fix: GHSA-426f-p74m-73fv — JWT JWKS issuer confusion auth bypass (CVSS 9.4)

Pin issuer and JWKS URL at startup from CLERK_PUBLISHABLE_KEY. Validate token iss claim before any JWKS fetch. Add issuer= to jwt.decode() with verify_iss=True.
feat: add linkedin and facebook feature flags, clean up dead code
2026-06-05 12:07:22 +05:30 · 2026-06-05 12:07:22 +05:30 · 2026-06-03 20:19:41 +05:30 · 2026-06-03 20:06:11 +05:30 · 2026-06-03 18:52:18 +05:30 · 2026-06-03 18:50:53 +05:30
377 changed files with 23038 additions and 48672 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -236,6 +236,9 @@ gsc_credentials_template.json
 .dmypy.json
 dmypy.json

+docs
+
+
 # Pyre type checker
 .pyre/

--- a/DELIVERY_SUMMARY.md
+++ b/DELIVERY_SUMMARY.md
@@ -1,521 +0,0 @@
-# 📋 Phase 2A Implementation Summary - What's Been Delivered
-
-**Date:** May 24, 2026 | **Session:** Complete Review & Status Report
-
---
-
-## 🎉 WHAT'S BEEN ACCOMPLISHED
-
-### ✅ Frontend Components: 6 Files Created
-
-1. **enterpriseSeoApi.ts** (650 lines)
-   - 15+ API methods with TypeScript signatures
-   - 20+ type-safe interfaces
-   - Request/response models matching backend expectations
-   - Error handling utilities
-   - Ready to call backend endpoints
-
-2. **llmInsightsGenerator.ts** (450 lines)
-   - 10+ insight generation methods
-   - 8 specialized LLM prompt templates
-   - Priority scoring algorithms
-   - Traffic projection calculations
-   - Effort assessment logic
-   - Phased implementation strategies
-
-3. **EnterpriseAuditResults.tsx** (800 lines)
-   - Executive summary section with overall score
-   - Technical audit with Core Web Vitals
-   - Keyword research with opportunity tables
-   - Competitive analysis
-   - 3-phase implementation roadmap
-   - AI insights with priority filtering
-   - Report download functionality
-
-4. **GSCAnalysisResults.tsx** (900 lines)
-   - Performance overview cards (4 key metrics)
-   - 4-tab interface for organized display
-   - Top keywords and pages tables
-   - Content opportunities with traffic projections
-   - Keywords needing attention section
-   - Technical signals monitoring
-   - Traffic potential summary
-
-5. **ActionableInsightsDisplay.tsx** (700 lines)
-   - Priority-ranked insights (1-10 scale)
-   - Impact vs Effort matrix visualization
-   - Traffic gain estimates per insight
-   - Step-by-step implementation guides
-   - Recommended tools per insight
-   - Filter controls (impact, effort, quick wins)
-   - Save/bookmark functionality
-
-6. **SEOAnalysisController.tsx** (750 lines)
-   - 5-step guided workflow with visual stepper
-   - Step 1: Website input form
-   - Step 2: Enterprise audit display
-   - Step 3: GSC analysis display
-   - Step 4: AI insights display
-   - Step 5: Review and download
-   - Real-time progress tracking (0-100%)
-   - Configuration options dialog
-   - Report generation and download
-
-### ✅ Dashboard Integration: 1 File Modified
-
-**SEODashboard.tsx**
- Added Tabs component from Material-UI
- Created 2-tab interface
- Tab 1: "📊 Overview" (existing functionality - preserved)
- Tab 2: "🔍 Enterprise Analysis" (new Phase 2A)
- Seamless tab navigation
- Full backward compatibility
-
-### ✅ Documentation: 7 Files Created
-
-1. **PHASE2A_INTEGRATION_GUIDE.md** (2,500+ words)
-   - Complete component specifications
-   - Feature descriptions
-   - Props interfaces
-   - Architecture overview
-   - Data flow visualization
-   - Implementation notes
-
-2. **PHASE2A_IMPLEMENTATION_REVIEW.md** (3,000+ words)
-   - Detailed completion status
-   - Backend endpoint requirements
-   - Phase-by-phase breakdown
-   - Success criteria
-   - Resource requirements
-
-3. **PHASE2A_NEXT_STEPS.md** (2,500+ words)
-   - Implementation roadmap
-   - Phase-by-phase guidance
-   - Backend code snippets
-   - Step-by-step instructions
-   - Resource planning
-
-4. **PHASE2A_STATUS_DASHBOARD.md** (2,000+ words)
-   - Real-time progress tracking
-   - Component breakdown
-   - Blocker identification
-   - Action items by priority
-   - Gantt chart view
-
-5. **PHASE2A_COMPLETE_REVIEW.md** (2,500+ words)
-   - Comprehensive review
-   - Metrics and completion status
-   - Success criteria evaluation
-   - Next actions summary
-
-6. **COMPILATION_FIXES.md** (1,000+ words)
-   - 14 TypeScript errors documented
-   - Root cause analysis
-   - Fixes applied
-   - Before/after code examples
-
-7. **QUICK_REFERENCE.md** (800 words)
-   - Quick status overview
-   - Action items
-   - Timeline summary
-   - Q&A section
-
-8. **FILE_INDEX.md** (500 words)
-   - Quick file navigation
-   - Component relationships
-   - File locations
-
---
-
-## 📊 METRICS
-
-### Code Statistics
-```
-Component                  Lines      Type             Status
-─────────────────────────────────────────────────────────────
-enterpriseSeoApi.ts        650        API Client       ✅ Complete
-llmInsightsGenerator.ts    450        Services         ✅ Complete
-EnterpriseAuditResults     800        Component        ✅ Complete
-GSCAnalysisResults         900        Component        ✅ Complete
-ActionableInsightsDisplay  700        Component        ✅ Complete
-SEOAnalysisController      750        Component        ✅ Complete
-SEODashboard (modified)     50        Integration      ✅ Complete
-─────────────────────────────────────────────────────────────
-TOTAL FRONTEND            4,850       Full Stack       ✅ 100%
-
-Documentation              12,000+     Guides           ✅ 100%
-─────────────────────────────────────────────────────────────
-TOTAL DELIVERED            16,850+                      ✅ 100%
-```
-
-### Component Coverage
-```
-Feature                    Coverage   Status
-────────────────────────────────────────────
-API Methods                15/15      ✅ 100%
-UI Components              50/50      ✅ 100%
-TypeScript Types           20/20      ✅ 100%
-LLM Prompts                8/8        ✅ 100%
-Error Handling             100%       ✅ 100%
-Loading States             100%       ✅ 100%
-Responsive Design          100%       ✅ 100%
-Accessibility              Full       ✅ 100%
-────────────────────────────────────────────
-OVERALL FRONTEND           ✅ 100% COMPLETE
-```
-
---
-
-## 🎯 COMPLETION STATUS BY PHASE
-
-### Phase 2A.0: Frontend ✅ COMPLETE
-```
-TARGET:       Build frontend UI for enterprise SEO analysis
-DELIVERED:    6 production-ready React components
-FEATURES:     50+ interactive UI elements
-QUALITY:      TypeScript strict mode, error handling, animations
-TESTING:      TypeScript compilation tests, type validation
-TIME:         3 days (May 21-23)
-EFFORT:       40 developer hours
-STATUS:       ✅ 100% COMPLETE - Ready for production
-```
-
-### Phase 2A.1: Backend Core 🔴 NOT STARTED
-```
-TARGET:       Implement 3 core backend endpoints
-REQUIRED:     Enterprise audit, GSC analysis, content opportunities
-EFFORT:       40-50 developer hours
-TIME:         1 week (target: May 24-30)
-STATUS:       🔴 0% - NOT STARTED - BLOCKING ALL TESTING
-CRITICAL:     YES - Must start immediately
-```
-
-### Phase 2A.2: LLM Integration 🔴 BLOCKED
-```
-TARGET:       Implement 8 LLM insight endpoints
-REQUIRED:     Audit insights, GSC insights, content strategy, etc.
-EFFORT:       40-50 developer hours
-TIME:         1 week (after Phase 2A.1)
-STATUS:       🔴 0% - BLOCKED BY PHASE 2A.1
-CRITICAL:     YES - Core feature
-```
-
-### Phase 2A.3: Infrastructure 🔴 BLOCKED
-```
-TARGET:       Add database and caching layer
-REQUIRED:     Redis, schema design, history storage
-BENEFIT:      10x performance improvement
-EFFORT:       30 developer hours
-TIME:         1 week (after Phase 2A.2)
-STATUS:       🔴 0% - BLOCKED BY PHASE 2A.2
-CRITICAL:     HIGH - For production
-```
-
-### Phase 2A.4: Testing 🔴 BLOCKED
-```
-TARGET:       Comprehensive testing and validation
-REQUIRED:     80%+ code coverage, all tests passing
-EFFORT:       50 developer hours
-TIME:         1-2 weeks (after Phase 2A.3)
-STATUS:       🔴 0% - BLOCKED BY PHASE 2A.3
-CRITICAL:     YES - Before deployment
-```
-
-### Phase 2A.5: Deployment 🔴 BLOCKED
-```
-TARGET:       Production deployment
-REQUIRED:     Documentation, deployment procedures, monitoring
-EFFORT:       30 developer hours
-TIME:         1 week (after Phase 2A.4)
-STATUS:       🔴 0% - BLOCKED BY PHASE 2A.4
-CRITICAL:     MEDIUM - Final step
-```
-
---
-
-## 📈 PROGRESS VISUALIZATION
-
-```
-OVERALL PROJECT PROGRESS: 20%
-
-Frontend:       ████████████████████░░░░░░░░░░░░░░░░░░░░░░  100% ✅
-Backend Core:   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%  🔴
-LLM Integration:░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%  🔴
-Infrastructure: ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%  🔴
-Testing:        ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%  🔴
-Deployment:     ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%  🔴
-──────────────────────────────────────────────────────────────────
-Average:        ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  20% 🟡
-
-BLOCKING FACTOR: Backend Implementation (0% complete)
-```
-
---
-
-## 🚀 DELIVERABLES CHECKLIST
-
-### Frontend Components
- [x] enterpriseSeoApi.ts - API client with 15+ methods
- [x] llmInsightsGenerator.ts - LLM prompt service
- [x] EnterpriseAuditResults.tsx - Audit display
- [x] GSCAnalysisResults.tsx - GSC display
- [x] ActionableInsightsDisplay.tsx - Insights display
- [x] SEOAnalysisController.tsx - Workflow orchestrator
- [x] SEODashboard.tsx - Tab integration
-
-### Documentation
- [x] PHASE2A_INTEGRATION_GUIDE.md - Component specs
- [x] PHASE2A_IMPLEMENTATION_REVIEW.md - Detailed review
- [x] PHASE2A_NEXT_STEPS.md - Implementation roadmap
- [x] PHASE2A_STATUS_DASHBOARD.md - Status tracking
- [x] PHASE2A_COMPLETE_REVIEW.md - Full review
- [x] COMPILATION_FIXES.md - Error fixes
- [x] QUICK_REFERENCE.md - Quick guide
- [x] FILE_INDEX.md - File navigation
-
-### Fixes & Improvements
- [x] Fixed 14 TypeScript compilation errors
- [x] Added type annotations to all map functions
- [x] Fixed Material-UI imports
- [x] Fixed component import paths
- [x] Added proper error handling
- [x] Implemented loading states
-
-### Quality Assurance
- [x] Full TypeScript type coverage
- [x] Responsive design verified
- [x] Error handling implemented
- [x] Loading states working
- [x] Animations configured
- [x] Accessibility considered
-
---
-
-## ⚠️ CRITICAL STATUS
-
-### Current Blocker: 🔴 Backend Not Implemented
-```
-IMPACT:    Prevents all functional testing
-SEVERITY:  CRITICAL - Production blocker
-TIMELINE:  1 week to resolve (Phase 2A.1)
-ACTION:    START IMMEDIATELY
-```
-
-### Blocking Items
- ❌ 3 core backend endpoints not implemented
- ❌ 8 LLM endpoints not implemented
- ❌ Database/caching not setup
- ❌ All testing blocked
- ❌ Production deployment blocked
-
-### Unblocking Path
-```
-TODAY        → Start Phase 2A.1
-May 30       → Complete Phase 2A.1 (3 endpoints)
-Jun 6        → Complete Phase 2A.2 (8 endpoints)
-Jun 13       → Complete Phase 2A.3 (caching/DB)
-Jun 20       → Complete Phase 2A.4 (testing)
-Jun 28       → Complete Phase 2A.5 (deployment)
-```
-
---
-
-## 📞 STAKEHOLDER SUMMARY
-
-### For Product Managers
- ✅ Frontend feature complete and visually impressive
- 🔴 Backend implementation critical path item
- 📅 5 weeks total timeline to production
- 💼 Enterprise SEO differentiation achieved
- 📈 Ready for customer demos (with mock data)
-
-### For Engineering Leads
- ✅ Frontend code is production-ready
- 🔴 Backend needs immediate attention
- 📋 Clear implementation roadmap provided
- 👥 Resource requirement: 2-3 backend developers
- ⏱️ Must start Phase 2A.1 today to maintain timeline
-
-### For Developers
- ✅ All components documented
- 📚 7 detailed guides provided
- 🎯 Clear next steps (Phase 2A.1)
- 🛠️ Backend architecture outlined
- 📍 Type definitions ready for implementation
-
-### For QA/Testing
- 🔴 Can't test end-to-end yet (no backend)
- ✅ Can test frontend components with mock data
- 📋 Test plan ready (see PHASE2A_STATUS_DASHBOARD.md)
- 👥 Need to be ready after Phase 2A.1
-
---
-
-## 🎯 SUCCESS CRITERIA MET
-
-### Frontend Completion ✅
- [x] All 6 components created
- [x] 4,850+ lines of production-ready code
- [x] Full TypeScript support
- [x] Material-UI integration
- [x] Error handling implemented
- [x] Loading states working
- [x] Responsive design
- [x] 14 compilation errors fixed
- [x] Zero technical debt
-
-### Documentation ✅
- [x] 8 comprehensive guides created
- [x] 12,000+ words of documentation
- [x] Backend implementation blueprint provided
- [x] Timeline and roadmap clear
- [x] Resource requirements defined
- [x] Success criteria specified
-
-### Integration ✅
- [x] Dashboard tab integration complete
- [x] Backward compatibility maintained
- [x] Existing features preserved
- [x] Seamless UX flow
-
-### Quality ✅
- [x] TypeScript strict mode
- [x] No technical debt
- [x] Clean architecture
- [x] Reusable components
- [x] Comprehensive error handling
-
---
-
-## 📊 WHAT'S LEFT TO DO
-
-### Phase 2A.1: Backend Core (NEXT)
-```
-Effort:    40-50 hours
-Timeline:  1 week
-Team:      2 developers
-Deliverable: 3 functional endpoints + tests
-Unblocks:  Everything else
-```
-
-### Phase 2A.2: LLM Integration (AFTER 2A.1)
-```
-Effort:    40-50 hours
-Timeline:  1 week
-Team:      1-2 developers
-Deliverable: 8 functional endpoints + prompt optimization
-Unblocks:  Insights generation
-```
-
-### Phase 2A.3: Infrastructure (AFTER 2A.2)
-```
-Effort:    30 hours
-Timeline:  1 week
-Team:      1 backend + DevOps
-Deliverable: Caching layer, database, monitoring
-Impact:    10x performance improvement
-```
-
-### Phase 2A.4: Testing (AFTER 2A.3)
-```
-Effort:    50 hours
-Timeline:  1-2 weeks
-Team:      2 QA + 1 dev
-Deliverable: 80%+ test coverage, all tests passing
-Must-have: Before production deployment
-```
-
-### Phase 2A.5: Deployment (AFTER 2A.4)
-```
-Effort:    30 hours
-Timeline:  1 week
-Team:      1 backend + DevOps
-Deliverable: Production release
-```
-
---
-
-## 💡 KEY INSIGHTS
-
-### Strengths
-1. **Frontend Complete** - Production-ready UI code
-2. **Well-Documented** - Clear guides for next phases
-3. **Clean Code** - Zero technical debt, maintainable
-4. **Type-Safe** - Full TypeScript support
-5. **User-Centric** - Great UX/UI with animations
-
-### Challenges
-1. **Backend Blocked** - Not started yet (critical blocker)
-2. **Timeline Risk** - 5-week path to production
-3. **Resource Dependent** - Needs 2-3 backend developers
-4. **LLM Integration** - Requires specialized setup
-5. **Testing Gap** - No tests yet
-
-### Opportunities
-1. **Differentiation** - First LLM-powered SEO dashboard
-2. **Monetization** - Premium enterprise feature
-3. **User Value** - Real traffic improvement guidance
-4. **Market Position** - Advanced SEO tooling
-5. **Scaling** - Foundation for more features
-
---
-
-## 🏁 FINAL STATUS
-
-```
-╔═══════════════════════════════════════════════════╗
-║        PHASE 2A DELIVERY SUMMARY                 ║
-╠═══════════════════════════════════════════════════╣
-║                                                   ║
-║  FRONTEND:         ✅ 100% COMPLETE              ║
-║  ├─ Components:    ✅ 6/6 created                ║
-║  ├─ Code:          ✅ 4,850+ lines               ║
-║  ├─ Documentation: ✅ 8 guides                   ║
-║  └─ Quality:       ✅ Production-ready           ║
-║                                                   ║
-║  BACKEND:          🔴 0% STARTED                 ║
-║  ├─ Endpoints:     🔴 0/12 implemented           ║
-║  ├─ Services:      🔴 0/3 created                ║
-║  ├─ Timeline:      ⏳ Ready to start              ║
-║  └─ Priority:      🔴 CRITICAL                   ║
-║                                                   ║
-║  OVERALL:          🟡 20% COMPLETE               ║
-║  ├─ Delivered:     4,850+ lines frontend         ║
-║  ├─ Needed:        2,650+ lines backend          ║
-║  ├─ Timeline:      5 weeks to production         ║
-║  └─ Next Step:     Start Phase 2A.1 TODAY        ║
-║                                                   ║
-╚═══════════════════════════════════════════════════╝
-```
-
---
-
-## ✨ CONCLUSION
-
-**Frontend Phase Complete** ✅  
-All frontend components are production-ready and fully documented.
-
-**Backend is Blocking** 🔴  
-Backend implementation is critical path. Must start immediately.
-
-**5-Week Path to Production** 📅  
-Clear roadmap provided for phases 2A.1 through 2A.5.
-
-**Ready for Next Phase** 🚀  
-All prerequisites met. Backend team can start Phase 2A.1 today.
-
---
-
-## 📞 Next Steps
-
-1. **Review** this summary with stakeholders
-2. **Allocate** 2-3 backend developers
-3. **Start** Phase 2A.1 implementation
-4. **Execute** according to timeline
-5. **Target** June 28, 2026 production release
-
---
-
-**Session Completed:** May 24, 2026  
-**Status:** Ready for Backend Implementation  
-**Questions?** See detailed documentation files
--- a/PHASE2A1_IMPLEMENTATION_STATUS.md
+++ b/PHASE2A1_IMPLEMENTATION_STATUS.md
@@ -1,440 +0,0 @@
-# Phase 2A.1: Backend Core Implementation - COMPLETE ✅
-
-**Status Date:** May 25, 2026  
-**Implementation Level:** 95% Complete - Router Registration Added  
-**Ready for Testing:** YES
-
---
-
-## 📋 What Was Found
-
-Phase 2A.1 backend implementation was **already substantially complete**. Today's work focused on ensuring proper activation and registration.
-
-### ✅ Already Implemented (95% Complete)
-
-#### 1. **Enterprise SEO Service** ✅ COMPLETE
-**File:** `backend/services/seo_tools/enterprise_seo_service.py` (400+ lines)
-
-**Features Implemented:**
- ✅ `execute_complete_audit()` - Comprehensive multi-tool orchestration
- ✅ Parallel execution of 5 audit components:
-  - Technical SEO audit (TechnicalSEOService)
-  - On-page SEO audit (OnPageSEOService)  
-  - PageSpeed analysis (PageSpeedService)
-  - Sitemap analysis (SitemapService)
-  - Content strategy analysis (ContentStrategyService)
- ✅ Competitive analysis across 5 competitors
- ✅ Overall score calculation (0-100)
- ✅ Priority actions aggregation
- ✅ AI insights generation
- ✅ Executive report generation
- ✅ Implementation timeline estimation
- ✅ Full error handling and logging
-
-**Methods Available:**
-```python
-async def execute_complete_audit(
-    website_url: str,
-    competitors: Optional[List[str]] = None,
-    target_keywords: Optional[List[str]] = None,
-    include_content_analysis: bool = True,
-    include_competitive_analysis: bool = True,
-    generate_executive_report: bool = True
-) -> Dict[str, Any]
-```
-
---
-
-#### 2. **GSC Analyzer Service** ✅ COMPLETE
-**File:** `backend/services/seo_tools/gsc_analyzer_service.py` (500+ lines)
-
-**Features Implemented:**
- ✅ `analyze_search_performance()` - Full GSC analysis pipeline
-  - Performance overview metrics
-  - Keyword-level analysis (top 10, trends, opportunities)
-  - Page-level performance breakdown
-  - Content opportunities identification (15+)
-  - Technical SEO signals monitoring
-  - Competitive positioning assessment
-  - Trend analysis
-  - AI recommendations
-
- ✅ `get_content_opportunities_report()` - Detailed content roadmap
-  - High-volume, low-CTR keywords
-  - Ranking improvement opportunities
-  - Content expansion candidates
-  - Priority-scored recommendations
-  - Phased implementation roadmap (Phase 1, 2, 3)
-  - Traffic potential calculations
-
- ✅ Helper methods for data analysis:
-  - `_fetch_gsc_data()` - GSC data retrieval
-  - `_analyze_performance_overview()` - Metrics aggregation
-  - `_analyze_keyword_performance()` - Keyword analysis
-  - `_analyze_page_performance()` - Page metrics
-  - `_identify_content_opportunities()` - Opportunity scoring
-  - `_analyze_technical_seo_signals()` - Technical monitoring
-  - `_analyze_competitive_position()` - Competitive benchmarking
-  - `_analyze_trends()` - Trend detection
-  - `_generate_ai_recommendations()` - LLM integration
-  - `health_check()` - Service health status
-
-**Mock Data Support:**
- Currently uses realistic mock data for demonstration
- Ready for real GSC API integration with user credentials
- Data structures match production API responses
-
---
-
-#### 3. **API Endpoints** ✅ COMPLETE
-**File:** `backend/routers/seo_tools.py` (1,100+ lines)
-
-**Endpoints Implemented:**
-
-| Endpoint | Method | Purpose | Status |
-|----------|--------|---------|--------|
-| `/api/seo/enterprise/complete-audit` | POST | Full audit execution | ✅ |
-| `/api/seo/enterprise/quick-audit` | POST | Quick audit variant | ✅ |
-| `/api/seo/gsc/analyze-search-performance` | POST | GSC analysis | ✅ |
-| `/api/seo/gsc/content-opportunities` | POST | Content roadmap | ✅ |
-| `/api/seo/enterprise/health` | GET | Health check | ✅ |
-
-**Request/Response Models** (Pydantic):
- ✅ `EnterpriseAuditRequest` - Structured input validation
- ✅ `GSCAnalysisRequest` - GSC parameters
- ✅ `ContentOpportunitiesRequest` - Content opportunities input
- ✅ `BaseResponse` - Standard response format
- ✅ `ErrorResponse` - Error handling
-
-**Response Format:**
-```python
-{
-  "success": bool,
-  "message": str,
-  "timestamp": datetime,
-  "execution_time": float,
-  "data": {
-    # Audit results or analysis data
-  }
-}
-```
-
---
-
-## 🔧 Today's Implementation Work
-
-### 1. **Router Registration Added** ✅
-**File Modified:** `backend/app.py` (Line 670)
-
-**What Was Done:**
-```python
-# Include SEO Tools router with enterprise audit and GSC analysis
-if seo_tools_router:
-    app.include_router(seo_tools_router)
-```
-
-**Why This Mattered:**
- Endpoints were implemented but NOT registered with FastAPI
- Without registration, the routes were unreachable
- Adding this line enables all endpoints at runtime
-
-**Location:** In the `if _is_full_mode():` block with other router registrations
-
---
-
-## 📊 Complete Feature Breakdown
-
-### Phase 2A.1 Feature Matrix
-
-| Feature | Component | Status | Lines | Completeness |
-|---------|-----------|--------|-------|--------------|
-| **Enterprise Audit** | enterprise_seo_service.py | ✅ Complete | 400+ | 100% |
-| **GSC Analysis** | gsc_analyzer_service.py | ✅ Complete | 500+ | 100% |
-| **Endpoints** | routers/seo_tools.py | ✅ Complete | 500+ | 100% |
-| **Router Registration** | app.py | ✅ Added | 3 | 100% |
-| **Error Handling** | All files | ✅ Complete | 100% | 100% |
-| **Logging** | All files | ✅ Complete | 100% | 100% |
-| **Request Validation** | routers/seo_tools.py | ✅ Complete | 100% | 100% |
-| **Response Formatting** | routers/seo_tools.py | ✅ Complete | 100% | 100% |
-| **Async/Parallel Execution** | service files | ✅ Complete | 100% | 100% |
-
---
-
-## 🎯 What Each Component Does
-
-### Enterprise Audit Workflow
-```
-1. Input Validation
-   ├─ Website URL
-   ├─ Competitors (max 5)
-   └─ Target keywords
-
-2. Parallel Execution (5 concurrent tasks)
-   ├─ Technical SEO Analysis
-   ├─ On-Page SEO Analysis
-   ├─ PageSpeed Insights
-   ├─ Sitemap Analysis
-   └─ Content Strategy Analysis
-
-3. Competitive Analysis
-   ├─ Benchmark against competitors
-   ├─ Identify advantages
-   └─ Identify gaps
-
-4. Score Aggregation
-   ├─ Calculate component scores
-   ├─ Overall score (0-100)
-   └─ Status determination
-
-5. Recommendations Aggregation
-   ├─ Prioritize actions
-   ├─ Estimate impact
-   └─ Create roadmap
-
-6. Report Generation
-   ├─ Executive summary
-   ├─ Component details
-   ├─ AI insights
-   └─ Next steps
-```
-
-### GSC Analysis Workflow
-```
-1. GSC Data Retrieval
-   ├─ Keywords performance
-   ├─ Pages performance
-   ├─ Device breakdown
-   └─ Search types
-
-2. Parallel Analyses (8 concurrent)
-   ├─ Performance overview
-   ├─ Keyword performance
-   ├─ Page performance
-   ├─ Content opportunities (15+)
-   ├─ Technical signals
-   ├─ Competitive position
-   ├─ Trends
-   └─ AI recommendations
-
-3. Opportunity Identification
-   ├─ High volume, low CTR
-   ├─ Ranking improvements
-   ├─ Content expansion
-   └─ Priority scoring
-
-4. Report Generation
-   ├─ Metrics summary
-   ├─ Opportunities list
-   ├─ Implementation phases
-   └─ Traffic projections
-```
-
---
-
-## 🚀 Ready for Testing
-
-### Test Endpoints Available
-
-**1. Enterprise Audit**
-```bash
-POST /api/seo/enterprise/complete-audit
-Content-Type: application/json
-
-{
-  "website_url": "https://example.com",
-  "competitors": ["https://competitor1.com", "https://competitor2.com"],
-  "target_keywords": ["keyword1", "keyword2"],
-  "include_content_analysis": true,
-  "include_competitive_analysis": true,
-  "generate_executive_report": true
-}
-```
-
-**Expected Response:**
-```json
-{
-  "success": true,
-  "message": "Complete enterprise audit executed successfully",
-  "execution_time": 45.23,
-  "data": {
-    "audit_id": "audit_20260525_143022",
-    "overall_score": 78,
-    "component_results": {...},
-    "priority_actions": [...],
-    "ai_insights": {...}
-  }
-}
-```
-
-**2. GSC Analysis**
-```bash
-POST /api/seo/gsc/analyze-search-performance
-Content-Type: application/json
-
-{
-  "site_url": "https://example.com",
-  "date_range_days": 90,
-  "include_opportunities": true,
-  "include_competitive": true
-}
-```
-
-**3. Content Opportunities**
-```bash
-POST /api/seo/gsc/content-opportunities
-Content-Type: application/json
-
-{
-  "site_url": "https://example.com",
-  "min_impressions": 100,
-  "date_range_days": 90
-}
-```
-
---
-
-## 📈 Implementation Statistics
-
-### Code Metrics
-```
-Backend Services:     900+ lines (2 files)
-Router Implementation: 500+ lines (1 file)
-Request Models:       400+ lines (in router)
-Total Backend Code:   1,800+ lines
-
-Endpoints:            5 POST/GET methods
-Service Methods:      15+ async methods
-Helper Methods:       20+ private methods
-Error Handlers:       Comprehensive
-```
-
-### Feature Coverage
-```
-✅ Complete audit orchestration
-✅ 5 parallel analysis components
-✅ Competitive benchmarking
-✅ Score aggregation
-✅ Priority recommendations
-✅ Executive reporting
-✅ GSC data integration
-✅ Opportunity identification
-✅ Trend analysis
-✅ AI insights generation
-✅ Content roadmapping
-✅ Implementation phasing
-✅ Error handling
-✅ Request validation
-✅ Response formatting
-✅ Async/concurrent execution
-✅ Comprehensive logging
-```
-
---
-
-## 🔗 Integration Points
-
-### Frontend Connected Points
-**From frontend/src/api/enterpriseSeoApi.ts:**
-```typescript
-✅ executeEnterpriseAudit() → POST /api/seo/enterprise/complete-audit
-✅ analyzeGSCSearchPerformance() → POST /api/seo/gsc/analyze-search-performance
-✅ getContentOpportunitiesReport() → POST /api/seo/gsc/content-opportunities
-```
-
-### Service Dependencies
-```
-enterpriseSEOService
-├─ TechnicalSEOService ✅
-├─ OnPageSEOService ✅
-├─ PageSpeedService ✅
-├─ SitemapService ✅
-├─ ContentStrategyService ✅
-└─ llm_text_gen (LLM provider) ✅
-
-GSCAnalyzerService
-├─ GSCService ✅
-└─ llm_text_gen (LLM provider) ✅
-```
-
---
-
-## ✨ Highlights
-
-### What Makes This Implementation Great
-1. **Parallel Execution** - 5 concurrent components run simultaneously
-2. **Type Safety** - Full Pydantic model validation
-3. **Error Resilience** - Individual component failures don't crash audit
-4. **Comprehensive Logging** - Every step tracked with loguru
-5. **Executive Focus** - Reports designed for stakeholder consumption
-6. **Scalable Design** - Ready for caching, database persistence, real APIs
-7. **AI Integration Ready** - LLM hooks built in for insights
-8. **Mock Data Support** - Works without real GSC credentials for testing
-
---
-
-## 🔄 Next Phases (Blocked Until This Is Tested)
-
-### Phase 2A.2: LLM Integration (Awaiting Completion of 2A.1)
- [ ] Integrate Claude/GPT APIs properly
- [ ] Refine LLM prompts with real data
- [ ] Add response caching
- [ ] Implement usage tracking
-
-### Phase 2A.3: Infrastructure (Awaiting Completion of 2A.2)
- [ ] Add Redis caching layer
- [ ] Database schema for history
- [ ] Performance optimization
- [ ] Monitoring setup
-
-### Phase 2A.4: Testing (Awaiting Completion of 2A.3)
- [ ] Unit tests for all services
- [ ] Integration tests for endpoints
- [ ] E2E tests with real data
- [ ] Performance validation
-
-### Phase 2A.5: Deployment (Awaiting Completion of 2A.4)
- [ ] API documentation
- [ ] Deployment procedures
- [ ] Monitoring setup
- [ ] Production release
-
---
-
-## 📝 Summary
-
-**Phase 2A.1 is 95% complete:**
- ✅ Enterprise SEO Service fully implemented
- ✅ GSC Analyzer Service fully implemented
- ✅ 5 API endpoints fully implemented
- ✅ Router registration added and enabled
- ✅ Error handling and logging implemented
- ✅ Request/response validation implemented
- ✅ Mock data for testing included
-
-**Ready to Test:**
- Backend is configured and endpoints are now accessible
- Frontend can call all three core endpoints
- Mock data will return realistic results
- Logging will track all operations
-
-**Timeline to Production:**
- Phase 2A.1: ✅ READY (just completed)
- Phase 2A.2: 1 week after 2A.1 tested
- Phase 2A.3: 1 week after 2A.2
- Phase 2A.4: 1-2 weeks after 2A.3
- Phase 2A.5: 1 week after 2A.4
-
-**Total: 5 weeks to production**
-
---
-
-## 🎉 Next Action
-
-**Start testing the endpoints!**
-
-1. Launch backend with `python start_alwrity_backend.py --dev`
-2. Send test request to `/api/seo/enterprise/complete-audit`
-3. Verify response with mock data
-4. Confirm integration with frontend
-5. Proceed to Phase 2A.2 if tests pass
-
--- a/PHASE2A_COMPLETE_REVIEW.md
+++ b/PHASE2A_COMPLETE_REVIEW.md
@@ -1,559 +0,0 @@
-# Phase 2A - Complete Review & Implementation Status
-
-**Generated:** May 24, 2026 | **Overall Status:** 20% Complete | **Blocking:** Backend Implementation
-
---
-
-## 🎯 EXECUTIVE SUMMARY
-
-### What Was Built ✅
-```
-FRONTEND IMPLEMENTATION: 100% COMPLETE
-├── 6 Production-Ready Components
-├── 4,850+ Lines of React/TypeScript
-├── 20+ Type-Safe Interfaces
-├── 50+ UI Components
-├── Full Material-UI Integration
-├── Framer Motion Animations
-├── Glass-morphism Design
-├── Responsive Layout
-└── Error Handling & Loading States
-    
-STATUS: ✅ PRODUCTION READY - Can start testing immediately
-```
-
-### What's Needed 🔴
-```
-BACKEND IMPLEMENTATION: 0% STARTED (BLOCKING)
-├── 12 API Endpoints Required
-├── 2,650+ Lines of Code Needed
-├── 3 Service Files (enterprise, GSC, LLM)
-├── LLM Integration
-├── Database Caching
-├── Error Handling
-└── Comprehensive Testing
-    
-STATUS: 🔴 NOT STARTED - Blocks all testing and validation
-```
-
-### Timeline 📅
-```
-Current Phase: Frontend Complete ✅
-Blocking Phase: Backend Core (Phase 2A.1)
-Critical Path: 5 weeks to production
-Resources: 2-3 developers
-Target Date: June 28, 2026
-```
-
---
-
-## 📊 DETAILED COMPLETION STATUS
-
-### Frontend Components Created
-
-#### 1. **enterpriseSeoApi.ts** ✅
-```
-PURPOSE:   Type-safe API client layer
-LINES:     650+
-EXPORTS:   - 15+ API methods
-           - 20+ TypeScript interfaces
-           - Error utilities
-FEATURES:  - Enterprise audit endpoints
-           - GSC analysis endpoints
-           - Content opportunity endpoints
-           - LLM insight endpoints
-           - Health check endpoint
-READY:     ✅ YES - Can call backend when ready
-```
-
-#### 2. **llmInsightsGenerator.ts** ✅
-```
-PURPOSE:   LLM prompt generation & insights service
-LINES:     450+
-EXPORTS:   - 10+ specialized methods
-           - 8 prompt templates
-           - Singleton instance
-FEATURES:  - Audit insights generation
-           - GSC insights generation
-           - Content strategy generation
-           - Traffic roadmap generation
-           - Priority scoring (1-10)
-           - Effort assessment
-           - Traffic gain calculation
-READY:     ✅ YES - Backend just needs to call
-```
-
-#### 3. **EnterpriseAuditResults.tsx** ✅
-```
-PURPOSE:   Display comprehensive enterprise audit results
-LINES:     800+
-FEATURES:  - Executive summary
-           - Technical audit findings
-           - Keyword research table
-           - Competitive analysis
-           - Implementation roadmap (3 phases)
-           - AI insights with filtering
-           - Report download
-STYLING:   ✅ Glass-morphism, animations, responsive
-STATE:     ✅ Local state management
-ERRORS:    ✅ Comprehensive error handling
-READY:     ✅ YES - Can render with mock data
-```
-
-#### 4. **GSCAnalysisResults.tsx** ✅
-```
-PURPOSE:   Display GSC search performance analysis
-LINES:     900+
-FEATURES:  - Performance overview (4 cards)
-           - 4-tab interface
-           - Top keywords table
-           - Top pages cards
-           - Content opportunities
-           - Keywords needing attention
-           - Technical signals
-           - Traffic potential
-STYLING:   ✅ Full Material-UI theming
-CHARTS:    ✅ Progress bars, trend indicators
-READY:     ✅ YES - Can render with mock data
-```
-
-#### 5. **ActionableInsightsDisplay.tsx** ✅
-```
-PURPOSE:   Display AI-powered actionable insights
-LINES:     700+
-FEATURES:  - Priority ranking (1-10 scale)
-           - Impact vs effort matrix
-           - Traffic gain estimates
-           - Implementation steps
-           - Recommended tools
-           - Filtering controls
-           - Save/bookmark functionality
-           - Phased strategies
-INTERACTIVITY: ✅ Full interactive UI
-READY:     ✅ YES - Fully functional UI
-```
-
-#### 6. **SEOAnalysisController.tsx** ✅
-```
-PURPOSE:   Main workflow orchestrator
-LINES:     750+
-FEATURES:  - 5-step guided workflow
-           - Visual stepper
-           - Website input form
-           - Real-time progress (0-100%)
-           - Result tabs
-           - Configuration dialog
-           - Report download
-           - Error handling
-STATE:     ✅ Local state + Zustand integration
-READY:     ✅ YES - Can orchestrate backend calls
-```
-
-#### 7. **SEODashboard.tsx (Modified)** ✅
-```
-PURPOSE:   Main dashboard with tab navigation
-CHANGES:   - Added Tabs component
-           - Tab 1: Overview (existing)
-           - Tab 2: Enterprise Analysis (new)
-           - Tab navigation UI
-INTEGRATION: ✅ Seamless
-BACKWARD COMPATIBILITY: ✅ Full
-READY:     ✅ YES - Tab switching works
-```
-
---
-
-## 🔴 Backend Implementation Status
-
-### Required Endpoints (12 Total)
-
-#### Core Endpoints (3) - PRIORITY 1
-```
-Endpoint 1: POST /api/seo-tools/enterprise/complete-audit
-Status:     🔴 NOT IMPLEMENTED
-Service:    enterprise_seo_service.py (needs creation)
-Effort:     HIGH (~400 lines)
-Purpose:    Complete enterprise SEO audit
-Inputs:     website_url, competitors, keywords
-Outputs:    Comprehensive audit result with 15+ fields
-Blocked:    ✓ Testing, ✓ Integration, ✓ Validation
-
-Endpoint 2: POST /api/seo-tools/gsc/analyze-search-performance
-Status:     🔴 NOT IMPLEMENTED
-Service:    gsc_analyzer_service.py (needs creation)
-Effort:     MEDIUM (~350 lines)
-Purpose:    Analyze GSC search performance
-Inputs:     site_url, date_range
-Outputs:    Search metrics, keywords, opportunities
-Blocked:    ✓ Testing, ✓ Integration, ✓ Validation
-
-Endpoint 3: POST /api/seo-tools/gsc/content-opportunities
-Status:     🔴 NOT IMPLEMENTED
-Service:    gsc_analyzer_service.py (shared)
-Effort:     MEDIUM (~300 lines)
-Purpose:    Identify content gaps and opportunities
-Inputs:     site_url, analysis_type
-Outputs:    Opportunity recommendations with ROI
-Blocked:    ✓ Testing, ✓ Integration, ✓ Validation
-```
-
-#### LLM Insight Endpoints (8) - PRIORITY 2
-```
-1. /api/seo-tools/llm/generate-audit-insights              🔴 0%
-2. /api/seo-tools/llm/generate-gsc-insights               🔴 0%
-3. /api/seo-tools/llm/generate-content-strategy           🔴 0%
-4. /api/seo-tools/llm/generate-traffic-roadmap            🔴 0%
-5. /api/seo-tools/llm/prioritized-recommendations         🔴 0%
-6. /api/seo-tools/llm/quick-wins                          🔴 0%
-7. /api/seo-tools/llm/competitive-insights                🔴 0%
-8. /api/seo-tools/llm/keyword-expansion                   🔴 0%
-
-Status:     All 🔴 NOT IMPLEMENTED
-Service:    llm_insights_service.py (needs creation)
-Effort:     HIGH (~500 lines)
-Purpose:    Generate LLM-powered actionable insights
-Inputs:     Analysis results + context
-Outputs:    Prioritized insights with traffic projections
-Blocked:    ✓ Insight generation, ✓ Traffic guidance
-```
-
-#### Support Endpoints (1) - PRIORITY 3
-```
-Endpoint: GET /api/seo-tools/enterprise/health
-Status:   🔴 NOT IMPLEMENTED
-Effort:   LOW (~50 lines)
-Purpose:  Health check for enterprise service
-Blocked:  ✓ Monitoring
-```
-
---
-
-## 📈 Completion Metrics
-
-### By Component Type
-```
-Component Type          Count  Status  Lines   Completion
-────────────────────────────────────────────────────────
-API Client Methods        15    ✅     650     100%
-Service Methods           10    ✅     450     100%
-UI Components             50    ✅    3,850    100%
-TypeScript Interfaces     20    ✅     N/A     100%
-API Endpoints            12    🔴    2,650     0%
-Service Files             3    🔴     N/A      0%
-Database Tables           2    🔴     N/A      0%
-────────────────────────────────────────────────────────
-TOTAL                     112   🟡    7,600    20%
-```
-
-### By Layer
-```
-Layer           Status    Completion   Details
-──────────────────────────────────────────────────────
-Frontend        ✅        100%         4,850 lines, ready
-Services        ⏳        50%          Prompts ready, backend logic pending
-Backend         🔴        0%           No endpoints implemented
-Database        🔴        0%           Schema design pending
-Infrastructure  🔴        0%           Cache/monitoring pending
-Testing         🔴        0%           Framework ready, tests pending
-──────────────────────────────────────────────────────
-AVERAGE         🟡        20%          Frontend heavy, backend needed
-```
-
---
-
-## 🚦 Implementation Phases Summary
-
-### Phase 2A.0: Frontend ✅ COMPLETE
-```
-STATUS:       ✅ COMPLETE
-TIMELINE:     3 days (completed May 21-23)
-EFFORT:       40 hours
-DELIVERABLE:  6 components, 4,850 lines
-QUALITY:      Production-ready
-TESTS:        TypeScript compilation tests ✅
-              14 compilation errors fixed ✅
-READY:        ✅ Can be deployed immediately
-BLOCKED:      Nothing - ready to go
-```
-
-### Phase 2A.1: Backend Core 🔴 NOT STARTED
-```
-STATUS:       🔴 NOT STARTED
-TIMELINE:     1 week (target: May 24-30)
-EFFORT:       40-50 hours (2 developers)
-DELIVERABLE:  3 endpoints, business logic
-INCLUDES:     - Enterprise audit service (~400 lines)
-              - GSC analyzer service (~350 lines)
-              - Routing updates (~50 lines)
-              - Error handling
-              - Unit tests (~100 lines)
-CRITICAL:     YES - Blocks all testing
-READY:        ⏳ Can start immediately
-BLOCKED:      Developer resources needed
-```
-
-### Phase 2A.2: LLM Integration 🔴 BLOCKED
-```
-STATUS:       🔴 BLOCKED (waiting for 2A.1)
-TIMELINE:     1 week (after Phase 2A.1)
-EFFORT:       40-50 hours
-DELIVERABLE:  8 endpoints, prompt templates
-INCLUDES:     - LLM insights service (~500 lines)
-              - 8 endpoint routes
-              - Prompt optimization
-              - Response parsing
-              - Caching strategy
-              - Performance tuning
-CRITICAL:     YES - Core feature
-READY:        🔴 Blocked by Phase 2A.1
-```
-
-### Phase 2A.3: Infrastructure 🔴 BLOCKED
-```
-STATUS:       🔴 BLOCKED (waiting for 2A.2)
-TIMELINE:     1 week
-EFFORT:       30 hours
-DELIVERABLE:  Caching layer, database, monitoring
-BENEFIT:      10x performance improvement
-CRITICAL:     HIGH (for production)
-READY:        🔴 Blocked by Phase 2A.2
-```
-
-### Phase 2A.4: Testing 🔴 BLOCKED
-```
-STATUS:       🔴 BLOCKED (waiting for 2A.3)
-TIMELINE:     1-2 weeks
-EFFORT:       50 hours
-DELIVERABLE:  80%+ test coverage, all tests passing
-INCLUDES:     - 50+ unit tests
-              - 20+ integration tests
-              - 10+ E2E tests
-              - Manual testing
-              - Performance validation
-              - Bug fixes
-CRITICAL:     YES - Must pass before deployment
-READY:        🔴 Blocked by Phase 2A.3
-```
-
-### Phase 2A.5: Deployment 🔴 BLOCKED
-```
-STATUS:       🔴 BLOCKED (waiting for 2A.4)
-TIMELINE:     1 week
-EFFORT:       30 hours
-DELIVERABLE:  Production release
-INCLUDES:     - Documentation
-              - Deployment procedures
-              - Monitoring setup
-              - Rollback procedures
-              - UAT support
-CRITICAL:     MEDIUM - Final step
-READY:        🔴 Blocked by Phase 2A.4
-```
-
---
-
-## ⚡ Critical Path to Production
-
-```
-May 24:  Phase 2A.0 Frontend ✅  Complete
-May 25:  START → Phase 2A.1 Backend Core 🔴
-May 30:  DONE → Phase 2A.1 (3 endpoints)
-Jun 1:   START → Phase 2A.2 LLM Integration 🔴
-Jun 6:   DONE → Phase 2A.2 (8 endpoints)
-Jun 7:   START → Phase 2A.3 Infrastructure 🔴
-Jun 13:  DONE → Phase 2A.3 (Caching/DB)
-Jun 14:  START → Phase 2A.4 Testing 🔴
-Jun 20:  DONE → Phase 2A.4 (80% coverage)
-Jun 21:  START → Phase 2A.5 Deployment 🔴
-Jun 28:  DONE → PRODUCTION READY ✅
-
-TOTAL: 5 weeks from today to production
-```
-
---
-
-## 📋 Documentation Deliverables
-
-All documents created in repo root:
-
-| Document | Purpose | Location | Status |
-|----------|---------|----------|--------|
-| **Integration Guide** | Frontend component specs | PHASE2A_INTEGRATION_GUIDE.md | ✅ Complete |
-| **Implementation Review** | Detailed review of all components | PHASE2A_IMPLEMENTATION_REVIEW.md | ✅ Complete |
-| **Next Steps** | Implementation roadmap | PHASE2A_NEXT_STEPS.md | ✅ Complete |
-| **Status Dashboard** | Real-time progress tracking | PHASE2A_STATUS_DASHBOARD.md | ✅ Complete |
-| **Compilation Fixes** | 14 TypeScript error resolutions | COMPILATION_FIXES.md | ✅ Complete |
-| **This File** | Complete review & summary | PHASE2A_COMPLETE_REVIEW.md | ✅ You are here |
-
---
-
-## 🎯 Success Criteria Status
-
-### Frontend Completion ✅
- [x] All 6 components created
- [x] 4,850+ lines of code
- [x] Type-safe TypeScript
- [x] Material-UI integration
- [x] Error handling
- [x] Loading states
- [x] Responsive design
- [x] All compilation errors fixed (14/14)
- [x] Production-ready code
-
-### Backend Requirements 🔴
- [ ] 3 core endpoints implemented
- [ ] 8 LLM endpoints implemented
- [ ] Business logic complete
- [ ] Error handling
- [ ] Unit tests passing
- [ ] Integration tests passing
- [ ] Performance benchmarks met
-
---
-
-## ⚠️ Current Blockers
-
-### Blocker #1: Backend Not Implemented (CRITICAL)
-```
-Issue:        Core endpoints not implemented
-Impact:       Blocks ALL testing and validation
-Severity:     CRITICAL - Production blocker
-Timeline:     1 week to resolve (Phase 2A.1)
-Action:       START IMMEDIATELY
-```
-
-### Blocker #2: LLM Service Not Implemented (CRITICAL)
-```
-Issue:        LLM integration endpoints missing
-Impact:       Blocks insight generation
-Severity:     CRITICAL - Core feature
-Timeline:     Blocked by Blocker #1, then 1 week
-Action:       Start after Phase 2A.1
-```
-
-### Blocker #3: Database/Caching Not Setup (HIGH)
-```
-Issue:        No caching layer or history storage
-Impact:       Performance issues, limited tracking
-Severity:     HIGH - Production impact
-Timeline:     Blocked by Blocker #2, then 1 week
-Action:       Start after Phase 2A.2
-```
-
---
-
-## 📞 Recommended Next Actions
-
-### TODAY (May 24)
-```
-1. [ ] Distribute this review to stakeholders
-2. [ ] Finalize backend resource allocation
-3. [ ] Setup development environment
-4. [ ] Create project plan for Phase 2A.1
-5. [ ] Assign backend developers
-```
-
-### THIS WEEK (May 24-30)
-```
-1. [ ] Complete Phase 2A.1 (3 core endpoints)
-2. [ ] Write unit tests
-3. [ ] Manual testing with real websites
-4. [ ] Performance baseline established
-5. [ ] Ready to move to Phase 2A.2
-```
-
-### NEXT WEEK (May 31-Jun 6)
-```
-1. [ ] Start Phase 2A.2 (LLM integration)
-2. [ ] Implement 8 LLM endpoints
-3. [ ] Optimize LLM prompts
-4. [ ] Setup caching layer (start)
-5. [ ] Begin comprehensive testing
-```
-
---
-
-## 💡 Key Takeaways
-
-### ✅ Strengths
-1. **Frontend Complete** - Production-ready UI
-2. **Well-Designed** - Clean architecture, reusable components
-3. **Type-Safe** - Full TypeScript coverage
-4. **Well-Documented** - Comprehensive guides provided
-5. **Zero Technical Debt** - Clean, maintainable code
-
-### 🔴 Concerns
-1. **Backend Not Started** - Critical blocker
-2. **Timeline Risk** - Backend needs 4 weeks
-3. **Resource Dependent** - Needs 2-3 developers
-4. **LLM Integration** - Requires specialized setup
-5. **Testing Gap** - No tests yet
-
-### 🟡 Opportunities
-1. **Feature Differentiation** - LLM-powered insights unique
-2. **Monetization** - Premium enterprise feature
-3. **Market Position** - Advanced SEO tooling
-4. **User Value** - Real traffic improvement guidance
-5. **Scaling Potential** - Foundation for more features
-
---
-
-## 📊 Final Status Summary
-
-```
-╔════════════════════════════════════════════════════════════╗
-║          PHASE 2A IMPLEMENTATION STATUS                   ║
-╠════════════════════════════════════════════════════════════╣
-║                                                             ║
-║  FRONTEND:     ✅ 100% COMPLETE (4,850 lines)              ║
-║  BACKEND:      🔴 0% STARTED (2,650 lines needed)          ║
-║  DATABASE:     🔴 0% STARTED (schema design pending)       ║
-║  TESTING:      🔴 0% STARTED (tests pending)               ║
-║  DEPLOYMENT:   🔴 0% STARTED (infrastructure pending)      ║
-║                                                             ║
-║  ─────────────────────────────────────────────────────    ║
-║  OVERALL:      🟡 20% COMPLETE                             ║
-║  ─────────────────────────────────────────────────────    ║
-║                                                             ║
-║  BLOCKING:     Backend implementation                      ║
-║  TIMELINE:     5 weeks to production                       ║
-║  RESOURCES:    2-3 developers needed                       ║
-║  TARGET:       June 28, 2026                               ║
-║                                                             ║
-║  NEXT STEP:    START PHASE 2A.1 IMMEDIATELY                ║
-║                                                             ║
-╚════════════════════════════════════════════════════════════╝
-```
-
---
-
-## 🚀 Ready to Proceed?
-
-### Frontend Status: ✅ READY
- Fully implemented and tested
- All components created
- No dependencies on backend
- Can be deployed anytime
-
-### Backend Status: 🔴 NOT READY
- Zero implementation
- Needs 4 weeks of work
- Blocks all functionality
- **ACTION REQUIRED: Start today**
-
-### Go/No-Go Decision
-```
-FRONTEND:  ✅ GO - Can proceed immediately
-BACKEND:   🔴 NO-GO - Must start Phase 2A.1
-OVERALL:   🔴 NO-GO until backend starts
-
-ACTION: Allocate resources NOW to Phase 2A.1
-IMPACT: 1-week delay → 2-month delay if not started
-```
-
---
-
-**Review Completed:** May 24, 2026  
-**Next Review:** After Phase 2A.1 Backend Implementation  
-**Questions?** Refer to specific implementation guides  
-**Ready to Start?** Begin Phase 2A.1 backend implementation immediately
--- a/PHASE2A_IMPLEMENTATION_REVIEW.md
+++ b/PHASE2A_IMPLEMENTATION_REVIEW.md
@@ -1,605 +0,0 @@
-# Phase 2A SEO Dashboard Implementation - Complete Review
-
-**Date:** May 24, 2026  
-**Status:** 🟡 FRONTEND COMPLETE | 🔴 BACKEND PENDING | 🟡 TESTING READY
-
---
-
-## 📊 Implementation Overview
-
-### Phase 2A Objectives
-1. ✅ Integrate enterprise SEO audit with dashboard
-2. ✅ Provide comprehensive GSC insights to end users
-3. ✅ Use LLM prompts for actionable insights
-4. ✅ Display traffic improvement strategies
-5. ⏳ Backend endpoint implementation (NOT STARTED)
-6. ⏳ End-to-end testing (PENDING BACKEND)
-
---
-
-## ✅ COMPLETED: Frontend Layer (100%)
-
-### Files Created: 6 Components
-
-#### 1. **enterpriseSeoApi.ts** (API Client Layer)
- **Status:** ✅ COMPLETE
- **Lines:** 650+
- **Purpose:** Type-safe API client for all Phase 2A endpoints
- **Exports:**
-  - 15+ API methods
-  - 20+ TypeScript interfaces
-  - Error handling utilities
- **Key Methods:**
-  - `executeEnterpriseAudit()`
-  - `analyzeGSCSearchPerformance()`
-  - `getContentOpportunitiesReport()`
-  - `generateAuditInsights()`
-  - `generateGSCInsights()`
-  - `getTrafficImprovementStrategies()`
- **Dependencies:** Uses existing `apiClient` and `longRunningApiClient`
- **Type Safety:** ✅ Full TypeScript strict mode support
-
-#### 2. **llmInsightsGenerator.ts** (Services Layer)
- **Status:** ✅ COMPLETE
- **Lines:** 450+
- **Purpose:** Convert analysis data to LLM-powered actionable insights
- **Exports:**
-  - 10+ specialized methods
-  - Prompt builder templates
-  - Singleton instance
- **Key Methods:**
-  - `generateEnterpriseAuditInsights()`
-  - `generateGSCAnalysisInsights()`
-  - `generateTrafficRoadmap()`
-  - `generatePrioritizedRecommendations()`
-  - `generateContentStrategy()`
-  - `generateCompetitiveInsights()`
-  - `generateKeywordExpansion()`
- **LLM Integration:** 8+ specialized prompt templates
- **Features:**
-  - Priority scoring (1-10 scale)
-  - Effort/impact assessment
-  - Traffic gain calculations
-  - Phased implementation strategies
-
-#### 3. **EnterpriseAuditResults.tsx** (Results Component)
- **Status:** ✅ COMPLETE
- **Lines:** 800+
- **Location:** `frontend/src/components/SEODashboard/components/`
- **Features:**
-  - Executive summary (overall score, traffic potential, time estimate)
-  - Technical audit section (Core Web Vitals, page speed, mobile usability)
-  - Keyword research table (opportunity scoring, volume, difficulty)
-  - Competitive analysis matrix
-  - Implementation roadmap (3 phases: quick wins, medium, long-term)
-  - AI insights panel with filtering
-  - Report download functionality
- **Styling:** Glass-morphism effects, animations, responsive design
- **Accessibility:** Proper semantic HTML, ARIA labels
- **Performance:** Optimized renders, memoization where needed
-
-#### 4. **GSCAnalysisResults.tsx** (Results Component)
- **Status:** ✅ COMPLETE
- **Lines:** 900+
- **Location:** `frontend/src/components/SEODashboard/components/`
- **Features:**
-  - Performance overview cards (clicks, impressions, CTR, position)
-  - 4-tab interface:
-    - Tab 1: Performance Overview
-    - Tab 2: Keywords Analysis
-    - Tab 3: Content Opportunities
-    - Tab 4: Technical Signals
-  - Top keywords and pages tables
-  - Content opportunities with traffic projections
-  - Keywords needing attention
-  - Traffic potential breakdown
-  - Technical signals dashboard
- **Data Visualization:** Charts, progress bars, trend indicators
- **Responsive:** Grid-based layout for all screen sizes
- **Interactivity:** Sortable tables, filterable lists
-
-#### 5. **ActionableInsightsDisplay.tsx** (Insights Component)
- **Status:** ✅ COMPLETE
- **Lines:** 700+
- **Location:** `frontend/src/components/SEODashboard/components/`
- **Features:**
-  - Priority-ranked insights (1-10 scale with color coding)
-  - Impact vs Effort matrix visualization
-  - Traffic gain estimates and ROI calculations
-  - Step-by-step implementation guides (expandable accordion)
-  - Recommended tools per insight
-  - Filter controls (by impact, by effort, quick wins only)
-  - Traffic improvement strategies section
-  - Bookmark and share functionality
-  - Save insights feature
- **UX:** Smooth animations, clear visual hierarchy
- **Accessibility:** Keyboard navigation support
-
-#### 6. **SEOAnalysisController.tsx** (Orchestration Component)
- **Status:** ✅ COMPLETE
- **Lines:** 750+
- **Location:** `frontend/src/components/SEODashboard/`
- **Purpose:** Main workflow orchestrator
- **Features:**
-  - 5-step guided workflow with visual stepper
-  - Step 1: Website Input (URL, competitors, keywords)
-  - Step 2: Enterprise Audit (with progress tracking)
-  - Step 3: GSC Analysis (simultaneous execution)
-  - Step 4: Generate AI Insights (LLM integration)
-  - Step 5: Review & Download (full report export)
-  - Real-time progress indicators (0-100%)
-  - Analysis configuration dialog
-  - Report download (JSON format)
-  - New analysis reset functionality
- **State Management:** Local state with Zustand integration points
- **Error Handling:** Comprehensive error displays
- **Loading States:** Smooth transitions and progress feedback
-
-### Dashboard Integration
- **Status:** ✅ COMPLETE
- **File Modified:** `SEODashboard.tsx`
- **Changes:**
-  - Added tab-based navigation system
-  - Tab 1: "📊 Overview" - Existing functionality (preserved)
-  - Tab 2: "🔍 Enterprise Analysis" - New Phase 2A tab
-  - Seamless tab switching with state management
-  - All existing features preserved
-
-### Compilation Status
- **Status:** ✅ FIXED
- **Errors Fixed:** 14/14
-  - 3 module path errors → Fixed import paths
-  - 2 Material-UI errors → Fixed import sources
-  - 9 TypeScript type errors → Added type annotations
- **Documentation:** `COMPILATION_FIXES.md` created
-
---
-
-## 🔴 PENDING: Backend Implementation (0%)
-
-### Required Endpoints: 12 Total
-
-#### Priority 1: Core Analysis Endpoints (3)
-1. **POST `/api/seo-tools/enterprise/complete-audit`**
-   - Input: `EnterpriseAuditRequest` (website_url, competitors, keywords)
-   - Output: `EnterpriseAuditResult` (comprehensive audit data)
-   - Backend File: `services/seo_tools/enterprise_seo_service.py`
-   - Status: 🔴 NOT IMPLEMENTED
-   - Effort: HIGH (requires multiple analysis modules)
-
-2. **POST `/api/seo-tools/gsc/analyze-search-performance`**
-   - Input: `GSCAnalysisRequest` (site_url, date_range)
-   - Output: `GSCAnalysisResult` (search performance data)
-   - Backend File: `services/seo_tools/gsc_analyzer_service.py`
-   - Status: 🔴 NOT IMPLEMENTED
-   - Effort: MEDIUM (GSC API integration needed)
-
-3. **POST `/api/seo-tools/gsc/content-opportunities`**
-   - Input: `ContentOpportunitiesRequest` (site_url, analysis_type)
-   - Output: `ContentOpportunitiesReport` (opportunity recommendations)
-   - Backend File: `services/seo_tools/gsc_analyzer_service.py`
-   - Status: 🔴 NOT IMPLEMENTED
-   - Effort: MEDIUM
-
-#### Priority 2: LLM Insight Endpoints (8)
-4. **POST `/api/seo-tools/llm/generate-audit-insights`**
-   - Converts audit results to actionable insights
-   - Status: 🔴 NOT IMPLEMENTED
-
-5. **POST `/api/seo-tools/llm/generate-gsc-insights`**
-   - Converts GSC data to search-focused insights
-   - Status: 🔴 NOT IMPLEMENTED
-
-6. **POST `/api/seo-tools/llm/generate-content-strategy`**
-   - Generates content gap analysis and strategy
-   - Status: 🔴 NOT IMPLEMENTED
-
-7. **POST `/api/seo-tools/llm/generate-traffic-roadmap`**
-   - Creates phased traffic improvement plan
-   - Status: 🔴 NOT IMPLEMENTED
-
-8. **POST `/api/seo-tools/llm/prioritized-recommendations`**
-   - Ranks all improvements by impact vs effort
-   - Status: 🔴 NOT IMPLEMENTED
-
-9. **POST `/api/seo-tools/llm/quick-wins`**
-   - Identifies quick wins (< 1 week implementation)
-   - Status: 🔴 NOT IMPLEMENTED
-
-10. **POST `/api/seo-tools/llm/competitive-insights`**
-    - Competitive positioning analysis
-    - Status: 🔴 NOT IMPLEMENTED
-
-11. **POST `/api/seo-tools/llm/keyword-expansion`**
-    - Keyword research and expansion
-    - Status: 🔴 NOT IMPLEMENTED
-
-#### Priority 3: Support Endpoints (1)
-12. **GET `/api/seo-tools/enterprise/health`**
-    - Health check for enterprise service
-    - Status: 🔴 NOT IMPLEMENTED
-
-### Backend Architecture Required
-```
-backend/
-├── services/
-│   └── seo_tools/
-│       ├── enterprise_seo_service.py (NEW)
-│       ├── gsc_analyzer_service.py (NEW)
-│       ├── llm_insights_service.py (NEW)
-│       └── ...
-├── routers/
-│   ├── seo_tools.py (EXISTING - needs updates)
-│   └── ...
-├── models/
-│   ├── seo_models.py (EXISTING - needs new types)
-│   └── ...
-└── api/
-    └── ... (existing structure)
-```
-
-### Backend Dependencies
- Google Search Console API (authentication ready ✅)
- LLM integration (Claude/GPT API)
- SEO analysis libraries (SEMrush API, Moz API, etc.)
- Database for caching results
- Authentication middleware (Clerk - ready ✅)
-
---
-
-## 🟡 TESTING STATUS (Ready for Backend)
-
-### Frontend Testing Readiness
- ✅ Component structure complete
- ✅ TypeScript types validated
- ✅ UI rendering verified
- ✅ Navigation works
- ⏳ Functional testing (pending mock data)
- ⏳ Integration testing (pending backend)
- ⏳ E2E testing (pending backend)
-
-### Test Data Mock Available
-```typescript
-// Mock data structure ready in llmInsightsGenerator.ts
-const mockEnterpriseAuditResult: EnterpriseAuditResult = {
-  website_url: 'https://example.com',
-  audit_date: '2026-05-24',
-  executive_summary: { /* ... */ },
-  // ... 15+ fields
-}
-```
-
---
-
-## 📈 Completion Metrics
-
-### Frontend Completion: 100%
-| Component | Status | Lines | Features |
-|-----------|--------|-------|----------|
-| API Client | ✅ COMPLETE | 650+ | 15+ methods, 20+ types |
-| LLM Service | ✅ COMPLETE | 450+ | 10+ methods, 8 prompts |
-| Audit Results | ✅ COMPLETE | 800+ | 8 sections, filtering |
-| GSC Results | ✅ COMPLETE | 900+ | 4 tabs, tables, charts |
-| Insights Display | ✅ COMPLETE | 700+ | Ranking, filtering, guides |
-| Controller | ✅ COMPLETE | 750+ | 5-step workflow, stepper |
-| Dashboard | ✅ COMPLETE | Modified | Tab integration |
-
-**Total Frontend Code:** ~4,850 lines | **Status:** ✅ PRODUCTION READY
-
-### Backend Completion: 0%
-| Endpoint | Priority | Status | Effort |
-|----------|----------|--------|--------|
-| Enterprise Audit | P1 | 🔴 0% | HIGH |
-| GSC Analysis | P1 | 🔴 0% | MEDIUM |
-| Content Opportunities | P1 | 🔴 0% | MEDIUM |
-| LLM Insights (8x) | P2 | 🔴 0% | HIGH |
-| Health Check | P3 | 🔴 0% | LOW |
-
-**Total Backend Work:** ~3,000+ lines needed | **Status:** 🔴 NOT STARTED
-
---
-
-## 🔄 Data Flow Architecture
-
-```
-User Input (Website URL)
-    ↓
-SEOAnalysisController (Frontend)
-    ├─→ enterpriseSeoAPI.executeEnterpriseAudit()
-    │   ├─→ POST /api/seo-tools/enterprise/complete-audit
-    │   └─→ Returns EnterpriseAuditResult
-    │
-    ├─→ enterpriseSeoAPI.analyzeGSCSearchPerformance()
-    │   ├─→ POST /api/seo-tools/gsc/analyze-search-performance
-    │   └─→ Returns GSCAnalysisResult
-    │
-    ├─→ EnterpriseAuditResults (Display)
-    │
-    ├─→ GSCAnalysisResults (Display)
-    │
-    ├─→ llmInsightsGenerator.generateEnterpriseAuditInsights()
-    │   ├─→ POST /api/seo-tools/llm/generate-audit-insights
-    │   └─→ Returns ActionableInsight[]
-    │
-    └─→ ActionableInsightsDisplay (Final Display)
-```
-
---
-
-## 📋 Next Implementation Phases
-
-### Phase 2A.1: Backend Core Endpoints (IMMEDIATE)
-**Timeline:** 1-2 weeks  
-**Priority:** CRITICAL  
-**Effort:** HIGH
-
-**Tasks:**
-1. Create `enterprise_seo_service.py`
-   - Technical SEO analysis (Core Web Vitals, speed, mobile)
-   - On-page analysis (meta tags, headings, content)
-   - Keyword research (volume, difficulty, ranking potential)
-   - Competitive benchmarking
-   - Implementation roadmap generation
-
-2. Create `gsc_analyzer_service.py`
-   - Google Search Console API integration
-   - Search performance metrics extraction
-   - Keyword opportunity identification
-   - Content gap analysis
-
-3. Update `routers/seo_tools.py`
-   - Add 3 core endpoint routes
-   - Add request/response validation
-   - Add error handling
-
-**Deliverables:**
- 3 functional endpoints
- Request/response validation
- Error handling
- Database caching (optional but recommended)
-
---
-
-### Phase 2A.2: LLM Integration Endpoints (CRITICAL)
-**Timeline:** 1-2 weeks  
-**Priority:** CRITICAL  
-**Effort:** HIGH
-
-**Tasks:**
-1. Create `llm_insights_service.py`
-   - LLM prompt templates for each insight type
-   - API integration with Claude/GPT
-   - Insight generation logic
-   - Caching for performance
-
-2. Implement 8 LLM endpoints
-   - Each endpoint accepts analysis result
-   - Calls LLM with specialized prompt
-   - Returns prioritized insights
-   - Includes traffic projections
-
-3. Prompt optimization
-   - Test with real SEO data
-   - Refine for accuracy
-   - Validate traffic projections
-
-**Deliverables:**
- 8 functional LLM endpoints
- Optimized prompts
- Caching layer
- Performance benchmarks
-
---
-
-### Phase 2A.3: Database & Caching (OPTIMIZATION)
-**Timeline:** 1 week  
-**Priority:** HIGH (for production)  
-**Effort:** MEDIUM
-
-**Tasks:**
-1. Design caching strategy
-   - Cache audit results (24-48 hours)
-   - Cache GSC data (12-24 hours)
-   - Cache LLM insights (48 hours)
-
-2. Implement caching layer
-   - Redis integration
-   - Cache invalidation logic
-   - TTL management
-
-3. Database storage
-   - Store analysis history
-   - Track user preferences
-   - Enable result comparison
-
-**Benefit:** 10x performance improvement for repeated analyses
-
---
-
-### Phase 2A.4: Testing & Validation (COMPREHENSIVE)
-**Timeline:** 1-2 weeks  
-**Priority:** HIGH  
-**Effort:** MEDIUM
-
-**Test Coverage:**
-1. Unit tests (50+ tests)
-   - Each service method
-   - Error scenarios
-   - Data validation
-
-2. Integration tests (20+ tests)
-   - End-to-end workflows
-   - API interactions
-   - LLM responses
-
-3. E2E tests (10+ tests)
-   - Frontend + Backend
-   - Real user workflows
-   - Performance benchmarks
-
-4. Manual testing
-   - Real websites (10+ test sites)
-   - GSC validation
-   - Insight accuracy
-   - UI/UX verification
-
-**Deliverables:**
- Test suite (80+ tests)
- Coverage report (80%+ coverage)
- Performance benchmarks
- Bug fix list
-
---
-
-### Phase 2A.5: Documentation & Deployment (FINAL)
-**Timeline:** 1 week  
-**Priority:** MEDIUM  
-**Effort:** LOW
-
-**Tasks:**
-1. API Documentation
-   - Endpoint specs
-   - Request/response examples
-   - Error codes
-   - Rate limiting
-
-2. User Documentation
-   - Feature guide
-   - Tutorial videos
-   - FAQs
-   - Troubleshooting
-
-3. Developer Documentation
-   - Architecture overview
-   - Setup guide
-   - Contributing guidelines
-   - Maintenance procedures
-
-4. Deployment
-   - Staging environment
-   - Production deployment
-   - Monitoring setup
-   - Rollback procedures
-
---
-
-## 🎯 Success Criteria
-
-### Phase 2A.1 (Backend Core)
- ✅ 3 endpoints fully functional
- ✅ Real enterprise audits working
- ✅ GSC data flowing to frontend
- ✅ All 14 frontend compilation errors resolved
-
-### Phase 2A.2 (LLM Integration)
- ✅ 8 LLM endpoints working
- ✅ Insights generated with traffic projections
- ✅ Priority scoring accurate (1-10 scale)
- ✅ Effort/impact assessment working
-
-### Phase 2A.3 (Database/Caching)
- ✅ Analysis history available
- ✅ Cache hit rate > 70%
- ✅ Query response time < 500ms
-
-### Phase 2A.4 (Testing)
- ✅ Test coverage > 80%
- ✅ All tests passing
- ✅ Performance benchmarks met
- ✅ No critical bugs
-
-### Phase 2A.5 (Documentation)
- ✅ All features documented
- ✅ Developer guide complete
- ✅ User guide complete
- ✅ Ready for production
-
---
-
-## 🚀 Estimated Timeline
-
-| Phase | Tasks | Timeline | Status |
-|-------|-------|----------|--------|
-| 2A.0 Frontend | 6 components | ✅ DONE | COMPLETE |
-| 2A.1 Backend Core | 3 endpoints | 1-2 weeks | ⏳ READY |
-| 2A.2 LLM Integration | 8 endpoints | 1-2 weeks | ⏳ BLOCKED |
-| 2A.3 DB/Caching | Optimization | 1 week | ⏳ BLOCKED |
-| 2A.4 Testing | Validation | 1-2 weeks | ⏳ BLOCKED |
-| 2A.5 Deployment | Release | 1 week | ⏳ BLOCKED |
-
-**Total Estimated:** 5-8 weeks  
-**Current Progress:** 20% (frontend only)  
-**Blocking Issue:** Backend endpoints not implemented
-
---
-
-## ⚠️ Critical Blockers
-
-### Immediate Blockers
-1. **Backend endpoints not implemented** - Blocks all functionality testing
-2. **No mock data** - Prevents UI testing with real-like data
-3. **No LLM service setup** - Blocks insight generation
-4. **GSC authentication** - Needs verification in production
-
-### Recommended Next Action
-**Start Phase 2A.1 immediately:** Implement the 3 core backend endpoints to unblock testing and validation.
-
---
-
-## 📊 Summary Dashboard
-
-```
-FRONTEND IMPLEMENTATION
-✅ API Client:              100% (650 lines)
-✅ LLM Service:             100% (450 lines)
-✅ Components:              100% (3,850 lines)
-✅ Integration:             100% (Complete)
-✅ Compilation:             100% (14 errors fixed)
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-Total Frontend:             ✅ 100% COMPLETE
-
-BACKEND IMPLEMENTATION
-🔴 Core Endpoints:          0% (Not started)
-🔴 LLM Endpoints:           0% (Not started)
-🔴 Database/Caching:        0% (Not started)
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-Total Backend:              🔴 0% NOT STARTED
-
-OVERALL PROJECT STATUS:     🟡 20% COMPLETE
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-Blocking: Backend Implementation
-Ready: Frontend Testing (awaiting backend)
-Next: Start Phase 2A.1 (Backend Core Endpoints)
-```
-
---
-
-## 📞 Action Items
-
-### For Frontend
- [ ] Run `npm run build` to verify all errors fixed
- [ ] Run `npm start` to launch development server
- [ ] Test tab navigation (Overview ↔ Enterprise Analysis)
- [ ] Verify component rendering with mock data
- [ ] Test responsive design on mobile/tablet
-
-### For Backend (IMMEDIATE)
- [ ] Create `services/seo_tools/enterprise_seo_service.py`
- [ ] Create `services/seo_tools/gsc_analyzer_service.py`
- [ ] Update `routers/seo_tools.py` with 3 new endpoints
- [ ] Implement request/response validation
- [ ] Add comprehensive error handling
- [ ] Test with real websites and GSC data
-
-### For DevOps
- [ ] Set up Redis caching layer
- [ ] Configure GSC API credentials
- [ ] Set up LLM API integration (Claude/GPT)
- [ ] Configure monitoring and logging
- [ ] Plan staging environment
-
---
-
-**Generated:** May 24, 2026  
-**Next Review:** After Phase 2A.1 Backend Implementation  
-**Questions?** Check `PHASE2A_INTEGRATION_GUIDE.md` or `COMPILATION_FIXES.md`
--- a/PHASE2A_NEXT_STEPS.md
+++ b/PHASE2A_NEXT_STEPS.md
@@ -1,667 +0,0 @@
-# Phase 2A Roadmap: Next Implementation Phases
-
-**Current Status:** Frontend 100% Complete → Backend 0% Started → Ready for Phase 2A.1
-
---
-
-## 🎯 Big Picture: What's Done vs What's Needed
-
-### ✅ COMPLETED (Frontend - 100%)
-
-```
-┌─────────────────────────────────────────────────────────┐
-│  USER INTERFACE LAYER (Complete & Ready)               │
-│                                                          │
-│  SEODashboard Tab: "🔍 Enterprise Analysis"             │
-│         ↓                                                │
-│  SEOAnalysisController (5-Step Workflow)                │
-│  ├─ Step 1: Website Input Form                          │
-│  ├─ Step 2: Enterprise Audit Display                    │
-│  ├─ Step 3: GSC Analysis Display                        │
-│  ├─ Step 4: AI Insights Display                         │
-│  └─ Step 5: Review & Download                           │
-└─────────────────────────────────────────────────────────┘
-                        ↓
-┌─────────────────────────────────────────────────────────┐
-│  SERVICE LAYER (Complete & Ready)                       │
-│                                                          │
-│  ├─ enterpriseSeoApi.ts (API Client)                    │
-│  │  ├─ executeEnterpriseAudit()                         │
-│  │  ├─ analyzeGSCSearchPerformance()                    │
-│  │  ├─ getContentOpportunitiesReport()                  │
-│  │  └─ ... 12 more methods                              │
-│  │                                                       │
-│  └─ llmInsightsGenerator.ts (Insights Service)          │
-│     ├─ generateEnterpriseAuditInsights()               │
-│     ├─ generateGSCAnalysisInsights()                   │
-│     ├─ generateTrafficRoadmap()                        │
-│     └─ ... 7 more insight methods                      │
-└─────────────────────────────────────────────────────────┘
-                        ↓
-                  🔴 BLOCKED HERE 🔴
-                  (Backend Missing)
-                        ↓
-┌─────────────────────────────────────────────────────────┐
-│  API ENDPOINTS (0% - Need Implementation)               │
-│                                                          │
-│  ❌ POST /api/seo-tools/enterprise/complete-audit      │
-│  ❌ POST /api/seo-tools/gsc/analyze-search-performance  │
-│  ❌ POST /api/seo-tools/gsc/content-opportunities       │
-│  ❌ POST /api/seo-tools/llm/generate-audit-insights     │
-│  ❌ ... 8 more LLM endpoints                            │
-└─────────────────────────────────────────────────────────┘
-```
-
---
-
-## 🔴 BLOCKER: Backend Not Implemented
-
-### Why Testing Can't Proceed
- ❌ No endpoints to call from frontend
- ❌ No data flowing to UI components
- ❌ Can't test end-to-end workflows
- ❌ Can't validate LLM insights
- ❌ Can't generate real reports
-
-### Immediate Impact
-```
-Frontend Ready ✅ → Can't Test → Can't Deploy ❌
-```
-
---
-
-## 📋 Phase 2A.1: Backend Core Endpoints (IMMEDIATE NEXT STEP)
-
-### What Needs to Be Built
-
-#### Endpoint 1: Enterprise Audit
-```
-POST /api/seo-tools/enterprise/complete-audit
-
-REQUEST:
-{
-  website_url: "https://example.com",
-  competitors?: ["https://competitor1.com"],
-  keywords?: ["target keyword 1"],
-  analysis_type: "complete" | "quick"
-}
-
-RESPONSE:
-{
-  executive_summary: { score, traffic_potential, time_to_implement },
-  technical_audit: { core_web_vitals, mobile_usability, page_speed },
-  keyword_research: [ { keyword, volume, difficulty, current_ranking } ],
-  competitive_analysis: { comparison, gaps, opportunities },
-  implementation_roadmap: [ { phase, tasks, timeline } ],
-  ... 15+ more fields
-}
-```
-
-**Backend Requirements:**
- SEO analysis library (e.g., SEMrush API, Moz API, or self-built)
- Technical audit tools (Core Web Vitals, page speed analysis)
- Keyword research integration
- Competitive analysis logic
- Data aggregation and formatting
-
-**Estimated Effort:** 400-600 lines of code
-
---
-
-#### Endpoint 2: GSC Analysis
-```
-POST /api/seo-tools/gsc/analyze-search-performance
-
-REQUEST:
-{
-  site_url: "https://example.com",
-  date_range: 90,  // days
-  include_competitors?: true
-}
-
-RESPONSE:
-{
-  performance_overview: { clicks, impressions, ctr, avg_position },
-  top_keywords: [ { keyword, clicks, impressions, ctr, position } ],
-  page_performance: [ { page_url, clicks, impressions, ctr, position } ],
-  keyword_analysis: { 
-    opportunities: [...],
-    declining_keywords: [...],
-    needs_attention: [...]
-  },
-  content_opportunities: [ { keyword, traffic_gain, priority } ],
-  technical_signals: { issues, fixes, score },
-  ... 10+ more fields
-}
-```
-
-**Backend Requirements:**
- Google Search Console API integration
- GSC authentication (already have credentials ✅)
- Data extraction and normalization
- Trend analysis
- Opportunity identification logic
-
-**Estimated Effort:** 300-400 lines of code
-
---
-
-#### Endpoint 3: Content Opportunities
-```
-POST /api/seo-tools/gsc/content-opportunities
-
-REQUEST:
-{
-  site_url: "https://example.com",
-  analysis_type: "gap_analysis" | "expansion" | "optimization"
-}
-
-RESPONSE:
-{
-  opportunities: [
-    {
-      keyword: "target keyword",
-      current_position: 15,
-      traffic_potential: 500,
-      difficulty: 45,
-      recommendation: "Create new article targeting this keyword",
-      priority: "high"
-    }
-  ],
-  total_traffic_potential: 15000,
-  quick_wins: [...],
-  competitive_gaps: [...]
-}
-```
-
-**Backend Requirements:**
- Keyword gap analysis logic
- Traffic potential calculation
- Difficulty scoring
- Competitive benchmarking
-
-**Estimated Effort:** 250-350 lines of code
-
---
-
-### Phase 2A.1 Implementation Steps
-
-#### Step 1: Setup Service Files (1 day)
-```python
-# backend/services/seo_tools/enterprise_seo_service.py
-class EnterpriseSEOService:
-    def execute_complete_audit(self, request: EnterpriseAuditRequest) -> EnterpriseAuditResult:
-        # Implement audit logic
-        pass
-    
-    def execute_quick_audit(self, request: QuickAuditRequest) -> EnterpriseAuditResult:
-        # Implement quick audit
-        pass
-
-# backend/services/seo_tools/gsc_analyzer_service.py
-class GSCAnalyzerService:
-    def analyze_search_performance(self, request: GSCAnalysisRequest) -> GSCAnalysisResult:
-        # Implement GSC analysis
-        pass
-    
-    def get_content_opportunities(self, request: ContentOpportunitiesRequest) -> ContentOpportunitiesReport:
-        # Implement opportunity analysis
-        pass
-```
-
-#### Step 2: Add Routes (1 day)
-```python
-# backend/routers/seo_tools.py - Add these routes:
-@router.post('/enterprise/complete-audit')
-async def complete_enterprise_audit(request: EnterpriseAuditRequest):
-    # Call EnterpriseSEOService
-    pass
-
-@router.post('/gsc/analyze-search-performance')
-async def analyze_gsc_performance(request: GSCAnalysisRequest):
-    # Call GSCAnalyzerService
-    pass
-
-@router.post('/gsc/content-opportunities')
-async def get_content_opportunities(request: ContentOpportunitiesRequest):
-    # Call GSCAnalyzerService
-    pass
-```
-
-#### Step 3: Implement Business Logic (2-3 days)
- Technical SEO analysis
- GSC data extraction
- Opportunity identification
- Data formatting
-
-#### Step 4: Testing (1-2 days)
- Unit tests for each method
- Integration tests
- Real website testing
- Error handling
-
-#### Step 5: Documentation (1 day)
- Endpoint documentation
- API specs
- Setup instructions
-
---
-
-## 📋 Phase 2A.2: LLM Integration (FOLLOWS PHASE 2A.1)
-
-### Once Backend Endpoints Working...
-
-#### Create LLM Service
-```python
-# backend/services/seo_tools/llm_insights_service.py
-class LLMInsightsService:
-    def generate_audit_insights(self, audit_result: EnterpriseAuditResult) -> List[ActionableInsight]:
-        prompt = self.build_audit_insight_prompt(audit_result)
-        response = llm_api.call(prompt)
-        return parse_insights(response)
-    
-    def generate_gsc_insights(self, gsc_result: GSCAnalysisResult) -> List[ActionableInsight]:
-        # Similar pattern
-        pass
-    
-    # 6 more methods for different insight types
-```
-
-#### Add LLM Endpoints (8 routes)
-1. `/api/seo-tools/llm/generate-audit-insights`
-2. `/api/seo-tools/llm/generate-gsc-insights`
-3. `/api/seo-tools/llm/generate-content-strategy`
-4. `/api/seo-tools/llm/generate-traffic-roadmap`
-5. `/api/seo-tools/llm/prioritized-recommendations`
-6. `/api/seo-tools/llm/quick-wins`
-7. `/api/seo-tools/llm/competitive-insights`
-8. `/api/seo-tools/llm/keyword-expansion`
-
-#### LLM Prompt Templates (Ready in Frontend)
-The `llmInsightsGenerator.ts` has all 8 prompt templates. Backend just needs to:
-1. Accept the prompt from frontend
-2. Call LLM API (Claude/GPT)
-3. Parse response
-4. Return formatted insights
-
---
-
-## 🚀 Recommended Implementation Sequence
-
-### Week 1: Phase 2A.1 Backend Core (CRITICAL)
-**Goal:** Get 3 core endpoints working
-
-```
-Day 1-2: Setup
-  ├─ Create enterprise_seo_service.py
-  ├─ Create gsc_analyzer_service.py
-  └─ Add routes to seo_tools.py
-
-Day 3-4: Implementation
-  ├─ Implement audit analysis logic
-  ├─ Integrate GSC API
-  └─ Add error handling
-
-Day 5: Testing
-  ├─ Unit tests
-  ├─ Integration tests
-  └─ Manual testing with real websites
-```
-
-**Deliverable:** 3 functional endpoints + tests
-
---
-
-### Week 2: Phase 2A.2 LLM Integration (CRITICAL)
-**Goal:** Get LLM insights working
-
-```
-Day 1-2: Setup
-  ├─ Create llm_insights_service.py
-  ├─ Setup LLM API (Claude/GPT)
-  └─ Add 8 LLM routes
-
-Day 3-4: Implementation
-  ├─ Implement insight generation
-  ├─ Integrate LLM prompts
-  └─ Add caching for performance
-
-Day 5: Testing
-  ├─ Test insight accuracy
-  ├─ Validate traffic projections
-  └─ Performance optimization
-```
-
-**Deliverable:** 8 functional LLM endpoints + tests
-
---
-
-### Week 3: Phase 2A.3 Optimization (RECOMMENDED)
-**Goal:** Add caching and database storage
-
-```
-Day 1-2: Caching Layer
-  ├─ Setup Redis
-  ├─ Implement cache strategy
-  └─ Cache invalidation logic
-
-Day 3-4: Database
-  ├─ Add analysis history storage
-  ├─ Enable result comparison
-  └─ Performance tuning
-
-Day 5: Monitoring
-  ├─ Setup logging
-  ├─ Performance monitoring
-  └─ Alerting
-```
-
-**Deliverable:** 10x performance improvement
-
---
-
-### Week 4: Phase 2A.4 Comprehensive Testing
-**Goal:** Validate everything works end-to-end
-
-```
-Day 1: Unit Testing
-  ├─ Service method tests (50+)
-  ├─ Error scenario tests
-  └─ Data validation tests
-
-Day 2: Integration Testing
-  ├─ API endpoint tests (20+)
-  ├─ Database integration tests
-  └─ LLM response tests
-
-Day 3: E2E Testing
-  ├─ Frontend + Backend workflows
-  ├─ Real website testing (10+ sites)
-  └─ Performance benchmarks
-
-Day 4-5: Bug Fixes
-  ├─ Fix identified issues
-  ├─ Performance optimization
-  └─ Edge case handling
-```
-
-**Deliverable:** 80%+ test coverage, all tests passing
-
---
-
-### Week 5: Phase 2A.5 Documentation & Deployment
-**Goal:** Document and release
-
-```
-Day 1-2: Documentation
-  ├─ API documentation
-  ├─ User guides
-  └─ Developer documentation
-
-Day 3-4: Deployment
-  ├─ Staging environment setup
-  ├─ Production deployment
-  └─ Monitoring setup
-
-Day 5: Validation
-  ├─ Production testing
-  ├─ User acceptance testing
-  └─ Rollback procedures
-```
-
-**Deliverable:** Production-ready release
-
---
-
-## 📊 Timeline & Resource Planning
-
-```
-                    Phase 2A.1        Phase 2A.2        Phase 2A.3    Phase 2A.4    Phase 2A.5
-Week                  Core              LLM               Cache         Test         Deploy
-────────────────────────────────────────────────────────────────────────────────────────────
-1  May 24-30     ████████████       
-                 (Backend Core)
-
-2  May 31-Jun 6                      ████████████      
-                                     (LLM Integration)
-
-3  Jun 7-13                                           ████████████
-                                                      (Optimization)
-
-4  Jun 14-20                                                        ████████████
-                                                                    (Testing)
-
-5  Jun 21-27                                                                      ████████████
-                                                                                  (Deployment)
-
-TOTAL:           5 working days      5 working days    5 working days 5 days     5 working days
-EFFORT:          80 hours (2x2)      80 hours (2x2)    40 hours      60 hours    40 hours
-TEAM:            2 Backend devs      1-2 Backend       1 Backend     2 QA/Dev    1 DevOps
-                                     devs              dev           1 Dev       1 Backend
-
-Progress:        20%                 40%               60%           80%         100%
-```
-
---
-
-## 🎯 Success Criteria for Each Phase
-
-### Phase 2A.1: Backend Core (WEEKS 1)
-✅ **MUST HAVE:**
- [ ] 3 endpoints responding correctly
- [ ] Request validation working
- [ ] Response formats match frontend expectations
- [ ] Error handling implemented
- [ ] All tests passing
-
-✅ **SHOULD HAVE:**
- [ ] Database caching setup
- [ ] Performance benchmarks met
- [ ] Edge cases handled
-
-⚠️ **NICE TO HAVE:**
- [ ] Advanced analytics
- [ ] Custom filters
-
---
-
-### Phase 2A.2: LLM Integration (WEEKS 2)
-✅ **MUST HAVE:**
- [ ] 8 LLM endpoints working
- [ ] Traffic projections accurate
- [ ] Priority scoring (1-10) implemented
- [ ] Effort assessment working
- [ ] All tests passing
-
-✅ **SHOULD HAVE:**
- [ ] Insights caching
- [ ] Response time < 5 seconds
- [ ] Prompt optimization complete
-
---
-
-### Phase 2A.3: Optimization (WEEKS 3)
-✅ **MUST HAVE:**
- [ ] Caching reduces response time by 80%
- [ ] History storage working
- [ ] Cache invalidation logic tested
-
-✅ **SHOULD HAVE:**
- [ ] Monitoring alerts set up
- [ ] Performance dashboard
-
---
-
-### Phase 2A.4: Testing (WEEKS 4)
-✅ **MUST HAVE:**
- [ ] 80%+ test coverage
- [ ] All tests passing
- [ ] No critical bugs
- [ ] Performance benchmarks met
-
---
-
-### Phase 2A.5: Deployment (WEEKS 5)
-✅ **MUST HAVE:**
- [ ] Production deployment successful
- [ ] Monitoring active
- [ ] User access working
- [ ] No data loss
-
---
-
-## 💡 Quick Reference: What to Build
-
-### Backend Structure Needed
-```
-backend/services/seo_tools/
-├── enterprise_seo_service.py       (New - 400 lines)
-├── gsc_analyzer_service.py         (New - 350 lines)
-├── llm_insights_service.py         (New - 500 lines)
-└── ...existing services...
-
-backend/routers/
-├── seo_tools.py                    (Update - +150 lines)
-└── ...existing routers...
-```
-
-### Database Schema Needed
-```sql
-- Store analysis results
-CREATE TABLE seo_analyses (
-  id UUID PRIMARY KEY,
-  user_id UUID,
-  website_url VARCHAR,
-  analysis_type VARCHAR,
-  results JSONB,
-  created_at TIMESTAMP,
-  cached_until TIMESTAMP
-);
-
-- Store insights
-CREATE TABLE insights (
-  id UUID PRIMARY KEY,
-  analysis_id UUID,
-  insight_text TEXT,
-  priority INT,
-  traffic_gain INT,
-  effort_level VARCHAR
-);
-```
-
-### Environment Setup Needed
-```
-# .env additions
-GSC_API_KEY=...
-LLM_API_KEY=...
-REDIS_URL=redis://localhost:6379
-DATABASE_URL=postgres://...
-```
-
---
-
-## ⚡ Quick Start for Phase 2A.1
-
-### 1. Create Service File Structure
-```python
-# backend/services/seo_tools/enterprise_seo_service.py
-from fastapi import HTTPException
-from typing import Optional, List
-
-class EnterpriseSEOService:
-    """Handles comprehensive enterprise SEO audits"""
-    
-    async def execute_complete_audit(self, website_url: str, competitors: Optional[List[str]] = None):
-        """Execute complete enterprise audit"""
-        try:
-            # 1. Technical audit
-            technical = await self._technical_audit(website_url)
-            
-            # 2. Keyword research
-            keywords = await self._keyword_research(website_url)
-            
-            # 3. Competitive analysis
-            competitive = await self._competitive_analysis(website_url, competitors)
-            
-            # 4. On-page analysis
-            on_page = await self._on_page_analysis(website_url)
-            
-            # 5. Generate roadmap
-            roadmap = self._generate_roadmap(technical, keywords, competitive, on_page)
-            
-            return {
-                'executive_summary': self._generate_summary(technical, keywords),
-                'technical_audit': technical,
-                'keyword_research': keywords,
-                'competitive_analysis': competitive,
-                'on_page_analysis': on_page,
-                'implementation_roadmap': roadmap,
-            }
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=str(e))
-    
-    async def _technical_audit(self, website_url: str):
-        # Implement technical SEO analysis
-        # Check Core Web Vitals, mobile usability, page speed, security, etc.
-        pass
-    
-    # ... more methods
-```
-
-### 2. Add Routes
-```python
-# backend/routers/seo_tools.py
-from backend.services.seo_tools.enterprise_seo_service import EnterpriseSEOService
-
-router = APIRouter()
-enterprise_service = EnterpriseSEOService()
-
-@router.post('/enterprise/complete-audit')
-async def complete_enterprise_audit(website_url: str, competitors: Optional[List[str]] = None):
-    return await enterprise_service.execute_complete_audit(website_url, competitors)
-```
-
-### 3. Test Endpoint
-```bash
-curl -X POST http://localhost:8000/api/seo-tools/enterprise/complete-audit \
-  -H "Content-Type: application/json" \
-  -d '{"website_url":"https://example.com"}'
-```
-
---
-
-## 🎬 Ready to Start?
-
-### Recommended Next Action
-**Start Phase 2A.1 today:** Implement the 3 core backend endpoints to unblock all testing.
-
-### Resources Provided
-1. ✅ `PHASE2A_INTEGRATION_GUIDE.md` - Complete frontend specs
-2. ✅ `COMPILATION_FIXES.md` - Fixed all 14 TypeScript errors
-3. ✅ Frontend code (4,850+ lines) - Ready to consume backend data
-4. ✅ LLM prompts in `llmInsightsGenerator.ts` - Ready to use
-5. ✅ Type definitions in `enterpriseSeoApi.ts` - Match backend models
-
-### What's Blocking
- ❌ Backend implementation NOT STARTED
- ❌ No core endpoints
- ❌ No LLM integration
- ❌ Can't test end-to-end
-
-### Next 24 Hours
- [ ] Review this document
- [ ] Estimate backend effort
- [ ] Plan resource allocation
- [ ] Start Phase 2A.1 implementation
- [ ] Setup development environment
-
---
-
-**Status:** Frontend 100% Complete → Backend Ready to Start  
-**Next Checkpoint:** Phase 2A.1 Complete (3 endpoints working)  
-**Timeline:** Can be done in 1-2 weeks with 2-3 developers  
-
-**Questions? Check:**
- `PHASE2A_IMPLEMENTATION_REVIEW.md` - This file (detailed review)
- `PHASE2A_INTEGRATION_GUIDE.md` - Frontend specifications
- `COMPILATION_FIXES.md` - TypeScript fixes applied
--- a/PHASE2A_STATUS_DASHBOARD.md
+++ b/PHASE2A_STATUS_DASHBOARD.md
@@ -1,460 +0,0 @@
-# 📊 Phase 2A Implementation Status Dashboard
-
-**Date:** May 24, 2026 | **Overall Progress:** 20% | **Current Phase:** Frontend Complete ✅
-
---
-
-## 🎯 Project Summary
-
-| Metric | Status | Details |
-|--------|--------|---------|
-| **Project Name** | Phase 2A SEO Dashboard | Enterprise SEO Analysis Integration |
-| **Current Phase** | Frontend Implementation | ✅ COMPLETE |
-| **Total Phases** | 5 | 2A.1 through 2A.5 |
-| **Overall Progress** | 20% | Frontend 100%, Backend 0% |
-| **Timeline** | 5-8 weeks | Started: May 24, Target: Jun 28 |
-| **Team Size** | 2-3 devs | Frontend ✅, Backend ⏳ |
-| **Blocking Issues** | 1 Critical | Backend not started |
-
---
-
-## 📈 Completion Status by Component
-
-### Frontend Layer: ✅ 100% COMPLETE
-
-```
-Component                    Status    Lines    Features    Tests
-─────────────────────────────────────────────────────────────────────────
-enterpriseSeoApi.ts          ✅        650+     15 methods  ✅ Types
-llmInsightsGenerator.ts      ✅        450+     10 methods  ✅ Types
-EnterpriseAuditResults       ✅        800+     8 sections  ✅ Rendering
-GSCAnalysisResults           ✅        900+     4 tabs      ✅ Rendering
-ActionableInsightsDisplay    ✅        700+     Filtering   ✅ Rendering
-SEOAnalysisController        ✅        750+     5-step flow ✅ Integration
-SEODashboard (modified)      ✅        ~50      Tab nav     ✅ Tab works
-─────────────────────────────────────────────────────────────────────────
-TOTAL FRONTEND               ✅        4,850    50+ features ✅ READY
-```
-
-### Backend Layer: 🔴 0% STARTED
-
-```
-Component                    Status    Priority Lines    Effort
-─────────────────────────────────────────────────────────────────────
-Enterprise Audit Endpoint    🔴        P1       ~400     HIGH
-GSC Analysis Endpoint        🔴        P1       ~350     MEDIUM
-Content Opportunities EP     🔴        P1       ~300     MEDIUM
-LLM Audit Insights EP        🔴        P2       ~200     MEDIUM
-LLM GSC Insights EP          🔴        P2       ~200     MEDIUM
-LLM Content Strategy EP      🔴        P2       ~150     LOW
-LLM Traffic Roadmap EP       🔴        P2       ~150     LOW
-LLM Recommendations EP       🔴        P2       ~150     LOW
-LLM Quick Wins EP            🔴        P2       ~100     LOW
-LLM Competitive EP           🔴        P2       ~100     LOW
-LLM Keyword Expansion EP     🔴        P2       ~100     LOW
-Health Check Endpoint        🔴        P3       ~50      LOW
-─────────────────────────────────────────────────────────────────────
-TOTAL BACKEND                🔴        N/A      ~2,650   HIGH
-```
-
-### Database & Infrastructure: 🔴 0% STARTED
-
-```
-Component                    Status    Priority Effort
-─────────────────────────────────────────────────────────────────
-Redis Caching Layer          🔴        P2       MEDIUM
-Analysis History DB          🔴        P2       LOW
-Performance Monitoring       🔴        P3       LOW
-Logging Infrastructure       🔴        P3       LOW
-```
-
---
-
-## 🎯 Phase Breakdown
-
-### Phase 2A.0: Frontend Implementation ✅
- **Status:** ✅ COMPLETE
- **Duration:** 3 days
- **Effort:** 40 hours
- **Team:** 1 Frontend Dev
- **Deliverable:** 6 components + full UI
-
-**What Was Done:**
- ✅ 4,850 lines of React/TypeScript code
- ✅ 20+ TypeScript interfaces
- ✅ 50+ UI components
- ✅ Dashboard integration
- ✅ Error handling
-
-**What's Next:** Phase 2A.1
-
---
-
-### Phase 2A.1: Backend Core Endpoints 🔴
- **Status:** 🔴 NOT STARTED
- **Duration:** 1 week
- **Effort:** 40-50 hours
- **Team:** 2 Backend Devs
- **Priority:** ⚠️ CRITICAL - BLOCKING ALL TESTING
-
-**What Needs to Be Done:**
- [ ] Enterprise audit service (400 lines)
- [ ] GSC analyzer service (350 lines)
- [ ] 3 API endpoints
- [ ] Request/response validation
- [ ] Error handling
- [ ] Unit tests
- [ ] Integration tests
-
-**Blocking Factors:**
- ❌ 3 core endpoints not implemented
- ❌ No business logic
- ❌ No data flowing to frontend
- ❌ Testing impossible
-
-**Success Criteria:**
- ✅ 3 endpoints functional
- ✅ Tests passing
- ✅ Real data flowing
- ✅ Frontend can make calls
-
---
-
-### Phase 2A.2: LLM Integration 🔴
- **Status:** 🔴 BLOCKED (Pending 2A.1)
- **Duration:** 1 week
- **Effort:** 40-50 hours
- **Team:** 1-2 Backend Devs
- **Priority:** ⚠️ CRITICAL
-
-**What Needs to Be Done:**
- [ ] LLM insights service (500 lines)
- [ ] 8 LLM endpoints
- [ ] Prompt optimization
- [ ] Response parsing
- [ ] Caching strategy
- [ ] Performance optimization
-
-**Dependencies:**
- ⏳ Depends on Phase 2A.1
- ⏳ Needs LLM API setup
- ⏳ Requires prompt templates (ready ✅)
-
---
-
-### Phase 2A.3: Database & Caching 🔴
- **Status:** 🔴 BLOCKED (Pending 2A.2)
- **Duration:** 1 week
- **Effort:** 30 hours
- **Team:** 1 Backend Dev + 1 DevOps
- **Priority:** HIGH (for production)
-
-**What Needs to Be Done:**
- [ ] Redis setup
- [ ] Cache invalidation logic
- [ ] Database schema
- [ ] History storage
- [ ] Performance tuning
-
-**Benefit:** 10x performance improvement
-
---
-
-### Phase 2A.4: Testing 🔴
- **Status:** 🔴 BLOCKED (Pending 2A.3)
- **Duration:** 1-2 weeks
- **Effort:** 50 hours
- **Team:** 2 QA + 1 Dev
- **Priority:** HIGH
-
-**What Needs to Be Done:**
- [ ] 50+ unit tests
- [ ] 20+ integration tests
- [ ] 10+ E2E tests
- [ ] Manual testing
- [ ] Performance validation
- [ ] Bug fixes
-
-**Target:** 80%+ code coverage
-
---
-
-### Phase 2A.5: Documentation & Deployment 🔴
- **Status:** 🔴 BLOCKED (Pending 2A.4)
- **Duration:** 1 week
- **Effort:** 30 hours
- **Team:** 1 Backend Dev + 1 DevOps
- **Priority:** MEDIUM
-
-**What Needs to Be Done:**
- [ ] API documentation
- [ ] User guides
- [ ] Developer documentation
- [ ] Deployment procedures
- [ ] Monitoring setup
- [ ] Rollback procedures
-
---
-
-## 📊 Overall Project Progress
-
-```
-TOTAL PROJECT PROGRESS: 20% COMPLETE
-═══════════════════════════════════════════════════════════════
-
-Frontend:        ████████████████████░░░░░░░░░░░░░░░░░░░░░░  100%
-Backend Core:    ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%
-LLM Integration: ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%
-Infrastructure:  ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%
-Testing:         ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%
-Deployment:      ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  0%
-
-WEEK-BY-WEEK PROJECTION:
-
-Week 1 (May 24-30):  ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  20%
-                     Frontend ✅ + Start Backend Core
-
-Week 2 (May 31-Jun6): ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  40%
-                     Backend Core ✅ + Start LLM
-
-Week 3 (Jun 7-13):   ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░  60%
-                     LLM Integration ✅ + Start DB/Cache
-
-Week 4 (Jun 14-20):  ████████████████░░░░░░░░░░░░░░░░░░░░░░░░  80%
-                     Infrastructure ✅ + Start Testing
-
-Week 5 (Jun 21-27):  ████████████████████░░░░░░░░░░░░░░░░░░░░  100%
-                     Testing + Deployment ✅
-```
-
---
-
-## ⚠️ Current Blockers
-
-### 🔴 CRITICAL: Backend Implementation Not Started
- **Impact:** Complete blocker for all testing
- **Severity:** Critical
- **Current Status:** 0% done
- **Time to Unblock:** 1 week
- **Action Required:** Start Phase 2A.1 immediately
-
-### 🟡 Dependencies
-| Phase | Depends On | Status |
-|-------|-----------|--------|
-| 2A.1  | N/A | 🔴 Blocked by resources |
-| 2A.2  | 2A.1 | 🔴 Blocked by 2A.1 |
-| 2A.3  | 2A.2 | 🔴 Blocked by 2A.2 |
-| 2A.4  | 2A.3 | 🔴 Blocked by 2A.3 |
-| 2A.5  | 2A.4 | 🔴 Blocked by 2A.4 |
-
---
-
-## 📋 Action Items by Priority
-
-### 🔴 IMMEDIATE (Next 24 Hours)
- [ ] Review this status dashboard
- [ ] Allocate backend development resources
- [ ] Setup development environment
- [ ] Start Phase 2A.1 backend core implementation
- [ ] Create service files (enterprise_seo_service.py, gsc_analyzer_service.py)
-
-### 🟡 SHORT TERM (Next Week)
- [ ] Complete Phase 2A.1 (3 endpoints working)
- [ ] Implement business logic for enterprise audit
- [ ] Integrate GSC API
- [ ] Write unit tests
- [ ] Manual testing with real websites
-
-### 🟢 MEDIUM TERM (2-3 Weeks)
- [ ] Start Phase 2A.2 LLM integration
- [ ] Implement 8 LLM endpoints
- [ ] Optimize LLM prompts
- [ ] Setup caching layer
- [ ] Begin comprehensive testing
-
-### 🔵 LONG TERM (4-5 Weeks)
- [ ] Complete all testing
- [ ] Deploy to staging
- [ ] UAT and bug fixes
- [ ] Deploy to production
- [ ] Monitor and optimize
-
---
-
-## 📞 Resource Requirements
-
-### Phase 2A.1 (Backend Core)
-```
-Role              Count  Hours/Week  Total Hours
-─────────────────────────────────────────────────
-Backend Dev       2      20         40 hours
-QA/Tester         0.5    5          5 hours
-DevOps            0      0          0 hours
-─────────────────────────────────────────────────
-TOTAL             2.5    25         45 hours
-```
-
-### Phase 2A.2 (LLM Integration)
-```
-Role              Count  Hours/Week  Total Hours
-─────────────────────────────────────────────────
-Backend Dev       1-2    20         40 hours
-LLM Specialist    0.5    5          5 hours
-QA/Tester         0.5    5          5 hours
-─────────────────────────────────────────────────
-TOTAL             2-2.5  30         50 hours
-```
-
-### Full Project (2A.1 through 2A.5)
-```
-Role              Total Hours
-─────────────────────────────────
-Backend Dev       ~250 hours
-Frontend Dev      40 hours (done)
-QA/Tester         ~80 hours
-DevOps            ~50 hours
-LLM Specialist    ~20 hours
-─────────────────────────────────
-TOTAL             ~440 hours
-```
-
---
-
-## 💰 ROI & Impact
-
-### Frontend ROI (Completed)
- ✅ 4,850 lines of production-ready code
- ✅ 50+ UI components
- ✅ Full enterprise SEO analysis UI
- ✅ LLM prompt integration ready
- ✅ Zero technical debt
-
-### Expected Backend ROI (Pending)
- 📊 Enterprise-grade SEO audit capability
- 📈 LLM-powered insights (8 types)
- 🚀 Traffic improvement guidance
- 💡 Competitive analysis
- 🎯 Implementation roadmaps
-
-### Business Impact
- Differentiator: First LLM-powered SEO dashboard
- Monetization: Premium feature for enterprise tier
- User Value: Actionable insights → Traffic growth
- Market Position: Advanced SEO intelligence
-
---
-
-## 🎯 Success Metrics
-
-### Phase 2A.1 Success
- [ ] 3 endpoints fully functional
- [ ] Response time < 10 seconds
- [ ] 95% uptime in testing
- [ ] All tests passing
- [ ] No critical bugs
-
-### Phase 2A.2 Success
- [ ] 8 LLM endpoints working
- [ ] Insights generate < 5 seconds
- [ ] Traffic projections ± 20% accuracy
- [ ] User satisfaction > 4.5/5
- [ ] No data corruption
-
-### Phase 2A.5 Success
- [ ] All tests passing
- [ ] 80%+ code coverage
- [ ] Performance benchmarks met
- [ ] Zero critical bugs
- [ ] User acceptance achieved
-
---
-
-## 📅 Gantt Chart View
-
-```
-Task                        May  Jun  Jul  Status
-────────────────────────────────────────────────────────
-Frontend (Done)             ✅            Complete
-├─ Phase 2A.0 Frontend      ✅
-│
-Backend & Infrastructure
-├─ Phase 2A.1 Core          ▓▓▓▓░░░░░░░░░  🔴 0%
-├─ Phase 2A.2 LLM                ▓▓▓▓░░░░░  🔴 0%
-├─ Phase 2A.3 DB/Cache                ▓▓▓  🔴 0%
-├─ Phase 2A.4 Testing                    ▓  🔴 0%
-└─ Phase 2A.5 Deploy                     ▓  🔴 0%
-
-Legend: ✅ Complete | ▓ In Progress | ░ Pending
-```
-
---
-
-## 📞 Next Steps (Quick Checklist)
-
-### Today (May 24)
- [ ] Team reviews this status document
- [ ] Stakeholder approval for Phase 2A.1
- [ ] Backend team setup environment
- [ ] Create JIRA tickets for Phase 2A.1
-
-### Tomorrow (May 25)
- [ ] Start Phase 2A.1 implementation
- [ ] Create service files
- [ ] Implement first endpoint
- [ ] Setup testing environment
-
-### This Week
- [ ] 3 core endpoints working
- [ ] Unit tests passing
- [ ] Manual testing on real sites
- [ ] Ready to move to Phase 2A.2
-
---
-
-## 📊 Key Metrics Dashboard
-
-| Metric | Current | Target | Status |
-|--------|---------|--------|--------|
-| Frontend Completion | 100% | 100% | ✅ On Track |
-| Backend Completion | 0% | 100% | 🔴 Blocked |
-| Test Coverage | N/A | 80% | ⏳ Pending |
-| Performance Target | N/A | <5s | ⏳ Pending |
-| Bug Count | 0 | 0 | ✅ On Track |
-| Deployment Readiness | 20% | 100% | 🟡 Need Backend |
-
---
-
-## 🎓 Documentation Provided
-
-| Document | Location | Status | Purpose |
-|----------|----------|--------|---------|
-| Integration Guide | `PHASE2A_INTEGRATION_GUIDE.md` | ✅ Ready | Frontend specs |
-| Implementation Review | `PHASE2A_IMPLEMENTATION_REVIEW.md` | ✅ Ready | Detailed review |
-| Next Steps | `PHASE2A_NEXT_STEPS.md` | ✅ Ready | Roadmap |
-| Compilation Fixes | `COMPILATION_FIXES.md` | ✅ Ready | Error resolution |
-| This File | `PHASE2A_STATUS_DASHBOARD.md` | ✅ Ready | Current status |
-
---
-
-## 🚀 Call to Action
-
-**IMMEDIATE ACTION REQUIRED:**
-
-Start Phase 2A.1 backend implementation to unblock:
- ✅ Frontend testing
- ✅ Integration testing  
- ✅ Full workflow validation
- ✅ Timeline adherence
-
-**Recommended Timeline:** Begin TODAY for June 28 completion
-
-**Resources Needed:** 2-3 backend developers for next 5 weeks
-
-**Expected Outcome:** Production-ready enterprise SEO dashboard with LLM-powered insights
-
---
-
-**Generated:** May 24, 2026  
-**Last Updated:** May 24, 2026  
-**Next Review:** Daily during Phase 2A.1  
-**Questions:** Check `PHASE2A_IMPLEMENTATION_REVIEW.md`
--- a/QUICK_REFERENCE.md
+++ b/QUICK_REFERENCE.md
@@ -1,342 +0,0 @@
-# Phase 2A - Quick Reference Guide
-
-**Last Updated:** May 24, 2026 | **Status:** Frontend 100% ✅ | Backend 0% 🔴
-
---
-
-## 📍 Where We Are
-
-```
-WHAT'S COMPLETE ✅
-├─ 6 React components (4,850 lines)
-├─ Type-safe API client (650 lines)
-├─ LLM prompts service (450 lines)
-├─ Dashboard tab integration
-├─ Error handling & loading states
-├─ Material-UI styling
-├─ Full TypeScript support
-└─ 14 compilation errors fixed
-
-WHAT'S BLOCKING 🔴
-├─ 12 backend endpoints (not started)
-├─ Enterprise audit service (not started)
-├─ GSC analyzer service (not started)
-├─ LLM insights service (not started)
-├─ Database/caching layer (not started)
-└─ All testing (can't start without backend)
-```
-
---
-
-## 🎯 Where We're Going
-
-### Phase 2A.1: Backend Core (NEXT - 1 week)
-**Priority:** 🔴 CRITICAL  
-**Effort:** 40-50 hours  
-**Team:** 2 backend developers
-
-**What to Build:**
- [x] Enterprise audit endpoint
- [x] GSC analysis endpoint
- [x] Content opportunities endpoint
- [x] Business logic
- [x] Error handling
- [x] Unit tests
-
-**Unblocks:**
- ✅ Frontend testing
- ✅ Integration testing
- ✅ End-to-end workflows
- ✅ Phase 2A.2
-
-### Phase 2A.2: LLM Integration (AFTER 2A.1 - 1 week)
-**Priority:** 🔴 CRITICAL  
-**Effort:** 40-50 hours  
-**Team:** 1-2 backend developers
-
-**What to Build:**
- [x] 8 LLM insight endpoints
- [x] Prompt optimization
- [x] Response parsing
- [x] Caching strategy
-
-**Unblocks:**
- ✅ Insight generation
- ✅ Traffic improvement guidance
- ✅ Phase 2A.3
-
-### Phase 2A.3: Infrastructure (AFTER 2A.2 - 1 week)
-**Priority:** HIGH  
-**Benefit:** 10x performance improvement
-
-**What to Build:**
- [x] Redis caching
- [x] Database schema
- [x] History storage
-
-### Phase 2A.4: Testing (AFTER 2A.3 - 1-2 weeks)
-**Priority:** HIGH  
-**Target:** 80%+ coverage
-
-**What to Build:**
- [x] 50+ unit tests
- [x] 20+ integration tests
- [x] 10+ E2E tests
-
-### Phase 2A.5: Deployment (AFTER 2A.4 - 1 week)
-**Priority:** MEDIUM
-
-**What to Build:**
- [x] API documentation
- [x] Deployment procedures
- [x] Monitoring setup
-
---
-
-## 📚 Documentation Map
-
-| Need | Document | Read Time |
-|------|----------|-----------|
-| **Full Implementation Details** | `PHASE2A_IMPLEMENTATION_REVIEW.md` | 20 min |
-| **Component Specifications** | `PHASE2A_INTEGRATION_GUIDE.md` | 15 min |
-| **Implementation Roadmap** | `PHASE2A_NEXT_STEPS.md` | 15 min |
-| **Status Tracking** | `PHASE2A_STATUS_DASHBOARD.md` | 10 min |
-| **Compilation Fixes** | `COMPILATION_FIXES.md` | 5 min |
-| **Complete Review** | `PHASE2A_COMPLETE_REVIEW.md` | 25 min |
-| **Quick Reference** | This File | 3 min |
-
---
-
-## 🔗 Key Files in Codebase
-
-### Frontend Components
-```
-frontend/src/api/
-├── enterpriseSeoApi.ts (650 lines)
-└── llmInsightsGenerator.ts (450 lines)
-
-frontend/src/components/SEODashboard/
-├── SEOAnalysisController.tsx (750 lines)
-└── components/
-    ├── EnterpriseAuditResults.tsx (800 lines)
-    ├── GSCAnalysisResults.tsx (900 lines)
-    └── ActionableInsightsDisplay.tsx (700 lines)
-
-frontend/src/components/SEODashboard/
-└── SEODashboard.tsx (modified - added tabs)
-```
-
-### Documentation
-```
-Root directory:
-├── PHASE2A_INTEGRATION_GUIDE.md
-├── PHASE2A_IMPLEMENTATION_REVIEW.md
-├── PHASE2A_NEXT_STEPS.md
-├── PHASE2A_STATUS_DASHBOARD.md
-├── PHASE2A_COMPLETE_REVIEW.md
-├── COMPILATION_FIXES.md
-└── FILE_INDEX.md
-```
-
-### Backend (Not Started)
-```
-backend/services/seo_tools/
-├── enterprise_seo_service.py (NEEDS CREATION)
-├── gsc_analyzer_service.py (NEEDS CREATION)
-└── llm_insights_service.py (NEEDS CREATION)
-
-backend/routers/
-└── seo_tools.py (NEEDS UPDATES - add 12 endpoints)
-```
-
---
-
-## ⚡ Quick Status Check
-
-### Frontend Ready?
-```
-✅ API client complete
-✅ All components created
-✅ Dashboard integrated
-✅ TypeScript errors fixed
-✅ Error handling in place
-✅ Loading states working
-= READY TO TEST (waiting for backend)
-```
-
-### Backend Ready?
-```
-🔴 No endpoints
-🔴 No services
-🔴 No database
-🔴 No LLM integration
-🔴 No tests
-= NOT READY (must start Phase 2A.1)
-```
-
-### Can We Deploy?
-```
-🔴 NO - Backend not implemented
-🔴 NO - No testing done
-🔴 NO - No production checks
-🔴 NO - No monitoring
-= BLOCKED (need 4+ weeks of backend work)
-```
-
---
-
-## 📞 Action Items
-
-### For Frontend Developers
- ✅ Review complete (all components ready)
- ✅ Testing ready (can start mock testing)
- ✅ Documentation complete
-
-### For Backend Developers
- [ ] **TODAY:** Review Phase 2A.1 requirements
- [ ] **TODAY:** Setup development environment
- [ ] **TODAY:** Create service file stubs
- [ ] **TOMORROW:** Start enterprise audit service
- [ ] **THIS WEEK:** Complete 3 core endpoints
-
-### For DevOps
- [ ] Plan infrastructure needs
- [ ] Setup Redis for caching
- [ ] Plan database schema
- [ ] Setup monitoring
-
-### For Product/Stakeholders
- [ ] Review documentation
- [ ] Approve timeline (5 weeks to production)
- [ ] Allocate resources (2-3 developers)
- [ ] Set success criteria
-
---
-
-## 🚀 How to Start Phase 2A.1
-
-### Step 1: Create Service File
-```python
-# backend/services/seo_tools/enterprise_seo_service.py
-
-class EnterpriseSEOService:
-    async def execute_complete_audit(self, website_url: str):
-        # Implement business logic
-        pass
-    
-    async def execute_quick_audit(self, website_url: str):
-        # Implement quick version
-        pass
-```
-
-### Step 2: Add Route
-```python
-# backend/routers/seo_tools.py
-
-@router.post('/enterprise/complete-audit')
-async def complete_audit(website_url: str):
-    service = EnterpriseSEOService()
-    return await service.execute_complete_audit(website_url)
-```
-
-### Step 3: Test
-```bash
-curl -X POST http://localhost:8000/api/seo-tools/enterprise/complete-audit
-```
-
-### Step 4: Implement
-Fill in business logic based on requirements in `PHASE2A_NEXT_STEPS.md`
-
---
-
-## 📊 Timeline at a Glance
-
-```
-Week 1: Phase 2A.1 Backend Core      [████░░░░░░░░░░░░░░░░░░░░]  20%
-Week 2: Phase 2A.2 LLM Integration   [████████░░░░░░░░░░░░░░░░]  40%
-Week 3: Phase 2A.3 Infrastructure    [████████████░░░░░░░░░░░░]  60%
-Week 4: Phase 2A.4 Testing           [████████████████░░░░░░░░]  80%
-Week 5: Phase 2A.5 Deployment        [████████████████████░░░░]  100%
-
-Target Completion: June 28, 2026
-```
-
---
-
-## ✨ Key Metrics
-
-| Metric | Current | Target | Status |
-|--------|---------|--------|--------|
-| Frontend Complete | 100% | 100% | ✅ On Track |
-| Backend Complete | 0% | 100% | 🔴 Blocked |
-| Test Coverage | - | 80% | ⏳ Pending |
-| Performance | - | <5s | ⏳ Pending |
-| Bugs | 0 | 0 | ✅ On Track |
-| Timeline | Week 1/5 | Week 5/5 | 🟡 At Risk |
-
---
-
-## 💬 Quick Q&A
-
-**Q: Is the frontend ready to ship?**  
-A: No, backend endpoints not implemented yet.
-
-**Q: How long until production?**  
-A: 5 weeks if we start Phase 2A.1 TODAY.
-
-**Q: What's blocking us?**  
-A: Backend implementation not started.
-
-**Q: How many developers needed?**  
-A: 2-3 backend developers for next 5 weeks.
-
-**Q: Can we test the frontend?**  
-A: Yes, with mock data. But can't test end-to-end without backend.
-
-**Q: What if we delay Phase 2A.1?**  
-A: Timeline pushes back 1 week per week of delay.
-
-**Q: Is there technical debt?**  
-A: No, frontend is clean and production-ready.
-
-**Q: What's the biggest risk?**  
-A: Backend implementation doesn't start immediately.
-
---
-
-## 🎯 Next Steps (24 Hours)
-
-1. **Discuss** this review with team
-2. **Allocate** 2-3 backend developers
-3. **Setup** development environment
-4. **Assign** Phase 2A.1 tasks
-5. **Start** implementation
-
---
-
-## 📞 Need More Details?
-
-| Topic | Document |
-|-------|----------|
-| Component Details | PHASE2A_INTEGRATION_GUIDE.md |
-| Backend Blueprint | PHASE2A_NEXT_STEPS.md |
-| Timeline & Resources | PHASE2A_IMPLEMENTATION_REVIEW.md |
-| Real-time Status | PHASE2A_STATUS_DASHBOARD.md |
-| Compilation Issues | COMPILATION_FIXES.md |
-
---
-
-## ✅ Sign-Off Checklist
-
- [ ] Reviewed frontend completion status
- [ ] Understand backend requirements
- [ ] Aware of 5-week timeline
- [ ] Know Phase 2A.1 is blocking factor
- [ ] Ready to allocate resources
- [ ] Agreed to start immediately
-
---
-
-**Status:** Frontend Ready ✅ | Backend Needed 🔴  
-**Action:** Start Phase 2A.1 TODAY  
-**Contact:** Check documentation for details
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/README.md
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/README.md
@@ -1,370 +0,0 @@
-Google Ads Generator
-Google Ads Generator Logo
-
-Overview
-The Google Ads Generator is an AI-powered tool designed to create high-converting Google Ads based on industry best practices. This tool helps marketers, business owners, and advertising professionals create optimized ad campaigns that maximize ROI and conversion rates.
-
-By leveraging advanced AI algorithms and proven advertising frameworks, the Google Ads Generator creates compelling ad copy, suggests optimal keywords, generates relevant extensions, and provides performance predictions—all tailored to your specific business needs and target audience.
-
-Table of Contents
-Features
-Getting Started
-User Interface
-Ad Creation Process
-Ad Types
-Quality Analysis
-Performance Simulation
-Best Practices
-Export Options
-Advanced Features
-Technical Details
-FAQ
-Troubleshooting
-Updates and Roadmap
-Features
-Core Features
-AI-Powered Ad Generation: Create compelling, high-converting Google Ads in seconds
-Multiple Ad Types: Support for Responsive Search Ads, Expanded Text Ads, Call-Only Ads, and Dynamic Search Ads
-Industry-Specific Templates: Tailored templates for 20+ industries
-Ad Extensions Generator: Automatically create Sitelinks, Callouts, and Structured Snippets
-Quality Score Analysis: Comprehensive scoring based on Google's quality factors
-Performance Prediction: Estimate CTR, conversion rates, and ROI
-A/B Testing: Generate multiple variations for testing
-Export Options: Export to CSV, Excel, Google Ads Editor CSV, and JSON
-Advanced Features
-Keyword Research Integration: Find high-performing keywords for your ads
-Competitor Analysis: Analyze competitor ads and identify opportunities
-Landing Page Suggestions: Recommendations for landing page optimization
-Budget Optimization: Suggestions for optimal budget allocation
-Ad Schedule Recommendations: Identify the best times to run your ads
-Audience Targeting Suggestions: Recommendations for demographic targeting
-Local Ad Optimization: Special features for local businesses
-E-commerce Ad Features: Product-specific ad generation
-Getting Started
-Prerequisites
-Alwrity AI Writer platform
-Basic understanding of Google Ads concepts
-Information about your business, products/services, and target audience
-Accessing the Tool
-Navigate to the Alwrity AI Writer platform
-Select "AI Google Ads Generator" from the tools menu
-Follow the guided setup process
-User Interface
-The Google Ads Generator features a user-friendly, tabbed interface designed to guide you through the ad creation process:
-
-Tab 1: Ad Creation
-This is where you'll input your business information and ad requirements:
-
-Business Information: Company name, industry, products/services
-Campaign Goals: Select from options like brand awareness, lead generation, sales, etc.
-Target Audience: Define your ideal customer
-Ad Type Selection: Choose from available ad formats
-USP and Benefits: Input your unique selling propositions and key benefits
-Keywords: Add target keywords or generate suggestions
-Landing Page URL: Specify where users will go after clicking your ad
-Budget Information: Set daily/monthly budget for performance predictions
-Tab 2: Ad Performance
-After generating ads, this tab provides detailed analysis:
-
-Quality Score: Overall score (1-10) with detailed breakdown
-Strengths & Improvements: What's good and what could be better
-Keyword Relevance: Analysis of keyword usage in ad elements
-CTR Prediction: Estimated click-through rate based on ad quality
-Conversion Potential: Estimated conversion rate
-Mobile Friendliness: Assessment of how well the ad performs on mobile
-Ad Policy Compliance: Check for potential policy violations
-Tab 3: Ad History
-Keep track of your generated ads:
-
-Saved Ads: Previously generated and saved ads
-Favorites: Ads you've marked as favorites
-Version History: Track changes and iterations
-Performance Notes: Add notes about real-world performance
-Tab 4: Best Practices
-Educational resources to improve your ads:
-
-Industry Guidelines: Best practices for your specific industry
-Ad Type Tips: Specific guidance for each ad type
-Quality Score Optimization: How to improve quality score
-Extension Strategies: How to effectively use ad extensions
-A/B Testing Guide: How to test and optimize your ads
-Ad Creation Process
-Step 1: Define Your Campaign
-Select your industry from the dropdown menu
-Choose your primary campaign goal
-Define your target audience
-Set your budget parameters
-Step 2: Input Business Details
-Enter your business name
-Provide your website URL
-Input your unique selling propositions
-List key product/service benefits
-Add any promotional offers or discounts
-Step 3: Keyword Selection
-Enter your primary keywords
-Use the integrated keyword research tool to find additional keywords
-Select keyword match types (broad, phrase, exact)
-Review keyword competition and volume metrics
-Step 4: Ad Type Selection
-Choose your preferred ad type
-Review the requirements and limitations for that ad type
-Select any additional features specific to that ad type
-Step 5: Generate Ads
-Click the "Generate Ads" button
-Review the generated ads
-Request variations if needed
-Save your favorite versions
-Step 6: Add Extensions
-Select which extension types to include
-Review and edit the generated extensions
-Add any custom extensions
-Step 7: Analyze and Optimize
-Review the quality score and analysis
-Make suggested improvements
-Regenerate ads if necessary
-Compare different versions
-Step 8: Export
-Choose your preferred export format
-Select which ads to include
-Download the file for import into Google Ads
-Ad Types
-Responsive Search Ads (RSA)
-The most flexible and recommended ad type, featuring:
-
-Up to 15 headlines (3 shown at a time)
-Up to 4 descriptions (2 shown at a time)
-Dynamic combination of elements based on performance
-Automatic testing of different combinations
-Expanded Text Ads (ETA)
-A more controlled ad format with:
-
-3 headlines
-2 descriptions
-Display URL with two path fields
-Fixed layout with no dynamic combinations
-Call-Only Ads
-Designed to drive phone calls rather than website visits:
-
-Business name
-Phone number
-Call-to-action text
-Description lines
-Verification URL (not shown to users)
-Dynamic Search Ads (DSA)
-Ads that use your website content to target relevant searches:
-
-Dynamic headline generation based on search queries
-Custom descriptions
-Landing page selection based on website content
-Requires website URL for crawling
-Quality Analysis
-Our comprehensive quality analysis evaluates your ads based on factors that influence Google's Quality Score:
-
-Headline Analysis
-Keyword Usage: Presence of keywords in headlines
-Character Count: Optimal length for visibility
-Power Words: Use of emotionally compelling words
-Clarity: Clear communication of value proposition
-Call to Action: Presence of action-oriented language
-Description Analysis
-Keyword Density: Optimal keyword usage
-Benefit Focus: Clear articulation of benefits
-Feature Inclusion: Mention of key features
-Urgency Elements: Time-limited offers or scarcity
-Call to Action: Clear next steps for the user
-URL Path Analysis
-Keyword Inclusion: Relevant keywords in display paths
-Readability: Clear, understandable paths
-Relevance: Connection to landing page content
-Overall Ad Relevance
-Keyword-to-Ad Relevance: Alignment between keywords and ad copy
-Ad-to-Landing Page Relevance: Consistency across the user journey
-Intent Match: Alignment with search intent
-Performance Simulation
-Our tool provides data-driven performance predictions based on:
-
-Click-Through Rate (CTR) Prediction
-Industry benchmarks
-Ad quality factors
-Keyword competition
-Ad position estimates
-Conversion Rate Prediction
-Industry averages
-Landing page quality
-Offer strength
-Call-to-action effectiveness
-Cost Estimation
-Keyword competition
-Quality Score impact
-Industry CPC averages
-Budget allocation
-ROI Calculation
-Estimated clicks
-Predicted conversions
-Average conversion value
-Cost projections
-Best Practices
-Our tool incorporates these Google Ads best practices:
-
-Headline Best Practices
-Include primary keywords in at least 2 headlines
-Use numbers and statistics when relevant
-Address user pain points directly
-Include your unique selling proposition
-Create a sense of urgency when appropriate
-Keep headlines under 30 characters for full visibility
-Use title case for better readability
-Include at least one call-to-action headline
-Description Best Practices
-Include primary and secondary keywords naturally
-Focus on benefits, not just features
-Address objections proactively
-Include specific offers or promotions
-End with a clear call to action
-Use all available character space (90 characters per description)
-Maintain consistent messaging with headlines
-Include trust signals (guarantees, social proof, etc.)
-Extension Best Practices
-Create at least 8 sitelinks for maximum visibility
-Use callouts to highlight additional benefits
-Include structured snippets relevant to your industry
-Ensure extensions don't duplicate headline content
-Make each extension unique and valuable
-Use specific, action-oriented language
-Keep sitelink text under 25 characters for mobile visibility
-Ensure landing pages for sitelinks are relevant and optimized
-Campaign Structure Best Practices
-Group closely related keywords together
-Create separate ad groups for different themes
-Align ad copy closely with keywords in each ad group
-Use a mix of match types for each keyword
-Include negative keywords to prevent irrelevant clicks
-Create separate campaigns for different goals or audiences
-Set appropriate bid adjustments for devices, locations, and schedules
-Implement conversion tracking for performance measurement
-Export Options
-The Google Ads Generator offers multiple export formats to fit your workflow:
-
-CSV Format
-Standard CSV format compatible with most spreadsheet applications
-Includes all ad elements and extensions
-Contains quality score and performance predictions
-Suitable for analysis and record-keeping
-Excel Format
-Formatted Excel workbook with multiple sheets
-Separate sheets for ads, extensions, and analysis
-Includes charts and visualizations of predicted performance
-Color-coded quality indicators
-Google Ads Editor CSV
-Specially formatted CSV for direct import into Google Ads Editor
-Follows Google's required format specifications
-Includes all necessary fields for campaign creation
-Ready for immediate upload to Google Ads Editor
-JSON Format
-Structured data format for programmatic use
-Complete ad data in machine-readable format
-Suitable for integration with other marketing tools
-Includes all metadata and analysis results
-Advanced Features
-Keyword Research Integration
-Access to keyword volume data
-Competition analysis
-Cost-per-click estimates
-Keyword difficulty scores
-Seasonal trend information
-Question-based keyword suggestions
-Long-tail keyword recommendations
-Competitor Analysis
-Identify competitors bidding on similar keywords
-Analyze competitor ad copy and messaging
-Identify gaps and opportunities
-Benchmark your ads against competitors
-Receive suggestions for differentiation
-Landing Page Suggestions
-Alignment with ad messaging
-Key elements to include
-Conversion optimization tips
-Mobile responsiveness recommendations
-Page speed improvement suggestions
-Call-to-action placement recommendations
-Local Ad Optimization
-Location extension suggestions
-Local keyword recommendations
-Geo-targeting strategies
-Local offer suggestions
-Community-focused messaging
-Location-specific call-to-actions
-Technical Details
-System Requirements
-Modern web browser (Chrome, Firefox, Safari, Edge)
-Internet connection
-Access to Alwrity AI Writer platform
-Data Privacy
-No permanent storage of business data
-Secure processing of all inputs
-Option to save ads to your account
-Compliance with data protection regulations
-API Integration
-Available API endpoints for programmatic access
-Documentation for developers
-Rate limits and authentication requirements
-Sample code for common use cases
-FAQ
-General Questions
-Q: How accurate are the performance predictions? A: Performance predictions are based on industry benchmarks and Google's published data. While they provide a good estimate, actual performance may vary based on numerous factors including competition, seasonality, and market conditions.
-
-Q: Can I edit the generated ads? A: Yes, all generated ads can be edited before export. You can modify headlines, descriptions, paths, and extensions to better fit your needs.
-
-Q: How many ads can I generate? A: The tool allows unlimited ad generation within your Alwrity subscription limits.
-
-Q: Are the generated ads compliant with Google's policies? A: The tool is designed to create policy-compliant ads, but we recommend reviewing Google's latest advertising policies as they may change over time.
-
-Technical Questions
-Q: Can I import my existing ads for optimization? A: Currently, the tool does not support importing existing ads, but this feature is on our roadmap.
-
-Q: How do I import the exported files into Google Ads? A: For Google Ads Editor CSV files, open Google Ads Editor, go to File > Import, and select your exported file. For other formats, you may need to manually create campaigns using the generated content.
-
-Q: Can I schedule automatic ad generation? A: Automated scheduling is not currently available but is planned for a future release.
-
-Troubleshooting
-Common Issues
-Issue: Generated ads don't include my keywords Solution: Ensure your keywords are relevant to your business description and offerings. Try using more specific keywords or providing more detailed business information.
-
-Issue: Quality score is consistently low Solution: Review the improvement suggestions in the Ad Performance tab. Common issues include keyword relevance, landing page alignment, and benefit clarity.
-
-Issue: Export file isn't importing correctly into Google Ads Editor Solution: Ensure you're selecting the "Google Ads Editor CSV" export format. If problems persist, check for special characters in your ad copy that might be causing formatting issues.
-
-Issue: Performance predictions seem unrealistic Solution: Adjust your industry selection and budget information to get more accurate predictions. Consider providing more specific audience targeting information.
-
-Updates and Roadmap
-Recent Updates
-Added support for Performance Max campaign recommendations
-Improved keyword research integration
-Enhanced mobile ad optimization
-Added 5 new industry templates
-Improved quality score algorithm
-Coming Soon
-Competitor ad analysis tool
-A/B testing performance simulator
-Landing page builder integration
-Automated ad scheduling recommendations
-Video ad script generator
-Google Shopping ad support
-Multi-language ad generation
-Custom template builder
-Support
-For additional help with the Google Ads Generator:
-
-Visit our Help Center
-Email support at support@example.com
-Join our Community Forum
-License
-The Google Ads Generator is part of the Alwrity AI Writer platform and is subject to the platform's terms of service and licensing agreements.
-
-Acknowledgments
-Google Ads API documentation
-Industry best practices from leading digital marketing experts
-User feedback and feature requests
-Last updated: [Current Date]
-
-Version: 1.0.0
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/init.py
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/init.py
@@ -1,9 +0,0 @@
-"""
-Google Ads Generator Module
-
-This module provides functionality for generating high-converting Google Ads.
-"""
-
-from .google_ads_generator import write_google_ads
-
-__all__ = ["write_google_ads"]
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_analyzer.py
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_analyzer.py
@@ -1,327 +0,0 @@
-"""
-Ad Analyzer Module
-
-This module provides functions for analyzing and scoring Google Ads.
-"""
-
-import re
-from typing import Dict, List, Any, Tuple
-import random
-from urllib.parse import urlparse
-
-def analyze_ad_quality(ad: Dict, primary_keywords: List[str], secondary_keywords: List[str], 
-business_name: str, call_to_action: str) -> Dict:
-"""
-Analyze the quality of a Google Ad based on best practices.
-
-Args:
-ad: Dictionary containing ad details
-primary_keywords: List of primary keywords
-secondary_keywords: List of secondary keywords
-business_name: Name of the business
-call_to_action: Call to action text
-
-Returns:
-Dictionary with analysis results
-"""
-# Initialize results
-strengths = []
-improvements = []
-
-# Get ad components
-headlines = ad.get("headlines", [])
-descriptions = ad.get("descriptions", [])
-path1 = ad.get("path1", "")
-path2 = ad.get("path2", "")
-
-# Check headline count
-if len(headlines) >= 10:
-strengths.append("Good number of headlines (10+) for optimization")
-elif len(headlines) >= 5:
-strengths.append("Adequate number of headlines for testing")
-else:
-improvements.append("Add more headlines (aim for 10+) to give Google's algorithm more options")
-
-# Check description count
-if len(descriptions) >= 4:
-strengths.append("Good number of descriptions (4+) for optimization")
-elif len(descriptions) >= 2:
-strengths.append("Adequate number of descriptions for testing")
-else:
-improvements.append("Add more descriptions (aim for 4+) to give Google's algorithm more options")
-
-# Check headline length
-long_headlines = [h for h in headlines if len(h) > 30]
-if long_headlines:
-improvements.append(f"{len(long_headlines)} headline(s) exceed 30 characters and may be truncated")
-else:
-strengths.append("All headlines are within the recommended length")
-
-# Check description length
-long_descriptions = [d for d in descriptions if len(d) > 90]
-if long_descriptions:
-improvements.append(f"{len(long_descriptions)} description(s) exceed 90 characters and may be truncated")
-else:
-strengths.append("All descriptions are within the recommended length")
-
-# Check keyword usage in headlines
-headline_keywords = []
-for kw in primary_keywords:
-if any(kw.lower() in h.lower() for h in headlines):
-headline_keywords.append(kw)
-
-if len(headline_keywords) == len(primary_keywords):
-strengths.append("All primary keywords are used in headlines")
-elif headline_keywords:
-strengths.append(f"{len(headline_keywords)} out of {len(primary_keywords)} primary keywords used in headlines")
-missing_kw = [kw for kw in primary_keywords if kw not in headline_keywords]
-improvements.append(f"Add these primary keywords to headlines: {', '.join(missing_kw)}")
-else:
-improvements.append("No primary keywords found in headlines - add keywords to improve relevance")
-
-# Check keyword usage in descriptions
-desc_keywords = []
-for kw in primary_keywords:
-if any(kw.lower() in d.lower() for d in descriptions):
-desc_keywords.append(kw)
-
-if len(desc_keywords) == len(primary_keywords):
-strengths.append("All primary keywords are used in descriptions")
-elif desc_keywords:
-strengths.append(f"{len(desc_keywords)} out of {len(primary_keywords)} primary keywords used in descriptions")
-missing_kw = [kw for kw in primary_keywords if kw not in desc_keywords]
-improvements.append(f"Add these primary keywords to descriptions: {', '.join(missing_kw)}")
-else:
-improvements.append("No primary keywords found in descriptions - add keywords to improve relevance")
-
-# Check for business name
-if any(business_name.lower() in h.lower() for h in headlines):
-strengths.append("Business name is included in headlines")
-else:
-improvements.append("Consider adding your business name to at least one headline")
-
-# Check for call to action
-if any(call_to_action.lower() in h.lower() for h in headlines) or any(call_to_action.lower() in d.lower() for d in descriptions):
-strengths.append("Call to action is included in the ad")
-else:
-improvements.append(f"Add your call to action '{call_to_action}' to at least one headline or description")
-
-# Check for numbers and statistics
-has_numbers = any(bool(re.search(r'\d+', h)) for h in headlines) or any(bool(re.search(r'\d+', d)) for d in descriptions)
-if has_numbers:
-strengths.append("Ad includes numbers or statistics which can improve CTR")
-else:
-improvements.append("Consider adding numbers or statistics to increase credibility and CTR")
-
-# Check for questions
-has_questions = any('?' in h for h in headlines) or any('?' in d for d in descriptions)
-if has_questions:
-strengths.append("Ad includes questions which can engage users")
-else:
-improvements.append("Consider adding a question to engage users")
-
-# Check for emotional triggers
-emotional_words = ['you', 'free', 'because', 'instantly', 'new', 'save', 'proven', 'guarantee', 'love', 'discover']
-has_emotional = any(any(word in h.lower() for word in emotional_words) for h in headlines) or \
-any(any(word in d.lower() for word in emotional_words) for d in descriptions)
-
-if has_emotional:
-strengths.append("Ad includes emotional trigger words which can improve engagement")
-else:
-improvements.append("Consider adding emotional trigger words to increase engagement")
-
-# Check for path relevance
-if any(kw.lower() in path1.lower() or kw.lower() in path2.lower() for kw in primary_keywords):
-strengths.append("Display URL paths include keywords which improves relevance")
-else:
-improvements.append("Add keywords to your display URL paths to improve relevance")
-
-# Return the analysis results
-return {
-"strengths": strengths,
-"improvements": improvements
-}
-
-def calculate_quality_score(ad: Dict, primary_keywords: List[str], landing_page: str, ad_type: str) -> Dict:
-"""
-Calculate a quality score for a Google Ad based on best practices.
-
-Args:
-ad: Dictionary containing ad details
-primary_keywords: List of primary keywords
-landing_page: Landing page URL
-ad_type: Type of Google Ad
-
-Returns:
-Dictionary with quality score components
-"""
-# Initialize scores
-keyword_relevance = 0
-ad_relevance = 0
-cta_effectiveness = 0
-landing_page_relevance = 0
-
-# Get ad components
-headlines = ad.get("headlines", [])
-descriptions = ad.get("descriptions", [])
-path1 = ad.get("path1", "")
-path2 = ad.get("path2", "")
-
-# Calculate keyword relevance (0-10)
-# Check if keywords are in headlines, descriptions, and paths
-keyword_in_headline = sum(1 for kw in primary_keywords if any(kw.lower() in h.lower() for h in headlines))
-keyword_in_description = sum(1 for kw in primary_keywords if any(kw.lower() in d.lower() for d in descriptions))
-keyword_in_path = sum(1 for kw in primary_keywords if kw.lower() in path1.lower() or kw.lower() in path2.lower())
-
-# Calculate score based on keyword presence
-if len(primary_keywords) > 0:
-headline_score = min(10, (keyword_in_headline / len(primary_keywords)) * 10)
-description_score = min(10, (keyword_in_description / len(primary_keywords)) * 10)
-path_score = min(10, (keyword_in_path / len(primary_keywords)) * 10)
-
-# Weight the scores (headlines most important)
-keyword_relevance = (headline_score * 0.6) + (description_score * 0.3) + (path_score * 0.1)
-else:
-keyword_relevance = 5  # Default score if no keywords provided
-
-# Calculate ad relevance (0-10)
-# Check for ad structure and content quality
-
-# Check headline count and length
-headline_count_score = min(10, (len(headlines) / 10) * 10)  # Ideal: 10+ headlines
-headline_length_score = 10 - min(10, (sum(1 for h in headlines if len(h) > 30) / max(1, len(headlines))) * 10)
-
-# Check description count and length
-description_count_score = min(10, (len(descriptions) / 4) * 10)  # Ideal: 4+ descriptions
-description_length_score = 10 - min(10, (sum(1 for d in descriptions if len(d) > 90) / max(1, len(descriptions))) * 10)
-
-# Check for emotional triggers, questions, numbers
-emotional_words = ['you', 'free', 'because', 'instantly', 'new', 'save', 'proven', 'guarantee', 'love', 'discover']
-emotional_score = min(10, sum(1 for h in headlines if any(word in h.lower() for word in emotional_words)) + 
-sum(1 for d in descriptions if any(word in d.lower() for word in emotional_words)))
-
-question_score = min(10, (sum(1 for h in headlines if '?' in h) + sum(1 for d in descriptions if '?' in d)) * 2)
-
-number_score = min(10, (sum(1 for h in headlines if bool(re.search(r'\d+', h))) + 
-sum(1 for d in descriptions if bool(re.search(r'\d+', d)))) * 2)
-
-# Calculate overall ad relevance score
-ad_relevance = (headline_count_score * 0.15) + (headline_length_score * 0.15) + \
-(description_count_score * 0.15) + (description_length_score * 0.15) + \
-(emotional_score * 0.2) + (question_score * 0.1) + (number_score * 0.1)
-
-# Calculate CTA effectiveness (0-10)
-# Check for clear call to action
-cta_phrases = ['get', 'buy', 'shop', 'order', 'sign up', 'register', 'download', 'learn', 'discover', 'find', 'call',
-'contact', 'request', 'start', 'try', 'join', 'subscribe', 'book', 'schedule', 'apply']
-
-cta_in_headline = any(any(phrase in h.lower() for phrase in cta_phrases) for h in headlines)
-cta_in_description = any(any(phrase in d.lower() for phrase in cta_phrases) for d in descriptions)
-
-if cta_in_headline and cta_in_description:
-cta_effectiveness = 10
-elif cta_in_headline:
-cta_effectiveness = 8
-elif cta_in_description:
-cta_effectiveness = 7
-else:
-cta_effectiveness = 4
-
-# Calculate landing page relevance (0-10)
-# In a real implementation, this would analyze the landing page content
-# For this example, we'll use a simplified approach
-
-if landing_page:
-# Check if domain seems relevant to keywords
-domain = urlparse(landing_page).netloc
-
-# Check if keywords are in the domain or path
-keyword_in_url = any(kw.lower() in landing_page.lower() for kw in primary_keywords)
-
-# Check if URL structure seems appropriate
-has_https = landing_page.startswith('https://')
-
-# Calculate landing page score
-landing_page_relevance = 5  # Base score
-
-if keyword_in_url:
-landing_page_relevance += 3
-
-if has_https:
-landing_page_relevance += 2
-
-# Cap at 10
-landing_page_relevance = min(10, landing_page_relevance)
-else:
-landing_page_relevance = 5  # Default score if no landing page provided
-
-# Calculate overall quality score (0-10)
-overall_score = (keyword_relevance * 0.4) + (ad_relevance * 0.3) + (cta_effectiveness * 0.2) + (landing_page_relevance * 0.1)
-
-# Calculate estimated CTR based on quality score
-# This is a simplified model - in reality, CTR depends on many factors
-base_ctr = {
-"Responsive Search Ad": 3.17,
-"Expanded Text Ad": 2.83,
-"Call-Only Ad": 3.48,
-"Dynamic Search Ad": 2.69
-}.get(ad_type, 3.0)
-
-# Adjust CTR based on quality score (±50%)
-quality_factor = (overall_score - 5) / 5  # -1 to 1
-estimated_ctr = base_ctr * (1 + (quality_factor * 0.5))
-
-# Calculate estimated conversion rate
-# Again, this is simplified - actual conversion rates depend on many factors
-base_conversion_rate = 3.75  # Average conversion rate for search ads
-
-# Adjust conversion rate based on quality score (±40%)
-estimated_conversion_rate = base_conversion_rate * (1 + (quality_factor * 0.4))
-
-# Return the quality score components
-return {
-"keyword_relevance": round(keyword_relevance, 1),
-"ad_relevance": round(ad_relevance, 1),
-"cta_effectiveness": round(cta_effectiveness, 1),
-"landing_page_relevance": round(landing_page_relevance, 1),
-"overall_score": round(overall_score, 1),
-"estimated_ctr": round(estimated_ctr, 2),
-"estimated_conversion_rate": round(estimated_conversion_rate, 2)
-}
-
-def analyze_keyword_relevance(keywords: List[str], ad_text: str) -> Dict:
-"""
-Analyze the relevance of keywords to ad text.
-
-Args:
-keywords: List of keywords to analyze
-ad_text: Combined ad text (headlines and descriptions)
-
-Returns:
-Dictionary with keyword relevance analysis
-"""
-results = {}
-
-for keyword in keywords:
-# Check if keyword is in ad text
-is_present = keyword.lower() in ad_text.lower()
-
-# Check if keyword is in the first 100 characters
-is_in_beginning = keyword.lower() in ad_text.lower()[:100]
-
-# Count occurrences
-occurrences = ad_text.lower().count(keyword.lower())
-
-# Calculate density
-density = (occurrences * len(keyword)) / len(ad_text) * 100 if len(ad_text) > 0 else 0
-
-# Store results
-results[keyword] = {
-"present": is_present,
-"in_beginning": is_in_beginning,
-"occurrences": occurrences,
-"density": round(density, 2),
-"optimal_density": 0.5 <= density <= 2.5
-}
-
-return results
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_extensions_generator.py
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_extensions_generator.py
@@ -1,320 +0,0 @@
-"""
-Ad Extensions Generator Module
-
-This module provides functions for generating various types of Google Ads extensions.
-"""
-
-from typing import Dict, List, Any, Optional
-import re
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-def generate_extensions(business_name: str, business_description: str, industry: str, 
-primary_keywords: List[str], unique_selling_points: List[str],
-landing_page: str) -> Dict:
-"""
-Generate a complete set of ad extensions based on business information.
-
-Args:
-business_name: Name of the business
-business_description: Description of the business
-industry: Industry of the business
-primary_keywords: List of primary keywords
-unique_selling_points: List of unique selling points
-landing_page: Landing page URL
-
-Returns:
-Dictionary with generated extensions
-"""
-# Generate sitelinks
-sitelinks = generate_sitelinks(business_name, business_description, industry, primary_keywords, landing_page)
-
-# Generate callouts
-callouts = generate_callouts(business_name, unique_selling_points, industry)
-
-# Generate structured snippets
-snippets = generate_structured_snippets(business_name, business_description, industry, primary_keywords)
-
-# Return all extensions
-return {
-"sitelinks": sitelinks,
-"callouts": callouts,
-"structured_snippets": snippets
-}
-
-def generate_sitelinks(business_name: str, business_description: str, industry: str, 
-primary_keywords: List[str], landing_page: str) -> List[Dict]:
-"""
-Generate sitelink extensions based on business information.
-
-Args:
-business_name: Name of the business
-business_description: Description of the business
-industry: Industry of the business
-primary_keywords: List of primary keywords
-landing_page: Landing page URL
-
-Returns:
-List of dictionaries with sitelink information
-"""
-# Define common sitelink types by industry
-industry_sitelinks = {
-"E-commerce": ["Shop Now", "Best Sellers", "New Arrivals", "Sale Items", "Customer Reviews", "About Us"],
-"SaaS/Technology": ["Features", "Pricing", "Demo", "Case Studies", "Support", "Blog"],
-"Healthcare": ["Services", "Locations", "Providers", "Insurance", "Patient Portal", "Contact Us"],
-"Education": ["Programs", "Admissions", "Campus", "Faculty", "Student Life", "Apply Now"],
-"Finance": ["Services", "Rates", "Calculators", "Locations", "Apply Now", "About Us"],
-"Real Estate": ["Listings", "Sell Your Home", "Neighborhoods", "Agents", "Mortgage", "Contact Us"],
-"Legal": ["Practice Areas", "Attorneys", "Results", "Testimonials", "Free Consultation", "Contact"],
-"Travel": ["Destinations", "Deals", "Book Now", "Reviews", "FAQ", "Contact Us"],
-"Food & Beverage": ["Menu", "Locations", "Order Online", "Reservations", "Catering", "About Us"]
-}
-
-# Get sitelinks for the specified industry, or use default
-sitelink_types = industry_sitelinks.get(industry, ["About Us", "Services", "Products", "Contact Us", "Testimonials", "FAQ"])
-
-# Generate sitelinks
-sitelinks = []
-base_url = landing_page.rstrip('/') if landing_page else ""
-
-for sitelink_type in sitelink_types:
-# Generate URL path based on sitelink type
-path = sitelink_type.lower().replace(' ', '-')
-url = f"{base_url}/{path}" if base_url else f"https://example.com/{path}"
-
-# Generate description based on sitelink type
-description = ""
-if sitelink_type == "About Us":
-description = f"Learn more about {business_name} and our mission."
-elif sitelink_type == "Services" or sitelink_type == "Products":
-description = f"Explore our range of {primary_keywords[0] if primary_keywords else 'offerings'}."
-elif sitelink_type == "Contact Us":
-description = f"Get in touch with our team for assistance."
-elif sitelink_type == "Testimonials" or sitelink_type == "Reviews":
-description = f"See what our customers say about us."
-elif sitelink_type == "FAQ":
-description = f"Find answers to common questions."
-elif sitelink_type == "Pricing" or sitelink_type == "Rates":
-description = f"View our competitive pricing options."
-elif sitelink_type == "Shop Now" or sitelink_type == "Order Online":
-description = f"Browse and purchase our {primary_keywords[0] if primary_keywords else 'products'} online."
-
-# Add the sitelink
-sitelinks.append({
-"text": sitelink_type,
-"url": url,
-"description": description
-})
-
-return sitelinks
-
-def generate_callouts(business_name: str, unique_selling_points: List[str], industry: str) -> List[str]:
-"""
-Generate callout extensions based on business information.
-
-Args:
-business_name: Name of the business
-unique_selling_points: List of unique selling points
-industry: Industry of the business
-
-Returns:
-List of callout texts
-"""
-# Use provided USPs if available
-if unique_selling_points and len(unique_selling_points) >= 4:
-# Ensure callouts are not too long (25 characters max)
-callouts = []
-for usp in unique_selling_points:
-if len(usp) <= 25:
-callouts.append(usp)
-else:
-# Try to truncate at a space
-truncated = usp[:22] + "..."
-callouts.append(truncated)
-
-return callouts[:8]  # Return up to 8 callouts
-
-# Define common callouts by industry
-industry_callouts = {
-"E-commerce": ["Free Shipping", "24/7 Customer Service", "Secure Checkout", "Easy Returns", "Price Match Guarantee", "Next Day Delivery", "Satisfaction Guaranteed", "Exclusive Deals"],
-"SaaS/Technology": ["24/7 Support", "Free Trial", "No Credit Card Required", "Easy Integration", "Data Security", "Cloud-Based", "Regular Updates", "Customizable"],
-"Healthcare": ["Board Certified", "Most Insurance Accepted", "Same-Day Appointments", "Compassionate Care", "State-of-the-Art Facility", "Experienced Staff", "Convenient Location", "Telehealth Available"],
-"Education": ["Accredited Programs", "Expert Faculty", "Financial Aid", "Career Services", "Small Class Sizes", "Flexible Schedule", "Online Options", "Hands-On Learning"],
-"Finance": ["FDIC Insured", "No Hidden Fees", "Personalized Service", "Online Banking", "Mobile App", "Low Interest Rates", "Financial Planning", "Retirement Services"],
-"Real Estate": ["Free Home Valuation", "Virtual Tours", "Experienced Agents", "Local Expertise", "Financing Available", "Property Management", "Commercial & Residential", "Investment Properties"],
-"Legal": ["Free Consultation", "No Win No Fee", "Experienced Attorneys", "24/7 Availability", "Proven Results", "Personalized Service", "Multiple Practice Areas", "Aggressive Representation"]
-}
-
-# Get callouts for the specified industry, or use default
-callouts = industry_callouts.get(industry, ["Professional Service", "Experienced Team", "Customer Satisfaction", "Quality Guaranteed", "Competitive Pricing", "Fast Service", "Personalized Solutions", "Trusted Provider"])
-
-return callouts
-
-def generate_structured_snippets(business_name: str, business_description: str, industry: str, primary_keywords: List[str]) -> Dict:
-"""
-Generate structured snippet extensions based on business information.
-
-Args:
-business_name: Name of the business
-business_description: Description of the business
-industry: Industry of the business
-primary_keywords: List of primary keywords
-
-Returns:
-Dictionary with structured snippet information
-"""
-# Define common snippet headers and values by industry
-industry_snippets = {
-"E-commerce": {
-"header": "Brands",
-"values": ["Nike", "Adidas", "Apple", "Samsung", "Sony", "LG", "Dell", "HP"]
-},
-"SaaS/Technology": {
-"header": "Services",
-"values": ["Cloud Storage", "Data Analytics", "CRM", "Project Management", "Email Marketing", "Cybersecurity", "API Integration", "Automation"]
-},
-"Healthcare": {
-"header": "Services",
-"values": ["Preventive Care", "Diagnostics", "Treatment", "Surgery", "Rehabilitation", "Counseling", "Telemedicine", "Wellness Programs"]
-},
-"Education": {
-"header": "Courses",
-"values": ["Business", "Technology", "Healthcare", "Design", "Engineering", "Education", "Arts", "Sciences"]
-},
-"Finance": {
-"header": "Services",
-"values": ["Checking Accounts", "Savings Accounts", "Loans", "Mortgages", "Investments", "Retirement Planning", "Insurance", "Wealth Management"]
-},
-"Real Estate": {
-"header": "Types",
-"values": ["Single-Family Homes", "Condos", "Townhouses", "Apartments", "Commercial", "Land", "New Construction", "Luxury Homes"]
-},
-"Legal": {
-"header": "Services",
-"values": ["Personal Injury", "Family Law", "Criminal Defense", "Estate Planning", "Business Law", "Immigration", "Real Estate Law", "Intellectual Property"]
-}
-}
-
-# Get snippets for the specified industry, or use default
-snippet_info = industry_snippets.get(industry, {
-"header": "Services",
-"values": ["Consultation", "Assessment", "Implementation", "Support", "Maintenance", "Training", "Customization", "Analysis"]
-})
-
-# If we have primary keywords, try to incorporate them
-if primary_keywords:
-# Try to determine a better header based on keywords
-service_keywords = ["service", "support", "consultation", "assistance", "help"]
-product_keywords = ["product", "item", "good", "merchandise"]
-brand_keywords = ["brand", "make", "manufacturer"]
-
-for kw in primary_keywords:
-kw_lower = kw.lower()
-if any(service_word in kw_lower for service_word in service_keywords):
-snippet_info["header"] = "Services"
-break
-elif any(product_word in kw_lower for product_word in product_keywords):
-snippet_info["header"] = "Products"
-break
-elif any(brand_word in kw_lower for brand_word in brand_keywords):
-snippet_info["header"] = "Brands"
-break
-
-return snippet_info
-
-def generate_custom_extensions(business_info: Dict, extension_type: str) -> Any:
-"""
-Generate custom extensions using AI based on business information.
-
-Args:
-business_info: Dictionary with business information
-extension_type: Type of extension to generate
-
-Returns:
-Generated extension data
-"""
-# Extract business information
-business_name = business_info.get("business_name", "")
-business_description = business_info.get("business_description", "")
-industry = business_info.get("industry", "")
-primary_keywords = business_info.get("primary_keywords", [])
-unique_selling_points = business_info.get("unique_selling_points", [])
-
-# Create a prompt based on extension type
-if extension_type == "sitelinks":
-prompt = f"""
-Generate 6 sitelink extensions for a Google Ads campaign for the following business:
-
-Business Name: {business_name}
-Business Description: {business_description}
-Industry: {industry}
-Keywords: {', '.join(primary_keywords)}
-
-For each sitelink, provide:
-1. Link text (max 25 characters)
-2. Description line 1 (max 35 characters)
-3. Description line 2 (max 35 characters)
-
-Format the response as a JSON array of objects with "text", "description1", and "description2" fields.
-"""
-elif extension_type == "callouts":
-prompt = f"""
-Generate 8 callout extensions for a Google Ads campaign for the following business:
-
-Business Name: {business_name}
-Business Description: {business_description}
-Industry: {industry}
-Keywords: {', '.join(primary_keywords)}
-Unique Selling Points: {', '.join(unique_selling_points)}
-
-Each callout should:
-1. Be 25 characters or less
-2. Highlight a feature, benefit, or unique selling point
-3. Be concise and impactful
-
-Format the response as a JSON array of strings.
-"""
-elif extension_type == "structured_snippets":
-prompt = f"""
-Generate structured snippet extensions for a Google Ads campaign for the following business:
-
-Business Name: {business_name}
-Business Description: {business_description}
-Industry: {industry}
-Keywords: {', '.join(primary_keywords)}
-
-Provide:
-1. The most appropriate header type (e.g., Brands, Services, Products, Courses, etc.)
-2. 8 values that are relevant to the business (each 25 characters or less)
-
-Format the response as a JSON object with "header" and "values" fields.
-"""
-else:
-return None
-
-# Generate the extensions using the LLM
-try:
-response = llm_text_gen(prompt)
-
-# Process the response based on extension type
-# In a real implementation, you would parse the JSON response
-# For this example, we'll return a placeholder
-
-if extension_type == "sitelinks":
-return [
-{"text": "About Us", "description1": "Learn about our company", "description2": "Our history and mission"},
-{"text": "Services", "description1": "Explore our service offerings", "description2": "Solutions for your needs"},
-{"text": "Products", "description1": "Browse our product catalog", "description2": "Quality items at great prices"},
-{"text": "Contact Us", "description1": "Get in touch with our team", "description2": "We're here to help you"},
-{"text": "Testimonials", "description1": "See what customers say", "description2": "Real reviews from real people"},
-{"text": "FAQ", "description1": "Frequently asked questions", "description2": "Find quick answers here"}
-]
-elif extension_type == "callouts":
-return ["Free Shipping", "24/7 Support", "Money-Back Guarantee", "Expert Team", "Premium Quality", "Fast Service", "Affordable Prices", "Satisfaction Guaranteed"]
-elif extension_type == "structured_snippets":
-return {"header": "Services", "values": ["Consultation", "Installation", "Maintenance", "Repair", "Training", "Support", "Design", "Analysis"]}
-else:
-return None
-
-except Exception as e:
-print(f"Error generating extensions: {str(e)}")
-return None
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_templates.py
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/ad_templates.py
@@ -1,219 +0,0 @@
-"""
-Ad Templates Module
-
-This module provides templates for different ad types and industries.
-"""
-
-from typing import Dict, List, Any
-
-def get_industry_templates(industry: str) -> Dict:
-"""
-Get ad templates specific to an industry.
-
-Args:
-industry: The industry to get templates for
-
-Returns:
-Dictionary with industry-specific templates
-"""
-# Define templates for different industries
-templates = {
-"E-commerce": {
-"headline_templates": [
-"{product} - {benefit} | {business_name}",
-"Shop {product} - {discount} Off Today",
-"Top-Rated {product} - Free Shipping",
-"{benefit} with Our {product}",
-"New {product} Collection - {benefit}",
-"{discount}% Off {product} - Limited Time",
-"Buy {product} Online - Fast Delivery",
-"{product} Sale Ends {timeframe}",
-"Best-Selling {product} from {business_name}",
-"Premium {product} - {benefit}"
-],
-"description_templates": [
-"Shop our selection of {product} and enjoy {benefit}. Free shipping on orders over ${amount}. Order now!",
-"Looking for quality {product}? Get {benefit} with our {product}. {discount} off your first order!",
-"{business_name} offers premium {product} with {benefit}. Shop online or visit our store today!",
-"Discover our {product} collection. {benefit} guaranteed or your money back. Order now and save {discount}!"
-],
-"emotional_triggers": ["exclusive", "limited time", "sale", "discount", "free shipping", "bestseller", "new arrival"],
-"call_to_actions": ["Shop Now", "Buy Today", "Order Online", "Get Yours", "Add to Cart", "Save Today"]
-},
-"SaaS/Technology": {
-"headline_templates": [
-"{product} Software - {benefit}",
-"Try {product} Free for {timeframe}",
-"{benefit} with Our {product} Platform",
-"{product} - Rated #1 for {feature}",
-"New {feature} in Our {product} Software",
-"{business_name} - {benefit} Software",
-"Streamline {pain_point} with {product}",
-"{product} Software - {discount} Off",
-"Enterprise-Grade {product} for {audience}",
-"{product} - {benefit} Guaranteed"
-],
-"description_templates": [
-"{business_name}'s {product} helps you {benefit}. Try it free for {timeframe}. No credit card required.",
-"Struggling with {pain_point}? Our {product} provides {benefit}. Join {number}+ satisfied customers.",
-"Our {product} platform offers {feature} to help you {benefit}. Rated {rating}/5 by {source}.",
-"{product} by {business_name}: {benefit} for your business. Plans starting at ${price}/month."
-],
-"emotional_triggers": ["efficient", "time-saving", "seamless", "integrated", "secure", "scalable", "innovative"],
-"call_to_actions": ["Start Free Trial", "Request Demo", "Learn More", "Sign Up Free", "Get Started", "See Plans"]
-},
-"Healthcare": {
-"headline_templates": [
-"{service} in {location} | {business_name}",
-"Expert {service} - {benefit}",
-"Quality {service} for {audience}",
-"{business_name} - {credential} {professionals}",
-"Same-Day {service} Appointments",
-"{service} Specialists in {location}",
-"Affordable {service} - {benefit}",
-"{symptom}? Get {service} Today",
-"Advanced {service} Technology",
-"Compassionate {service} Care"
-],
-"description_templates": [
-"{business_name} provides expert {service} with {benefit}. Our {credential} team is ready to help. Schedule today!",
-"Experiencing {symptom}? Our {professionals} offer {service} with {benefit}. Most insurance accepted.",
-"Quality {service} in {location}. {benefit} from our experienced team. Call now to schedule your appointment.",
-"Our {service} center provides {benefit} for {audience}. Open {days} with convenient hours."
-],
-"emotional_triggers": ["trusted", "experienced", "compassionate", "advanced", "personalized", "comprehensive", "gentle"],
-"call_to_actions": ["Schedule Now", "Book Appointment", "Call Today", "Free Consultation", "Learn More", "Find Relief"]
-},
-"Real Estate": {
-"headline_templates": [
-"{property_type} in {location} | {business_name}",
-"{property_type} for {price_range} - {location}",
-"Find Your Dream {property_type} in {location}",
-"{feature} {property_type} - {location}",
-"New {property_type} Listings in {location}",
-"Sell Your {property_type} in {timeframe}",
-"{business_name} - {credential} {professionals}",
-"{property_type} {benefit} - {location}",
-"Exclusive {property_type} Listings",
-"{number}+ {property_type} Available Now"
-],
-"description_templates": [
-"Looking for {property_type} in {location}? {business_name} offers {benefit}. Browse our listings or call us today!",
-"Sell your {property_type} in {location} with {business_name}. Our {professionals} provide {benefit}. Free valuation!",
-"{business_name}: {credential} {professionals} helping you find the perfect {property_type} in {location}. Call now!",
-"Discover {feature} {property_type} in {location}. Prices from {price_range}. Schedule a viewing today!"
-],
-"emotional_triggers": ["dream home", "exclusive", "luxury", "investment", "perfect location", "spacious", "modern"],
-"call_to_actions": ["View Listings", "Schedule Viewing", "Free Valuation", "Call Now", "Learn More", "Get Pre-Approved"]
-}
-}
-
-# Return templates for the specified industry, or a default if not found
-return templates.get(industry, {
-"headline_templates": [
-"{product/service} - {benefit} | {business_name}",
-"Professional {product/service} - {benefit}",
-"{benefit} with Our {product/service}",
-"{business_name} - {credential} {product/service}",
-"Quality {product/service} for {audience}",
-"Affordable {product/service} - {benefit}",
-"{product/service} in {location}",
-"{feature} {product/service} by {business_name}",
-"Experienced {product/service} Provider",
-"{product/service} - Satisfaction Guaranteed"
-],
-"description_templates": [
-"{business_name} offers professional {product/service} with {benefit}. Contact us today to learn more!",
-"Looking for quality {product/service}? {business_name} provides {benefit}. Call now for more information.",
-"Our {product/service} helps you {benefit}. Trusted by {number}+ customers. Contact us today!",
-"{business_name}: {credential} {product/service} provider. We offer {benefit} for {audience}. Learn more!"
-],
-"emotional_triggers": ["professional", "quality", "trusted", "experienced", "affordable", "reliable", "satisfaction"],
-"call_to_actions": ["Contact Us", "Learn More", "Call Now", "Get Quote", "Visit Website", "Schedule Consultation"]
-})
-
-def get_ad_type_templates(ad_type: str) -> Dict:
-"""
-Get templates specific to an ad type.
-
-Args:
-ad_type: The ad type to get templates for
-
-Returns:
-Dictionary with ad type-specific templates
-"""
-# Define templates for different ad types
-templates = {
-"Responsive Search Ad": {
-"headline_count": 15,
-"description_count": 4,
-"headline_max_length": 30,
-"description_max_length": 90,
-"best_practices": [
-"Include at least 3 headlines with keywords",
-"Create headlines with different lengths",
-"Include at least 1 headline with a call to action",
-"Include at least 1 headline with your brand name",
-"Create descriptions that complement each other",
-"Include keywords in at least 2 descriptions",
-"Include a call to action in at least 1 description"
-]
-},
-"Expanded Text Ad": {
-"headline_count": 3,
-"description_count": 2,
-"headline_max_length": 30,
-"description_max_length": 90,
-"best_practices": [
-"Include keywords in Headline 1",
-"Use a call to action in Headline 2 or 3",
-"Include your brand name in one headline",
-"Make descriptions complementary but able to stand alone",
-"Include keywords in at least one description",
-"Include a call to action in at least one description"
-]
-},
-"Call-Only Ad": {
-"headline_count": 2,
-"description_count": 2,
-"headline_max_length": 30,
-"description_max_length": 90,
-"best_practices": [
-"Focus on encouraging phone calls",
-"Include language like 'Call now', 'Speak to an expert', etc.",
-"Mention phone availability (e.g., '24/7', 'Available now')",
-"Include benefits of calling rather than clicking",
-"Be clear about who will answer the call",
-"Include any special offers for callers"
-]
-},
-"Dynamic Search Ad": {
-"headline_count": 0,  # Headlines are dynamically generated
-"description_count": 2,
-"headline_max_length": 0,  # N/A
-"description_max_length": 90,
-"best_practices": [
-"Create descriptions that work with any dynamically generated headline",
-"Focus on your unique selling points",
-"Include a strong call to action",
-"Highlight benefits that apply across your product/service range",
-"Avoid specific product mentions that might not match the dynamic headline"
-]
-}
-}
-
-# Return templates for the specified ad type, or a default if not found
-return templates.get(ad_type, {
-"headline_count": 3,
-"description_count": 2,
-"headline_max_length": 30,
-"description_max_length": 90,
-"best_practices": [
-"Include keywords in headlines",
-"Use a call to action",
-"Include your brand name",
-"Make descriptions informative and compelling",
-"Include keywords in descriptions",
-"Highlight unique selling points"
-]
-})
--- a/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/google_ads_generator.py
+++ b/ToBeMigrated/ai_marketing_tools/ai_google_ads_generator/google_ads_generator.py
--- a/ToBeMigrated/ai_seo_tools/ENTERPRISE_FEATURES.md
+++ b/ToBeMigrated/ai_seo_tools/ENTERPRISE_FEATURES.md
@@ -1,215 +0,0 @@
-# Alwrity Enterprise SEO Features
-
-## 🚀 Overview
-
-Alwrity's AI SEO Tools have been enhanced with enterprise-level features that provide comprehensive SEO management, advanced analytics, and AI-powered strategic insights. These enhancements transform Alwrity from a collection of individual tools into a unified enterprise SEO command center.
-
-## 🏢 Enterprise SEO Suite
-
-### Unified Command Center (`enterprise_seo_suite.py`)
-
-The Enterprise SEO Suite serves as a central orchestrator for all SEO activities, providing:
-
-#### Core Workflows
- **Complete SEO Audit**: Comprehensive site analysis combining technical, content, and performance metrics
- **Content Strategy Development**: AI-powered content planning with market intelligence
- **Search Intelligence Analysis**: Deep GSC data analysis with actionable insights
- **Performance Monitoring**: Continuous tracking and optimization recommendations
-
-#### Key Features
- **Intelligent Workflow Orchestration**: Automatically sequences and coordinates multiple SEO analyses
- **AI-Powered Recommendations**: Uses advanced AI to generate strategic insights and action plans
- **Enterprise Reporting**: Comprehensive reports suitable for executive and team consumption
- **Scalable Architecture**: Designed to handle multiple sites and large datasets
-
-### Enterprise-Level Capabilities
- Multi-site management support
- Role-based access controls (planned)
- Team collaboration features (planned)
- Advanced reporting and dashboards
- API integration capabilities
-
-## 📊 Google Search Console Intelligence
-
-### Advanced GSC Integration (`google_search_console_integration.py`)
-
-Transforms raw GSC data into strategic insights with:
-
-#### Search Performance Analysis
- **Comprehensive Metrics**: Clicks, impressions, CTR, and position tracking
- **Trend Analysis**: Week-over-week and month-over-month performance trends
- **Keyword Performance**: Deep analysis of keyword opportunities and optimization potential
- **Page Performance**: Identification of top-performing and underperforming pages
-
-#### Content Opportunities Engine
- **CTR Optimization**: Identifies high-impression, low-CTR keywords for meta optimization
- **Position Improvement**: Highlights keywords ranking 11-20 for content enhancement
- **Content Gap Detection**: Discovers missing keyword opportunities
- **Technical Issue Detection**: Identifies potential crawl and indexing problems
-
-#### AI-Powered Insights
- **Strategic Recommendations**: AI analysis of search data for actionable insights
- **Immediate Opportunities**: Quick wins identified within 0-30 days
- **Long-term Strategy**: 3-12 month strategic planning recommendations
- **Competitive Analysis**: Market position assessment and improvement strategies
-
-### Demo Mode & Real Integration
- **Demo Mode**: Realistic sample data for testing and exploration
- **GSC API Integration**: Ready for real Google Search Console API connection
- **Credentials Management**: Secure handling of GSC API credentials
- **Data Export**: Full analysis export in JSON and CSV formats
-
-## 🧠 AI Content Strategy Generator
-
-### Comprehensive Strategy Development (`ai_content_strategy.py`)
-
-Creates complete content strategies using AI market intelligence:
-
-#### Business Context Analysis
- **Market Positioning**: AI analysis of competitive landscape and opportunities
- **Content Gap Identification**: Discovers missing content themes in the industry
- **Competitive Advantage Mapping**: Identifies unique positioning opportunities
- **Audience Intelligence**: Deep insights into target audience needs and preferences
-
-#### Content Pillar Development
- **Strategic Pillars**: 4-6 content themes aligned with business goals
- **Keyword Mapping**: Target keywords and semantic variations for each pillar
- **Content Type Recommendations**: Optimal content formats for each pillar
- **Success Metrics**: KPIs and measurement frameworks for each pillar
-
-#### Content Calendar Planning
- **Automated Scheduling**: AI-generated content calendar with optimal timing
- **Resource Planning**: Time estimates and resource allocation
- **Priority Scoring**: Content prioritization based on impact and effort
- **Distribution Mapping**: Multi-channel content distribution strategy
-
-#### Topic Cluster Strategy
- **SEO-Optimized Clusters**: Topic clusters designed for search dominance
- **Pillar Page Strategy**: Hub-and-spoke content architecture
- **Internal Linking Plans**: Strategic linking for SEO authority building
- **Content Relationship Mapping**: How content pieces support each other
-
-### Implementation Support
- **Phase-Based Roadmap**: 3-phase implementation plan with milestones
- **KPI Framework**: Comprehensive measurement and tracking system
- **Resource Requirements**: Budget and team resource planning
- **Risk Mitigation**: Strategies to avoid common content pitfalls
-
-## 🔧 Enhanced Technical Capabilities
-
-### Advanced SEO Workflows
- **Multi-Tool Orchestration**: Seamless integration between all SEO tools
- **Data Correlation**: Cross-referencing insights from multiple analyses
- **Automated Recommendations**: AI-generated action plans with priority scoring
- **Performance Tracking**: Before/after analysis and improvement measurement
-
-### Enterprise Data Management
- **Large Dataset Handling**: Optimized for enterprise-scale websites
- **Historical Data Tracking**: Long-term trend analysis and comparison
- **Data Export & Integration**: API-ready for integration with other tools
- **Security & Privacy**: Enterprise-grade data handling and security
-
-## 📈 Advanced Analytics & Reporting
-
-### Performance Dashboards
- **Executive Summaries**: High-level insights for leadership teams
- **Detailed Analytics**: In-depth analysis for SEO practitioners
- **Trend Visualization**: Interactive charts and performance tracking
- **Competitive Benchmarking**: Market position and competitor analysis
-
-### ROI Measurement
- **Impact Quantification**: Measuring SEO improvements in business terms
- **Cost-Benefit Analysis**: ROI calculation for SEO investments
- **Performance Attribution**: Connecting SEO efforts to business outcomes
- **Forecasting Models**: Predictive analytics for future performance
-
-## 🎯 Strategic Planning Features
-
-### Market Intelligence
- **Industry Analysis**: AI-powered market research and trend identification
- **Competitive Intelligence**: Deep analysis of competitor content strategies
- **Opportunity Mapping**: Identification of untapped market opportunities
- **Risk Assessment**: Potential challenges and mitigation strategies
-
-### Long-term Planning
- **Strategic Roadmaps**: 6-12 month SEO strategy development
- **Resource Planning**: Team and budget allocation recommendations
- **Technology Roadmap**: Tool and platform evolution planning
- **Scalability Planning**: Growth-oriented SEO architecture
-
-## 🚀 Implementation Benefits
-
-### For Enterprise Teams
- **Unified Workflow**: Single platform for all SEO activities
- **Team Collaboration**: Shared insights and coordinated strategies
- **Scalable Operations**: Handle multiple sites and large datasets
- **Executive Reporting**: Clear ROI and performance communication
-
-### For SEO Professionals
- **Advanced Insights**: AI-powered analysis beyond basic tools
- **Time Efficiency**: Automated workflows and intelligent recommendations
- **Strategic Focus**: Less time on analysis, more on strategy execution
- **Competitive Advantage**: Access to enterprise-level intelligence
-
-### For Business Leaders
- **Clear ROI**: Quantified business impact of SEO investments
- **Strategic Alignment**: SEO strategy aligned with business objectives
- **Risk Management**: Proactive identification and mitigation of SEO risks
- **Competitive Intelligence**: Market position and improvement opportunities
-
-## 🔄 Integration Architecture
-
-### Modular Design
- **Tool Independence**: Each tool can function independently
- **Workflow Integration**: Tools work together in intelligent sequences
- **API-First**: Ready for integration with external systems
- **Extensible Framework**: Easy to add new tools and capabilities
-
-### Data Flow
- **Centralized Data Management**: Unified data storage and processing
- **Cross-Tool Insights**: Data sharing between different analyses
- **Historical Tracking**: Long-term data retention and trend analysis
- **Real-time Updates**: Live data integration and analysis
-
-## 📋 Getting Started
-
-### For New Users
-1. Start with the **Enterprise SEO Suite** for comprehensive analysis
-2. Use **Demo Mode** to explore features with sample data
-3. Configure **Google Search Console** integration for real data
-4. Generate your first **AI Content Strategy** for strategic planning
-
-### For Existing Users
-1. Explore the new **Enterprise tab** in the SEO dashboard
-2. Connect your **Google Search Console** for enhanced insights
-3. Generate comprehensive **content strategies** using AI
-4. Utilize **workflow orchestration** for multi-tool analysis
-
-### Implementation Timeline
- **Week 1**: Tool exploration and data connection
- **Week 2-3**: Initial audits and strategy development
- **Month 1**: Content implementation and optimization
- **Month 2-3**: Performance tracking and strategy refinement
-
-## 🔮 Future Enhancements
-
-### Planned Features
- **Multi-site Management**: Centralized management of multiple websites
- **Team Collaboration**: Role-based access and collaborative workflows
- **Advanced Integrations**: CRM, Analytics, and Marketing Platform connections
- **Machine Learning Models**: Custom AI models for specific industries
- **Predictive Analytics**: Forecasting SEO performance and opportunities
-
-### Roadmap
- **Q1**: Multi-site support and team collaboration features
- **Q2**: Advanced integrations and custom AI models
- **Q3**: Predictive analytics and forecasting capabilities
- **Q4**: Industry-specific optimization and enterprise scalability
-
---
-
-## 🎯 Conclusion
-
-These enterprise enhancements transform Alwrity into a comprehensive SEO management platform that rivals expensive enterprise solutions while maintaining ease of use and AI-powered intelligence. The combination of technical excellence, strategic insight, and practical implementation makes it suitable for everything from small businesses to large enterprises.
-
-The modular architecture ensures that users can adopt features gradually while the unified workflow orchestration provides the power of enterprise-level SEO management when needed. 
--- a/ToBeMigrated/ai_seo_tools/README.md
+++ b/ToBeMigrated/ai_seo_tools/README.md
@@ -1,251 +0,0 @@
-# 🚀 Alwrity's Enterprise AI SEO Tools Suite
-
-**Transform your SEO strategy with AI-powered enterprise-level tools and intelligent workflows**
-
-Alwrity's AI SEO Tools have evolved into a comprehensive enterprise suite that combines individual optimization tools with intelligent workflow orchestration, providing everything from basic SEO tasks to advanced strategic analysis and competitive intelligence.
-
---
-
-## 🌟 **What's New: Enterprise Features**
-
-### 🎯 **Enterprise SEO Command Center**
- **Unified Workflow Orchestration**: Combines all tools into intelligent, automated workflows
- **Complete SEO Audits**: Comprehensive analysis covering technical, content, competitive, and performance aspects
- **AI-Powered Strategic Recommendations**: Advanced insights with prioritized action plans
- **Enterprise-Level Reporting**: Professional dashboards with ROI measurement and executive summaries
-
-### 📊 **Google Search Console Intelligence**
- **Advanced GSC Integration**: Deep analysis of search performance data with AI insights
- **Content Opportunities Engine**: Identifies high-impact optimization opportunities
- **Search Intelligence Workflows**: Transforms GSC data into actionable content strategies
- **Competitive Position Analysis**: Market positioning insights based on search performance
-
-### 🧠 **AI Content Strategy Generator**
- **Comprehensive Strategy Development**: AI-powered content planning with market intelligence
- **Content Pillar Architecture**: Topic cluster strategies with keyword mapping
- **Implementation Roadmaps**: Phase-based execution plans with resource estimation
- **Business Context Analysis**: Industry-specific insights and competitive positioning
-
---
-
-## 🛠️ **Complete Tool Suite**
-
-### **🏢 Enterprise Suite**
-| Tool | Description | Key Features |
-|------|-------------|--------------|
-| **Enterprise SEO Command Center** | Unified workflow orchestration | Complete audits, AI recommendations, strategic planning |
-| **Google Search Console Intelligence** | Advanced GSC data analysis | Content opportunities, search intelligence, competitive analysis |
-| **AI Content Strategy Generator** | Comprehensive content planning | Market intelligence, topic clusters, implementation roadmaps |
-
-### **📊 Analytics & Intelligence**
-| Tool | Description | Key Features |
-|------|-------------|--------------|
-| **Enhanced Content Gap Analysis** | Advanced competitive content analysis | Advertools integration, AI insights, opportunity identification |
-| **Technical SEO Crawler** | Site-wide technical analysis | Performance metrics, crawl analysis, AI recommendations |
-| **Competitive Intelligence** | Market positioning analysis | Competitor benchmarking, strategic insights, market opportunities |
-
-### **🔧 Technical SEO**
-| Tool | Description | Key Features |
-|------|-------------|--------------|
-| **On-Page SEO Analyzer** | Comprehensive page optimization | Meta analysis, content optimization, readability scoring |
-| **URL SEO Checker** | Individual URL analysis | Technical factors, optimization recommendations |
-| **Google PageSpeed Insights** | Performance analysis | Core Web Vitals, speed optimization, mobile performance |
-
-### **📝 Content & Strategy**
-| Tool | Description | Key Features |
-|------|-------------|--------------|
-| **Content Calendar Planner** | Strategic content planning | Editorial calendars, topic scheduling, resource planning |
-| **Topic Cluster Generator** | Content architecture planning | Pillar pages, cluster content, internal linking strategies |
-| **Content Performance Analyzer** | Content effectiveness analysis | Performance metrics, optimization recommendations |
-
-### **⚡ Quick Optimization Tools**
-| Tool | Description | Key Features |
-|------|-------------|--------------|
-| **Meta Description Generator** | SEO-friendly meta descriptions | Keyword optimization, CTR enhancement, length optimization |
-| **Content Title Generator** | Attention-grabbing titles | Keyword integration, engagement optimization, SERP visibility |
-| **OpenGraph Generator** | Social media optimization | Facebook/LinkedIn optimization, visual appeal, click enhancement |
-| **Image Alt Text Generator** | AI-powered alt text creation | SEO optimization, accessibility compliance, image discoverability |
-| **Schema Markup Generator** | Structured data creation | Rich snippets, search enhancement, content understanding |
-| **Twitter Tags Generator** | Twitter optimization | Engagement enhancement, visibility improvement, social sharing |
-
---
-
-## 🎯 **Enterprise Workflows**
-
-### **🔍 Complete SEO Audit Workflow**
-1. **Technical SEO Analysis** - Site-wide technical health assessment
-2. **Content Gap Analysis** - Competitive content opportunities identification
-3. **On-Page Optimization** - Page-level SEO factor analysis
-4. **Performance Analysis** - Speed, mobile, and Core Web Vitals assessment
-5. **AI Strategic Recommendations** - Prioritized action plan with impact estimates
-
-### **📊 Search Intelligence Workflow**
-1. **GSC Data Analysis** - Comprehensive search performance review
-2. **Content Opportunity Identification** - High-impact optimization targets
-3. **Competitive Position Assessment** - Market positioning analysis
-4. **Strategic Content Planning** - Data-driven content strategy development
-
-### **🧠 Content Strategy Workflow**
-1. **Business Context Analysis** - Industry and competitive landscape assessment
-2. **Content Pillar Development** - Topic cluster architecture creation
-3. **Content Calendar Planning** - Strategic content scheduling and resource allocation
-4. **Implementation Roadmap** - Phase-based execution with timeline and priorities
-
---
-
-## 🚀 **Getting Started**
-
-### **For New Users**
-1. **Start with Basic Tools** - Use individual optimization tools for immediate wins
-2. **Explore Analytics** - Try content gap analysis and technical crawling
-3. **Upgrade to Enterprise** - Access unified workflows and AI-powered insights
-
-### **For Existing Users**
-1. **Access Enterprise Suite** - Navigate to the new Enterprise tab in the dashboard
-2. **Run Complete Audit** - Execute comprehensive SEO analysis workflows
-3. **Implement AI Recommendations** - Follow prioritized action plans for maximum impact
-
-### **For Enterprise Teams**
-1. **Configure GSC Integration** - Connect your Google Search Console for advanced insights
-2. **Develop Content Strategy** - Use AI-powered planning for strategic content development
-3. **Monitor and Optimize** - Leverage continuous monitoring and optimization workflows
-
---
-
-## 📈 **Business Impact**
-
-### **Immediate Benefits (0-30 days)**
- ✅ **Quick Wins Identification** - AI-powered immediate optimization opportunities
- ✅ **Technical Issue Resolution** - Critical SEO problems with prioritized fixes
- ✅ **Content Optimization** - Existing page improvements for better performance
- ✅ **Performance Enhancement** - Speed and mobile optimization recommendations
-
-### **Strategic Growth (1-6 months)**
- 📈 **Content Strategy Execution** - Systematic content development with topic clusters
- 📈 **Competitive Positioning** - Market advantage through strategic content gaps
- 📈 **Authority Building** - Thought leadership content and link-worthy assets
- 📈 **Search Visibility** - Improved rankings through comprehensive optimization
-
-### **Long-term Success (6-12 months)**
- 🏆 **Market Leadership** - Dominant search presence in target markets
- 🏆 **Organic Growth** - Sustainable traffic and conversion improvements
- 🏆 **Competitive Advantage** - Advanced SEO capabilities beyond competitors
- 🏆 **ROI Optimization** - Measurable business impact and revenue growth
-
---
-
-## 🔧 **Technical Architecture**
-
-### **Modular Design**
- **Independent Tools** - Each tool functions standalone for specific tasks
- **Workflow Integration** - Tools combine seamlessly in enterprise workflows
- **API-Ready Architecture** - External system integration capabilities
- **Scalable Infrastructure** - Handles enterprise-level data and analysis
-
-### **AI Integration**
- **Advanced Language Models** - GPT-powered analysis and recommendations
- **Contextual Intelligence** - Business-specific insights and strategies
- **Continuous Learning** - Improving recommendations based on performance data
- **Multi-Modal Analysis** - Text, data, and performance metric integration
-
-### **Data Management**
- **Secure Processing** - Enterprise-grade data security and privacy
- **Real-time Analysis** - Live data processing and immediate insights
- **Historical Tracking** - Performance monitoring and trend analysis
- **Export Capabilities** - Comprehensive reporting and data portability
-
---
-
-## 🎯 **Use Cases by Role**
-
-### **SEO Professionals**
- **Comprehensive Audits** - Complete site analysis with actionable recommendations
- **Competitive Intelligence** - Market positioning and opportunity identification
- **Strategic Planning** - Long-term SEO roadmaps with business alignment
- **Performance Monitoring** - Continuous optimization and improvement tracking
-
-### **Content Marketers**
- **Content Strategy Development** - AI-powered planning with market intelligence
- **Topic Research** - Data-driven content ideas and keyword opportunities
- **Performance Analysis** - Content effectiveness measurement and optimization
- **Editorial Planning** - Strategic content calendars with resource allocation
-
-### **Business Leaders**
- **ROI Measurement** - Clear business impact and performance metrics
- **Strategic Insights** - Market opportunities and competitive positioning
- **Resource Planning** - Efficient allocation of SEO and content resources
- **Executive Reporting** - High-level dashboards and strategic recommendations
-
-### **Agencies & Consultants**
- **Client Audits** - Professional-grade analysis and reporting
- **Scalable Solutions** - Multi-client management and optimization
- **Competitive Analysis** - Market intelligence and positioning strategies
- **Value Demonstration** - Clear ROI and performance improvement tracking
-
---
-
-## 🔮 **Future Roadmap**
-
-### **Planned Enhancements**
- 🔄 **Real-time Monitoring** - Continuous SEO health tracking and alerts
- 🤖 **Advanced AI Models** - Enhanced analysis and prediction capabilities
- 🌐 **Multi-language Support** - Global SEO optimization and analysis
- 📱 **Mobile App** - On-the-go SEO monitoring and management
- 🔗 **Enhanced Integrations** - More third-party tool connections and APIs
-
-### **Advanced Features in Development**
- **Predictive SEO Analytics** - Forecast performance and opportunity identification
- **Automated Optimization** - AI-driven automatic SEO improvements
- **Voice Search Optimization** - Emerging search behavior analysis
- **Local SEO Suite** - Location-based optimization and management
- **E-commerce SEO** - Specialized tools for online retail optimization
-
---
-
-## 📚 **Resources & Support**
-
-### **Documentation**
- 📖 **Enterprise Features Guide** - Comprehensive feature documentation
- 🎥 **Video Tutorials** - Step-by-step workflow demonstrations
- 📋 **Best Practices** - Industry-standard SEO optimization guidelines
- 🔧 **API Documentation** - Integration guides and technical specifications
-
-### **Support Channels**
- 💬 **Community Forum** - User discussions and knowledge sharing
- 📧 **Email Support** - Direct assistance for technical issues
- 🎓 **Training Programs** - Advanced SEO strategy and tool mastery
- 🤝 **Consulting Services** - Strategic SEO planning and implementation
-
---
-
-## 🏁 **Action Plan: Maximize Your SEO Success**
-
-### **Phase 1: Foundation (Week 1-2)**
-1. **Complete SEO Audit** - Run comprehensive analysis to identify opportunities
-2. **Fix Critical Issues** - Address high-priority technical and content problems
-3. **Optimize Existing Content** - Improve meta tags, titles, and on-page elements
-4. **Set Up Monitoring** - Configure GSC integration and performance tracking
-
-### **Phase 2: Strategic Development (Week 3-8)**
-1. **Develop Content Strategy** - Create comprehensive content pillars and clusters
-2. **Implement Technical Fixes** - Address performance and crawlability issues
-3. **Build Content Calendar** - Plan strategic content development and publishing
-4. **Monitor Competitive Position** - Track market positioning and opportunities
-
-### **Phase 3: Growth & Optimization (Week 9-24)**
-1. **Execute Content Strategy** - Publish high-quality, optimized content consistently
-2. **Build Authority** - Develop thought leadership and link-worthy content
-3. **Expand Market Presence** - Target new keywords and market segments
-4. **Measure and Refine** - Continuously optimize based on performance data
-
-### **Phase 4: Market Leadership (Month 6+)**
-1. **Dominate Target Markets** - Achieve top rankings for primary keywords
-2. **Scale Successful Strategies** - Expand winning approaches to new areas
-3. **Innovation Leadership** - Stay ahead with emerging SEO trends and techniques
-4. **Sustainable Growth** - Maintain and improve market position continuously
-
---
-
-**Ready to transform your SEO strategy?** Start with our Enterprise SEO Command Center and experience the power of AI-driven SEO optimization at scale.
-
-🚀 **[Launch Enterprise SEO Suite](./enterprise_seo_suite.py)** | 📊 **[Explore GSC Intelligence](./google_search_console_integration.py)** | 🧠 **[Generate Content Strategy](./ai_content_strategy.py)**
--- a/ToBeMigrated/ai_seo_tools/TBD
+++ b/ToBeMigrated/ai_seo_tools/TBD
@@ -1,68 +0,0 @@
-https://github.com/greghub/website-launch-checklist
-https://github.com/marcobiedermann/search-engine-optimization
-https://developers.google.com/speed/docs/insights/v5/get-started
-https://developers.google.com/search/apis/indexing-api/v3/prereqs
-https://developer.chrome.com/docs/lighthouse/overview/#cli
-
-APIs
-https://docs.ayrshare.com/
-https://github.com/dataforseo/PythonClient
-https://mysiteauditor.com/api
-
-https://github.com/searchsolved/search-solved-public-seo/blob/main/keyword-research/low-competition-keyword-finder-serp-api/low_competition_finder_serp_api.py
-
-### Structured Data
-
- [Facebook Debugger](https://developers.facebook.com/tools/debug) - Enter the URL you want to scrape to see how the page's markup appears to Facebook.
- [Pinterest](https://developers.pinterest.com/rich_pins/validator/) - Validate your Rich Pins and apply to get them on Pinterest.
- [Structured Data Testing Tool](https://developers.google.com/structured-data/testing-tool/) - Paste in your rich snippets or url to test it.
- [Twitter card validator](https://cards-dev.twitter.com/validator) - Enter the URL of the page with the meta tags to validate.
-
-https://github.com/sethblack/python-seo-analyzer
-
-https://www.holisticseo.digital/python-seo/analyse-compare-robots-txt/
-
-https://github.com/Nv7-GitHub/googlesearch
-https://www.semrush.com/blog/python-for-google-search/
-
-https://www.kaggle.com/code/eliasdabbas/botpresso-crawl-audit-analysis
-https://www.kaggle.com/code/eliasdabbas/nike-xml-sitemap-audit-analysis
-https://www.kaggle.com/code/eliasdabbas/twitter-user-account-analysis-python-sejournal
-https://www.kaggle.com/code/eliasdabbas/seo-crawl-analysis-template
-https://www.kaggle.com/code/eliasdabbas/advertools-seo-crawl-analysis-template
-
-https://www.semrush.com/blog/content-analysis-xml-sitemaps-python/
-
-
-different configurations that influence your technical SEO and how to optimize them to maximize your organic search visibility. 
-
-ALwrity’ll cover:
-
-    HTTP status
-
-    URL structure
-
-    Website links
-
-    XML sitemaps
-
-    Robots.txt
-
-    Meta robots tag
-
-    Canonicalization
-
-    JavaScript usage
-
-    HTTPS usage
-
-    Mobile friendliness
-
-    Structured data
-
-    Core Web Vitals
-
-    Hreflang annotations
-
-
-
--- a/ToBeMigrated/ai_seo_tools/ai_content_strategy.py
+++ b/ToBeMigrated/ai_seo_tools/ai_content_strategy.py
@@ -1,954 +0,0 @@
-"""
-AI-Powered Content Strategy Generator
-
-Creates comprehensive content strategies using AI analysis of SEO data,
-competitor insights, and market trends for enterprise content planning.
-"""
-
-import streamlit as st
-import pandas as pd
-import numpy as np
-from typing import Dict, Any, List, Optional, Tuple
-from datetime import datetime, timedelta
-import json
-from loguru import logger
-import plotly.express as px
-import plotly.graph_objects as go
-
-# Import AI modules
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-class AIContentStrategyGenerator:
-    """
-    Enterprise AI-powered content strategy generator with market intelligence.
-    """
-    
-    def __init__(self):
-        """Initialize the content strategy generator."""
-        logger.info("AI Content Strategy Generator initialized")
-    
-    def generate_content_strategy(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Generate comprehensive AI-powered content strategy.
-        
-        Args:
-            business_info: Business and industry information
-            
-        Returns:
-            Complete content strategy with recommendations
-        """
-        try:
-            st.info("🧠 Generating AI-powered content strategy...")
-            
-            # Analyze business context
-            business_analysis = self._analyze_business_context(business_info)
-            
-            # Generate content pillars
-            content_pillars = self._generate_content_pillars(business_info, business_analysis)
-            
-            # Create content calendar
-            content_calendar = self._create_content_calendar(content_pillars, business_info)
-            
-            # Generate topic clusters
-            topic_clusters = self._generate_topic_clusters(business_info, content_pillars)
-            
-            # Create distribution strategy
-            distribution_strategy = self._create_distribution_strategy(business_info)
-            
-            # Generate KPI framework
-            kpi_framework = self._create_kpi_framework(business_info)
-            
-            # Create implementation roadmap
-            implementation_roadmap = self._create_implementation_roadmap(business_info)
-            
-            strategy_results = {
-                'business_info': business_info,
-                'generation_timestamp': datetime.utcnow().isoformat(),
-                'business_analysis': business_analysis,
-                'content_pillars': content_pillars,
-                'content_calendar': content_calendar,
-                'topic_clusters': topic_clusters,
-                'distribution_strategy': distribution_strategy,
-                'kpi_framework': kpi_framework,
-                'implementation_roadmap': implementation_roadmap,
-                'ai_insights': self._generate_strategic_insights(business_info, content_pillars)
-            }
-            
-            return strategy_results
-            
-        except Exception as e:
-            error_msg = f"Error generating content strategy: {str(e)}"
-            logger.error(error_msg, exc_info=True)
-            return {'error': error_msg}
-    
-    def _analyze_business_context(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze business context for strategic insights."""
-        try:
-            # Create AI prompt for business analysis
-            analysis_prompt = f"""
-            Analyze this business context for content strategy development:
-            
-            BUSINESS DETAILS:
-            - Industry: {business_info.get('industry', 'Not specified')}
-            - Target Audience: {business_info.get('target_audience', 'Not specified')}
-            - Business Goals: {business_info.get('business_goals', 'Not specified')}
-            - Content Objectives: {business_info.get('content_objectives', 'Not specified')}
-            - Budget: {business_info.get('budget', 'Not specified')}
-            - Timeline: {business_info.get('timeline', 'Not specified')}
-            
-            Provide analysis on:
-            1. Market positioning opportunities
-            2. Content gaps in the industry
-            3. Competitive advantages to leverage
-            4. Audience pain points and interests
-            5. Seasonal content opportunities
-            6. Content format preferences for this audience
-            7. Distribution channel recommendations
-            
-            Format as structured insights with specific recommendations.
-            """
-            
-            ai_analysis = llm_text_gen(
-                analysis_prompt,
-                system_prompt="You are a content strategy expert analyzing business context for strategic content planning."
-            )
-            
-            return {
-                'full_analysis': ai_analysis,
-                'market_position': self._extract_market_position(ai_analysis),
-                'content_gaps': self._extract_content_gaps(ai_analysis),
-                'competitive_advantages': self._extract_competitive_advantages(ai_analysis),
-                'audience_insights': self._extract_audience_insights(ai_analysis)
-            }
-            
-        except Exception as e:
-            logger.error(f"Business analysis error: {str(e)}")
-            return {'error': str(e)}
-    
-    def _generate_content_pillars(self, business_info: Dict[str, Any], business_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Generate strategic content pillars."""
-        try:
-            pillars_prompt = f"""
-            Create content pillars for this business based on the analysis:
-            
-            BUSINESS CONTEXT:
-            - Industry: {business_info.get('industry', 'Not specified')}
-            - Target Audience: {business_info.get('target_audience', 'Not specified')}
-            - Business Goals: {business_info.get('business_goals', 'Not specified')}
-            
-            ANALYSIS INSIGHTS:
-            {business_analysis.get('full_analysis', 'No analysis available')}
-            
-            Generate 4-6 content pillars that:
-            1. Align with business goals
-            2. Address audience needs
-            3. Differentiate from competitors
-            4. Support SEO objectives
-            5. Enable consistent content creation
-            
-            For each pillar, provide:
-            - Name and description
-            - Target keywords/topics
-            - Content types suitable for this pillar
-            - Success metrics
-            - Example content ideas (5)
-            
-            Format as JSON structure.
-            """
-            
-            ai_pillars = llm_text_gen(
-                pillars_prompt,
-                system_prompt="You are a content strategist creating strategic content pillars. Return structured data."
-            )
-            
-            # Parse and structure the pillars
-            pillars = [
-                {
-                    'id': 1,
-                    'name': 'Thought Leadership',
-                    'description': 'Position as industry expert through insights and trends',
-                    'target_keywords': ['industry trends', 'expert insights', 'market analysis'],
-                    'content_types': ['Blog posts', 'Whitepapers', 'Webinars', 'Podcasts'],
-                    'success_metrics': ['Brand mentions', 'Expert citations', 'Speaking invitations'],
-                    'content_ideas': [
-                        'Industry trend predictions for 2024',
-                        'Expert roundtable discussions',
-                        'Market analysis reports',
-                        'Innovation case studies',
-                        'Future of industry insights'
-                    ]
-                },
-                {
-                    'id': 2,
-                    'name': 'Educational Content',
-                    'description': 'Educate audience on best practices and solutions',
-                    'target_keywords': ['how to', 'best practices', 'tutorials', 'guides'],
-                    'content_types': ['Tutorials', 'Guides', 'Video content', 'Infographics'],
-                    'success_metrics': ['Organic traffic', 'Time on page', 'Social shares'],
-                    'content_ideas': [
-                        'Step-by-step implementation guides',
-                        'Best practices checklists',
-                        'Common mistakes to avoid',
-                        'Tool comparison guides',
-                        'Quick tip series'
-                    ]
-                },
-                {
-                    'id': 3,
-                    'name': 'Customer Success',
-                    'description': 'Showcase success stories and build trust',
-                    'target_keywords': ['case study', 'success story', 'results', 'testimonials'],
-                    'content_types': ['Case studies', 'Customer stories', 'Testimonials', 'Reviews'],
-                    'success_metrics': ['Lead generation', 'Conversion rate', 'Trust signals'],
-                    'content_ideas': [
-                        'Detailed customer case studies',
-                        'Before/after transformations',
-                        'ROI success stories',
-                        'Customer interview series',
-                        'Implementation timelines'
-                    ]
-                },
-                {
-                    'id': 4,
-                    'name': 'Product Education',
-                    'description': 'Educate on product features and benefits',
-                    'target_keywords': ['product features', 'benefits', 'use cases', 'comparison'],
-                    'content_types': ['Product demos', 'Feature guides', 'Comparison content'],
-                    'success_metrics': ['Product adoption', 'Trial conversions', 'Feature usage'],
-                    'content_ideas': [
-                        'Feature deep-dive tutorials',
-                        'Use case demonstrations',
-                        'Product comparison guides',
-                        'Integration tutorials',
-                        'Advanced tips and tricks'
-                    ]
-                }
-            ]
-            
-            return pillars
-            
-        except Exception as e:
-            logger.error(f"Content pillars error: {str(e)}")
-            return []
-    
-    def _create_content_calendar(self, content_pillars: List[Dict[str, Any]], business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """Create comprehensive content calendar."""
-        timeline = business_info.get('timeline', '3 months')
-        
-        # Generate calendar structure based on timeline
-        if '3 months' in timeline or '90 days' in timeline:
-            periods = 12  # Weekly planning
-            period_type = 'week'
-        elif '6 months' in timeline:
-            periods = 24  # Bi-weekly planning
-            period_type = 'bi-week'
-        elif '1 year' in timeline or '12 months' in timeline:
-            periods = 52  # Weekly planning for a year
-            period_type = 'week'
-        else:
-            periods = 12  # Default to 3 months
-            period_type = 'week'
-        
-        calendar_items = []
-        pillar_rotation = 0
-        
-        for period in range(1, periods + 1):
-            # Rotate through content pillars
-            current_pillar = content_pillars[pillar_rotation % len(content_pillars)]
-            
-            # Generate content for this period
-            content_item = {
-                'period': period,
-                'period_type': period_type,
-                'pillar': current_pillar['name'],
-                'content_type': current_pillar['content_types'][0],  # Primary type
-                'topic': current_pillar['content_ideas'][period % len(current_pillar['content_ideas'])],
-                'target_keywords': current_pillar['target_keywords'][:2],  # Top 2 keywords
-                'distribution_channels': ['Blog', 'Social Media', 'Email'],
-                'priority': 'High' if period <= periods // 3 else 'Medium',
-                'estimated_hours': np.random.randint(4, 12),
-                'success_metrics': current_pillar['success_metrics']
-            }
-            
-            calendar_items.append(content_item)
-            pillar_rotation += 1
-        
-        return {
-            'timeline': timeline,
-            'total_periods': periods,
-            'period_type': period_type,
-            'calendar_items': calendar_items,
-            'pillar_distribution': self._calculate_pillar_distribution(calendar_items, content_pillars)
-        }
-    
-    def _generate_topic_clusters(self, business_info: Dict[str, Any], content_pillars: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Generate SEO topic clusters."""
-        clusters = []
-        
-        for pillar in content_pillars:
-            # Create topic cluster for each pillar
-            cluster = {
-                'cluster_name': f"{pillar['name']} Cluster",
-                'pillar_id': pillar['id'],
-                'primary_topic': pillar['target_keywords'][0] if pillar['target_keywords'] else pillar['name'],
-                'supporting_topics': pillar['target_keywords'][1:] if len(pillar['target_keywords']) > 1 else [],
-                'content_pieces': [
-                    {
-                        'type': 'Pillar Page',
-                        'title': f"Complete Guide to {pillar['name']}",
-                        'target_keyword': pillar['target_keywords'][0] if pillar['target_keywords'] else pillar['name'],
-                        'word_count': '3000-5000',
-                        'priority': 'High'
-                    }
-                ],
-                'internal_linking_strategy': f"Link all {pillar['name'].lower()} content to pillar page",
-                'seo_opportunity': f"Dominate {pillar['target_keywords'][0] if pillar['target_keywords'] else pillar['name']} search results"
-            }
-            
-            # Add supporting content pieces
-            for i, idea in enumerate(pillar['content_ideas'][:3]):  # Top 3 ideas
-                cluster['content_pieces'].append({
-                    'type': 'Supporting Content',
-                    'title': idea,
-                    'target_keyword': pillar['target_keywords'][i % len(pillar['target_keywords'])] if pillar['target_keywords'] else idea,
-                    'word_count': '1500-2500',
-                    'priority': 'Medium'
-                })
-            
-            clusters.append(cluster)
-        
-        return clusters
-    
-    def _create_distribution_strategy(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """Create content distribution strategy."""
-        return {
-            'primary_channels': [
-                {
-                    'channel': 'Company Blog',
-                    'content_types': ['Long-form articles', 'Guides', 'Case studies'],
-                    'frequency': 'Weekly',
-                    'audience_reach': 'High',
-                    'seo_value': 'High'
-                },
-                {
-                    'channel': 'LinkedIn',
-                    'content_types': ['Professional insights', 'Industry news', 'Thought leadership'],
-                    'frequency': 'Daily',
-                    'audience_reach': 'Medium',
-                    'seo_value': 'Medium'
-                },
-                {
-                    'channel': 'Email Newsletter',
-                    'content_types': ['Curated insights', 'Product updates', 'Educational content'],
-                    'frequency': 'Bi-weekly',
-                    'audience_reach': 'High',
-                    'seo_value': 'Low'
-                }
-            ],
-            'secondary_channels': [
-                {
-                    'channel': 'YouTube',
-                    'content_types': ['Tutorial videos', 'Webinars', 'Product demos'],
-                    'frequency': 'Bi-weekly',
-                    'audience_reach': 'Medium',
-                    'seo_value': 'High'
-                },
-                {
-                    'channel': 'Industry Publications',
-                    'content_types': ['Guest articles', 'Expert quotes', 'Research insights'],
-                    'frequency': 'Monthly',
-                    'audience_reach': 'Medium',
-                    'seo_value': 'High'
-                }
-            ],
-            'repurposing_strategy': {
-                'blog_post_to_social': 'Extract key insights for LinkedIn posts',
-                'long_form_to_video': 'Create video summaries of detailed guides',
-                'case_study_to_multiple': 'Create infographics, social posts, and email content',
-                'webinar_to_content': 'Extract blog posts, social content, and email series'
-            }
-        }
-    
-    def _create_kpi_framework(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """Create KPI measurement framework."""
-        return {
-            'primary_kpis': [
-                {
-                    'metric': 'Organic Traffic Growth',
-                    'target': '25% increase per quarter',
-                    'measurement': 'Google Analytics',
-                    'frequency': 'Monthly'
-                },
-                {
-                    'metric': 'Lead Generation',
-                    'target': '50 qualified leads per month',
-                    'measurement': 'CRM tracking',
-                    'frequency': 'Weekly'
-                },
-                {
-                    'metric': 'Brand Awareness',
-                    'target': '15% increase in brand mentions',
-                    'measurement': 'Social listening tools',
-                    'frequency': 'Monthly'
-                }
-            ],
-            'content_kpis': [
-                {
-                    'metric': 'Content Engagement',
-                    'target': '5% average engagement rate',
-                    'measurement': 'Social media analytics',
-                    'frequency': 'Weekly'
-                },
-                {
-                    'metric': 'Content Shares',
-                    'target': '100 shares per piece',
-                    'measurement': 'Social sharing tracking',
-                    'frequency': 'Per content piece'
-                },
-                {
-                    'metric': 'Time on Page',
-                    'target': '3+ minutes average',
-                    'measurement': 'Google Analytics',
-                    'frequency': 'Monthly'
-                }
-            ],
-            'seo_kpis': [
-                {
-                    'metric': 'Keyword Rankings',
-                    'target': 'Top 10 for 20 target keywords',
-                    'measurement': 'SEO tools',
-                    'frequency': 'Weekly'
-                },
-                {
-                    'metric': 'Backlink Growth',
-                    'target': '10 quality backlinks per month',
-                    'measurement': 'Backlink analysis tools',
-                    'frequency': 'Monthly'
-                }
-            ]
-        }
-    
-    def _create_implementation_roadmap(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """Create implementation roadmap."""
-        return {
-            'phase_1': {
-                'name': 'Foundation (Month 1)',
-                'objectives': ['Content audit', 'Pillar page creation', 'Basic SEO setup'],
-                'deliverables': ['Content strategy document', '4 pillar pages', 'SEO foundation'],
-                'success_criteria': ['All pillar pages published', 'SEO tracking implemented']
-            },
-            'phase_2': {
-                'name': 'Content Creation (Months 2-3)',
-                'objectives': ['Regular content publication', 'Social media activation', 'Email marketing'],
-                'deliverables': ['24 blog posts', 'Social media calendar', 'Email sequences'],
-                'success_criteria': ['Consistent publishing schedule', '20% traffic increase']
-            },
-            'phase_3': {
-                'name': 'Optimization (Months 4-6)',
-                'objectives': ['Performance optimization', 'Advanced SEO', 'Conversion optimization'],
-                'deliverables': ['Optimized content', 'Advanced SEO implementation', 'Conversion funnels'],
-                'success_criteria': ['50% traffic increase', 'Improved conversion rates']
-            }
-        }
-    
-    # Utility methods
-    def _extract_market_position(self, analysis: str) -> str:
-        """Extract market positioning from AI analysis."""
-        return "Market positioning insights extracted from AI analysis"
-    
-    def _extract_content_gaps(self, analysis: str) -> List[str]:
-        """Extract content gaps from AI analysis."""
-        return ["Educational content gap", "Technical documentation gap", "Case study gap"]
-    
-    def _extract_competitive_advantages(self, analysis: str) -> List[str]:
-        """Extract competitive advantages from AI analysis."""
-        return ["Unique technology approach", "Industry expertise", "Customer success focus"]
-    
-    def _extract_audience_insights(self, analysis: str) -> Dict[str, Any]:
-        """Extract audience insights from AI analysis."""
-        return {
-            'pain_points': ["Complex implementation", "Limited resources", "ROI concerns"],
-            'content_preferences': ["Visual content", "Step-by-step guides", "Real examples"],
-            'consumption_patterns': ["Mobile-first", "Video preferred", "Quick consumption"]
-        }
-    
-    def _calculate_pillar_distribution(self, calendar_items: List[Dict[str, Any]], content_pillars: List[Dict[str, Any]]) -> Dict[str, int]:
-        """Calculate content distribution across pillars."""
-        distribution = {}
-        for pillar in content_pillars:
-            count = len([item for item in calendar_items if item['pillar'] == pillar['name']])
-            distribution[pillar['name']] = count
-        return distribution
-    
-    def _generate_strategic_insights(self, business_info: Dict[str, Any], content_pillars: List[Dict[str, Any]]) -> Dict[str, Any]:
-        """Generate strategic insights and recommendations."""
-        return {
-            'key_insights': [
-                "Focus on educational content for early funnel engagement",
-                "Leverage customer success stories for conversion",
-                "Develop thought leadership for brand authority",
-                "Create product education for user adoption"
-            ],
-            'strategic_recommendations': [
-                "Implement topic cluster strategy for SEO dominance",
-                "Create pillar page for each content theme",
-                "Develop comprehensive content repurposing workflow",
-                "Establish thought leadership through industry insights"
-            ],
-            'risk_mitigation': [
-                "Diversify content topics to avoid algorithm dependency",
-                "Create evergreen content for long-term value",
-                "Build email list to reduce platform dependency",
-                "Monitor competitor content to maintain differentiation"
-            ]
-        }
-
-
-def render_ai_content_strategy():
-    """Render the AI Content Strategy interface."""
-    
-    st.title("🧠 AI Content Strategy Generator")
-    st.markdown("**Generate comprehensive content strategies powered by AI intelligence**")
-    
-    # Configuration form
-    st.header("📋 Business Information")
-    
-    with st.form("content_strategy_form"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            industry = st.selectbox(
-                "Industry",
-                [
-                    "Technology & Software",
-                    "Marketing & Advertising",
-                    "Healthcare",
-                    "Finance & Fintech",
-                    "E-commerce",
-                    "Education",
-                    "Manufacturing",
-                    "Professional Services",
-                    "Other"
-                ],
-                index=0
-            )
-            
-            target_audience = st.text_area(
-                "Target Audience",
-                placeholder="Describe your ideal customers, their roles, challenges, and goals...",
-                height=100
-            )
-            
-            business_goals = st.multiselect(
-                "Business Goals",
-                [
-                    "Increase brand awareness",
-                    "Generate leads",
-                    "Drive website traffic",
-                    "Establish thought leadership",
-                    "Improve customer education",
-                    "Support sales process",
-                    "Enhance customer retention",
-                    "Launch new product/service"
-                ]
-            )
-        
-        with col2:
-            content_objectives = st.multiselect(
-                "Content Objectives",
-                [
-                    "SEO improvement",
-                    "Social media engagement",
-                    "Email marketing",
-                    "Lead nurturing",
-                    "Customer education",
-                    "Brand storytelling",
-                    "Product demonstration",
-                    "Community building"
-                ]
-            )
-            
-            budget = st.selectbox(
-                "Monthly Content Budget",
-                [
-                    "No budget",
-                    "Under $1,000",
-                    "$1,000 - $5,000",
-                    "$5,000 - $10,000",
-                    "$10,000 - $25,000",
-                    "$25,000+"
-                ]
-            )
-            
-            timeline = st.selectbox(
-                "Strategy Timeline",
-                [
-                    "3 months",
-                    "6 months",
-                    "1 year",
-                    "Ongoing"
-                ]
-            )
-        
-        # Additional context
-        st.subheader("Additional Context")
-        
-        current_challenges = st.text_area(
-            "Current Content Challenges",
-            placeholder="What content challenges are you currently facing?",
-            height=80
-        )
-        
-        competitive_landscape = st.text_area(
-            "Competitive Landscape",
-            placeholder="Describe your main competitors and their content approach...",
-            height=80
-        )
-        
-        submit_strategy = st.form_submit_button("🧠 Generate AI Content Strategy", type="primary")
-    
-    # Process strategy generation
-    if submit_strategy:
-        if target_audience and business_goals and content_objectives:
-            # Prepare business information
-            business_info = {
-                'industry': industry,
-                'target_audience': target_audience,
-                'business_goals': business_goals,
-                'content_objectives': content_objectives,
-                'budget': budget,
-                'timeline': timeline,
-                'current_challenges': current_challenges,
-                'competitive_landscape': competitive_landscape
-            }
-            
-            # Initialize generator
-            if 'strategy_generator' not in st.session_state:
-                st.session_state.strategy_generator = AIContentStrategyGenerator()
-            
-            generator = st.session_state.strategy_generator
-            
-            with st.spinner("🧠 Generating AI-powered content strategy..."):
-                strategy_results = generator.generate_content_strategy(business_info)
-            
-            if 'error' not in strategy_results:
-                st.success("✅ Content strategy generated successfully!")
-                
-                # Store results in session state
-                st.session_state.strategy_results = strategy_results
-                
-                # Display results
-                render_strategy_results_dashboard(strategy_results)
-            else:
-                st.error(f"❌ Strategy generation failed: {strategy_results['error']}")
-        else:
-            st.warning("⚠️ Please fill in target audience, business goals, and content objectives.")
-    
-    # Show previous results if available
-    elif 'strategy_results' in st.session_state:
-        st.info("🧠 Showing previous strategy results")
-        render_strategy_results_dashboard(st.session_state.strategy_results)
-
-
-def render_strategy_results_dashboard(results: Dict[str, Any]):
-    """Render comprehensive strategy results dashboard."""
-    
-    # Strategy overview
-    st.header("📊 Content Strategy Overview")
-    
-    business_analysis = results.get('business_analysis', {})
-    content_pillars = results.get('content_pillars', [])
-    content_calendar = results.get('content_calendar', {})
-    
-    # Key metrics overview
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.metric("Content Pillars", len(content_pillars))
-    
-    with col2:
-        calendar_items = content_calendar.get('calendar_items', [])
-        st.metric("Content Pieces", len(calendar_items))
-    
-    with col3:
-        timeline = content_calendar.get('timeline', 'Not specified')
-        st.metric("Timeline", timeline)
-    
-    with col4:
-        total_hours = sum(item.get('estimated_hours', 0) for item in calendar_items)
-        st.metric("Est. Hours", f"{total_hours}h")
-    
-    # Strategy tabs
-    tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
-        "🧠 AI Insights",
-        "🏛️ Content Pillars",
-        "📅 Content Calendar",
-        "🎯 Topic Clusters",
-        "📢 Distribution",
-        "📊 Implementation"
-    ])
-    
-    with tab1:
-        if business_analysis:
-            st.subheader("Business Analysis & Insights")
-            
-            # Market positioning
-            market_position = business_analysis.get('market_position', '')
-            if market_position:
-                st.markdown("#### 🎯 Market Positioning")
-                st.info(market_position)
-            
-            # Content gaps
-            content_gaps = business_analysis.get('content_gaps', [])
-            if content_gaps:
-                st.markdown("#### 🔍 Content Gaps Identified")
-                for gap in content_gaps:
-                    st.warning(f"📌 {gap}")
-            
-            # Competitive advantages
-            advantages = business_analysis.get('competitive_advantages', [])
-            if advantages:
-                st.markdown("#### 🏆 Competitive Advantages")
-                for advantage in advantages:
-                    st.success(f"✅ {advantage}")
-            
-            # AI insights
-            ai_insights = results.get('ai_insights', {})
-            if ai_insights:
-                st.markdown("#### 🧠 Strategic AI Insights")
-                
-                insights = ai_insights.get('key_insights', [])
-                for insight in insights:
-                    st.info(f"💡 {insight}")
-                
-                recommendations = ai_insights.get('strategic_recommendations', [])
-                if recommendations:
-                    st.markdown("#### 🎯 Strategic Recommendations")
-                    for rec in recommendations:
-                        st.success(f"📋 {rec}")
-    
-    with tab2:
-        if content_pillars:
-            st.subheader("Content Pillars Strategy")
-            
-            # Pillars overview chart
-            pillar_names = [pillar['name'] for pillar in content_pillars]
-            pillar_ideas = [len(pillar['content_ideas']) for pillar in content_pillars]
-            
-            fig = px.bar(
-                x=pillar_names,
-                y=pillar_ideas,
-                title="Content Ideas per Pillar",
-                labels={'x': 'Content Pillars', 'y': 'Number of Ideas'}
-            )
-            st.plotly_chart(fig, use_container_width=True)
-            
-            # Detailed pillar information
-            for pillar in content_pillars:
-                with st.expander(f"🏛️ {pillar['name']}", expanded=False):
-                    st.markdown(f"**Description:** {pillar['description']}")
-                    
-                    col1, col2 = st.columns(2)
-                    
-                    with col1:
-                        st.markdown("**Target Keywords:**")
-                        for keyword in pillar['target_keywords']:
-                            st.code(keyword)
-                        
-                        st.markdown("**Content Types:**")
-                        for content_type in pillar['content_types']:
-                            st.write(f"• {content_type}")
-                    
-                    with col2:
-                        st.markdown("**Success Metrics:**")
-                        for metric in pillar['success_metrics']:
-                            st.write(f"📊 {metric}")
-                        
-                        st.markdown("**Content Ideas:**")
-                        for idea in pillar['content_ideas']:
-                            st.write(f"💡 {idea}")
-    
-    with tab3:
-        if content_calendar:
-            st.subheader("Content Calendar & Planning")
-            
-            calendar_items = content_calendar.get('calendar_items', [])
-            
-            if calendar_items:
-                # Calendar overview
-                df_calendar = pd.DataFrame(calendar_items)
-                
-                # Priority distribution
-                priority_counts = df_calendar['priority'].value_counts()
-                fig_priority = px.pie(
-                    values=priority_counts.values,
-                    names=priority_counts.index,
-                    title="Content Priority Distribution"
-                )
-                st.plotly_chart(fig_priority, use_container_width=True)
-                
-                # Content calendar table
-                st.markdown("#### 📅 Detailed Content Calendar")
-                
-                display_df = df_calendar[[
-                    'period', 'pillar', 'content_type', 'topic', 
-                    'priority', 'estimated_hours'
-                ]].copy()
-                
-                display_df.columns = [
-                    'Period', 'Pillar', 'Content Type', 'Topic', 
-                    'Priority', 'Est. Hours'
-                ]
-                
-                st.dataframe(
-                    display_df,
-                    column_config={
-                        "Priority": st.column_config.SelectboxColumn(
-                            "Priority",
-                            options=["High", "Medium", "Low"]
-                        ),
-                        "Est. Hours": st.column_config.NumberColumn(
-                            "Est. Hours",
-                            format="%d h"
-                        )
-                    },
-                    hide_index=True,
-                    use_container_width=True
-                )
-                
-                # Export calendar
-                csv = df_calendar.to_csv(index=False)
-                st.download_button(
-                    label="📥 Download Content Calendar",
-                    data=csv,
-                    file_name=f"content_calendar_{datetime.now().strftime('%Y%m%d')}.csv",
-                    mime="text/csv"
-                )
-    
-    with tab4:
-        topic_clusters = results.get('topic_clusters', [])
-        if topic_clusters:
-            st.subheader("SEO Topic Clusters")
-            
-            for cluster in topic_clusters:
-                with st.expander(f"🎯 {cluster['cluster_name']}", expanded=False):
-                    col1, col2 = st.columns(2)
-                    
-                    with col1:
-                        st.markdown(f"**Primary Topic:** {cluster['primary_topic']}")
-                        st.markdown(f"**SEO Opportunity:** {cluster['seo_opportunity']}")
-                        st.markdown(f"**Linking Strategy:** {cluster['internal_linking_strategy']}")
-                    
-                    with col2:
-                        st.markdown("**Supporting Topics:**")
-                        for topic in cluster['supporting_topics']:
-                            st.code(topic)
-                    
-                    st.markdown("**Content Pieces:**")
-                    content_pieces = cluster['content_pieces']
-                    df_pieces = pd.DataFrame(content_pieces)
-                    st.dataframe(df_pieces, hide_index=True, use_container_width=True)
-    
-    with tab5:
-        distribution_strategy = results.get('distribution_strategy', {})
-        if distribution_strategy:
-            st.subheader("Content Distribution Strategy")
-            
-            # Primary channels
-            primary_channels = distribution_strategy.get('primary_channels', [])
-            if primary_channels:
-                st.markdown("#### 📢 Primary Distribution Channels")
-                df_primary = pd.DataFrame(primary_channels)
-                st.dataframe(df_primary, hide_index=True, use_container_width=True)
-            
-            # Secondary channels
-            secondary_channels = distribution_strategy.get('secondary_channels', [])
-            if secondary_channels:
-                st.markdown("#### 📺 Secondary Distribution Channels")
-                df_secondary = pd.DataFrame(secondary_channels)
-                st.dataframe(df_secondary, hide_index=True, use_container_width=True)
-            
-            # Repurposing strategy
-            repurposing = distribution_strategy.get('repurposing_strategy', {})
-            if repurposing:
-                st.markdown("#### ♻️ Content Repurposing Strategy")
-                for strategy, description in repurposing.items():
-                    st.write(f"**{strategy.replace('_', ' ').title()}:** {description}")
-    
-    with tab6:
-        # Implementation roadmap
-        roadmap = results.get('implementation_roadmap', {})
-        kpi_framework = results.get('kpi_framework', {})
-        
-        if roadmap:
-            st.subheader("Implementation Roadmap")
-            
-            for phase_key, phase_data in roadmap.items():
-                with st.expander(f"📋 {phase_data['name']}", expanded=False):
-                    st.markdown(f"**Objectives:**")
-                    for objective in phase_data['objectives']:
-                        st.write(f"• {objective}")
-                    
-                    st.markdown(f"**Deliverables:**")
-                    for deliverable in phase_data['deliverables']:
-                        st.write(f"📦 {deliverable}")
-                    
-                    st.markdown(f"**Success Criteria:**")
-                    for criteria in phase_data['success_criteria']:
-                        st.write(f"✅ {criteria}")
-        
-        if kpi_framework:
-            st.subheader("KPI Framework")
-            
-            # Primary KPIs
-            primary_kpis = kpi_framework.get('primary_kpis', [])
-            if primary_kpis:
-                st.markdown("#### 🎯 Primary KPIs")
-                df_primary_kpis = pd.DataFrame(primary_kpis)
-                st.dataframe(df_primary_kpis, hide_index=True, use_container_width=True)
-            
-            # Content KPIs
-            content_kpis = kpi_framework.get('content_kpis', [])
-            if content_kpis:
-                st.markdown("#### 📝 Content KPIs")
-                df_content_kpis = pd.DataFrame(content_kpis)
-                st.dataframe(df_content_kpis, hide_index=True, use_container_width=True)
-    
-    # Export functionality
-    st.markdown("---")
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📥 Export Full Strategy", use_container_width=True):
-            strategy_json = json.dumps(results, indent=2, default=str)
-            st.download_button(
-                label="Download JSON Strategy",
-                data=strategy_json,
-                file_name=f"content_strategy_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                mime="application/json"
-            )
-    
-    with col2:
-        if st.button("📊 Export Calendar", use_container_width=True):
-            calendar_items = content_calendar.get('calendar_items', [])
-            if calendar_items:
-                df_calendar = pd.DataFrame(calendar_items)
-                csv = df_calendar.to_csv(index=False)
-                st.download_button(
-                    label="Download CSV Calendar",
-                    data=csv,
-                    file_name=f"content_calendar_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                    mime="text/csv"
-                )
-    
-    with col3:
-        if st.button("🔄 Generate New Strategy", use_container_width=True):
-            if 'strategy_results' in st.session_state:
-                del st.session_state.strategy_results
-            st.rerun()
-
-
-# Main execution
-if __name__ == "__main__":
-    render_ai_content_strategy() 
--- a/ToBeMigrated/ai_seo_tools/enterprise_seo_suite.py
+++ b/ToBeMigrated/ai_seo_tools/enterprise_seo_suite.py
@@ -1,919 +0,0 @@
-"""
-Enterprise SEO Command Center
-
-Unified AI-powered SEO suite that orchestrates all existing tools into 
-intelligent workflows for enterprise-level SEO management.
-"""
-
-import streamlit as st
-import asyncio
-import pandas as pd
-from typing import Dict, Any, List, Optional, Tuple
-from datetime import datetime, timedelta
-import json
-from loguru import logger
-
-# Import existing SEO tools
-from .on_page_seo_analyzer import fetch_seo_data
-from .content_gap_analysis.enhanced_analyzer import EnhancedContentGapAnalyzer
-from .technical_seo_crawler.crawler import TechnicalSEOCrawler
-from .weburl_seo_checker import url_seo_checker
-from .google_pagespeed_insights import google_pagespeed_insights
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Import the new enterprise tools
-from .google_search_console_integration import GoogleSearchConsoleAnalyzer, render_gsc_integration
-from .ai_content_strategy import AIContentStrategyGenerator, render_ai_content_strategy
-
-class EnterpriseSEOSuite:
-    """
-    Enterprise-level SEO suite orchestrating all tools into intelligent workflows.
-    """
-    
-    def __init__(self):
-        """Initialize the enterprise SEO suite."""
-        self.gap_analyzer = EnhancedContentGapAnalyzer()
-        self.technical_crawler = TechnicalSEOCrawler()
-        
-        # Initialize new enterprise tools
-        self.gsc_analyzer = GoogleSearchConsoleAnalyzer()
-        self.content_strategy_generator = AIContentStrategyGenerator()
-        
-        # SEO workflow templates
-        self.workflow_templates = {
-            'complete_audit': 'Complete SEO Audit',
-            'content_strategy': 'Content Strategy Development',
-            'technical_optimization': 'Technical SEO Optimization',
-            'competitor_intelligence': 'Competitive Intelligence',
-            'keyword_domination': 'Keyword Domination Strategy',
-            'local_seo': 'Local SEO Optimization',
-            'enterprise_monitoring': 'Enterprise SEO Monitoring'
-        }
-        
-        logger.info("Enterprise SEO Suite initialized")
-    
-    async def execute_complete_seo_audit(self, website_url: str, competitors: List[str], 
-                                       target_keywords: List[str]) -> Dict[str, Any]:
-        """
-        Execute a comprehensive enterprise SEO audit combining all tools.
-        
-        Args:
-            website_url: Primary website to audit
-            competitors: List of competitor URLs (max 5)
-            target_keywords: Primary keywords to optimize for
-            
-        Returns:
-            Comprehensive audit results with prioritized action plan
-        """
-        try:
-            st.info("🚀 Initiating Complete Enterprise SEO Audit...")
-            
-            audit_results = {
-                'audit_timestamp': datetime.utcnow().isoformat(),
-                'website_url': website_url,
-                'competitors': competitors[:5],
-                'target_keywords': target_keywords,
-                'technical_audit': {},
-                'content_analysis': {},
-                'competitive_intelligence': {},
-                'on_page_analysis': {},
-                'performance_metrics': {},
-                'strategic_recommendations': {},
-                'priority_action_plan': []
-            }
-            
-            # Phase 1: Technical SEO Audit
-            with st.expander("🔧 Technical SEO Analysis", expanded=True):
-                st.info("Analyzing technical SEO factors...")
-                technical_results = await self._run_technical_audit(website_url)
-                audit_results['technical_audit'] = technical_results
-                st.success("✅ Technical audit completed")
-            
-            # Phase 2: Content Gap Analysis
-            with st.expander("📊 Content Intelligence Analysis", expanded=True):
-                st.info("Analyzing content gaps and opportunities...")
-                content_results = await self._run_content_analysis(
-                    website_url, competitors, target_keywords
-                )
-                audit_results['content_analysis'] = content_results
-                st.success("✅ Content analysis completed")
-            
-            # Phase 3: On-Page SEO Analysis
-            with st.expander("🔍 On-Page SEO Analysis", expanded=True):
-                st.info("Analyzing on-page SEO factors...")
-                onpage_results = await self._run_onpage_analysis(website_url)
-                audit_results['on_page_analysis'] = onpage_results
-                st.success("✅ On-page analysis completed")
-            
-            # Phase 4: Performance Analysis
-            with st.expander("⚡ Performance Analysis", expanded=True):
-                st.info("Analyzing website performance...")
-                performance_results = await self._run_performance_analysis(website_url)
-                audit_results['performance_metrics'] = performance_results
-                st.success("✅ Performance analysis completed")
-            
-            # Phase 5: AI-Powered Strategic Recommendations
-            with st.expander("🤖 AI Strategic Analysis", expanded=True):
-                st.info("Generating AI-powered strategic recommendations...")
-                strategic_analysis = await self._generate_strategic_recommendations(audit_results)
-                audit_results['strategic_recommendations'] = strategic_analysis
-                
-                # Generate prioritized action plan
-                action_plan = await self._create_priority_action_plan(audit_results)
-                audit_results['priority_action_plan'] = action_plan
-                st.success("✅ Strategic analysis completed")
-            
-            return audit_results
-            
-        except Exception as e:
-            error_msg = f"Error in complete SEO audit: {str(e)}"
-            logger.error(error_msg, exc_info=True)
-            st.error(error_msg)
-            return {'error': error_msg}
-    
-    async def _run_technical_audit(self, website_url: str) -> Dict[str, Any]:
-        """Run comprehensive technical SEO audit."""
-        try:
-            # Use existing technical crawler
-            technical_results = self.technical_crawler.analyze_website_technical_seo(
-                website_url, crawl_depth=3, max_pages=100
-            )
-            
-            # Enhance with additional technical checks
-            enhanced_results = {
-                'crawler_results': technical_results,
-                'critical_issues': self._identify_critical_technical_issues(technical_results),
-                'performance_score': self._calculate_technical_score(technical_results),
-                'priority_fixes': self._prioritize_technical_fixes(technical_results)
-            }
-            
-            return enhanced_results
-            
-        except Exception as e:
-            logger.error(f"Technical audit error: {str(e)}")
-            return {'error': str(e)}
-    
-    async def _run_content_analysis(self, website_url: str, competitors: List[str], 
-                                  keywords: List[str]) -> Dict[str, Any]:
-        """Run comprehensive content gap analysis."""
-        try:
-            # Use existing content gap analyzer
-            content_results = self.gap_analyzer.analyze_comprehensive_gap(
-                website_url, competitors, keywords, industry="general"
-            )
-            
-            # Enhance with content strategy insights
-            enhanced_results = {
-                'gap_analysis': content_results,
-                'content_opportunities': self._identify_content_opportunities(content_results),
-                'keyword_strategy': self._develop_keyword_strategy(content_results),
-                'competitive_advantages': self._find_competitive_advantages(content_results)
-            }
-            
-            return enhanced_results
-            
-        except Exception as e:
-            logger.error(f"Content analysis error: {str(e)}")
-            return {'error': str(e)}
-    
-    async def _run_onpage_analysis(self, website_url: str) -> Dict[str, Any]:
-        """Run on-page SEO analysis."""
-        try:
-            # Use existing on-page analyzer
-            onpage_data = fetch_seo_data(website_url)
-            
-            # Enhanced analysis
-            enhanced_results = {
-                'seo_data': onpage_data,
-                'optimization_score': self._calculate_onpage_score(onpage_data),
-                'meta_optimization': self._analyze_meta_optimization(onpage_data),
-                'content_optimization': self._analyze_content_optimization(onpage_data)
-            }
-            
-            return enhanced_results
-            
-        except Exception as e:
-            logger.error(f"On-page analysis error: {str(e)}")
-            return {'error': str(e)}
-    
-    async def _run_performance_analysis(self, website_url: str) -> Dict[str, Any]:
-        """Run website performance analysis."""
-        try:
-            # Comprehensive performance metrics
-            performance_results = {
-                'core_web_vitals': await self._analyze_core_web_vitals(website_url),
-                'loading_performance': await self._analyze_loading_performance(website_url),
-                'mobile_optimization': await self._analyze_mobile_optimization(website_url),
-                'performance_score': 0  # Will be calculated
-            }
-            
-            # Calculate overall performance score
-            performance_results['performance_score'] = self._calculate_performance_score(
-                performance_results
-            )
-            
-            return performance_results
-            
-        except Exception as e:
-            logger.error(f"Performance analysis error: {str(e)}")
-            return {'error': str(e)}
-    
-    async def _generate_strategic_recommendations(self, audit_results: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate AI-powered strategic recommendations."""
-        try:
-            # Compile audit summary for AI analysis
-            audit_summary = {
-                'technical_score': audit_results.get('technical_audit', {}).get('performance_score', 0),
-                'content_gaps': len(audit_results.get('content_analysis', {}).get('content_opportunities', [])),
-                'onpage_score': audit_results.get('on_page_analysis', {}).get('optimization_score', 0),
-                'performance_score': audit_results.get('performance_metrics', {}).get('performance_score', 0)
-            }
-            
-            strategic_prompt = f"""
-            Analyze this comprehensive SEO audit and provide strategic recommendations:
-            
-            AUDIT SUMMARY:
-            - Technical SEO Score: {audit_summary['technical_score']}/100
-            - Content Gaps Identified: {audit_summary['content_gaps']}
-            - On-Page SEO Score: {audit_summary['onpage_score']}/100
-            - Performance Score: {audit_summary['performance_score']}/100
-            
-            DETAILED FINDINGS:
-            Technical Issues: {json.dumps(audit_results.get('technical_audit', {}), indent=2)[:1000]}
-            Content Opportunities: {json.dumps(audit_results.get('content_analysis', {}), indent=2)[:1000]}
-            
-            Provide strategic recommendations in these categories:
-            
-            1. IMMEDIATE WINS (0-30 days):
-               - Quick technical fixes with high impact
-               - Content optimizations for existing pages
-               - Critical performance improvements
-               
-            2. STRATEGIC INITIATIVES (1-3 months):
-               - Content strategy development
-               - Technical architecture improvements
-               - Competitive positioning strategies
-               
-            3. LONG-TERM GROWTH (3-12 months):
-               - Authority building strategies
-               - Market expansion opportunities
-               - Advanced SEO techniques
-               
-            4. RISK MITIGATION:
-               - Technical vulnerabilities to address
-               - Content gaps that competitors could exploit
-               - Performance issues affecting user experience
-               
-            Provide specific, actionable recommendations with expected impact and effort estimates.
-            """
-            
-            strategic_analysis = llm_text_gen(
-                strategic_prompt,
-                system_prompt="You are an enterprise SEO strategist with 10+ years of experience. Provide detailed, actionable recommendations based on comprehensive audit data."
-            )
-            
-            return {
-                'full_analysis': strategic_analysis,
-                'immediate_wins': self._extract_immediate_wins(strategic_analysis),
-                'strategic_initiatives': self._extract_strategic_initiatives(strategic_analysis),
-                'long_term_growth': self._extract_long_term_growth(strategic_analysis),
-                'risk_mitigation': self._extract_risk_mitigation(strategic_analysis)
-            }
-            
-        except Exception as e:
-            logger.error(f"Strategic analysis error: {str(e)}")
-            return {'error': str(e)}
-    
-    async def _create_priority_action_plan(self, audit_results: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Create prioritized action plan from audit results."""
-        try:
-            action_plan = []
-            
-            # Extract recommendations from all analysis phases
-            strategic_recs = audit_results.get('strategic_recommendations', {})
-            
-            # Immediate wins (High priority, low effort)
-            immediate_wins = strategic_recs.get('immediate_wins', [])
-            for win in immediate_wins[:5]:
-                action_plan.append({
-                    'category': 'Immediate Win',
-                    'priority': 'Critical',
-                    'effort': 'Low',
-                    'timeframe': '0-30 days',
-                    'action': win,
-                    'expected_impact': 'High',
-                    'source': 'Strategic Analysis'
-                })
-            
-            # Technical fixes
-            technical_issues = audit_results.get('technical_audit', {}).get('critical_issues', [])
-            for issue in technical_issues[:3]:
-                action_plan.append({
-                    'category': 'Technical SEO',
-                    'priority': 'High',
-                    'effort': 'Medium',
-                    'timeframe': '1-4 weeks',
-                    'action': issue,
-                    'expected_impact': 'High',
-                    'source': 'Technical Audit'
-                })
-            
-            # Content opportunities
-            content_ops = audit_results.get('content_analysis', {}).get('content_opportunities', [])
-            for opportunity in content_ops[:3]:
-                action_plan.append({
-                    'category': 'Content Strategy',
-                    'priority': 'Medium',
-                    'effort': 'High',
-                    'timeframe': '2-8 weeks',
-                    'action': opportunity,
-                    'expected_impact': 'Medium',
-                    'source': 'Content Analysis'
-                })
-            
-            # Sort by priority and expected impact
-            priority_order = {'Critical': 0, 'High': 1, 'Medium': 2, 'Low': 3}
-            action_plan.sort(key=lambda x: priority_order.get(x['priority'], 4))
-            
-            return action_plan[:15]  # Top 15 actions
-            
-        except Exception as e:
-            logger.error(f"Action plan creation error: {str(e)}")
-            return []
-    
-    # Utility methods for analysis
-    def _identify_critical_technical_issues(self, technical_results: Dict[str, Any]) -> List[str]:
-        """Identify critical technical SEO issues."""
-        critical_issues = []
-        
-        # Add logic to identify critical technical issues
-        # This would analyze the technical_results and extract critical problems
-        
-        return critical_issues
-    
-    def _calculate_technical_score(self, technical_results: Dict[str, Any]) -> int:
-        """Calculate technical SEO score."""
-        # Implement scoring algorithm based on technical audit results
-        return 75  # Placeholder
-    
-    def _prioritize_technical_fixes(self, technical_results: Dict[str, Any]) -> List[str]:
-        """Prioritize technical fixes by impact and effort."""
-        # Implement prioritization logic
-        return ["Fix broken links", "Optimize images", "Improve page speed"]
-    
-    def _identify_content_opportunities(self, content_results: Dict[str, Any]) -> List[str]:
-        """Identify top content opportunities."""
-        # Extract content opportunities from gap analysis
-        return ["Create FAQ content", "Develop comparison guides", "Write how-to articles"]
-    
-    def _develop_keyword_strategy(self, content_results: Dict[str, Any]) -> Dict[str, Any]:
-        """Develop keyword strategy from content analysis."""
-        return {
-            'primary_keywords': [],
-            'secondary_keywords': [],
-            'long_tail_opportunities': [],
-            'competitor_gaps': []
-        }
-    
-    def _find_competitive_advantages(self, content_results: Dict[str, Any]) -> List[str]:
-        """Find competitive advantages from analysis."""
-        return ["Unique content angles", "Underserved niches", "Technical superiority"]
-    
-    def _calculate_onpage_score(self, onpage_data: Dict[str, Any]) -> int:
-        """Calculate on-page SEO score."""
-        return 80  # Placeholder
-    
-    def _analyze_meta_optimization(self, onpage_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze meta tag optimization."""
-        return {'title_optimization': 'good', 'description_optimization': 'needs_work'}
-    
-    def _analyze_content_optimization(self, onpage_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze content optimization."""
-        return {'keyword_density': 'optimal', 'content_length': 'adequate'}
-    
-    async def _analyze_core_web_vitals(self, website_url: str) -> Dict[str, Any]:
-        """Analyze Core Web Vitals."""
-        return {'lcp': 2.5, 'fid': 100, 'cls': 0.1}
-    
-    async def _analyze_loading_performance(self, website_url: str) -> Dict[str, Any]:
-        """Analyze loading performance."""
-        return {'ttfb': 200, 'fcp': 1.5, 'speed_index': 3.0}
-    
-    async def _analyze_mobile_optimization(self, website_url: str) -> Dict[str, Any]:
-        """Analyze mobile optimization."""
-        return {'mobile_friendly': True, 'responsive_design': True}
-    
-    def _calculate_performance_score(self, performance_results: Dict[str, Any]) -> int:
-        """Calculate overall performance score."""
-        return 85  # Placeholder
-    
-    def _extract_immediate_wins(self, analysis: str) -> List[str]:
-        """Extract immediate wins from strategic analysis."""
-        # Parse the AI analysis and extract immediate wins
-        lines = analysis.split('\n')
-        wins = []
-        in_immediate_section = False
-        
-        for line in lines:
-            if 'IMMEDIATE WINS' in line.upper():
-                in_immediate_section = True
-                continue
-            elif 'STRATEGIC INITIATIVES' in line.upper():
-                in_immediate_section = False
-                continue
-            
-            if in_immediate_section and line.strip().startswith('-'):
-                wins.append(line.strip().lstrip('- '))
-        
-        return wins[:5]
-    
-    def _extract_strategic_initiatives(self, analysis: str) -> List[str]:
-        """Extract strategic initiatives from analysis."""
-        # Similar extraction logic for strategic initiatives
-        return ["Develop content hub", "Implement schema markup", "Build authority pages"]
-    
-    def _extract_long_term_growth(self, analysis: str) -> List[str]:
-        """Extract long-term growth strategies."""
-        return ["Market expansion", "Authority building", "Advanced technical SEO"]
-    
-    def _extract_risk_mitigation(self, analysis: str) -> List[str]:
-        """Extract risk mitigation strategies."""
-        return ["Fix technical vulnerabilities", "Address content gaps", "Improve performance"]
-
-    def execute_content_strategy_workflow(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Execute comprehensive content strategy workflow using AI insights.
-        
-        Args:
-            business_info: Business context and objectives
-            
-        Returns:
-            Complete content strategy with implementation plan
-        """
-        try:
-            st.info("🧠 Executing AI-powered content strategy workflow...")
-            
-            # Generate AI content strategy
-            content_strategy = self.content_strategy_generator.generate_content_strategy(business_info)
-            
-            # If GSC data is available, enhance with search insights
-            if business_info.get('gsc_site_url'):
-                gsc_insights = self.gsc_analyzer.analyze_search_performance(
-                    business_info['gsc_site_url'],
-                    business_info.get('gsc_date_range', 90)
-                )
-                content_strategy['gsc_insights'] = gsc_insights
-            
-            # Generate SEO-optimized content recommendations
-            seo_content_recs = self._generate_seo_content_recommendations(content_strategy)
-            content_strategy['seo_recommendations'] = seo_content_recs
-            
-            return content_strategy
-            
-        except Exception as e:
-            logger.error(f"Content strategy workflow error: {str(e)}")
-            return {'error': str(e)}
-    
-    def execute_search_intelligence_workflow(self, site_url: str, date_range: int = 90) -> Dict[str, Any]:
-        """
-        Execute comprehensive search intelligence workflow using GSC data.
-        
-        Args:
-            site_url: Website URL registered in GSC
-            date_range: Analysis period in days
-            
-        Returns:
-            Complete search intelligence analysis with actionable insights
-        """
-        try:
-            st.info("📊 Executing search intelligence workflow...")
-            
-            # Analyze GSC performance
-            gsc_analysis = self.gsc_analyzer.analyze_search_performance(site_url, date_range)
-            
-            # Enhance with technical SEO analysis
-            technical_analysis = self.technical_crawler.crawl_and_analyze(site_url)
-            gsc_analysis['technical_insights'] = technical_analysis
-            
-            # Generate content gap analysis based on GSC keywords
-            if gsc_analysis.get('keyword_analysis'):
-                keywords = [kw['keyword'] for kw in gsc_analysis['keyword_analysis'].get('high_volume_keywords', [])]
-                content_gaps = self.gap_analyzer.analyze_content_gaps(
-                    keywords[:10],  # Top 10 keywords
-                    site_url
-                )
-                gsc_analysis['content_gap_analysis'] = content_gaps
-            
-            # Generate comprehensive recommendations
-            search_recommendations = self._generate_search_intelligence_recommendations(gsc_analysis)
-            gsc_analysis['comprehensive_recommendations'] = search_recommendations
-            
-            return gsc_analysis
-            
-        except Exception as e:
-            logger.error(f"Search intelligence workflow error: {str(e)}")
-            return {'error': str(e)}
-    
-    def _generate_seo_content_recommendations(self, content_strategy: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate SEO-optimized content recommendations based on strategy."""
-        try:
-            content_pillars = content_strategy.get('content_pillars', [])
-            
-            seo_recommendations = {
-                'keyword_optimization': [],
-                'content_structure': [],
-                'internal_linking': [],
-                'technical_seo': []
-            }
-            
-            for pillar in content_pillars:
-                # Keyword optimization recommendations
-                for keyword in pillar.get('target_keywords', []):
-                    seo_recommendations['keyword_optimization'].append({
-                        'pillar': pillar['name'],
-                        'keyword': keyword,
-                        'recommendation': f"Create comprehensive content targeting '{keyword}' with semantic variations",
-                        'priority': 'High' if keyword in pillar['target_keywords'][:2] else 'Medium'
-                    })
-                
-                # Content structure recommendations
-                seo_recommendations['content_structure'].append({
-                    'pillar': pillar['name'],
-                    'recommendation': f"Create pillar page for {pillar['name']} with supporting cluster content",
-                    'structure': 'Pillar + Cluster model'
-                })
-            
-            # Internal linking strategy
-            seo_recommendations['internal_linking'] = [
-                "Link all cluster content to relevant pillar pages",
-                "Create topic-based internal linking structure",
-                "Use contextual anchor text with target keywords",
-                "Implement breadcrumb navigation for topic clusters"
-            ]
-            
-            # Technical SEO recommendations
-            seo_recommendations['technical_seo'] = [
-                "Optimize page speed for all content pages",
-                "Implement structured data for articles",
-                "Create XML sitemap sections for content categories",
-                "Optimize images with descriptive alt text"
-            ]
-            
-            return seo_recommendations
-            
-        except Exception as e:
-            logger.error(f"SEO content recommendations error: {str(e)}")
-            return {'error': str(e)}
-    
-    def _generate_search_intelligence_recommendations(self, gsc_analysis: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate comprehensive recommendations from search intelligence analysis."""
-        try:
-            recommendations = {
-                'immediate_actions': [],
-                'content_opportunities': [],
-                'technical_improvements': [],
-                'strategic_initiatives': []
-            }
-            
-            # Extract content opportunities from GSC analysis
-            content_opps = gsc_analysis.get('content_opportunities', [])
-            for opp in content_opps[:5]:  # Top 5 opportunities
-                recommendations['content_opportunities'].append({
-                    'type': opp['type'],
-                    'keyword': opp['keyword'],
-                    'action': opp['opportunity'],
-                    'priority': opp['priority'],
-                    'estimated_impact': opp['potential_impact']
-                })
-            
-            # Technical improvements from analysis
-            technical_insights = gsc_analysis.get('technical_insights', {})
-            if technical_insights.get('crawl_issues_indicators'):
-                for issue in technical_insights['crawl_issues_indicators']:
-                    recommendations['technical_improvements'].append({
-                        'issue': issue,
-                        'priority': 'High',
-                        'category': 'Crawl & Indexing'
-                    })
-            
-            # Immediate actions based on performance
-            performance = gsc_analysis.get('performance_overview', {})
-            if performance.get('avg_ctr', 0) < 2:
-                recommendations['immediate_actions'].append({
-                    'action': 'Improve meta descriptions and titles for better CTR',
-                    'expected_impact': 'Increase CTR by 1-2%',
-                    'timeline': '2-4 weeks'
-                })
-            
-            if performance.get('avg_position', 0) > 10:
-                recommendations['immediate_actions'].append({
-                    'action': 'Focus on improving content quality for top keywords',
-                    'expected_impact': 'Improve average position by 2-5 ranks',
-                    'timeline': '4-8 weeks'
-                })
-            
-            # Strategic initiatives
-            competitive_analysis = gsc_analysis.get('competitive_analysis', {})
-            if competitive_analysis.get('market_position') in ['Challenger', 'Emerging Player']:
-                recommendations['strategic_initiatives'].append({
-                    'initiative': 'Develop thought leadership content strategy',
-                    'goal': 'Improve market position and brand authority',
-                    'timeline': '3-6 months'
-                })
-            
-            return recommendations
-            
-        except Exception as e:
-            logger.error(f"Search intelligence recommendations error: {str(e)}")
-            return {'error': str(e)}
-
-def render_enterprise_seo_suite():
-    """Render the Enterprise SEO Command Center interface."""
-    
-    st.set_page_config(
-        page_title="Enterprise SEO Command Center",
-        page_icon="🚀",
-        layout="wide"
-    )
-    
-    st.title("🚀 Enterprise SEO Command Center")
-    st.markdown("**Unified AI-powered SEO suite orchestrating all tools into intelligent workflows**")
-    
-    # Initialize suite
-    if 'enterprise_seo_suite' not in st.session_state:
-        st.session_state.enterprise_seo_suite = EnterpriseSEOSuite()
-    
-    suite = st.session_state.enterprise_seo_suite
-    
-    # Workflow selection
-    st.sidebar.header("🎯 SEO Workflow Selection")
-    selected_workflow = st.sidebar.selectbox(
-        "Choose Workflow",
-        list(suite.workflow_templates.keys()),
-        format_func=lambda x: suite.workflow_templates[x]
-    )
-    
-    # Main workflow interface
-    if selected_workflow == 'complete_audit':
-        st.header("🔍 Complete Enterprise SEO Audit")
-        render_complete_audit_interface(suite)
-    elif selected_workflow == 'content_strategy':
-        st.header("📊 Content Strategy Development")
-        render_content_strategy_interface(suite)
-    elif selected_workflow == 'technical_optimization':
-        st.header("🔧 Technical SEO Optimization")
-        render_technical_optimization_interface(suite)
-    else:
-        st.info(f"Workflow '{suite.workflow_templates[selected_workflow]}' is being developed.")
-
-def render_complete_audit_interface(suite: EnterpriseSEOSuite):
-    """Render the complete audit workflow interface."""
-    
-    # Input form
-    with st.form("enterprise_audit_form"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            website_url = st.text_input(
-                "Website URL",
-                value="https://example.com",
-                help="Enter your website URL for comprehensive analysis"
-            )
-            
-            target_keywords = st.text_area(
-                "Target Keywords (one per line)",
-                value="AI content creation\nSEO tools\ncontent optimization",
-                help="Enter your primary keywords to optimize for"
-            )
-        
-        with col2:
-            competitors = st.text_area(
-                "Competitor URLs (one per line)",
-                value="https://jasper.ai\nhttps://copy.ai\nhttps://writesonic.com",
-                help="Enter up to 5 competitor URLs for analysis"
-            )
-        
-        submit_audit = st.form_submit_button("🚀 Start Complete SEO Audit", type="primary")
-    
-    # Process audit
-    if submit_audit:
-        if website_url and target_keywords:
-            # Parse inputs
-            keywords_list = [k.strip() for k in target_keywords.split('\n') if k.strip()]
-            competitors_list = [c.strip() for c in competitors.split('\n') if c.strip()]
-            
-            # Run audit
-            with st.spinner("🔍 Running comprehensive SEO audit..."):
-                audit_results = asyncio.run(
-                    suite.execute_complete_seo_audit(
-                        website_url, competitors_list, keywords_list
-                    )
-                )
-            
-            if 'error' not in audit_results:
-                st.success("✅ Enterprise SEO audit completed!")
-                
-                # Display results dashboard
-                render_audit_results_dashboard(audit_results)
-            else:
-                st.error(f"❌ Audit failed: {audit_results['error']}")
-        else:
-            st.warning("⚠️ Please enter website URL and target keywords.")
-
-def render_audit_results_dashboard(results: Dict[str, Any]):
-    """Render comprehensive audit results dashboard."""
-    
-    # Priority Action Plan (Most Important)
-    st.header("📋 Priority Action Plan")
-    action_plan = results.get('priority_action_plan', [])
-    
-    if action_plan:
-        # Display as interactive table
-        df_actions = pd.DataFrame(action_plan)
-        
-        # Style the dataframe
-        st.dataframe(
-            df_actions,
-            column_config={
-                "category": "Category",
-                "priority": st.column_config.SelectboxColumn(
-                    "Priority",
-                    options=["Critical", "High", "Medium", "Low"]
-                ),
-                "effort": "Effort Level",
-                "timeframe": "Timeline",
-                "action": "Action Required",
-                "expected_impact": "Expected Impact"
-            },
-            hide_index=True,
-            use_container_width=True
-        )
-    
-    # Key Metrics Overview
-    st.header("📊 SEO Health Dashboard")
-    
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        technical_score = results.get('technical_audit', {}).get('performance_score', 0)
-        st.metric("Technical SEO", f"{technical_score}/100", delta=None)
-    
-    with col2:
-        onpage_score = results.get('on_page_analysis', {}).get('optimization_score', 0)
-        st.metric("On-Page SEO", f"{onpage_score}/100", delta=None)
-    
-    with col3:
-        performance_score = results.get('performance_metrics', {}).get('performance_score', 0)
-        st.metric("Performance", f"{performance_score}/100", delta=None)
-    
-    with col4:
-        content_gaps = len(results.get('content_analysis', {}).get('content_opportunities', []))
-        st.metric("Content Opportunities", content_gaps, delta=None)
-    
-    # Detailed Analysis Sections
-    tab1, tab2, tab3, tab4, tab5 = st.tabs([
-        "🤖 Strategic Insights",
-        "🔧 Technical Analysis", 
-        "📊 Content Intelligence",
-        "🔍 On-Page Analysis",
-        "⚡ Performance Metrics"
-    ])
-    
-    with tab1:
-        strategic_recs = results.get('strategic_recommendations', {})
-        if strategic_recs:
-            st.subheader("AI-Powered Strategic Recommendations")
-            
-            # Immediate wins
-            immediate_wins = strategic_recs.get('immediate_wins', [])
-            if immediate_wins:
-                st.markdown("#### 🚀 Immediate Wins (0-30 days)")
-                for win in immediate_wins[:5]:
-                    st.success(f"✅ {win}")
-            
-            # Strategic initiatives
-            strategic_initiatives = strategic_recs.get('strategic_initiatives', [])
-            if strategic_initiatives:
-                st.markdown("#### 📈 Strategic Initiatives (1-3 months)")
-                for initiative in strategic_initiatives[:3]:
-                    st.info(f"📋 {initiative}")
-            
-            # Full analysis
-            full_analysis = strategic_recs.get('full_analysis', '')
-            if full_analysis:
-                with st.expander("🧠 Complete Strategic Analysis"):
-                    st.write(full_analysis)
-    
-    with tab2:
-        technical_audit = results.get('technical_audit', {})
-        if technical_audit:
-            st.subheader("Technical SEO Analysis")
-            
-            critical_issues = technical_audit.get('critical_issues', [])
-            if critical_issues:
-                st.markdown("#### ⚠️ Critical Issues")
-                for issue in critical_issues:
-                    st.error(f"🚨 {issue}")
-            
-            priority_fixes = technical_audit.get('priority_fixes', [])
-            if priority_fixes:
-                st.markdown("#### 🔧 Priority Fixes")
-                for fix in priority_fixes:
-                    st.warning(f"🛠️ {fix}")
-    
-    with tab3:
-        content_analysis = results.get('content_analysis', {})
-        if content_analysis:
-            st.subheader("Content Intelligence")
-            
-            content_opportunities = content_analysis.get('content_opportunities', [])
-            if content_opportunities:
-                st.markdown("#### 📝 Content Opportunities")
-                for opportunity in content_opportunities[:5]:
-                    st.info(f"💡 {opportunity}")
-            
-            competitive_advantages = content_analysis.get('competitive_advantages', [])
-            if competitive_advantages:
-                st.markdown("#### 🏆 Competitive Advantages")
-                for advantage in competitive_advantages:
-                    st.success(f"⭐ {advantage}")
-    
-    with tab4:
-        onpage_analysis = results.get('on_page_analysis', {})
-        if onpage_analysis:
-            st.subheader("On-Page SEO Analysis")
-            
-            meta_optimization = onpage_analysis.get('meta_optimization', {})
-            content_optimization = onpage_analysis.get('content_optimization', {})
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                st.markdown("#### 🏷️ Meta Tag Optimization")
-                st.json(meta_optimization)
-            
-            with col2:
-                st.markdown("#### 📄 Content Optimization")
-                st.json(content_optimization)
-    
-    with tab5:
-        performance_metrics = results.get('performance_metrics', {})
-        if performance_metrics:
-            st.subheader("Performance Analysis")
-            
-            core_vitals = performance_metrics.get('core_web_vitals', {})
-            loading_performance = performance_metrics.get('loading_performance', {})
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                st.markdown("#### ⚡ Core Web Vitals")
-                st.json(core_vitals)
-            
-            with col2:
-                st.markdown("#### 🚀 Loading Performance")
-                st.json(loading_performance)
-    
-    # Export functionality
-    st.markdown("---")
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📥 Export Full Report", use_container_width=True):
-            # Create downloadable report
-            report_json = json.dumps(results, indent=2, default=str)
-            st.download_button(
-                label="Download JSON Report",
-                data=report_json,
-                file_name=f"seo_audit_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                mime="application/json"
-            )
-    
-    with col2:
-        if st.button("📊 Export Action Plan", use_container_width=True):
-            # Create CSV of action plan
-            df_actions = pd.DataFrame(action_plan)
-            csv = df_actions.to_csv(index=False)
-            st.download_button(
-                label="Download CSV Action Plan",
-                data=csv,
-                file_name=f"action_plan_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                mime="text/csv"
-            )
-    
-    with col3:
-        if st.button("🔄 Schedule Follow-up Audit", use_container_width=True):
-            st.info("Follow-up scheduling feature coming soon!")
-
-def render_content_strategy_interface(suite: EnterpriseSEOSuite):
-    """Render content strategy development interface."""
-    st.info("🚧 Content Strategy Development workflow coming soon!")
-
-def render_technical_optimization_interface(suite: EnterpriseSEOSuite):
-    """Render technical optimization interface."""
-    st.info("🚧 Technical SEO Optimization workflow coming soon!")
-
-
-# Main execution
-if __name__ == "__main__":
-    render_enterprise_seo_suite() 
--- a/ToBeMigrated/ai_seo_tools/google_pagespeed_insights.py
+++ b/ToBeMigrated/ai_seo_tools/google_pagespeed_insights.py
@@ -1,135 +0,0 @@
-import requests
-import streamlit as st
-import json
-import pandas as pd
-import plotly.express as px
-from tenacity import retry, stop_after_attempt, wait_random_exponential
-from datetime import datetime
-
-def run_pagespeed(url, api_key=None, strategy='DESKTOP', locale='en'):
-    """Fetches and processes PageSpeed Insights data."""
-    serviceurl = 'https://www.googleapis.com/pagespeedonline/v5/runPagespeed'
-    base_url = f"{serviceurl}?url={url}&strategy={strategy}&locale={locale}&category=performance&category=accessibility&category=best-practices&category=seo"
-    
-    if api_key:
-        base_url += f"&key={api_key}"
-
-    try:
-        response = requests.get(base_url)
-        response.raise_for_status()  # Raise an exception for bad status codes
-        data = response.json()
-        return data
-    except requests.exceptions.RequestException as e:
-        st.error(f"Error fetching PageSpeed Insights data: {e}")
-        return None
-
-def display_results(data):
-    """Presents PageSpeed Insights data in a user-friendly format."""
-    st.subheader("PageSpeed Insights Report")
-
-    # Extract scores from the PageSpeed Insights data
-    scores = {
-        "Performance": data['lighthouseResult']['categories']['performance']['score'] * 100,
-        "Accessibility": data['lighthouseResult']['categories']['accessibility']['score'] * 100,
-        "SEO": data['lighthouseResult']['categories']['seo']['score'] * 100,
-        "Best Practices": data['lighthouseResult']['categories']['best-practices']['score'] * 100
-    }
-
-    descriptions = {
-        "Performance": data['lighthouseResult']['categories']['performance'].get('description', "This score represents Google's assessment of your page's speed. A higher percentage indicates better performance."),
-        "Accessibility": data['lighthouseResult']['categories']['accessibility'].get('description', "This score evaluates how accessible your page is to users with disabilities. A higher percentage means better accessibility."),
-        "SEO": data['lighthouseResult']['categories']['seo'].get('description', "This score measures how well your page is optimized for search engines. A higher percentage indicates better SEO practices."),
-        "Best Practices": data['lighthouseResult']['categories']['best-practices'].get('description', "This score reflects how well your page follows best practices for web development. A higher percentage signifies adherence to best practices.")
-    }
-
-    for category, score in scores.items():
-        st.metric(label=f"Overall {category} Score", value=f"{score:.0f}%", help=descriptions[category])
-
-    # Display additional metrics
-    st.subheader("Additional Metrics")
-    additional_metrics = {
-        "First Contentful Paint (FCP)": data['lighthouseResult']['audits']['first-contentful-paint']['displayValue'],
-        "Largest Contentful Paint (LCP)": data['lighthouseResult']['audits']['largest-contentful-paint']['displayValue'],
-        "Time to Interactive (TTI)": data['lighthouseResult']['audits']['interactive']['displayValue'],
-        "Total Blocking Time (TBT)": data['lighthouseResult']['audits']['total-blocking-time']['displayValue'],
-        "Cumulative Layout Shift (CLS)": data['lighthouseResult']['audits']['cumulative-layout-shift']['displayValue']
-    }
-
-    st.table(pd.DataFrame(additional_metrics.items(), columns=["Metric", "Value"]))
-
-    # Display Network Requests
-    st.subheader("Network Requests")
-    if 'network-requests' in data['lighthouseResult']['audits']:
-        network_requests = [
-            {
-                "End Time": item.get("endTime", "N/A"),
-                "Start Time": item.get("startTime", "N/A"),
-                "Transfer Size (MB)": round(item.get("transferSize", 0) / 1048576, 2),
-                "Resource Size (MB)": round(item.get("resourceSize", 0) / 1048576, 2),
-                "URL": item.get("url", "N/A")
-            }
-            for item in data["lighthouseResult"]["audits"]["network-requests"]["details"]["items"]
-            if item.get("transferSize", 0) > 100000 or item.get("resourceSize", 0) > 100000
-        ]
-        if network_requests:
-            st.dataframe(pd.DataFrame(network_requests), use_container_width=True)
-        else:
-            st.write("No significant network requests found.")
-
-    # Display Mainthread Work Breakdown
-    st.subheader("Mainthread Work Breakdown")
-    if 'mainthread-work-breakdown' in data['lighthouseResult']['audits']:
-        mainthread_data = [
-            {"Process": item.get("groupLabel", "N/A"), "Duration (ms)": item.get("duration", "N/A")}
-            for item in data["lighthouseResult"]["audits"]["mainthread-work-breakdown"]["details"]["items"] if item.get("duration", "N/A") != "N/A"
-        ]
-        if mainthread_data:
-            fig = px.bar(pd.DataFrame(mainthread_data), x="Process", y="Duration (ms)", title="Mainthread Work Breakdown", labels={"Process": "Process", "Duration (ms)": "Duration (ms)"})
-            st.plotly_chart(fig, use_container_width=True)
-        else:
-            st.write("No significant main thread work breakdown data found.")
-
-    # Display other metrics
-    metrics = [
-        ("Use of Passive Event Listeners", 'uses-passive-event-listeners', ["URL", "Code Line"]),
-        ("DOM Size", 'dom-size', ["Score", "DOM Size"]),
-        ("Offscreen Images", 'offscreen-images', ["URL", "Total Bytes", "Wasted Bytes", "Wasted Percentage"]),
-        ("Critical Request Chains", 'critical-request-chains', ["URL", "Start Time", "End Time", "Transfer Size", "Chain"]),
-        ("Total Bytes Weight", 'total-byte-weight', ["URL", "Total Bytes"]),
-        ("Render Blocking Resources", 'render-blocking-resources', ["URL", "Total Bytes", "Wasted Milliseconds"]),
-        ("Use of Rel Preload", 'uses-rel-preload', ["URL", "Wasted Milliseconds"])
-    ]
-
-    for metric_title, audit_key, columns in metrics:
-        st.subheader(metric_title)
-        if audit_key in data['lighthouseResult']['audits']:
-            details = data['lighthouseResult']['audits'][audit_key].get("details", {}).get("items", [])
-            if details:
-                st.table(pd.DataFrame(details, columns=columns))
-            else:
-                st.write(f"No significant {metric_title.lower()} data found.")
-
-def google_pagespeed_insights():
-    st.markdown("<h1 style='text-align: center; color: #1565C0;'>PageSpeed Insights Analyzer</h1>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: center;'>Get detailed insights into your website's performance! Powered by Google PageSpeed Insights <a href='https://developer.chrome.com/docs/lighthouse/overview/'>[Learn More]</a></h3>", unsafe_allow_html=True)
-
-    # User Input
-    with st.form("pagespeed_form"):
-        url = st.text_input("Enter Website URL", placeholder="https://www.example.com")
-        api_key = st.text_input("Enter Google API Key (Optional)", placeholder="Your API Key", help="Get your API key here: [https://developers.google.com/speed/docs/insights/v5/get-started#key]")
-        device = st.selectbox("Choose Device", ["Mobile", "Desktop"])
-        locale = st.selectbox("Choose Locale", ["en", "fr", "es", "de", "ja"])
-        categories = st.multiselect("Select Categories to Analyze", ['PERFORMANCE', 'ACCESSIBILITY', 'BEST_PRACTICES', 'SEO'], default=['PERFORMANCE', 'ACCESSIBILITY', 'BEST_PRACTICES', 'SEO'])
-
-        submitted = st.form_submit_button("Analyze")
-
-    if submitted:
-        if not url:
-            st.error("Please provide the website URL.")
-        else:
-            strategy = 'mobile' if device == "Mobile" else 'desktop'
-            data = run_pagespeed(url, api_key, strategy=strategy, locale=locale)
-            if data:
-                display_results(data)
-            else:
-                st.error("Failed to retrieve PageSpeed Insights data.")
--- a/ToBeMigrated/ai_seo_tools/google_search_console_integration.py
+++ b/ToBeMigrated/ai_seo_tools/google_search_console_integration.py
@@ -1,864 +0,0 @@
-"""
-Google Search Console Integration for Enterprise SEO
-
-Connects GSC data with AI-powered content strategy and keyword intelligence.
-Provides enterprise-level search performance insights and content recommendations.
-"""
-
-import streamlit as st
-import pandas as pd
-import numpy as np
-from typing import Dict, Any, List, Optional, Tuple
-from datetime import datetime, timedelta
-import json
-from loguru import logger
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-
-# Import AI modules
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-class GoogleSearchConsoleAnalyzer:
-    """
-    Enterprise Google Search Console analyzer with AI-powered insights.
-    """
-    
-    def __init__(self):
-        """Initialize the GSC analyzer."""
-        self.gsc_client = None  # Will be initialized when credentials are provided
-        logger.info("Google Search Console Analyzer initialized")
-    
-    def analyze_search_performance(self, site_url: str, date_range: int = 90) -> Dict[str, Any]:
-        """
-        Analyze comprehensive search performance from GSC data.
-        
-        Args:
-            site_url: Website URL registered in GSC
-            date_range: Number of days to analyze (default 90)
-            
-        Returns:
-            Comprehensive search performance analysis
-        """
-        try:
-            st.info("📊 Analyzing Google Search Console data...")
-            
-            # Simulate GSC data for demonstration (replace with actual GSC API calls)
-            search_data = self._get_mock_gsc_data(site_url, date_range)
-            
-            # Perform comprehensive analysis
-            analysis_results = {
-                'site_url': site_url,
-                'analysis_period': f"Last {date_range} days",
-                'analysis_timestamp': datetime.utcnow().isoformat(),
-                'performance_overview': self._analyze_performance_overview(search_data),
-                'keyword_analysis': self._analyze_keyword_performance(search_data),
-                'page_analysis': self._analyze_page_performance(search_data),
-                'content_opportunities': self._identify_content_opportunities(search_data),
-                'technical_insights': self._analyze_technical_seo_signals(search_data),
-                'competitive_analysis': self._analyze_competitive_position(search_data),
-                'ai_recommendations': self._generate_ai_recommendations(search_data)
-            }
-            
-            return analysis_results
-            
-        except Exception as e:
-            error_msg = f"Error analyzing search performance: {str(e)}"
-            logger.error(error_msg, exc_info=True)
-            return {'error': error_msg}
-    
-    def _get_mock_gsc_data(self, site_url: str, days: int) -> Dict[str, pd.DataFrame]:
-        """
-        Generate mock GSC data for demonstration.
-        In production, this would fetch real data from GSC API.
-        """
-        # Generate mock keyword data
-        keywords_data = []
-        sample_keywords = [
-            "AI content creation", "SEO tools", "content optimization", "blog writing AI",
-            "meta description generator", "keyword research", "technical SEO", "content strategy",
-            "on-page optimization", "SERP analysis", "content gap analysis", "SEO audit"
-        ]
-        
-        for keyword in sample_keywords:
-            # Generate realistic performance data
-            impressions = np.random.randint(100, 10000)
-            clicks = int(impressions * np.random.uniform(0.02, 0.15))  # CTR between 2-15%
-            position = np.random.uniform(3, 25)
-            
-            keywords_data.append({
-                'keyword': keyword,
-                'impressions': impressions,
-                'clicks': clicks,
-                'ctr': (clicks / impressions) * 100,
-                'position': position
-            })
-        
-        # Generate mock page data
-        pages_data = []
-        sample_pages = [
-            "/blog/ai-content-creation-guide", "/tools/seo-analyzer", "/features/content-optimization",
-            "/blog/technical-seo-checklist", "/tools/keyword-research", "/blog/content-strategy-2024",
-            "/tools/meta-description-generator", "/blog/on-page-seo-guide", "/features/enterprise-seo"
-        ]
-        
-        for page in sample_pages:
-            impressions = np.random.randint(500, 5000)
-            clicks = int(impressions * np.random.uniform(0.03, 0.12))
-            position = np.random.uniform(5, 20)
-            
-            pages_data.append({
-                'page': page,
-                'impressions': impressions,
-                'clicks': clicks,
-                'ctr': (clicks / impressions) * 100,
-                'position': position
-            })
-        
-        # Generate time series data
-        time_series_data = []
-        for i in range(days):
-            date = datetime.now() - timedelta(days=i)
-            daily_clicks = np.random.randint(50, 500)
-            daily_impressions = np.random.randint(1000, 8000)
-            
-            time_series_data.append({
-                'date': date.strftime('%Y-%m-%d'),
-                'clicks': daily_clicks,
-                'impressions': daily_impressions,
-                'ctr': (daily_clicks / daily_impressions) * 100,
-                'position': np.random.uniform(8, 15)
-            })
-        
-        return {
-            'keywords': pd.DataFrame(keywords_data),
-            'pages': pd.DataFrame(pages_data),
-            'time_series': pd.DataFrame(time_series_data)
-        }
-    
-    def _analyze_performance_overview(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Analyze overall search performance metrics."""
-        keywords_df = search_data['keywords']
-        time_series_df = search_data['time_series']
-        
-        # Calculate totals and averages
-        total_clicks = keywords_df['clicks'].sum()
-        total_impressions = keywords_df['impressions'].sum()
-        avg_ctr = (total_clicks / total_impressions) * 100 if total_impressions > 0 else 0
-        avg_position = keywords_df['position'].mean()
-        
-        # Calculate trends
-        recent_clicks = time_series_df.head(7)['clicks'].mean()
-        previous_clicks = time_series_df.tail(7)['clicks'].mean()
-        clicks_trend = ((recent_clicks - previous_clicks) / previous_clicks * 100) if previous_clicks > 0 else 0
-        
-        recent_impressions = time_series_df.head(7)['impressions'].mean()
-        previous_impressions = time_series_df.tail(7)['impressions'].mean()
-        impressions_trend = ((recent_impressions - previous_impressions) / previous_impressions * 100) if previous_impressions > 0 else 0
-        
-        # Top performing keywords
-        top_keywords = keywords_df.nlargest(5, 'clicks')[['keyword', 'clicks', 'impressions', 'position']].to_dict('records')
-        
-        # Opportunity keywords (high impressions, low CTR)
-        opportunity_keywords = keywords_df[
-            (keywords_df['impressions'] > keywords_df['impressions'].median()) &
-            (keywords_df['ctr'] < 3)
-        ].nlargest(5, 'impressions')[['keyword', 'impressions', 'ctr', 'position']].to_dict('records')
-        
-        return {
-            'total_clicks': int(total_clicks),
-            'total_impressions': int(total_impressions),
-            'avg_ctr': round(avg_ctr, 2),
-            'avg_position': round(avg_position, 1),
-            'clicks_trend': round(clicks_trend, 1),
-            'impressions_trend': round(impressions_trend, 1),
-            'top_keywords': top_keywords,
-            'opportunity_keywords': opportunity_keywords
-        }
-    
-    def _analyze_keyword_performance(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Analyze keyword performance and opportunities."""
-        keywords_df = search_data['keywords']
-        
-        # Keyword categorization
-        high_volume_keywords = keywords_df[keywords_df['impressions'] > keywords_df['impressions'].quantile(0.8)]
-        low_competition_keywords = keywords_df[keywords_df['position'] <= 10]
-        optimization_opportunities = keywords_df[
-            (keywords_df['position'] > 10) & 
-            (keywords_df['position'] <= 20) &
-            (keywords_df['impressions'] > 100)
-        ]
-        
-        # Content gap analysis
-        missing_keywords = self._identify_missing_keywords(keywords_df)
-        
-        # Seasonal trends analysis
-        seasonal_insights = self._analyze_seasonal_trends(keywords_df)
-        
-        return {
-            'total_keywords': len(keywords_df),
-            'high_volume_keywords': high_volume_keywords.to_dict('records'),
-            'ranking_keywords': low_competition_keywords.to_dict('records'),
-            'optimization_opportunities': optimization_opportunities.to_dict('records'),
-            'missing_keywords': missing_keywords,
-            'seasonal_insights': seasonal_insights,
-            'keyword_distribution': {
-                'positions_1_3': len(keywords_df[keywords_df['position'] <= 3]),
-                'positions_4_10': len(keywords_df[(keywords_df['position'] > 3) & (keywords_df['position'] <= 10)]),
-                'positions_11_20': len(keywords_df[(keywords_df['position'] > 10) & (keywords_df['position'] <= 20)]),
-                'positions_21_plus': len(keywords_df[keywords_df['position'] > 20])
-            }
-        }
-    
-    def _analyze_page_performance(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Analyze page-level performance."""
-        pages_df = search_data['pages']
-        
-        # Top performing pages
-        top_pages = pages_df.nlargest(10, 'clicks')
-        
-        # Underperforming pages (high impressions, low clicks)
-        underperforming_pages = pages_df[
-            (pages_df['impressions'] > pages_df['impressions'].median()) &
-            (pages_df['ctr'] < 2)
-        ].nlargest(5, 'impressions')
-        
-        # Page type analysis
-        page_types = self._categorize_pages(pages_df)
-        
-        return {
-            'top_pages': top_pages.to_dict('records'),
-            'underperforming_pages': underperforming_pages.to_dict('records'),
-            'page_types_performance': page_types,
-            'total_pages': len(pages_df)
-        }
-    
-    def _identify_content_opportunities(self, search_data: Dict[str, pd.DataFrame]) -> List[Dict[str, Any]]:
-        """Identify content creation and optimization opportunities."""
-        keywords_df = search_data['keywords']
-        
-        opportunities = []
-        
-        # High impression, low CTR keywords need content optimization
-        low_ctr_keywords = keywords_df[
-            (keywords_df['impressions'] > 500) & 
-            (keywords_df['ctr'] < 3)
-        ]
-        
-        for _, keyword_row in low_ctr_keywords.iterrows():
-            opportunities.append({
-                'type': 'Content Optimization',
-                'keyword': keyword_row['keyword'],
-                'opportunity': f"Optimize existing content for '{keyword_row['keyword']}' to improve CTR from {keyword_row['ctr']:.1f}%",
-                'potential_impact': 'High',
-                'current_position': round(keyword_row['position'], 1),
-                'impressions': int(keyword_row['impressions']),
-                'priority': 'High' if keyword_row['impressions'] > 1000 else 'Medium'
-            })
-        
-        # Position 11-20 keywords need content improvement
-        position_11_20 = keywords_df[
-            (keywords_df['position'] > 10) & 
-            (keywords_df['position'] <= 20) &
-            (keywords_df['impressions'] > 100)
-        ]
-        
-        for _, keyword_row in position_11_20.iterrows():
-            opportunities.append({
-                'type': 'Content Enhancement',
-                'keyword': keyword_row['keyword'],
-                'opportunity': f"Enhance content for '{keyword_row['keyword']}' to move from position {keyword_row['position']:.1f} to first page",
-                'potential_impact': 'Medium',
-                'current_position': round(keyword_row['position'], 1),
-                'impressions': int(keyword_row['impressions']),
-                'priority': 'Medium'
-            })
-        
-        # Sort by potential impact and impressions
-        opportunities = sorted(opportunities, key=lambda x: x['impressions'], reverse=True)
-        
-        return opportunities[:10]  # Top 10 opportunities
-    
-    def _analyze_technical_seo_signals(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Analyze technical SEO signals from search data."""
-        keywords_df = search_data['keywords']
-        pages_df = search_data['pages']
-        
-        # Analyze performance patterns that might indicate technical issues
-        technical_insights = {
-            'crawl_issues_indicators': [],
-            'mobile_performance': {},
-            'core_web_vitals_impact': {},
-            'indexing_insights': {}
-        }
-        
-        # Identify potential crawl issues
-        very_low_impressions = keywords_df[keywords_df['impressions'] < 10]
-        if len(very_low_impressions) > len(keywords_df) * 0.3:  # If 30%+ have very low impressions
-            technical_insights['crawl_issues_indicators'].append(
-                "High percentage of keywords with very low impressions may indicate crawl or indexing issues"
-            )
-        
-        # Mobile performance indicators
-        avg_mobile_position = keywords_df['position'].mean()  # In real implementation, this would be mobile-specific
-        technical_insights['mobile_performance'] = {
-            'avg_mobile_position': round(avg_mobile_position, 1),
-            'mobile_optimization_needed': avg_mobile_position > 15
-        }
-        
-        return technical_insights
-    
-    def _analyze_competitive_position(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Analyze competitive positioning based on search data."""
-        keywords_df = search_data['keywords']
-        
-        # Calculate competitive metrics
-        dominant_keywords = len(keywords_df[keywords_df['position'] <= 3])
-        competitive_keywords = len(keywords_df[(keywords_df['position'] > 3) & (keywords_df['position'] <= 10)])
-        losing_keywords = len(keywords_df[keywords_df['position'] > 10])
-        
-        competitive_strength = (dominant_keywords * 3 + competitive_keywords * 2 + losing_keywords * 1) / len(keywords_df)
-        
-        return {
-            'dominant_keywords': dominant_keywords,
-            'competitive_keywords': competitive_keywords,
-            'losing_keywords': losing_keywords,
-            'competitive_strength_score': round(competitive_strength, 2),
-            'market_position': self._determine_market_position(competitive_strength)
-        }
-    
-    def _generate_ai_recommendations(self, search_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
-        """Generate AI-powered recommendations based on search data."""
-        try:
-            keywords_df = search_data['keywords']
-            pages_df = search_data['pages']
-            
-            # Prepare data summary for AI analysis
-            top_keywords = keywords_df.nlargest(5, 'impressions')['keyword'].tolist()
-            avg_position = keywords_df['position'].mean()
-            total_impressions = keywords_df['impressions'].sum()
-            total_clicks = keywords_df['clicks'].sum()
-            avg_ctr = (total_clicks / total_impressions * 100) if total_impressions > 0 else 0
-            
-            # Create comprehensive prompt for AI analysis
-            ai_prompt = f"""
-            Analyze this Google Search Console data and provide strategic SEO recommendations:
-            
-            SEARCH PERFORMANCE SUMMARY:
-            - Total Keywords Tracked: {len(keywords_df)}
-            - Total Impressions: {total_impressions:,}
-            - Total Clicks: {total_clicks:,}
-            - Average CTR: {avg_ctr:.2f}%
-            - Average Position: {avg_position:.1f}
-            
-            TOP PERFORMING KEYWORDS:
-            {', '.join(top_keywords)}
-            
-            PERFORMANCE DISTRIBUTION:
-            - Keywords ranking 1-3: {len(keywords_df[keywords_df['position'] <= 3])}
-            - Keywords ranking 4-10: {len(keywords_df[(keywords_df['position'] > 3) & (keywords_df['position'] <= 10)])}
-            - Keywords ranking 11-20: {len(keywords_df[(keywords_df['position'] > 10) & (keywords_df['position'] <= 20)])}
-            - Keywords ranking 21+: {len(keywords_df[keywords_df['position'] > 20])}
-            
-            TOP PAGES BY TRAFFIC:
-            {pages_df.nlargest(3, 'clicks')['page'].tolist()}
-            
-            Based on this data, provide:
-            
-            1. IMMEDIATE OPTIMIZATION OPPORTUNITIES (0-30 days):
-               - Specific keywords to optimize for better CTR
-               - Pages that need content updates
-               - Quick technical wins
-            
-            2. CONTENT STRATEGY RECOMMENDATIONS (1-3 months):
-               - New content topics based on keyword gaps
-               - Content enhancement priorities
-               - Internal linking opportunities
-            
-            3. LONG-TERM SEO STRATEGY (3-12 months):
-               - Market expansion opportunities
-               - Authority building topics
-               - Competitive positioning strategies
-            
-            4. TECHNICAL SEO PRIORITIES:
-               - Performance issues affecting rankings
-               - Mobile optimization needs
-               - Core Web Vitals improvements
-            
-            Provide specific, actionable recommendations with expected impact and priority levels.
-            """
-            
-            ai_analysis = llm_text_gen(
-                ai_prompt,
-                system_prompt="You are an enterprise SEO strategist analyzing Google Search Console data. Provide specific, data-driven recommendations that will improve search performance."
-            )
-            
-            return {
-                'full_analysis': ai_analysis,
-                'immediate_opportunities': self._extract_immediate_opportunities(ai_analysis),
-                'content_strategy': self._extract_content_strategy(ai_analysis),
-                'long_term_strategy': self._extract_long_term_strategy(ai_analysis),
-                'technical_priorities': self._extract_technical_priorities(ai_analysis)
-            }
-            
-        except Exception as e:
-            logger.error(f"AI recommendations error: {str(e)}")
-            return {'error': str(e)}
-    
-    # Utility methods
-    def _identify_missing_keywords(self, keywords_df: pd.DataFrame) -> List[str]:
-        """Identify potential missing keywords based on current keyword performance."""
-        # In a real implementation, this would use keyword research APIs
-        existing_keywords = set(keywords_df['keyword'].str.lower())
-        
-        potential_keywords = [
-            "AI writing tools", "content automation", "SEO content generator",
-            "blog post optimizer", "meta tag generator", "keyword analyzer"
-        ]
-        
-        missing = [kw for kw in potential_keywords if kw.lower() not in existing_keywords]
-        return missing[:5]
-    
-    def _analyze_seasonal_trends(self, keywords_df: pd.DataFrame) -> Dict[str, Any]:
-        """Analyze seasonal trends in keyword performance."""
-        # Placeholder for seasonal analysis
-        return {
-            'seasonal_keywords': [],
-            'trend_analysis': "Seasonal analysis requires historical data spanning multiple seasons"
-        }
-    
-    def _categorize_pages(self, pages_df: pd.DataFrame) -> Dict[str, Any]:
-        """Categorize pages by type and analyze performance."""
-        page_types = {
-            'Blog Posts': {'count': 0, 'total_clicks': 0, 'avg_position': 0},
-            'Product Pages': {'count': 0, 'total_clicks': 0, 'avg_position': 0},
-            'Tool Pages': {'count': 0, 'total_clicks': 0, 'avg_position': 0},
-            'Other': {'count': 0, 'total_clicks': 0, 'avg_position': 0}
-        }
-        
-        for _, page_row in pages_df.iterrows():
-            page_url = page_row['page']
-            clicks = page_row['clicks']
-            position = page_row['position']
-            
-            if '/blog/' in page_url:
-                page_types['Blog Posts']['count'] += 1
-                page_types['Blog Posts']['total_clicks'] += clicks
-                page_types['Blog Posts']['avg_position'] += position
-            elif '/tools/' in page_url:
-                page_types['Tool Pages']['count'] += 1
-                page_types['Tool Pages']['total_clicks'] += clicks
-                page_types['Tool Pages']['avg_position'] += position
-            elif '/features/' in page_url or '/product/' in page_url:
-                page_types['Product Pages']['count'] += 1
-                page_types['Product Pages']['total_clicks'] += clicks
-                page_types['Product Pages']['avg_position'] += position
-            else:
-                page_types['Other']['count'] += 1
-                page_types['Other']['total_clicks'] += clicks
-                page_types['Other']['avg_position'] += position
-        
-        # Calculate averages
-        for page_type in page_types:
-            if page_types[page_type]['count'] > 0:
-                page_types[page_type]['avg_position'] = round(
-                    page_types[page_type]['avg_position'] / page_types[page_type]['count'], 1
-                )
-        
-        return page_types
-    
-    def _determine_market_position(self, competitive_strength: float) -> str:
-        """Determine market position based on competitive strength score."""
-        if competitive_strength >= 2.5:
-            return "Market Leader"
-        elif competitive_strength >= 2.0:
-            return "Strong Competitor"
-        elif competitive_strength >= 1.5:
-            return "Emerging Player"
-        else:
-            return "Challenger"
-    
-    def _extract_immediate_opportunities(self, analysis: str) -> List[str]:
-        """Extract immediate opportunities from AI analysis."""
-        lines = analysis.split('\n')
-        opportunities = []
-        in_immediate_section = False
-        
-        for line in lines:
-            if 'IMMEDIATE OPTIMIZATION' in line.upper():
-                in_immediate_section = True
-                continue
-            elif 'CONTENT STRATEGY' in line.upper():
-                in_immediate_section = False
-                continue
-            
-            if in_immediate_section and line.strip().startswith('-'):
-                opportunities.append(line.strip().lstrip('- '))
-        
-        return opportunities[:5]
-    
-    def _extract_content_strategy(self, analysis: str) -> List[str]:
-        """Extract content strategy recommendations from AI analysis."""
-        return ["Develop topic clusters", "Create comparison content", "Build FAQ sections"]
-    
-    def _extract_long_term_strategy(self, analysis: str) -> List[str]:
-        """Extract long-term strategy from AI analysis."""
-        return ["Build domain authority", "Expand to new markets", "Develop thought leadership content"]
-    
-    def _extract_technical_priorities(self, analysis: str) -> List[str]:
-        """Extract technical priorities from AI analysis."""
-        return ["Improve page speed", "Optimize mobile experience", "Fix crawl errors"]
-
-
-def render_gsc_integration():
-    """Render the Google Search Console integration interface."""
-    
-    st.title("📊 Google Search Console Intelligence")
-    st.markdown("**AI-powered insights from your Google Search Console data**")
-    
-    # Initialize analyzer
-    if 'gsc_analyzer' not in st.session_state:
-        st.session_state.gsc_analyzer = GoogleSearchConsoleAnalyzer()
-    
-    analyzer = st.session_state.gsc_analyzer
-    
-    # Configuration section
-    st.header("🔧 Configuration")
-    
-    with st.expander("📋 Setup Instructions", expanded=False):
-        st.markdown("""
-        ### Setting up Google Search Console Integration
-        
-        1. **Verify your website** in Google Search Console
-        2. **Enable the Search Console API** in Google Cloud Console
-        3. **Create service account credentials** and download the JSON file
-        4. **Upload credentials** using the file uploader below
-        
-        📚 [Detailed Setup Guide](https://developers.google.com/webmaster-tools/search-console-api-original/v3/prereqs)
-        """)
-    
-    # Input form
-    with st.form("gsc_analysis_form"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            site_url = st.text_input(
-                "Site URL",
-                value="https://example.com",
-                help="Enter your website URL as registered in Google Search Console"
-            )
-            
-            date_range = st.selectbox(
-                "Analysis Period",
-                [30, 60, 90, 180],
-                index=2,
-                help="Number of days to analyze"
-            )
-        
-        with col2:
-            # Credentials upload (placeholder)
-            credentials_file = st.file_uploader(
-                "GSC API Credentials (JSON)",
-                type=['json'],
-                help="Upload your Google Search Console API credentials file"
-            )
-            
-            demo_mode = st.checkbox(
-                "Demo Mode",
-                value=True,
-                help="Use demo data for testing (no credentials needed)"
-            )
-        
-        submit_analysis = st.form_submit_button("📊 Analyze Search Performance", type="primary")
-    
-    # Process analysis
-    if submit_analysis:
-        if site_url and (demo_mode or credentials_file):
-            with st.spinner("📊 Analyzing Google Search Console data..."):
-                analysis_results = analyzer.analyze_search_performance(site_url, date_range)
-            
-            if 'error' not in analysis_results:
-                st.success("✅ Search Console analysis completed!")
-                
-                # Store results in session state
-                st.session_state.gsc_results = analysis_results
-                
-                # Display results
-                render_gsc_results_dashboard(analysis_results)
-            else:
-                st.error(f"❌ Analysis failed: {analysis_results['error']}")
-        else:
-            st.warning("⚠️ Please enter site URL and upload credentials (or enable demo mode).")
-    
-    # Show previous results if available
-    elif 'gsc_results' in st.session_state:
-        st.info("📊 Showing previous analysis results")
-        render_gsc_results_dashboard(st.session_state.gsc_results)
-
-
-def render_gsc_results_dashboard(results: Dict[str, Any]):
-    """Render comprehensive GSC analysis results."""
-    
-    # Performance overview
-    st.header("📊 Search Performance Overview")
-    
-    overview = results['performance_overview']
-    
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.metric(
-            "Total Clicks",
-            f"{overview['total_clicks']:,}",
-            delta=f"{overview['clicks_trend']:+.1f}%" if overview['clicks_trend'] != 0 else None
-        )
-    
-    with col2:
-        st.metric(
-            "Total Impressions",
-            f"{overview['total_impressions']:,}",
-            delta=f"{overview['impressions_trend']:+.1f}%" if overview['impressions_trend'] != 0 else None
-        )
-    
-    with col3:
-        st.metric(
-            "Average CTR",
-            f"{overview['avg_ctr']:.2f}%"
-        )
-    
-    with col4:
-        st.metric(
-            "Average Position",
-            f"{overview['avg_position']:.1f}"
-        )
-    
-    # Content opportunities (Most important section)
-    st.header("🎯 Content Opportunities")
-    
-    opportunities = results['content_opportunities']
-    if opportunities:
-        # Display as interactive table
-        df_opportunities = pd.DataFrame(opportunities)
-        
-        st.dataframe(
-            df_opportunities,
-            column_config={
-                "type": "Opportunity Type",
-                "keyword": "Keyword",
-                "opportunity": "Description",
-                "potential_impact": st.column_config.SelectboxColumn(
-                    "Impact",
-                    options=["High", "Medium", "Low"]
-                ),
-                "current_position": st.column_config.NumberColumn(
-                    "Current Position",
-                    format="%.1f"
-                ),
-                "impressions": st.column_config.NumberColumn(
-                    "Impressions",
-                    format="%d"
-                ),
-                "priority": st.column_config.SelectboxColumn(
-                    "Priority",
-                    options=["High", "Medium", "Low"]
-                )
-            },
-            hide_index=True,
-            use_container_width=True
-        )
-    
-    # Detailed analysis tabs
-    tab1, tab2, tab3, tab4, tab5 = st.tabs([
-        "🤖 AI Insights",
-        "🎯 Keyword Analysis",
-        "📄 Page Performance",
-        "🏆 Competitive Position",
-        "🔧 Technical Signals"
-    ])
-    
-    with tab1:
-        ai_recs = results.get('ai_recommendations', {})
-        if ai_recs and 'error' not in ai_recs:
-            st.subheader("AI-Powered Recommendations")
-            
-            # Immediate opportunities
-            immediate_ops = ai_recs.get('immediate_opportunities', [])
-            if immediate_ops:
-                st.markdown("#### 🚀 Immediate Optimizations (0-30 days)")
-                for op in immediate_ops:
-                    st.success(f"✅ {op}")
-            
-            # Content strategy
-            content_strategy = ai_recs.get('content_strategy', [])
-            if content_strategy:
-                st.markdown("#### 📝 Content Strategy (1-3 months)")
-                for strategy in content_strategy:
-                    st.info(f"📋 {strategy}")
-            
-            # Full analysis
-            full_analysis = ai_recs.get('full_analysis', '')
-            if full_analysis:
-                with st.expander("🧠 Complete AI Analysis"):
-                    st.write(full_analysis)
-    
-    with tab2:
-        keyword_analysis = results.get('keyword_analysis', {})
-        if keyword_analysis:
-            st.subheader("Keyword Performance Analysis")
-            
-            # Keyword distribution chart
-            dist = keyword_analysis['keyword_distribution']
-            fig = px.pie(
-                values=[dist['positions_1_3'], dist['positions_4_10'], dist['positions_11_20'], dist['positions_21_plus']],
-                names=['Positions 1-3', 'Positions 4-10', 'Positions 11-20', 'Positions 21+'],
-                title="Keyword Position Distribution"
-            )
-            st.plotly_chart(fig, use_container_width=True)
-            
-            # High volume keywords
-            high_volume = keyword_analysis.get('high_volume_keywords', [])
-            if high_volume:
-                st.markdown("#### 📈 High Volume Keywords")
-                st.dataframe(pd.DataFrame(high_volume), hide_index=True)
-            
-            # Optimization opportunities
-            opt_opportunities = keyword_analysis.get('optimization_opportunities', [])
-            if opt_opportunities:
-                st.markdown("#### 🎯 Optimization Opportunities (Positions 11-20)")
-                st.dataframe(pd.DataFrame(opt_opportunities), hide_index=True)
-    
-    with tab3:
-        page_analysis = results.get('page_analysis', {})
-        if page_analysis:
-            st.subheader("Page Performance Analysis")
-            
-            # Top pages
-            top_pages = page_analysis.get('top_pages', [])
-            if top_pages:
-                st.markdown("#### 🏆 Top Performing Pages")
-                st.dataframe(pd.DataFrame(top_pages), hide_index=True)
-            
-            # Underperforming pages
-            underperforming = page_analysis.get('underperforming_pages', [])
-            if underperforming:
-                st.markdown("#### ⚠️ Underperforming Pages (High Impressions, Low CTR)")
-                st.dataframe(pd.DataFrame(underperforming), hide_index=True)
-            
-            # Page types performance
-            page_types = page_analysis.get('page_types_performance', {})
-            if page_types:
-                st.markdown("#### 📊 Performance by Page Type")
-                
-                # Create visualization
-                types = []
-                clicks = []
-                positions = []
-                
-                for page_type, data in page_types.items():
-                    if data['count'] > 0:
-                        types.append(page_type)
-                        clicks.append(data['total_clicks'])
-                        positions.append(data['avg_position'])
-                
-                if types:
-                    col1, col2 = st.columns(2)
-                    
-                    with col1:
-                        fig_clicks = px.bar(x=types, y=clicks, title="Total Clicks by Page Type")
-                        st.plotly_chart(fig_clicks, use_container_width=True)
-                    
-                    with col2:
-                        fig_position = px.bar(x=types, y=positions, title="Average Position by Page Type")
-                        st.plotly_chart(fig_position, use_container_width=True)
-    
-    with tab4:
-        competitive_analysis = results.get('competitive_analysis', {})
-        if competitive_analysis:
-            st.subheader("Competitive Position Analysis")
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                st.metric("Market Position", competitive_analysis['market_position'])
-                st.metric("Competitive Strength", f"{competitive_analysis['competitive_strength_score']}/3.0")
-            
-            with col2:
-                # Competitive distribution
-                comp_data = {
-                    'Dominant (1-3)': competitive_analysis['dominant_keywords'],
-                    'Competitive (4-10)': competitive_analysis['competitive_keywords'],
-                    'Losing (11+)': competitive_analysis['losing_keywords']
-                }
-                
-                fig = px.bar(
-                    x=list(comp_data.keys()),
-                    y=list(comp_data.values()),
-                    title="Keyword Competitive Position"
-                )
-                st.plotly_chart(fig, use_container_width=True)
-    
-    with tab5:
-        technical_insights = results.get('technical_insights', {})
-        if technical_insights:
-            st.subheader("Technical SEO Signals")
-            
-            # Crawl issues indicators
-            crawl_issues = technical_insights.get('crawl_issues_indicators', [])
-            if crawl_issues:
-                st.markdown("#### ⚠️ Potential Issues")
-                for issue in crawl_issues:
-                    st.warning(f"🚨 {issue}")
-            
-            # Mobile performance
-            mobile_perf = technical_insights.get('mobile_performance', {})
-            if mobile_perf:
-                st.markdown("#### 📱 Mobile Performance")
-                col1, col2 = st.columns(2)
-                
-                with col1:
-                    st.metric("Avg Mobile Position", f"{mobile_perf.get('avg_mobile_position', 0):.1f}")
-                
-                with col2:
-                    if mobile_perf.get('mobile_optimization_needed', False):
-                        st.warning("📱 Mobile optimization needed")
-                    else:
-                        st.success("📱 Mobile performance good")
-    
-    # Export functionality
-    st.markdown("---")
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📥 Export Full Report", use_container_width=True):
-            report_json = json.dumps(results, indent=2, default=str)
-            st.download_button(
-                label="Download JSON Report",
-                data=report_json,
-                file_name=f"gsc_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                mime="application/json"
-            )
-    
-    with col2:
-        if st.button("📊 Export Opportunities", use_container_width=True):
-            if opportunities:
-                df_opportunities = pd.DataFrame(opportunities)
-                csv = df_opportunities.to_csv(index=False)
-                st.download_button(
-                    label="Download CSV Opportunities",
-                    data=csv,
-                    file_name=f"content_opportunities_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                    mime="text/csv"
-                )
-    
-    with col3:
-        if st.button("🔄 Refresh Analysis", use_container_width=True):
-            # Clear cached results to force refresh
-            if 'gsc_results' in st.session_state:
-                del st.session_state.gsc_results
-            st.rerun()
-
-
-# Main execution
-if __name__ == "__main__":
-    render_gsc_integration()
--- a/ToBeMigrated/ai_seo_tools/image_alt_text_generator.py
+++ b/ToBeMigrated/ai_seo_tools/image_alt_text_generator.py
@@ -1,112 +0,0 @@
-import streamlit as st
-import base64
-import requests
-from PIL import Image
-import os
-
-
-def encode_image(image_path):
-    """
-    Encodes an image to base64 format.
-
-    Args:
-        image_path (str): Path to the image file.
-
-    Returns:
-        str: Base64 encoded string of the image.
-
-    Raises:
-        ValueError: If the image path is invalid.
-    """
-    safe_root = os.getenv('SAFE_ROOT_DIRECTORY', '/safe/root/directory')  # Use an environment variable for the safe root directory
-    normalized_path = os.path.normpath(image_path)
-    if not normalized_path.startswith(safe_root):
-        raise ValueError("Invalid image path")
-    with open(normalized_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-
-def get_image_description(image_path):
-    """
-    Generates a description for the given image using an external API.
-
-    Args:
-        image_path (str): Path to the image file.
-
-    Returns:
-        str: Description of the image.
-
-    Raises:
-        ValueError: If the image path is invalid.
-    """
-    safe_root = os.getenv('SAFE_ROOT_DIRECTORY', '/safe/root/directory')  # Use an environment variable for the safe root directory
-    normalized_path = os.path.normpath(image_path)
-    if not normalized_path.startswith(safe_root):
-        raise ValueError("Invalid image path")
-    base64_image = encode_image(normalized_path)
-
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
-    }
-
-    payload = {
-        "model": "gpt-4o-mini",
-        "messages": [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": """You are an SEO expert specializing in writing optimized Alt text for images. 
-            Your goal is to create clear, descriptive, and concise Alt text that accurately represents 
-            the content and context of the given image. Make sure your response is optimized for search engines and accessibility."""
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}"
-                        }
-                    }
-                ]
-            }
-        ],
-        "max_tokens": 300
-    }
-
-    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-    response_data = response.json()
-
-    # Extract the content field from the response
-    content = response_data['choices'][0]['message']['content']
-    return content
-
-
-def alt_text_gen():
-    """
-    Streamlit app function to generate Alt text for an uploaded image.
-    """
-    st.title("Image Description Generator")
-
-    image_path = st.text_input("Enter the full path of the image file", help="Provide the full path to a .jpg, .jpeg, or .png image file")
-
-    if image_path:
-        if os.path.exists(image_path) and image_path.lower().endswith(('jpg', 'jpeg', 'png')):
-            try:
-                image = Image.open(image_path)
-                st.image(image, caption='Uploaded Image', use_column_width=True)
-
-                if st.button("Get Image Alt Text"):
-                    with st.spinner("Generating Alt Text..."):
-                        try:
-                            description = get_image_description(image_path)
-                            st.success("Alt Text generated successfully!")
-                            st.write("Alt Text:", description)
-                        except Exception as e:
-                            st.error(f"Error generating description: {e}")
-            except Exception as e:
-                st.error(f"Error processing image: {e}")
-        else:
-            st.error("Please enter a valid image file path ending with .jpg, .jpeg, or .png")
-    else:
-        st.info("Please enter the full path of an image file.")
--- a/ToBeMigrated/ai_seo_tools/meta_desc_generator.py
+++ b/ToBeMigrated/ai_seo_tools/meta_desc_generator.py
@@ -1,110 +0,0 @@
-import os
-import json
-import streamlit as st
-from tenacity import retry, stop_after_attempt, wait_random_exponential
-from loguru import logger
-import sys
-
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def metadesc_generator_main():
-    """
-    Streamlit app for generating SEO-optimized blog meta descriptions.
-    """
-    st.title("✍️ Alwrity - AI Blog Meta Description Generator")
-    st.markdown(
-        "Create compelling, SEO-optimized meta descriptions in just a few clicks. Perfect for enhancing your blog's click-through rates!"
-    )
-
-    # Input section
-    with st.expander("**PRO-TIP** - Read the instructions below. 🚀", expanded=True):
-        col1, col2, _ = st.columns([5, 5, 0.5])
-
-        # Column 1: Keywords and Tone
-        with col1:
-            keywords = st.text_input(
-                "🔑 Target Keywords (comma-separated):",
-                placeholder="e.g., content marketing, SEO, social media, online business",
-                help="Enter your target keywords, separated by commas. 📝",
-            )
-
-            tone_options = ["General", "Informative", "Engaging", "Humorous", "Intriguing", "Playful"]
-            tone = st.selectbox(
-                "🎨 Desired Tone (optional):",
-                options=tone_options,
-                help="Choose the overall tone you want for your meta description. 🎭",
-            )
-
-        # Column 2: Search Intent and Language
-        with col2:
-            search_type = st.selectbox(
-                "🔍 Search Intent:",
-                ("Informational Intent", "Commercial Intent", "Transactional Intent", "Navigational Intent"),
-                index=0,
-            )
-
-            language_options = ["English", "Spanish", "French", "German", "Other"]
-            language_choice = st.selectbox(
-                "🌐 Preferred Language:",
-                options=language_options,
-                help="Select the language for your meta description. 🗣️",
-            )
-
-            language = (
-                st.text_input(
-                    "Specify Other Language:",
-                    placeholder="e.g., Italian, Chinese",
-                    help="Enter your preferred language. 🌍",
-                )
-                if language_choice == "Other"
-                else language_choice
-            )
-
-    # Generate Meta Description button
-    if st.button("**✨ Generate Meta Description ✨**"):
-        if not keywords.strip():
-            st.error("**🫣 Target Keywords are required! Please provide at least one keyword.**")
-            return
-
-        with st.spinner("Crafting your Meta descriptions... ⏳"):
-            blog_metadesc = generate_blog_metadesc(keywords, tone, search_type, language)
-            if blog_metadesc:
-                st.success("**🎉 Meta Descriptions Generated Successfully! 🚀**")
-                with st.expander("**Your SEO-Boosting Blog Meta Descriptions 🎆🎇**", expanded=True):
-                    st.markdown(blog_metadesc)
-            else:
-                st.error("💥 **Failed to generate blog meta description. Please try again!**")
-
-
-def generate_blog_metadesc(keywords, tone, search_type, language):
-    """
-    Generate blog meta descriptions using LLM.
-
-    Args:
-        keywords (str): Comma-separated target keywords.
-        tone (str): Desired tone for the meta description.
-        search_type (str): Search intent type.
-        language (str): Preferred language for the description.
-
-    Returns:
-        str: Generated meta descriptions or error message.
-    """
-    prompt = f"""
-        Craft 3 engaging and SEO-friendly meta descriptions for a blog post based on the following details:
-
-        Blog Post Keywords: {keywords}
-        Search Intent Type: {search_type}
-        Desired Tone: {tone}
-        Preferred Language: {language}
-
-        Output Format:
-
-        Respond with 3 compelling and concise meta descriptions, approximately 155-160 characters long, that incorporate the target keywords, reflect the blog post content, resonate with the target audience, and entice users to click through to read the full article.
-    """
-    try:
-        return llm_text_gen(prompt)
-    except Exception as err:
-        logger.error(f"Error generating meta description: {err}")
-        st.error(f"💥 Error: Failed to generate response from LLM: {err}")
-        return None
--- a/ToBeMigrated/ai_seo_tools/on_page_seo_analyzer.py
+++ b/ToBeMigrated/ai_seo_tools/on_page_seo_analyzer.py
--- a/ToBeMigrated/ai_seo_tools/opengraph_generator.py
+++ b/ToBeMigrated/ai_seo_tools/opengraph_generator.py
@@ -1,129 +0,0 @@
-import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def generate_og_tags(url, title_hint, description_hint, platform="General"):
-    """
-    Generate Open Graph tags based on the provided URL, title hint, description hint, and platform.
-
-    Args:
-        url (str): The URL of the webpage.
-        title_hint (str): A hint for the title.
-        description_hint (str): A hint for the description.
-        platform (str): The platform for which to generate the tags (General, Facebook, or Twitter).
-
-    Returns:
-        str: The generated Open Graph tags or an error message.
-    """
-    # Create a prompt for the text generation model
-    prompt = (
-        f"Generate Open Graph tags for the following page:\nURL: {url}\n"
-        f"Title hint: {title_hint}\nDescription hint: {description_hint}"
-    )
-    if platform == "Facebook":
-        prompt += "\nSpecifically for Facebook"
-    elif platform == "Twitter":
-        prompt += "\nSpecifically for Twitter"
-
-    try:
-        # Generate Open Graph tags using the text generation model
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        st.error(f"Failed to generate Open Graph tags: {err}")
-        return None
-
-
-def extract_default_og_tags(url):
-    """
-    Extract default Open Graph tags from the provided URL.
-
-    Args:
-        url (str): The URL of the webpage.
-
-    Returns:
-        tuple: A tuple containing the title, description, and image URL, or None in case of an error.
-    """
-    try:
-        # Fetch the HTML content of the URL
-        response = requests.get(url)
-        response.raise_for_status()
-
-        # Parse the HTML content using BeautifulSoup
-        soup = BeautifulSoup(response.content, 'html.parser')
-
-        # Extract the title, description, and image URL
-        title = soup.find('title').text if soup.find('title') else None
-        description = soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else None
-        image_url = soup.find('meta', attrs={'property': 'og:image'})['content'] if soup.find('meta', attrs={'property': 'og:image'}) else None
-
-        return title, description, image_url
-
-    except requests.exceptions.RequestException as req_err:
-        st.error(f"Error fetching the URL: {req_err}")
-        return None, None, None
-
-    except Exception as err:
-        st.error(f"Error parsing the HTML content: {err}")
-        return None, None, None
-
-
-def og_tag_generator():
-    """Main function to run the Streamlit app."""
-    st.title("AI Open Graph Tag Generator")
-
-    # Platform selection
-    platform = st.selectbox(
-        "**Select the platform**",
-        ["General", "Facebook", "Twitter"],
-        help="Choose the platform for which you want to generate Open Graph tags."
-    )
-
-    # URL input
-    url = st.text_input(
-        "**Enter the URL of the page to generate Open Graph tags for:**",
-        placeholder="e.g., https://example.com",
-        help="Provide the URL of the page you want to generate Open Graph tags for."
-    )
-
-    if url:
-        # Extract default Open Graph tags
-        title, description, image_url = extract_default_og_tags(url)
-
-        # Title hint input
-        title_hint = st.text_input(
-            "**Modify existing title or suggest a new one (optional):**",
-            value=title if title else "",
-            placeholder="e.g., Amazing Blog Post Title"
-        )
-
-        # Description hint input
-        description_hint = st.text_area(
-            "**Modify existing description or suggest a new one (optional):**",
-            value=description if description else "",
-            placeholder="e.g., This is a detailed description of the content."
-        )
-
-        # Image URL hint input
-        image_hint = st.text_input(
-            "**Use this image or suggest a new URL (optional):**",
-            value=image_url if image_url else "",
-            placeholder="e.g., https://example.com/image.jpg"
-        )
-
-        # Generate Open Graph tags
-        if st.button("Generate Open Graph Tags"):
-            with st.spinner("Generating Open Graph tags..."):
-                try:
-                    og_tags = generate_og_tags(url, title_hint, description_hint, platform)
-                    if og_tags:
-                        st.success("Open Graph tags generated successfully!")
-                        st.markdown(og_tags)
-                    else:
-                        st.error("Failed to generate Open Graph tags.")
-                except Exception as e:
-                    st.error(f"Failed to generate Open Graph tags: {e}")
-    else:
-        st.info("Please enter a URL to generate Open Graph tags.")
--- a/ToBeMigrated/ai_seo_tools/opengraph_image_generate.py
+++ b/ToBeMigrated/ai_seo_tools/opengraph_image_generate.py
@@ -1,2 +0,0 @@
-
-ogImage TBD
--- a/ToBeMigrated/ai_seo_tools/optimize_images_for_upload.py
+++ b/ToBeMigrated/ai_seo_tools/optimize_images_for_upload.py
@@ -1,187 +0,0 @@
-import os
-import sys
-import tinify
-from PIL import Image
-from loguru import logger
-from dotenv import load_dotenv
-import streamlit as st
-from tempfile import NamedTemporaryFile
-
-# Load environment variables
-load_dotenv()
-
-# Set Tinyfy API key from environment variable
-TINIFY_API_KEY = os.getenv('TINIFY_API_KEY')
-if TINIFY_API_KEY:
-    tinify.key = TINIFY_API_KEY
-
-def setup_logger() -> None:
-    """Configure the logger."""
-    logger.remove()
-    logger.add(
-        sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-setup_logger()
-
-def compress_image(image: Image.Image, quality: int = 45, resize: tuple = None, preserve_exif: bool = False) -> Image.Image:
-    """
-    Compress and optionally resize an image.
-    
-    Args:
-        image (PIL.Image): Image object to compress.
-        quality (int): Quality of the output image (1-100).
-        resize (tuple): Tuple (width, height) to resize the image.
-        preserve_exif (bool): Preserve EXIF data if True.
-    
-    Returns:
-        PIL.Image: The compressed and resized image object.
-    """
-    try:
-        if image.mode == 'RGBA':
-            logger.info("Converting RGBA image to RGB.")
-            image = image.convert('RGB')
-
-        exif = image.info.get('exif') if preserve_exif and 'exif' in image.info else None
-
-        if resize:
-            image = image.resize(resize, Image.LANCZOS)
-            logger.info(f"Resized image to {resize}")
-
-        with NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-            temp_path = temp_file.name
-            try:
-                image.save(temp_path, optimize=True, quality=quality, exif=exif)
-            except Exception as exif_error:
-                logger.warning(f"Error saving image with EXIF: {exif_error}. Saving without EXIF.")
-                image.save(temp_path, optimize=True, quality=quality)
-
-        logger.info("Image compression successful.")
-        return Image.open(temp_path)
-    
-    except Exception as e:
-        logger.error(f"Error compressing image: {e}")
-        st.error("Failed to compress the image. Please try again.")
-        return None
-
-def convert_to_webp(image: Image.Image, image_path: str) -> str:
-    """
-    Convert an image to WebP format.
-    
-    Args:
-        image (PIL.Image): Image object to convert.
-        image_path (str): Path to save the WebP image.
-    
-    Returns:
-        str: Path to the WebP image.
-    """
-    try:
-        webp_path = os.path.splitext(image_path)[0] + '.webp'
-        image.save(webp_path, 'WEBP', quality=80, method=6)
-        return webp_path
-    except Exception as e:
-        logger.error(f"Error converting image to WebP: {e}")
-        st.error("Failed to convert the image to WebP format. Please try again.")
-        return None
-
-def compress_image_tinyfy(image_path: str) -> None:
-    """
-    Compress an image using Tinyfy API.
-    
-    Args:
-        image_path (str): Path to the image to be compressed.
-    
-    Returns:
-        None
-    """
-    try:
-        if not tinify.key:
-            logger.warning("Tinyfy API key is not set. Skipping Tinyfy compression.")
-            return
-        
-        source = tinify.from_file(image_path)
-        source.to_file(image_path)
-        logger.info("Tinyfy compression successful.")
-    except tinify.errors.AccountError:
-        logger.error("Verify your Tinyfy API key and account limit.")
-        st.warning("Tinyfy compression failed. Check your API key and account limit.")
-    except Exception as e:
-        logger.error(f"Error during Tinyfy compression: {e}")
-        st.warning("Tinyfy compression failed. Ensure the API key is set.")
-
-def optimize_image(image: Image.Image, image_path: str, quality: int, resize: tuple, preserve_exif: bool) -> str:
-    """
-    Optimize the image by compressing and converting it to WebP, with optional Tinyfy compression.
-
-    Args:
-        image (PIL.Image): The original image.
-        image_path (str): The path to the image file.
-        quality (int): Quality level for compression.
-        resize (tuple): Dimensions to resize the image.
-        preserve_exif (bool): Whether to preserve EXIF data.
-
-    Returns:
-        str: Path to the optimized WebP image, or None if failed.
-    """
-    logger.info("Starting image optimization process...")
-
-    compressed_image = compress_image(image, quality, resize, preserve_exif)
-    if compressed_image is None:
-        return None
-
-    webp_path = convert_to_webp(compressed_image, image_path)
-    if webp_path is None:
-        return None
-
-    if tinify.key:
-        compress_image_tinyfy(webp_path)
-    else:
-        logger.info("Tinyfy key not provided, skipping Tinyfy compression.")
-
-    return webp_path
-
-def main_img_optimizer() -> None:
-    st.title("ALwrity Image Optimizer")
-    st.markdown("## Upload an image to optimize its size and format.")
-
-    input_tinify_key = st.text_input("Optional: Enter your Tinyfy API Key")
-    if input_tinify_key:
-        tinify.key = input_tinify_key
-
-    uploaded_file = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png', 'gif', 'bmp', 'webp'])
-
-    if uploaded_file:
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Original Image", use_column_width=True)
-
-        quality = st.slider("Compression Quality", 1, 100, 45)
-        preserve_exif = st.checkbox("Preserve EXIF Data", value=False)
-        resize = st.checkbox("Resize Image")
-
-        if resize:
-            width = st.number_input("Width", value=image.width)
-            height = st.number_input("Height", value=image.height)
-            resize_dims = (width, height)
-        else:
-            resize_dims = None
-
-        if st.button("Optimize Image"):
-            with st.spinner("Optimizing..."):
-                if tinify.key:
-                    st.info("Tinyfy compression will be applied.")
-                
-                webp_path = optimize_image(image, uploaded_file.name, quality, resize_dims, preserve_exif)
-
-                if webp_path:
-                    st.image(webp_path, caption="Optimized Image (WebP)", use_column_width=True)
-                    st.success("Image optimization completed!")
-
-                    with open(webp_path, "rb") as file:
-                        st.download_button(
-                            label="Download Optimized Image",
-                            data=file,
-                            file_name=os.path.basename(webp_path),
-                            mime="image/webp"
-                        )
--- a/ToBeMigrated/ai_seo_tools/seo_analyzer_api.py
+++ b/ToBeMigrated/ai_seo_tools/seo_analyzer_api.py
@@ -1,340 +0,0 @@
-"""
-FastAPI endpoint for the Comprehensive SEO Analyzer
-Provides data for the React SEO Dashboard
-"""
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, HttpUrl
-from typing import List, Optional, Dict, Any
-from datetime import datetime
-import json
-
-from .comprehensive_seo_analyzer import ComprehensiveSEOAnalyzer, SEOAnalysisResult
-
-app = FastAPI(
-    title="Comprehensive SEO Analyzer API",
-    description="API for analyzing website SEO performance with actionable insights",
-    version="1.0.0"
-)
-
-# Initialize the analyzer
-seo_analyzer = ComprehensiveSEOAnalyzer()
-
-class SEOAnalysisRequest(BaseModel):
-    url: HttpUrl
-    target_keywords: Optional[List[str]] = None
-
-class SEOAnalysisResponse(BaseModel):
-    url: str
-    timestamp: datetime
-    overall_score: int
-    health_status: str
-    critical_issues: List[str]
-    warnings: List[str]
-    recommendations: List[str]
-    data: Dict[str, Any]
-    success: bool
-    message: str
-
-@app.post("/analyze-seo", response_model=SEOAnalysisResponse)
-async def analyze_seo(request: SEOAnalysisRequest):
-    """
-    Analyze a URL for comprehensive SEO performance
-    
-    Args:
-        request: SEOAnalysisRequest containing URL and optional target keywords
-        
-    Returns:
-        SEOAnalysisResponse with detailed analysis results
-    """
-    try:
-        # Convert URL to string
-        url_str = str(request.url)
-        
-        # Perform analysis
-        result = seo_analyzer.analyze_url(url_str, request.target_keywords)
-        
-        # Convert to response format
-        response_data = {
-            'url': result.url,
-            'timestamp': result.timestamp,
-            'overall_score': result.overall_score,
-            'health_status': result.health_status,
-            'critical_issues': result.critical_issues,
-            'warnings': result.warnings,
-            'recommendations': result.recommendations,
-            'data': result.data,
-            'success': True,
-            'message': f"SEO analysis completed successfully for {result.url}"
-        }
-        
-        return SEOAnalysisResponse(**response_data)
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error analyzing SEO: {str(e)}"
-        )
-
-@app.get("/health")
-async def health_check():
-    """Health check endpoint"""
-    return {
-        "status": "healthy",
-        "timestamp": datetime.now(),
-        "service": "Comprehensive SEO Analyzer API"
-    }
-
-@app.get("/analysis-summary/{url:path}")
-async def get_analysis_summary(url: str):
-    """
-    Get a quick summary of SEO analysis for a URL
-    
-    Args:
-        url: The URL to analyze
-        
-    Returns:
-        Summary of SEO analysis
-    """
-    try:
-        # Ensure URL has protocol
-        if not url.startswith(('http://', 'https://')):
-            url = f"https://{url}"
-        
-        # Perform analysis
-        result = seo_analyzer.analyze_url(url)
-        
-        # Create summary
-        summary = {
-            "url": result.url,
-            "overall_score": result.overall_score,
-            "health_status": result.health_status,
-            "critical_issues_count": len(result.critical_issues),
-            "warnings_count": len(result.warnings),
-            "recommendations_count": len(result.recommendations),
-            "top_issues": result.critical_issues[:3],
-            "top_recommendations": result.recommendations[:3],
-            "analysis_timestamp": result.timestamp.isoformat()
-        }
-        
-        return summary
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error getting analysis summary: {str(e)}"
-        )
-
-@app.get("/seo-metrics/{url:path}")
-async def get_seo_metrics(url: str):
-    """
-    Get detailed SEO metrics for dashboard display
-    
-    Args:
-        url: The URL to analyze
-        
-    Returns:
-        Detailed SEO metrics for React dashboard
-    """
-    try:
-        # Ensure URL has protocol
-        if not url.startswith(('http://', 'https://')):
-            url = f"https://{url}"
-        
-        # Perform analysis
-        result = seo_analyzer.analyze_url(url)
-        
-        # Extract metrics for dashboard
-        metrics = {
-            "overall_score": result.overall_score,
-            "health_status": result.health_status,
-            "url_structure_score": result.data.get('url_structure', {}).get('score', 0),
-            "meta_data_score": result.data.get('meta_data', {}).get('score', 0),
-            "content_score": result.data.get('content_analysis', {}).get('score', 0),
-            "technical_score": result.data.get('technical_seo', {}).get('score', 0),
-            "performance_score": result.data.get('performance', {}).get('score', 0),
-            "accessibility_score": result.data.get('accessibility', {}).get('score', 0),
-            "user_experience_score": result.data.get('user_experience', {}).get('score', 0),
-            "security_score": result.data.get('security_headers', {}).get('score', 0)
-        }
-        
-        # Add detailed data for each category
-        dashboard_data = {
-            "metrics": metrics,
-            "critical_issues": result.critical_issues,
-            "warnings": result.warnings,
-            "recommendations": result.recommendations,
-            "detailed_analysis": {
-                "url_structure": result.data.get('url_structure', {}),
-                "meta_data": result.data.get('meta_data', {}),
-                "content_analysis": result.data.get('content_analysis', {}),
-                "technical_seo": result.data.get('technical_seo', {}),
-                "performance": result.data.get('performance', {}),
-                "accessibility": result.data.get('accessibility', {}),
-                "user_experience": result.data.get('user_experience', {}),
-                "security_headers": result.data.get('security_headers', {}),
-                "keyword_analysis": result.data.get('keyword_analysis', {})
-            },
-            "timestamp": result.timestamp.isoformat(),
-            "url": result.url
-        }
-        
-        return dashboard_data
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error getting SEO metrics: {str(e)}"
-        )
-
-@app.post("/batch-analyze")
-async def batch_analyze(urls: List[str]):
-    """
-    Analyze multiple URLs in batch
-    
-    Args:
-        urls: List of URLs to analyze
-        
-    Returns:
-        Batch analysis results
-    """
-    try:
-        results = []
-        
-        for url in urls:
-            try:
-                # Ensure URL has protocol
-                if not url.startswith(('http://', 'https://')):
-                    url = f"https://{url}"
-                
-                # Perform analysis
-                result = seo_analyzer.analyze_url(url)
-                
-                # Add to results
-                results.append({
-                    "url": result.url,
-                    "overall_score": result.overall_score,
-                    "health_status": result.health_status,
-                    "critical_issues_count": len(result.critical_issues),
-                    "warnings_count": len(result.warnings),
-                    "success": True
-                })
-                
-            except Exception as e:
-                # Add error result
-                results.append({
-                    "url": url,
-                    "overall_score": 0,
-                    "health_status": "error",
-                    "critical_issues_count": 0,
-                    "warnings_count": 0,
-                    "success": False,
-                    "error": str(e)
-                })
-        
-        return {
-            "total_urls": len(urls),
-            "successful_analyses": len([r for r in results if r['success']]),
-            "failed_analyses": len([r for r in results if not r['success']]),
-            "results": results
-        }
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error in batch analysis: {str(e)}"
-        )
-
-# Enhanced prompts for better results
-ENHANCED_PROMPTS = {
-    "critical_issue": "🚨 CRITICAL: This issue is severely impacting your SEO performance and must be fixed immediately.",
-    "warning": "⚠️ WARNING: This could be improved to boost your search rankings.",
-    "recommendation": "💡 RECOMMENDATION: Implement this to improve your SEO score.",
-    "excellent": "🎉 EXCELLENT: Your SEO is performing very well in this area!",
-    "good": "✅ GOOD: Your SEO is performing well, with room for minor improvements.",
-    "needs_improvement": "🔧 NEEDS IMPROVEMENT: Several areas need attention to boost your SEO.",
-    "poor": "❌ POOR: Significant improvements needed across multiple areas."
-}
-
-def enhance_analysis_result(result: SEOAnalysisResult) -> SEOAnalysisResult:
-    """
-    Enhance analysis results with better prompts and user-friendly language
-    """
-    # Enhance critical issues
-    enhanced_critical_issues = []
-    for issue in result.critical_issues:
-        enhanced_issue = f"{ENHANCED_PROMPTS['critical_issue']} {issue}"
-        enhanced_critical_issues.append(enhanced_issue)
-    
-    # Enhance warnings
-    enhanced_warnings = []
-    for warning in result.warnings:
-        enhanced_warning = f"{ENHANCED_PROMPTS['warning']} {warning}"
-        enhanced_warnings.append(enhanced_warning)
-    
-    # Enhance recommendations
-    enhanced_recommendations = []
-    for rec in result.recommendations:
-        enhanced_rec = f"{ENHANCED_PROMPTS['recommendation']} {rec}"
-        enhanced_recommendations.append(enhanced_rec)
-    
-    # Create enhanced result
-    enhanced_result = SEOAnalysisResult(
-        url=result.url,
-        timestamp=result.timestamp,
-        overall_score=result.overall_score,
-        health_status=result.health_status,
-        critical_issues=enhanced_critical_issues,
-        warnings=enhanced_warnings,
-        recommendations=enhanced_recommendations,
-        data=result.data
-    )
-    
-    return enhanced_result
-
-@app.post("/analyze-seo-enhanced", response_model=SEOAnalysisResponse)
-async def analyze_seo_enhanced(request: SEOAnalysisRequest):
-    """
-    Analyze a URL with enhanced, user-friendly prompts
-    
-    Args:
-        request: SEOAnalysisRequest containing URL and optional target keywords
-        
-    Returns:
-        SEOAnalysisResponse with enhanced, user-friendly analysis results
-    """
-    try:
-        # Convert URL to string
-        url_str = str(request.url)
-        
-        # Perform analysis
-        result = seo_analyzer.analyze_url(url_str, request.target_keywords)
-        
-        # Enhance results
-        enhanced_result = enhance_analysis_result(result)
-        
-        # Convert to response format
-        response_data = {
-            'url': enhanced_result.url,
-            'timestamp': enhanced_result.timestamp,
-            'overall_score': enhanced_result.overall_score,
-            'health_status': enhanced_result.health_status,
-            'critical_issues': enhanced_result.critical_issues,
-            'warnings': enhanced_result.warnings,
-            'recommendations': enhanced_result.recommendations,
-            'data': enhanced_result.data,
-            'success': True,
-            'message': f"Enhanced SEO analysis completed successfully for {enhanced_result.url}"
-        }
-        
-        return SEOAnalysisResponse(**response_data)
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error analyzing SEO: {str(e)}"
-        )
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000) 
--- a/ToBeMigrated/ai_seo_tools/seo_structured_data.py
+++ b/ToBeMigrated/ai_seo_tools/seo_structured_data.py
@@ -1,130 +0,0 @@
-import streamlit as st
-import json
-from datetime import date
-from dotenv import load_dotenv
-
-from ..ai_web_researcher.firecrawl_web_crawler import scrape_url
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-# Load environment variables
-load_dotenv()
-
-# Define a dictionary for schema types
-schema_types = {
-    "Article": {
-        "fields": ["Headline", "Author", "Date Published", "Keywords"],
-        "schema_type": "Article",
-    },
-    "Product": {
-        "fields": ["Name", "Description", "Price", "Brand", "Image URL"],
-        "schema_type": "Product",
-    },
-    "Recipe": {
-        "fields": ["Name", "Ingredients", "Cooking Time", "Serving Size", "Image URL"],
-        "schema_type": "Recipe",
-    },
-    "Event": {
-        "fields": ["Name", "Start Date", "End Date", "Location", "Description"],
-        "schema_type": "Event",
-    },
-    "LocalBusiness": {
-        "fields": ["Name", "Address", "Phone Number", "Opening Hours", "Image URL"],
-        "schema_type": "LocalBusiness",
-    },
-    # ... (add more schema types as needed)
-}
-
-def generate_json_data(content_type, details, url):
-    """Generates structured data (JSON-LD) based on user input."""
-    try:
-        scraped_text = scrape_url(url)
-    except Exception as err:
-        st.error(f"Failed to scrape web page from URL: {url} - Error: {err}")
-        return
-
-    schema = schema_types.get(content_type)
-    if not schema:
-        st.error(f"Invalid content type: {content_type}")
-        return
-
-    data = {
-        "@context": "https://schema.org",
-        "@type": schema["schema_type"],
-    }
-    for field in schema["fields"]:
-        value = details.get(field)
-        if isinstance(value, date):
-            value = value.isoformat()
-        data[field] = value if value else "N/A"  # Use placeholder values if input is missing
-
-    if url:
-        data['url'] = url
-
-    llm_structured_data = get_llm_structured_data(content_type, data, scraped_text)
-    return llm_structured_data
-
-def get_llm_structured_data(content_type, data, scraped_text):
-    """Function to get structured data from LLM."""
-    prompt = f"""Given the following information:
-
-        HTML Content: <<<HTML>>> {scraped_text} <<<END_HTML>>>
-        Content Type: <<<CONTENT_TYPE>>> {content_type} <<<END_CONTENT_TYPE>>>
-        Additional Relevant Data: <<<ADDITIONAL_DATA>>> {data} <<<END_ADDITIONAL_DATA>>>
-
-        Create a detailed structured data (JSON-LD) script for SEO purposes. 
-        The structured data should help search engines understand the content and features of the webpage, enhancing its visibility and potential for rich snippets in search results.
-        
-        Detailed Steps:
-        Parse the HTML content to extract relevant information like the title, main heading, and body content.
-        Use the contentType to determine the structured data type (e.g., Article, Product, Recipe).
-        Integrate the additional relevant data (e.g., author, datePublished, keywords) into the structured data.
-        Ensure all URLs, images, and other attributes are correctly formatted and included.
-        Validate the generated JSON-LD to ensure it meets schema.org standards and is free of errors.
-
-        Expected Output:
-        Generate a JSON-LD structured data snippet based on the provided inputs."""
-
-    try:
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        st.error(f"Failed to get response from LLM: {err}")
-        return
-
-def ai_structured_data():
-    st.title("📝 Generate Structured Data for SEO 🚀")
-    st.markdown("**Make your content more discoverable with rich snippets.**")
-
-    content_type = st.selectbox("**Select Content Type**", list(schema_types.keys()))
-
-    details = {}
-    schema_fields = schema_types[content_type]["fields"]
-    num_fields = len(schema_fields)
-
-    url = st.text_input("**URL :**", placeholder="Enter the URL of your webpage")
-    for i in range(0, num_fields, 2):
-        cols = st.columns(2)
-        for j in range(2):
-            if i + j < num_fields:
-                field = schema_fields[i + j]
-                if "Date" in field:
-                    details[field] = cols[j].date_input(field)
-                else:
-                    details[field] = cols[j].text_input(field, placeholder=f"Enter {field.lower()}")
-
-    if st.button("Generate Structured Data"):
-        if not url:
-            st.error("URL is required to generate structured data.")
-            return
-
-        structured_data = generate_json_data(content_type, details, url)
-        if structured_data:
-            st.subheader("Generated Structured Data (JSON-LD):")
-            st.markdown(structured_data)
-
-            st.download_button(
-                label="Download JSON-LD",
-                data=structured_data,
-                file_name=f"{content_type}_structured_data.json",
-                mime="application/json",
-            )
--- a/ToBeMigrated/ai_seo_tools/sitemap_analysis.py
+++ b/ToBeMigrated/ai_seo_tools/sitemap_analysis.py
@@ -1,340 +0,0 @@
-import streamlit as st
-import advertools as adv
-import pandas as pd
-import plotly.graph_objects as go
-from urllib.error import URLError
-import xml.etree.ElementTree as ET
-import requests
-
-
-def main():
-    """
-    Main function to run the Sitemap Analyzer Streamlit app.
-    """
-    st.title("📊 Sitemap Analyzer")
-    st.write("""
-        This tool analyzes a website's sitemap to understand its content structure and publishing trends. 
-        Enter a sitemap URL to start your analysis.
-    """)
-
-    sitemap_url = st.text_input(
-        "Please enter the sitemap URL:", 
-        "https://www.example.com/sitemap.xml"
-    )
-
-    if st.button("Analyze Sitemap"):
-        try:
-            sitemap_df = fetch_all_sitemaps(sitemap_url)
-            if sitemap_df is not None and not sitemap_df.empty:
-                sitemap_df = process_lastmod_column(sitemap_df)
-                ppmonth = analyze_content_trends(sitemap_df)
-                sitemap_df = categorize_and_shorten_sitemaps(sitemap_df)
-                
-                display_key_metrics(sitemap_df, ppmonth)
-                plot_sitemap_content_distribution(sitemap_df)
-                plot_content_trends(ppmonth)
-                plot_content_type_breakdown(sitemap_df)
-                plot_publishing_frequency(sitemap_df)
-
-                st.success("🎉 Analysis complete!")
-            else:
-                st.error("No valid URLs found in the sitemap.")
-        except URLError as e:
-            st.error(f"Error fetching the sitemap: {e}")
-        except Exception as e:
-            st.error(f"An unexpected error occurred: {e}")
-
-
-def fetch_all_sitemaps(sitemap_url):
-    """
-    Fetches all sitemaps from the provided sitemap URL and concatenates their URLs into a DataFrame.
-
-    Parameters:
-    sitemap_url (str): The URL of the sitemap.
-
-    Returns:
-    DataFrame: A DataFrame containing all URLs from the sitemaps.
-    """
-    st.write(f"🚀 Fetching and analyzing the sitemap: {sitemap_url}...")
-
-    try:
-        sitemap_df = fetch_sitemap(sitemap_url)
-        
-        if sitemap_df is not None:
-            all_sitemaps = sitemap_df.loc[
-                sitemap_df['loc'].str.contains('sitemap'), 
-                'loc'
-            ].tolist()
-            
-            if all_sitemaps:
-                st.write(
-                    f"🔄 Found {len(all_sitemaps)} additional sitemaps. Fetching data from them..."
-                )
-                all_urls_df = pd.DataFrame()
-
-                for sitemap in all_sitemaps:
-                    try:
-                        st.write(f"Fetching URLs from {sitemap}...")
-                        temp_df = fetch_sitemap(sitemap)
-                        if temp_df is not None:
-                            all_urls_df = pd.concat(
-                                [all_urls_df, temp_df], ignore_index=True
-                            )
-                    except Exception as e:
-                        st.error(f"Error fetching {sitemap}: {e}")
-
-                st.write(
-                    f"✅ Successfully fetched {len(all_urls_df)} URLs from all sitemaps."
-                )
-                return all_urls_df
-            
-            else:
-                st.write(f"✅ Successfully fetched {len(sitemap_df)} URLs from the main sitemap.")
-                return sitemap_df
-        else:
-            return None
-
-    except Exception as e:
-        st.error(f"⚠️ Error fetching the sitemap: {e}")
-        return None
-
-
-def fetch_sitemap(url):
-    """
-    Fetches and parses the sitemap from the provided URL.
-
-    Parameters:
-    url (str): The URL of the sitemap.
-
-    Returns:
-    DataFrame: A DataFrame containing the URLs from the sitemap.
-    """
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        
-        ET.fromstring(response.content)
-        
-        sitemap_df = adv.sitemap_to_df(url)
-        return sitemap_df
-
-    except requests.RequestException as e:
-        st.error(f"⚠️ Request error: {e}")
-        return None
-    except ET.ParseError as e:
-        st.error(f"⚠️ XML parsing error: {e}")
-        return None
-
-
-def process_lastmod_column(sitemap_df):
-    """
-    Processes the 'lastmod' column in the sitemap DataFrame by converting it to DateTime format and setting it as the index.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-
-    Returns:
-    DataFrame: The processed sitemap DataFrame with 'lastmod' as the index.
-    """
-    st.write("📅 Converting 'lastmod' column to DateTime format and setting it as the index...")
-
-    try:
-        sitemap_df = sitemap_df.dropna(subset=['lastmod'])
-        sitemap_df['lastmod'] = pd.to_datetime(sitemap_df['lastmod'])
-        sitemap_df.set_index('lastmod', inplace=True)
-        
-        st.write("✅ 'lastmod' column successfully converted to DateTime format and set as the index.")
-        return sitemap_df
-
-    except Exception as e:
-        st.error(f"⚠️ Error processing the 'lastmod' column: {e}")
-        return None
-
-
-def categorize_and_shorten_sitemaps(sitemap_df):
-    """
-    Categorizes and shortens the sitemap names in the sitemap DataFrame.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-
-    Returns:
-    DataFrame: The sitemap DataFrame with categorized and shortened sitemap names.
-    """
-    st.write("🔍 Categorizing and shortening sitemap names...")
-
-    try:
-        sitemap_df['sitemap_name'] = sitemap_df['sitemap'].str.split('/').str[4]
-        sitemap_df['sitemap_name'] = sitemap_df['sitemap_name'].replace({
-            'sitemap-site-kasko-fiyatlari.xml': 'Kasko',
-            'sitemap-site-bireysel.xml': 'Personal',
-            'sitemap-site-kurumsal.xml': 'Cooperate',
-            'sitemap-site-arac-sigortasi.xml': 'Car',
-            'sitemap-site.xml': 'Others'
-        })
-
-        st.write("✅ Sitemap names categorized and shortened.")
-        return sitemap_df
-
-    except Exception as e:
-        st.error(f"⚠️ Error categorizing sitemap names: {e}")
-        return sitemap_df
-
-
-def analyze_content_trends(sitemap_df):
-    """
-    Analyzes content publishing trends in the sitemap DataFrame.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-
-    Returns:
-    Series: A Series representing the number of contents published each month.
-    """
-    st.write("📅 Analyzing content publishing trends...")
-    
-    try:
-        ppmonth = sitemap_df.resample('M').size()
-        sitemap_df['monthly_count'] = sitemap_df.index.to_period('M').value_counts().sort_index()
-        
-        st.write("✅ Content trends analysis completed.")
-        return ppmonth
-    
-    except Exception as e:
-        st.error(f"⚠️ Error during content trends analysis: {e}")
-        return pd.Series()
-
-
-def display_key_metrics(sitemap_df, ppmonth):
-    """
-    Displays key metrics of the sitemap analysis.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-    ppmonth (Series): The Series representing the number of contents published each month.
-    """
-    st.write("### Key Metrics")
-    
-    total_urls = len(sitemap_df)
-    total_articles = ppmonth.sum()
-    average_frequency = ppmonth.mean()
-    
-    st.write(f"**Total URLs Found:** {total_urls:,}")
-    st.write(f"**Total Articles Published:** {total_articles:,}")
-    st.write(f"**Average Monthly Publishing Frequency:** {average_frequency:.2f} articles/month")
-
-
-def plot_sitemap_content_distribution(sitemap_df):
-    """
-    Plots the content distribution by sitemap categories.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-    """
-    st.write("📊 Visualizing content amount by sitemap categories...")
-
-    try:
-        if 'sitemap_name' in sitemap_df.columns:
-            stmc = sitemap_df.groupby('sitemap_name').size()
-            fig = go.Figure()
-            fig.add_bar(x=stmc.index, y=stmc.values, name='Sitemap Categories')
-            fig.update_layout(
-                title='Content Amount by Sitemap Categories',
-                xaxis_title='Sitemap Categories',
-                yaxis_title='Number of Articles',
-                paper_bgcolor='#E5ECF6'
-            )
-            st.plotly_chart(fig)
-        else:
-            st.warning("⚠️ The 'sitemap_name' column is missing in the data.")
-    
-    except Exception as e:
-        st.error(f"⚠️ Error during sitemap content distribution plotting: {e}")
-
-
-def plot_content_trends(ppmonth):
-    """
-    Plots the content publishing trends over time.
-
-    Parameters:
-    ppmonth (Series): The Series representing the number of contents published each month.
-    """
-    st.write("📈 Plotting content publishing trends over time...")
-
-    try:
-        fig = go.Figure()
-        fig.add_scatter(x=ppmonth.index, y=ppmonth.values, mode='lines+markers', name='Publishing Trends')
-        fig.update_layout(
-            title='Content Publishing Trends Over Time',
-            xaxis_title='Month',
-            yaxis_title='Number of Articles',
-            paper_bgcolor='#E5ECF6'
-        )
-        st.plotly_chart(fig)
-    
-    except Exception as e:
-        st.error(f"⚠️ Error during content trends plotting: {e}")
-
-
-def plot_content_type_breakdown(sitemap_df):
-    """
-    Plots the content type breakdown.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-    """
-    st.write("🔍 Plotting content type breakdown...")
-
-    try:
-        if 'sitemap_name' in sitemap_df.columns and not sitemap_df['sitemap_name'].empty:
-            content_type_counts = sitemap_df['sitemap_name'].value_counts()
-            st.write("Content Type Counts:", content_type_counts)
-            
-            if not content_type_counts.empty:
-                fig = go.Figure(data=[go.Pie(labels=content_type_counts.index, values=content_type_counts.values)])
-                fig.update_layout(
-                    title='Content Type Breakdown',
-                    paper_bgcolor='#E5ECF6'
-                )
-                st.plotly_chart(fig)
-            else:
-                st.warning("⚠️ No content types to display.")
-        else:
-            st.warning("⚠️ The 'sitemap_name' column is missing or empty.")
-    
-    except Exception as e:
-        st.error(f"⚠️ Error during content type breakdown plotting: {e}")
-
-
-def plot_publishing_frequency(sitemap_df):
-    """
-    Plots the publishing frequency by month.
-
-    Parameters:
-    sitemap_df (DataFrame): The sitemap DataFrame.
-    """
-    st.write("📆 Plotting publishing frequency by month...")
-
-    try:
-        if not sitemap_df.empty:
-            frequency_by_month = sitemap_df.index.to_period('M').value_counts().sort_index()
-            frequency_by_month.index = frequency_by_month.index.astype(str)
-            
-            fig = go.Figure()
-            fig.add_bar(x=frequency_by_month.index, y=frequency_by_month.values, name='Publishing Frequency')
-            fig.update_layout(
-                title='Publishing Frequency by Month',
-                xaxis_title='Month',
-                yaxis_title='Number of Articles',
-                paper_bgcolor='#E5ECF6'
-            )
-            st.plotly_chart(fig)
-        else:
-            st.warning("⚠️ No data available to plot publishing frequency.")
-    
-    except Exception as e:
-        st.error(f"⚠️ Error during publishing frequency plotting: {e}")
-
-
-if __name__ == "__main__":
-    main()
--- a/ToBeMigrated/ai_seo_tools/technical_seo_crawler/init.py
+++ b/ToBeMigrated/ai_seo_tools/technical_seo_crawler/init.py
@@ -1,22 +0,0 @@
-"""
-Technical SEO Crawler Package.
-
-This package provides comprehensive technical SEO analysis capabilities
-with advertools integration and AI-powered recommendations.
-
-Components:
- TechnicalSEOCrawler: Core crawler with technical analysis
- TechnicalSEOCrawlerUI: Streamlit interface for the crawler
-"""
-
-from .crawler import TechnicalSEOCrawler
-from .ui import TechnicalSEOCrawlerUI, render_technical_seo_crawler
-
-__version__ = "1.0.0"
-__author__ = "ALwrity"
-
-__all__ = [
-    'TechnicalSEOCrawler',
-    'TechnicalSEOCrawlerUI', 
-    'render_technical_seo_crawler'
-] 
--- a/ToBeMigrated/ai_seo_tools/technical_seo_crawler/crawler.py
+++ b/ToBeMigrated/ai_seo_tools/technical_seo_crawler/crawler.py
@@ -1,709 +0,0 @@
-"""
-Comprehensive Technical SEO Crawler using Advertools Integration.
-
-This module provides advanced site-wide technical SEO analysis using:
- adv.crawl: Complete website crawling and analysis
- adv.crawl_headers: HTTP headers and server analysis
- adv.crawl_images: Image optimization analysis
- adv.url_to_df: URL structure optimization
- AI-powered technical recommendations
-"""
-
-import streamlit as st
-import pandas as pd
-import advertools as adv
-from typing import Dict, Any, List, Optional, Tuple
-from urllib.parse import urlparse, urljoin
-import tempfile
-import os
-from datetime import datetime
-import json
-from collections import Counter, defaultdict
-from loguru import logger
-import numpy as np
-
-# Import existing modules
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
-
-class TechnicalSEOCrawler:
-    """Comprehensive technical SEO crawler with advertools integration."""
-    
-    def __init__(self):
-        """Initialize the technical SEO crawler."""
-        self.temp_dir = tempfile.mkdtemp()
-        logger.info("TechnicalSEOCrawler initialized")
-    
-    def analyze_website_technical_seo(self, website_url: str, crawl_depth: int = 3, 
-                                    max_pages: int = 500) -> Dict[str, Any]:
-        """
-        Perform comprehensive technical SEO analysis.
-        
-        Args:
-            website_url: Website URL to analyze
-            crawl_depth: How deep to crawl (1-5)
-            max_pages: Maximum pages to crawl (50-1000)
-            
-        Returns:
-            Comprehensive technical SEO analysis results
-        """
-        try:
-            st.info("🚀 Starting Comprehensive Technical SEO Crawl...")
-            
-            # Initialize results structure
-            results = {
-                'analysis_timestamp': datetime.utcnow().isoformat(),
-                'website_url': website_url,
-                'crawl_settings': {
-                    'depth': crawl_depth,
-                    'max_pages': max_pages
-                },
-                'crawl_overview': {},
-                'technical_issues': {},
-                'performance_analysis': {},
-                'content_analysis': {},
-                'url_structure': {},
-                'image_optimization': {},
-                'security_headers': {},
-                'mobile_seo': {},
-                'structured_data': {},
-                'ai_recommendations': {}
-            }
-            
-            # Phase 1: Core Website Crawl
-            with st.expander("🕷️ Website Crawling Progress", expanded=True):
-                crawl_data = self._perform_comprehensive_crawl(website_url, crawl_depth, max_pages)
-                results['crawl_overview'] = crawl_data
-                st.success(f"✅ Crawled {crawl_data.get('pages_crawled', 0)} pages")
-            
-            # Phase 2: Technical Issues Detection
-            with st.expander("🔍 Technical Issues Analysis", expanded=True):
-                technical_issues = self._analyze_technical_issues(crawl_data)
-                results['technical_issues'] = technical_issues
-                st.success("✅ Identified technical SEO issues")
-            
-            # Phase 3: Performance Analysis
-            with st.expander("⚡ Performance Analysis", expanded=True):
-                performance = self._analyze_performance_metrics(crawl_data)
-                results['performance_analysis'] = performance
-                st.success("✅ Analyzed website performance metrics")
-            
-            # Phase 4: Content & Structure Analysis
-            with st.expander("📊 Content Structure Analysis", expanded=True):
-                content_analysis = self._analyze_content_structure(crawl_data)
-                results['content_analysis'] = content_analysis
-                st.success("✅ Analyzed content structure and optimization")
-            
-            # Phase 5: URL Structure Optimization
-            with st.expander("🔗 URL Structure Analysis", expanded=True):
-                url_analysis = self._analyze_url_structure(crawl_data)
-                results['url_structure'] = url_analysis
-                st.success("✅ Analyzed URL structure and patterns")
-            
-            # Phase 6: Image SEO Analysis
-            with st.expander("🖼️ Image SEO Analysis", expanded=True):
-                image_analysis = self._analyze_image_seo(website_url)
-                results['image_optimization'] = image_analysis
-                st.success("✅ Analyzed image optimization")
-            
-            # Phase 7: Security & Headers Analysis
-            with st.expander("🛡️ Security Headers Analysis", expanded=True):
-                security_analysis = self._analyze_security_headers(website_url)
-                results['security_headers'] = security_analysis
-                st.success("✅ Analyzed security headers")
-            
-            # Phase 8: Mobile SEO Analysis
-            with st.expander("📱 Mobile SEO Analysis", expanded=True):
-                mobile_analysis = self._analyze_mobile_seo(crawl_data)
-                results['mobile_seo'] = mobile_analysis
-                st.success("✅ Analyzed mobile SEO factors")
-            
-            # Phase 9: AI-Powered Recommendations
-            with st.expander("🤖 AI Technical Recommendations", expanded=True):
-                ai_recommendations = self._generate_technical_recommendations(results)
-                results['ai_recommendations'] = ai_recommendations
-                st.success("✅ Generated AI-powered technical recommendations")
-            
-            return results
-            
-        except Exception as e:
-            error_msg = f"Error in technical SEO analysis: {str(e)}"
-            logger.error(error_msg, exc_info=True)
-            st.error(error_msg)
-            return {'error': error_msg}
-    
-    def _perform_comprehensive_crawl(self, website_url: str, depth: int, max_pages: int) -> Dict[str, Any]:
-        """Perform comprehensive website crawl using adv.crawl."""
-        try:
-            st.info("🕷️ Crawling website for comprehensive analysis...")
-            
-            # Create crawl output file
-            crawl_file = os.path.join(self.temp_dir, "technical_crawl.jl")
-            
-            # Configure crawl settings for technical SEO
-            custom_settings = {
-                'DEPTH_LIMIT': depth,
-                'CLOSESPIDER_PAGECOUNT': max_pages,
-                'DOWNLOAD_DELAY': 0.5,  # Be respectful
-                'CONCURRENT_REQUESTS': 8,
-                'ROBOTSTXT_OBEY': True,
-                'USER_AGENT': 'ALwrity-TechnicalSEO-Crawler/1.0',
-                'COOKIES_ENABLED': False,
-                'TELNETCONSOLE_ENABLED': False,
-                'LOG_LEVEL': 'WARNING'
-            }
-            
-            # Start crawl
-            adv.crawl(
-                url_list=[website_url],
-                output_file=crawl_file,
-                follow_links=True,
-                custom_settings=custom_settings
-            )
-            
-            # Read and process crawl results
-            if os.path.exists(crawl_file):
-                crawl_df = pd.read_json(crawl_file, lines=True)
-                
-                # Basic crawl statistics
-                crawl_overview = {
-                    'pages_crawled': len(crawl_df),
-                    'status_codes': crawl_df['status'].value_counts().to_dict(),
-                    'crawl_file_path': crawl_file,
-                    'crawl_dataframe': crawl_df,
-                    'domains_found': crawl_df['url'].apply(lambda x: urlparse(x).netloc).nunique(),
-                    'avg_response_time': crawl_df.get('download_latency', pd.Series()).mean(),
-                    'total_content_size': crawl_df.get('size', pd.Series()).sum()
-                }
-                
-                return crawl_overview
-            else:
-                st.error("Crawl file not created")
-                return {}
-                
-        except Exception as e:
-            st.error(f"Error in website crawl: {str(e)}")
-            return {}
-    
-    def _analyze_technical_issues(self, crawl_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze technical SEO issues from crawl data."""
-        try:
-            st.info("🔍 Detecting technical SEO issues...")
-            
-            if 'crawl_dataframe' not in crawl_data:
-                return {}
-            
-            df = crawl_data['crawl_dataframe']
-            
-            technical_issues = {
-                'http_errors': {},
-                'redirect_issues': {},
-                'duplicate_content': {},
-                'missing_elements': {},
-                'page_speed_issues': {},
-                'crawlability_issues': {}
-            }
-            
-            # HTTP Status Code Issues
-            error_codes = df[df['status'] >= 400]['status'].value_counts().to_dict()
-            technical_issues['http_errors'] = {
-                'total_errors': len(df[df['status'] >= 400]),
-                'error_breakdown': error_codes,
-                'error_pages': df[df['status'] >= 400][['url', 'status']].to_dict('records')[:50]
-            }
-            
-            # Redirect Analysis
-            redirects = df[df['status'].isin([301, 302, 303, 307, 308])]
-            technical_issues['redirect_issues'] = {
-                'total_redirects': len(redirects),
-                'redirect_chains': self._find_redirect_chains(redirects),
-                'redirect_types': redirects['status'].value_counts().to_dict()
-            }
-            
-            # Duplicate Content Detection
-            if 'title' in df.columns:
-                duplicate_titles = df['title'].value_counts()
-                duplicate_titles = duplicate_titles[duplicate_titles > 1]
-                
-                technical_issues['duplicate_content'] = {
-                    'duplicate_titles': len(duplicate_titles),
-                    'duplicate_title_groups': duplicate_titles.to_dict(),
-                    'pages_with_duplicate_titles': df[df['title'].isin(duplicate_titles.index)][['url', 'title']].to_dict('records')[:20]
-                }
-            
-            # Missing Elements Analysis
-            missing_elements = {
-                'missing_titles': len(df[(df['title'].isna()) | (df['title'] == '')]) if 'title' in df.columns else 0,
-                'missing_meta_desc': len(df[(df['meta_desc'].isna()) | (df['meta_desc'] == '')]) if 'meta_desc' in df.columns else 0,
-                'missing_h1': len(df[(df['h1'].isna()) | (df['h1'] == '')]) if 'h1' in df.columns else 0
-            }
-            technical_issues['missing_elements'] = missing_elements
-            
-            # Page Speed Issues
-            if 'download_latency' in df.columns:
-                slow_pages = df[df['download_latency'] > 3.0]  # Pages taking >3s
-                technical_issues['page_speed_issues'] = {
-                    'slow_pages_count': len(slow_pages),
-                    'avg_load_time': df['download_latency'].mean(),
-                    'slowest_pages': slow_pages.nlargest(10, 'download_latency')[['url', 'download_latency']].to_dict('records')
-                }
-            
-            return technical_issues
-            
-        except Exception as e:
-            st.error(f"Error analyzing technical issues: {str(e)}")
-            return {}
-    
-    def _analyze_performance_metrics(self, crawl_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze website performance metrics."""
-        try:
-            st.info("⚡ Analyzing performance metrics...")
-            
-            if 'crawl_dataframe' not in crawl_data:
-                return {}
-            
-            df = crawl_data['crawl_dataframe']
-            
-            performance = {
-                'load_time_analysis': {},
-                'content_size_analysis': {},
-                'server_performance': {},
-                'optimization_opportunities': []
-            }
-            
-            # Load Time Analysis
-            if 'download_latency' in df.columns:
-                load_times = df['download_latency'].dropna()
-                performance['load_time_analysis'] = {
-                    'avg_load_time': load_times.mean(),
-                    'median_load_time': load_times.median(),
-                    'p95_load_time': load_times.quantile(0.95),
-                    'fastest_page': load_times.min(),
-                    'slowest_page': load_times.max(),
-                    'pages_over_3s': len(load_times[load_times > 3]),
-                    'performance_distribution': {
-                        'fast_pages': len(load_times[load_times <= 1]),
-                        'moderate_pages': len(load_times[(load_times > 1) & (load_times <= 3)]),
-                        'slow_pages': len(load_times[load_times > 3])
-                    }
-                }
-            
-            # Content Size Analysis
-            if 'size' in df.columns:
-                sizes = df['size'].dropna()
-                performance['content_size_analysis'] = {
-                    'avg_page_size': sizes.mean(),
-                    'median_page_size': sizes.median(),
-                    'largest_page': sizes.max(),
-                    'smallest_page': sizes.min(),
-                    'pages_over_1mb': len(sizes[sizes > 1048576]),  # 1MB
-                    'total_content_size': sizes.sum()
-                }
-            
-            # Server Performance
-            status_codes = df['status'].value_counts()
-            total_pages = len(df)
-            performance['server_performance'] = {
-                'success_rate': status_codes.get(200, 0) / total_pages * 100,
-                'error_rate': sum(status_codes.get(code, 0) for code in range(400, 600)) / total_pages * 100,
-                'redirect_rate': sum(status_codes.get(code, 0) for code in [301, 302, 303, 307, 308]) / total_pages * 100
-            }
-            
-            return performance
-            
-        except Exception as e:
-            st.error(f"Error analyzing performance: {str(e)}")
-            return {}
-    
-    def _analyze_content_structure(self, crawl_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze content structure and SEO elements."""
-        try:
-            st.info("📊 Analyzing content structure...")
-            
-            if 'crawl_dataframe' not in crawl_data:
-                return {}
-            
-            df = crawl_data['crawl_dataframe']
-            
-            content_analysis = {
-                'title_analysis': {},
-                'meta_description_analysis': {},
-                'heading_structure': {},
-                'internal_linking': {},
-                'content_optimization': {}
-            }
-            
-            # Title Analysis
-            if 'title' in df.columns:
-                titles = df['title'].dropna()
-                title_lengths = titles.str.len()
-                
-                content_analysis['title_analysis'] = {
-                    'avg_title_length': title_lengths.mean(),
-                    'title_length_distribution': {
-                        'too_short': len(title_lengths[title_lengths < 30]),
-                        'optimal': len(title_lengths[(title_lengths >= 30) & (title_lengths <= 60)]),
-                        'too_long': len(title_lengths[title_lengths > 60])
-                    },
-                    'duplicate_titles': len(titles.value_counts()[titles.value_counts() > 1]),
-                    'missing_titles': len(df) - len(titles)
-                }
-            
-            # Meta Description Analysis
-            if 'meta_desc' in df.columns:
-                meta_descs = df['meta_desc'].dropna()
-                meta_lengths = meta_descs.str.len()
-                
-                content_analysis['meta_description_analysis'] = {
-                    'avg_meta_length': meta_lengths.mean(),
-                    'meta_length_distribution': {
-                        'too_short': len(meta_lengths[meta_lengths < 120]),
-                        'optimal': len(meta_lengths[(meta_lengths >= 120) & (meta_lengths <= 160)]),
-                        'too_long': len(meta_lengths[meta_lengths > 160])
-                    },
-                    'missing_meta_descriptions': len(df) - len(meta_descs)
-                }
-            
-            # Heading Structure Analysis
-            heading_cols = [col for col in df.columns if col.startswith('h') and col[1:].isdigit()]
-            if heading_cols:
-                heading_analysis = {}
-                for col in heading_cols:
-                    headings = df[col].dropna()
-                    heading_analysis[f'{col}_usage'] = {
-                        'pages_with_heading': len(headings),
-                        'usage_rate': len(headings) / len(df) * 100,
-                        'avg_length': headings.str.len().mean() if len(headings) > 0 else 0
-                    }
-                content_analysis['heading_structure'] = heading_analysis
-            
-            # Internal Linking Analysis
-            if 'links_internal' in df.columns:
-                internal_links = df['links_internal'].apply(lambda x: len(x) if isinstance(x, list) else 0)
-                content_analysis['internal_linking'] = {
-                    'avg_internal_links': internal_links.mean(),
-                    'pages_with_no_internal_links': len(internal_links[internal_links == 0]),
-                    'max_internal_links': internal_links.max(),
-                    'internal_link_distribution': internal_links.describe().to_dict()
-                }
-            
-            return content_analysis
-            
-        except Exception as e:
-            st.error(f"Error analyzing content structure: {str(e)}")
-            return {}
-    
-    def _analyze_url_structure(self, crawl_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze URL structure and optimization using adv.url_to_df."""
-        try:
-            st.info("🔗 Analyzing URL structure...")
-            
-            if 'crawl_dataframe' not in crawl_data:
-                return {}
-            
-            df = crawl_data['crawl_dataframe']
-            urls = df['url'].tolist()
-            
-            # Use advertools to analyze URL structure
-            url_df = adv.url_to_df(urls)
-            
-            url_analysis = {
-                'url_length_analysis': {},
-                'url_structure_patterns': {},
-                'url_optimization': {},
-                'path_analysis': {}
-            }
-            
-            # URL Length Analysis
-            url_lengths = url_df['url'].str.len()
-            url_analysis['url_length_analysis'] = {
-                'avg_url_length': url_lengths.mean(),
-                'max_url_length': url_lengths.max(),
-                'long_urls_count': len(url_lengths[url_lengths > 100]),
-                'url_length_distribution': url_lengths.describe().to_dict()
-            }
-            
-            # Path Depth Analysis
-            if 'dir_1' in url_df.columns:
-                path_depths = url_df.apply(lambda row: sum(1 for i in range(1, 10) if f'dir_{i}' in row and pd.notna(row[f'dir_{i}'])), axis=1)
-                url_analysis['path_analysis'] = {
-                    'avg_path_depth': path_depths.mean(),
-                    'max_path_depth': path_depths.max(),
-                    'deep_paths_count': len(path_depths[path_depths > 4]),
-                    'path_depth_distribution': path_depths.value_counts().to_dict()
-                }
-            
-            # URL Structure Patterns
-            domains = url_df['netloc'].value_counts()
-            schemes = url_df['scheme'].value_counts()
-            
-            url_analysis['url_structure_patterns'] = {
-                'domains_found': domains.to_dict(),
-                'schemes_used': schemes.to_dict(),
-                'subdomain_usage': len(url_df[url_df['netloc'].str.contains('\.', regex=True)]),
-                'https_usage': schemes.get('https', 0) / len(url_df) * 100
-            }
-            
-            # URL Optimization Issues
-            optimization_issues = []
-            
-            # Check for non-HTTPS URLs
-            if schemes.get('http', 0) > 0:
-                optimization_issues.append(f"{schemes.get('http', 0)} pages not using HTTPS")
-            
-            # Check for long URLs
-            long_urls = len(url_lengths[url_lengths > 100])
-            if long_urls > 0:
-                optimization_issues.append(f"{long_urls} URLs are too long (>100 characters)")
-            
-            # Check for deep paths
-            if 'path_analysis' in url_analysis:
-                deep_paths = url_analysis['path_analysis']['deep_paths_count']
-                if deep_paths > 0:
-                    optimization_issues.append(f"{deep_paths} URLs have deep path structures (>4 levels)")
-            
-            url_analysis['url_optimization'] = {
-                'issues_found': len(optimization_issues),
-                'optimization_recommendations': optimization_issues
-            }
-            
-            return url_analysis
-            
-        except Exception as e:
-            st.error(f"Error analyzing URL structure: {str(e)}")
-            return {}
-    
-    def _analyze_image_seo(self, website_url: str) -> Dict[str, Any]:
-        """Analyze image SEO using adv.crawl_images."""
-        try:
-            st.info("🖼️ Analyzing image SEO...")
-            
-            # Create image crawl output file
-            image_file = os.path.join(self.temp_dir, "image_crawl.jl")
-            
-            # Crawl images
-            adv.crawl_images(
-                url_list=[website_url],
-                output_file=image_file,
-                custom_settings={
-                    'DEPTH_LIMIT': 2,
-                    'CLOSESPIDER_PAGECOUNT': 100,
-                    'DOWNLOAD_DELAY': 1
-                }
-            )
-            
-            image_analysis = {
-                'image_count': 0,
-                'alt_text_analysis': {},
-                'image_format_analysis': {},
-                'image_size_analysis': {},
-                'optimization_opportunities': []
-            }
-            
-            if os.path.exists(image_file):
-                image_df = pd.read_json(image_file, lines=True)
-                
-                image_analysis['image_count'] = len(image_df)
-                
-                # Alt text analysis
-                if 'img_alt' in image_df.columns:
-                    alt_texts = image_df['img_alt'].dropna()
-                    missing_alt = len(image_df) - len(alt_texts)
-                    
-                    image_analysis['alt_text_analysis'] = {
-                        'images_with_alt': len(alt_texts),
-                        'images_missing_alt': missing_alt,
-                        'alt_text_coverage': len(alt_texts) / len(image_df) * 100,
-                        'avg_alt_length': alt_texts.str.len().mean() if len(alt_texts) > 0 else 0
-                    }
-                
-                # Image format analysis
-                if 'img_src' in image_df.columns:
-                    # Extract file extensions
-                    extensions = image_df['img_src'].str.extract(r'\.([a-zA-Z]{2,4})(?:\?|$)')
-                    format_counts = extensions[0].value_counts()
-                    
-                    image_analysis['image_format_analysis'] = {
-                        'format_distribution': format_counts.to_dict(),
-                        'modern_format_usage': format_counts.get('webp', 0) + format_counts.get('avif', 0)
-                    }
-            
-            return image_analysis
-            
-        except Exception as e:
-            st.error(f"Error analyzing images: {str(e)}")
-            return {}
-    
-    def _analyze_security_headers(self, website_url: str) -> Dict[str, Any]:
-        """Analyze security headers using adv.crawl_headers."""
-        try:
-            st.info("🛡️ Analyzing security headers...")
-            
-            # Create headers output file
-            headers_file = os.path.join(self.temp_dir, "security_headers.jl")
-            
-            # Crawl headers
-            adv.crawl_headers([website_url], output_file=headers_file)
-            
-            security_analysis = {
-                'security_headers_present': {},
-                'security_score': 0,
-                'security_recommendations': []
-            }
-            
-            if os.path.exists(headers_file):
-                headers_df = pd.read_json(headers_file, lines=True)
-                
-                # Check for important security headers
-                security_headers = {
-                    'X-Frame-Options': 'resp_headers_X-Frame-Options',
-                    'X-Content-Type-Options': 'resp_headers_X-Content-Type-Options',
-                    'X-XSS-Protection': 'resp_headers_X-XSS-Protection',
-                    'Strict-Transport-Security': 'resp_headers_Strict-Transport-Security',
-                    'Content-Security-Policy': 'resp_headers_Content-Security-Policy',
-                    'Referrer-Policy': 'resp_headers_Referrer-Policy'
-                }
-                
-                headers_present = {}
-                for header_name, column_name in security_headers.items():
-                    is_present = column_name in headers_df.columns and headers_df[column_name].notna().any()
-                    headers_present[header_name] = is_present
-                
-                security_analysis['security_headers_present'] = headers_present
-                
-                # Calculate security score
-                present_count = sum(headers_present.values())
-                security_analysis['security_score'] = (present_count / len(security_headers)) * 100
-                
-                # Generate recommendations
-                recommendations = []
-                for header_name, is_present in headers_present.items():
-                    if not is_present:
-                        recommendations.append(f"Add {header_name} header for improved security")
-                
-                security_analysis['security_recommendations'] = recommendations
-            
-            return security_analysis
-            
-        except Exception as e:
-            st.error(f"Error analyzing security headers: {str(e)}")
-            return {}
-    
-    def _analyze_mobile_seo(self, crawl_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Analyze mobile SEO factors."""
-        try:
-            st.info("📱 Analyzing mobile SEO factors...")
-            
-            if 'crawl_dataframe' not in crawl_data:
-                return {}
-            
-            df = crawl_data['crawl_dataframe']
-            
-            mobile_analysis = {
-                'viewport_analysis': {},
-                'mobile_optimization': {},
-                'responsive_design_indicators': {}
-            }
-            
-            # Viewport meta tag analysis
-            if 'viewport' in df.columns:
-                viewport_present = df['viewport'].notna().sum()
-                mobile_analysis['viewport_analysis'] = {
-                    'pages_with_viewport': viewport_present,
-                    'viewport_coverage': viewport_present / len(df) * 100,
-                    'pages_missing_viewport': len(df) - viewport_present
-                }
-            
-            # Check for mobile-specific meta tags and indicators
-            mobile_indicators = []
-            
-            # Check for touch icons
-            if any('touch-icon' in col for col in df.columns):
-                mobile_indicators.append("Touch icons configured")
-            
-            # Check for responsive design indicators in content
-            # This is a simplified check - in practice, you'd analyze CSS and page structure
-            mobile_analysis['mobile_optimization'] = {
-                'mobile_indicators_found': len(mobile_indicators),
-                'mobile_indicators': mobile_indicators
-            }
-            
-            return mobile_analysis
-            
-        except Exception as e:
-            st.error(f"Error analyzing mobile SEO: {str(e)}")
-            return {}
-    
-    def _generate_technical_recommendations(self, results: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate AI-powered technical SEO recommendations."""
-        try:
-            st.info("🤖 Generating technical recommendations...")
-            
-            # Prepare technical analysis summary for AI
-            technical_summary = {
-                'website_url': results.get('website_url', ''),
-                'pages_crawled': results.get('crawl_overview', {}).get('pages_crawled', 0),
-                'error_count': results.get('technical_issues', {}).get('http_errors', {}).get('total_errors', 0),
-                'avg_load_time': results.get('performance_analysis', {}).get('load_time_analysis', {}).get('avg_load_time', 0),
-                'security_score': results.get('security_headers', {}).get('security_score', 0),
-                'missing_titles': results.get('content_analysis', {}).get('title_analysis', {}).get('missing_titles', 0),
-                'missing_meta_desc': results.get('content_analysis', {}).get('meta_description_analysis', {}).get('missing_meta_descriptions', 0)
-            }
-            
-            # Generate AI recommendations
-            prompt = f"""
-            As a technical SEO expert, analyze this comprehensive website audit and provide prioritized recommendations:
-
-            WEBSITE: {technical_summary['website_url']}
-            PAGES ANALYZED: {technical_summary['pages_crawled']}
-            
-            TECHNICAL ISSUES:
-            - HTTP Errors: {technical_summary['error_count']}
-            - Average Load Time: {technical_summary['avg_load_time']:.2f}s
-            - Security Score: {technical_summary['security_score']:.1f}%
-            - Missing Titles: {technical_summary['missing_titles']}
-            - Missing Meta Descriptions: {technical_summary['missing_meta_desc']}
-
-            PROVIDE:
-            1. Critical Issues (Fix Immediately)
-            2. High Priority Optimizations
-            3. Medium Priority Improvements
-            4. Long-term Technical Strategy
-            5. Specific Implementation Steps
-            6. Expected Impact Assessment
-
-            Format as JSON with clear priorities and actionable recommendations.
-            """
-            
-            ai_response = llm_text_gen(
-                prompt=prompt,
-                system_prompt="You are a senior technical SEO specialist with expertise in website optimization, Core Web Vitals, and search engine best practices.",
-                response_format="json_object"
-            )
-            
-            if ai_response:
-                return ai_response
-            else:
-                return {'recommendations': ['AI recommendations temporarily unavailable']}
-                
-        except Exception as e:
-            st.error(f"Error generating recommendations: {str(e)}")
-            return {}
-    
-    def _find_redirect_chains(self, redirects_df: pd.DataFrame) -> List[Dict[str, Any]]:
-        """Find redirect chains in the crawled data."""
-        # Simplified redirect chain detection
-        # In a full implementation, you'd trace the redirect paths
-        redirect_chains = []
-        
-        if len(redirects_df) > 0:
-            # Group redirects by status code
-            for status_code in redirects_df['status'].unique():
-                status_redirects = redirects_df[redirects_df['status'] == status_code]
-                redirect_chains.append({
-                    'status_code': int(status_code),
-                    'count': len(status_redirects),
-                    'examples': status_redirects['url'].head(5).tolist()
-                })
-        
-        return redirect_chains 
--- a/ToBeMigrated/ai_seo_tools/technical_seo_crawler/ui.py
+++ b/ToBeMigrated/ai_seo_tools/technical_seo_crawler/ui.py
@@ -1,968 +0,0 @@
-"""
-Technical SEO Crawler UI with Comprehensive Analysis Dashboard.
-
-This module provides a professional Streamlit interface for the Technical SEO Crawler
-with detailed analysis results, visualization, and export capabilities.
-"""
-
-import streamlit as st
-import pandas as pd
-from typing import Dict, Any, List
-import json
-from datetime import datetime
-import io
-import base64
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-
-from .crawler import TechnicalSEOCrawler
-from lib.alwrity_ui.dashboard_styles import apply_dashboard_style, render_dashboard_header
-
-class TechnicalSEOCrawlerUI:
-    """Professional UI for Technical SEO Crawler."""
-    
-    def __init__(self):
-        """Initialize the Technical SEO Crawler UI."""
-        self.crawler = TechnicalSEOCrawler()
-        
-        # Apply dashboard styling
-        apply_dashboard_style()
-    
-    def render(self):
-        """Render the Technical SEO Crawler interface."""
-        
-        # Enhanced dashboard header
-        render_dashboard_header(
-            "🔧 Technical SEO Crawler",
-            "Comprehensive site-wide technical SEO analysis with AI-powered recommendations. Identify and fix technical issues that impact your search rankings."
-        )
-        
-        # Main content area
-        with st.container():
-            # Analysis input form
-            self._render_crawler_form()
-        
-        # Session state for results
-        if 'technical_seo_results' in st.session_state and st.session_state.technical_seo_results:
-            st.markdown("---")
-            self._render_results_dashboard(st.session_state.technical_seo_results)
-    
-    def _render_crawler_form(self):
-        """Render the crawler configuration form."""
-        st.markdown("## 🚀 Configure Technical SEO Audit")
-        
-        with st.form("technical_seo_crawler_form"):
-            # Website URL input
-            col1, col2 = st.columns([3, 1])
-            
-            with col1:
-                website_url = st.text_input(
-                    "🌐 Website URL to Audit",
-                    placeholder="https://yourwebsite.com",
-                    help="Enter the website URL for comprehensive technical SEO analysis"
-                )
-            
-            with col2:
-                audit_type = st.selectbox(
-                    "🎯 Audit Type",
-                    options=["Standard", "Deep", "Quick"],
-                    help="Choose the depth of analysis"
-                )
-            
-            # Crawl configuration
-            st.markdown("### ⚙️ Crawl Configuration")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                if audit_type == "Quick":
-                    crawl_depth = st.slider("Crawl Depth", 1, 2, 1)
-                    max_pages = st.slider("Max Pages", 10, 100, 50)
-                elif audit_type == "Deep":
-                    crawl_depth = st.slider("Crawl Depth", 1, 5, 4)
-                    max_pages = st.slider("Max Pages", 100, 1000, 500)
-                else:  # Standard
-                    crawl_depth = st.slider("Crawl Depth", 1, 4, 3)
-                    max_pages = st.slider("Max Pages", 50, 500, 200)
-            
-            with col2:
-                analyze_images = st.checkbox(
-                    "🖼️ Analyze Images",
-                    value=True,
-                    help="Include image SEO analysis"
-                )
-                
-                analyze_security = st.checkbox(
-                    "🛡️ Security Headers",
-                    value=True,
-                    help="Analyze security headers"
-                )
-            
-            with col3:
-                analyze_mobile = st.checkbox(
-                    "📱 Mobile SEO",
-                    value=True,
-                    help="Include mobile SEO analysis"
-                )
-                
-                ai_recommendations = st.checkbox(
-                    "🤖 AI Recommendations",
-                    value=True,
-                    help="Generate AI-powered recommendations"
-                )
-            
-            # Analysis scope
-            st.markdown("### 🎯 Analysis Scope")
-            
-            analysis_options = st.multiselect(
-                "Select Analysis Components",
-                options=[
-                    "Technical Issues Detection",
-                    "Performance Analysis", 
-                    "Content Structure Analysis",
-                    "URL Structure Optimization",
-                    "Internal Linking Analysis",
-                    "Duplicate Content Detection"
-                ],
-                default=[
-                    "Technical Issues Detection",
-                    "Performance Analysis", 
-                    "Content Structure Analysis"
-                ],
-                help="Choose which analysis components to include"
-            )
-            
-            # Submit button
-            submitted = st.form_submit_button(
-                "🚀 Start Technical SEO Audit",
-                use_container_width=True,
-                type="primary"
-            )
-            
-            if submitted:
-                # Validate inputs
-                if not website_url or not website_url.startswith(('http://', 'https://')):
-                    st.error("❌ Please enter a valid website URL starting with http:// or https://")
-                    return
-                
-                # Run technical SEO analysis
-                self._run_technical_analysis(
-                    website_url=website_url,
-                    crawl_depth=crawl_depth,
-                    max_pages=max_pages,
-                    options={
-                        'analyze_images': analyze_images,
-                        'analyze_security': analyze_security,
-                        'analyze_mobile': analyze_mobile,
-                        'ai_recommendations': ai_recommendations,
-                        'analysis_scope': analysis_options
-                    }
-                )
-    
-    def _run_technical_analysis(self, website_url: str, crawl_depth: int, 
-                               max_pages: int, options: Dict[str, Any]):
-        """Run the technical SEO analysis."""
-        
-        try:
-            with st.spinner("🔄 Running Comprehensive Technical SEO Audit..."):
-                
-                # Initialize progress tracking
-                progress_bar = st.progress(0)
-                status_text = st.empty()
-                
-                # Update progress
-                progress_bar.progress(10)
-                status_text.text("🚀 Initializing technical SEO crawler...")
-                
-                # Run comprehensive analysis
-                results = self.crawler.analyze_website_technical_seo(
-                    website_url=website_url,
-                    crawl_depth=crawl_depth,
-                    max_pages=max_pages
-                )
-                
-                progress_bar.progress(100)
-                status_text.text("✅ Technical SEO audit complete!")
-                
-                # Store results in session state
-                st.session_state.technical_seo_results = results
-                
-                # Clear progress indicators
-                progress_bar.empty()
-                status_text.empty()
-                
-                if 'error' in results:
-                    st.error(f"❌ Analysis failed: {results['error']}")
-                else:
-                    st.success("🎉 Technical SEO Audit completed successfully!")
-                    st.balloons()
-                    
-                    # Rerun to show results
-                    st.rerun()
-                
-        except Exception as e:
-            st.error(f"❌ Error running technical analysis: {str(e)}")
-    
-    def _render_results_dashboard(self, results: Dict[str, Any]):
-        """Render the comprehensive results dashboard."""
-        
-        if 'error' in results:
-            st.error(f"❌ Analysis Error: {results['error']}")
-            return
-        
-        # Results header
-        st.markdown("## 📊 Technical SEO Audit Results")
-        
-        # Key metrics overview
-        self._render_metrics_overview(results)
-        
-        # Detailed analysis tabs
-        self._render_detailed_analysis(results)
-        
-        # Export functionality
-        self._render_export_options(results)
-    
-    def _render_metrics_overview(self, results: Dict[str, Any]):
-        """Render key metrics overview."""
-        
-        st.markdown("### 📈 Audit Overview")
-        
-        # Create metrics columns
-        col1, col2, col3, col4, col5, col6 = st.columns(6)
-        
-        with col1:
-            pages_crawled = results.get('crawl_overview', {}).get('pages_crawled', 0)
-            st.metric(
-                "🕷️ Pages Crawled",
-                pages_crawled,
-                help="Total pages analyzed"
-            )
-        
-        with col2:
-            error_count = results.get('technical_issues', {}).get('http_errors', {}).get('total_errors', 0)
-            st.metric(
-                "❌ HTTP Errors",
-                error_count,
-                delta=f"-{error_count}" if error_count > 0 else None,
-                help="Pages with HTTP errors (4xx, 5xx)"
-            )
-        
-        with col3:
-            avg_load_time = results.get('performance_analysis', {}).get('load_time_analysis', {}).get('avg_load_time', 0)
-            st.metric(
-                "⚡ Avg Load Time",
-                f"{avg_load_time:.2f}s",
-                delta=f"+{avg_load_time:.2f}s" if avg_load_time > 3 else None,
-                help="Average page load time"
-            )
-        
-        with col4:
-            security_score = results.get('security_headers', {}).get('security_score', 0)
-            st.metric(
-                "🛡️ Security Score",
-                f"{security_score:.0f}%",
-                delta=f"{security_score:.0f}%" if security_score < 100 else None,
-                help="Security headers implementation score"
-            )
-        
-        with col5:
-            missing_titles = results.get('content_analysis', {}).get('title_analysis', {}).get('missing_titles', 0)
-            st.metric(
-                "📝 Missing Titles",
-                missing_titles,
-                delta=f"-{missing_titles}" if missing_titles > 0 else None,
-                help="Pages without title tags"
-            )
-        
-        with col6:
-            image_count = results.get('image_optimization', {}).get('image_count', 0)
-            st.metric(
-                "🖼️ Images Analyzed",
-                image_count,
-                help="Total images found and analyzed"
-            )
-        
-        # Analysis timestamp
-        if results.get('analysis_timestamp'):
-            timestamp = datetime.fromisoformat(results['analysis_timestamp'].replace('Z', '+00:00'))
-            st.caption(f"📅 Audit completed: {timestamp.strftime('%Y-%m-%d %H:%M:%S UTC')}")
-    
-    def _render_detailed_analysis(self, results: Dict[str, Any]):
-        """Render detailed analysis in tabs."""
-        
-        # Create main analysis tabs
-        tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
-            "🔍 Technical Issues",
-            "⚡ Performance",
-            "📊 Content Analysis", 
-            "🔗 URL Structure",
-            "🖼️ Image SEO",
-            "🛡️ Security",
-            "🤖 AI Recommendations"
-        ])
-        
-        with tab1:
-            self._render_technical_issues(results.get('technical_issues', {}))
-        
-        with tab2:
-            self._render_performance_analysis(results.get('performance_analysis', {}))
-        
-        with tab3:
-            self._render_content_analysis(results.get('content_analysis', {}))
-        
-        with tab4:
-            self._render_url_structure(results.get('url_structure', {}))
-        
-        with tab5:
-            self._render_image_analysis(results.get('image_optimization', {}))
-        
-        with tab6:
-            self._render_security_analysis(results.get('security_headers', {}))
-        
-        with tab7:
-            self._render_ai_recommendations(results.get('ai_recommendations', {}))
-    
-    def _render_technical_issues(self, technical_data: Dict[str, Any]):
-        """Render technical issues analysis."""
-        
-        st.markdown("### 🔍 Technical SEO Issues")
-        
-        if not technical_data:
-            st.info("No technical issues data available")
-            return
-        
-        # HTTP Errors
-        if technical_data.get('http_errors'):
-            http_errors = technical_data['http_errors']
-            
-            st.markdown("#### ❌ HTTP Status Code Errors")
-            
-            if http_errors.get('total_errors', 0) > 0:
-                st.error(f"Found {http_errors['total_errors']} pages with HTTP errors!")
-                
-                # Error breakdown chart
-                if http_errors.get('error_breakdown'):
-                    error_df = pd.DataFrame(
-                        list(http_errors['error_breakdown'].items()),
-                        columns=['Status Code', 'Count']
-                    )
-                    
-                    fig = px.bar(error_df, x='Status Code', y='Count', 
-                               title="HTTP Error Distribution")
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                # Error pages table
-                if http_errors.get('error_pages'):
-                    st.markdown("**Pages with Errors:**")
-                    error_pages_df = pd.DataFrame(http_errors['error_pages'])
-                    st.dataframe(error_pages_df, use_container_width=True)
-            else:
-                st.success("✅ No HTTP errors found!")
-        
-        # Redirect Issues
-        if technical_data.get('redirect_issues'):
-            redirect_data = technical_data['redirect_issues']
-            
-            st.markdown("#### 🔄 Redirect Analysis")
-            
-            total_redirects = redirect_data.get('total_redirects', 0)
-            
-            if total_redirects > 0:
-                st.warning(f"Found {total_redirects} redirect(s)")
-                
-                # Redirect types
-                if redirect_data.get('redirect_types'):
-                    redirect_df = pd.DataFrame(
-                        list(redirect_data['redirect_types'].items()),
-                        columns=['Redirect Type', 'Count']
-                    )
-                    st.bar_chart(redirect_df.set_index('Redirect Type'))
-            else:
-                st.success("✅ No redirects found")
-        
-        # Duplicate Content
-        if technical_data.get('duplicate_content'):
-            duplicate_data = technical_data['duplicate_content']
-            
-            st.markdown("#### 📋 Duplicate Content Issues")
-            
-            duplicate_titles = duplicate_data.get('duplicate_titles', 0)
-            
-            if duplicate_titles > 0:
-                st.warning(f"Found {duplicate_titles} duplicate title(s)")
-                
-                # Show duplicate title groups
-                if duplicate_data.get('pages_with_duplicate_titles'):
-                    duplicate_df = pd.DataFrame(duplicate_data['pages_with_duplicate_titles'])
-                    st.dataframe(duplicate_df, use_container_width=True)
-            else:
-                st.success("✅ No duplicate titles found")
-        
-        # Missing Elements
-        if technical_data.get('missing_elements'):
-            missing_data = technical_data['missing_elements']
-            
-            st.markdown("#### 📝 Missing SEO Elements")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                missing_titles = missing_data.get('missing_titles', 0)
-                if missing_titles > 0:
-                    st.error(f"Missing Titles: {missing_titles}")
-                else:
-                    st.success("All pages have titles ✅")
-            
-            with col2:
-                missing_meta = missing_data.get('missing_meta_desc', 0)
-                if missing_meta > 0:
-                    st.error(f"Missing Meta Descriptions: {missing_meta}")
-                else:
-                    st.success("All pages have meta descriptions ✅")
-            
-            with col3:
-                missing_h1 = missing_data.get('missing_h1', 0)
-                if missing_h1 > 0:
-                    st.error(f"Missing H1 tags: {missing_h1}")
-                else:
-                    st.success("All pages have H1 tags ✅")
-    
-    def _render_performance_analysis(self, performance_data: Dict[str, Any]):
-        """Render performance analysis."""
-        
-        st.markdown("### ⚡ Website Performance Analysis")
-        
-        if not performance_data:
-            st.info("No performance data available")
-            return
-        
-        # Load Time Analysis
-        if performance_data.get('load_time_analysis'):
-            load_time_data = performance_data['load_time_analysis']
-            
-            st.markdown("#### 🚀 Page Load Time Analysis")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                avg_load = load_time_data.get('avg_load_time', 0)
-                st.metric("Average Load Time", f"{avg_load:.2f}s")
-            
-            with col2:
-                median_load = load_time_data.get('median_load_time', 0)
-                st.metric("Median Load Time", f"{median_load:.2f}s")
-            
-            with col3:
-                p95_load = load_time_data.get('p95_load_time', 0)
-                st.metric("95th Percentile", f"{p95_load:.2f}s")
-            
-            # Performance distribution
-            if load_time_data.get('performance_distribution'):
-                perf_dist = load_time_data['performance_distribution']
-                
-                # Create pie chart for performance distribution
-                labels = ['Fast (≤1s)', 'Moderate (1-3s)', 'Slow (>3s)']
-                values = [
-                    perf_dist.get('fast_pages', 0),
-                    perf_dist.get('moderate_pages', 0),
-                    perf_dist.get('slow_pages', 0)
-                ]
-                
-                fig = px.pie(values=values, names=labels, 
-                           title="Page Load Time Distribution")
-                st.plotly_chart(fig, use_container_width=True)
-        
-        # Content Size Analysis
-        if performance_data.get('content_size_analysis'):
-            size_data = performance_data['content_size_analysis']
-            
-            st.markdown("#### 📦 Content Size Analysis")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                avg_size = size_data.get('avg_page_size', 0)
-                st.metric("Average Page Size", f"{avg_size/1024:.1f} KB")
-            
-            with col2:
-                largest_size = size_data.get('largest_page', 0)
-                st.metric("Largest Page", f"{largest_size/1024:.1f} KB")
-            
-            with col3:
-                large_pages = size_data.get('pages_over_1mb', 0)
-                st.metric("Pages >1MB", large_pages)
-        
-        # Server Performance
-        if performance_data.get('server_performance'):
-            server_data = performance_data['server_performance']
-            
-            st.markdown("#### 🖥️ Server Performance")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                success_rate = server_data.get('success_rate', 0)
-                st.metric("Success Rate", f"{success_rate:.1f}%")
-            
-            with col2:
-                error_rate = server_data.get('error_rate', 0)
-                st.metric("Error Rate", f"{error_rate:.1f}%")
-            
-            with col3:
-                redirect_rate = server_data.get('redirect_rate', 0)
-                st.metric("Redirect Rate", f"{redirect_rate:.1f}%")
-    
-    def _render_content_analysis(self, content_data: Dict[str, Any]):
-        """Render content structure analysis."""
-        
-        st.markdown("### 📊 Content Structure Analysis")
-        
-        if not content_data:
-            st.info("No content analysis data available")
-            return
-        
-        # Title Analysis
-        if content_data.get('title_analysis'):
-            title_data = content_data['title_analysis']
-            
-            st.markdown("#### 📝 Title Tag Analysis")
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                avg_title_length = title_data.get('avg_title_length', 0)
-                st.metric("Average Title Length", f"{avg_title_length:.0f} chars")
-                
-                duplicate_titles = title_data.get('duplicate_titles', 0)
-                st.metric("Duplicate Titles", duplicate_titles)
-            
-            with col2:
-                # Title length distribution
-                if title_data.get('title_length_distribution'):
-                    length_dist = title_data['title_length_distribution']
-                    
-                    labels = ['Too Short (<30)', 'Optimal (30-60)', 'Too Long (>60)']
-                    values = [
-                        length_dist.get('too_short', 0),
-                        length_dist.get('optimal', 0),
-                        length_dist.get('too_long', 0)
-                    ]
-                    
-                    fig = px.pie(values=values, names=labels, 
-                               title="Title Length Distribution")
-                    st.plotly_chart(fig, use_container_width=True)
-        
-        # Meta Description Analysis
-        if content_data.get('meta_description_analysis'):
-            meta_data = content_data['meta_description_analysis']
-            
-            st.markdown("#### 🏷️ Meta Description Analysis")
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                avg_meta_length = meta_data.get('avg_meta_length', 0)
-                st.metric("Average Meta Length", f"{avg_meta_length:.0f} chars")
-                
-                missing_meta = meta_data.get('missing_meta_descriptions', 0)
-                st.metric("Missing Meta Descriptions", missing_meta)
-            
-            with col2:
-                # Meta length distribution
-                if meta_data.get('meta_length_distribution'):
-                    meta_dist = meta_data['meta_length_distribution']
-                    
-                    labels = ['Too Short (<120)', 'Optimal (120-160)', 'Too Long (>160)']
-                    values = [
-                        meta_dist.get('too_short', 0),
-                        meta_dist.get('optimal', 0),
-                        meta_dist.get('too_long', 0)
-                    ]
-                    
-                    fig = px.pie(values=values, names=labels, 
-                               title="Meta Description Length Distribution")
-                    st.plotly_chart(fig, use_container_width=True)
-        
-        # Heading Structure
-        if content_data.get('heading_structure'):
-            heading_data = content_data['heading_structure']
-            
-            st.markdown("#### 📋 Heading Structure Analysis")
-            
-            # Create heading usage chart
-            heading_usage = []
-            for heading_type, data in heading_data.items():
-                heading_usage.append({
-                    'Heading': heading_type.replace('_usage', '').upper(),
-                    'Usage Rate': data.get('usage_rate', 0),
-                    'Pages': data.get('pages_with_heading', 0)
-                })
-            
-            if heading_usage:
-                heading_df = pd.DataFrame(heading_usage)
-                
-                fig = px.bar(heading_df, x='Heading', y='Usage Rate',
-                           title="Heading Tag Usage Rates")
-                st.plotly_chart(fig, use_container_width=True)
-                
-                st.dataframe(heading_df, use_container_width=True)
-    
-    def _render_url_structure(self, url_data: Dict[str, Any]):
-        """Render URL structure analysis."""
-        
-        st.markdown("### 🔗 URL Structure Analysis")
-        
-        if not url_data:
-            st.info("No URL structure data available")
-            return
-        
-        # URL Length Analysis
-        if url_data.get('url_length_analysis'):
-            length_data = url_data['url_length_analysis']
-            
-            st.markdown("#### 📏 URL Length Analysis")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                avg_length = length_data.get('avg_url_length', 0)
-                st.metric("Average URL Length", f"{avg_length:.0f} chars")
-            
-            with col2:
-                max_length = length_data.get('max_url_length', 0)
-                st.metric("Longest URL", f"{max_length:.0f} chars")
-            
-            with col3:
-                long_urls = length_data.get('long_urls_count', 0)
-                st.metric("URLs >100 chars", long_urls)
-        
-        # URL Structure Patterns
-        if url_data.get('url_structure_patterns'):
-            pattern_data = url_data['url_structure_patterns']
-            
-            st.markdown("#### 🏗️ URL Structure Patterns")
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                https_usage = pattern_data.get('https_usage', 0)
-                st.metric("HTTPS Usage", f"{https_usage:.1f}%")
-            
-            with col2:
-                subdomain_usage = pattern_data.get('subdomain_usage', 0)
-                st.metric("Subdomains Found", subdomain_usage)
-        
-        # Path Analysis
-        if url_data.get('path_analysis'):
-            path_data = url_data['path_analysis']
-            
-            st.markdown("#### 📂 Path Depth Analysis")
-            
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                avg_depth = path_data.get('avg_path_depth', 0)
-                st.metric("Average Path Depth", f"{avg_depth:.1f}")
-            
-            with col2:
-                max_depth = path_data.get('max_path_depth', 0)
-                st.metric("Maximum Depth", max_depth)
-            
-            with col3:
-                deep_paths = path_data.get('deep_paths_count', 0)
-                st.metric("Deep Paths (>4)", deep_paths)
-        
-        # Optimization Issues
-        if url_data.get('url_optimization'):
-            opt_data = url_data['url_optimization']
-            
-            st.markdown("#### ⚠️ URL Optimization Issues")
-            
-            issues_found = opt_data.get('issues_found', 0)
-            recommendations = opt_data.get('optimization_recommendations', [])
-            
-            if issues_found > 0:
-                st.warning(f"Found {issues_found} URL optimization issue(s)")
-                
-                for rec in recommendations:
-                    st.write(f"• {rec}")
-            else:
-                st.success("✅ No URL optimization issues found")
-    
-    def _render_image_analysis(self, image_data: Dict[str, Any]):
-        """Render image SEO analysis."""
-        
-        st.markdown("### 🖼️ Image SEO Analysis")
-        
-        if not image_data:
-            st.info("No image analysis data available")
-            return
-        
-        # Image overview
-        image_count = image_data.get('image_count', 0)
-        st.metric("Total Images Found", image_count)
-        
-        if image_count > 0:
-            # Alt text analysis
-            if image_data.get('alt_text_analysis'):
-                alt_data = image_data['alt_text_analysis']
-                
-                st.markdown("#### 📝 Alt Text Analysis")
-                
-                col1, col2, col3 = st.columns(3)
-                
-                with col1:
-                    images_with_alt = alt_data.get('images_with_alt', 0)
-                    st.metric("Images with Alt Text", images_with_alt)
-                
-                with col2:
-                    images_missing_alt = alt_data.get('images_missing_alt', 0)
-                    st.metric("Missing Alt Text", images_missing_alt)
-                
-                with col3:
-                    alt_coverage = alt_data.get('alt_text_coverage', 0)
-                    st.metric("Alt Text Coverage", f"{alt_coverage:.1f}%")
-            
-            # Image format analysis
-            if image_data.get('image_format_analysis'):
-                format_data = image_data['image_format_analysis']
-                
-                st.markdown("#### 🎨 Image Format Analysis")
-                
-                if format_data.get('format_distribution'):
-                    format_dist = format_data['format_distribution']
-                    
-                    format_df = pd.DataFrame(
-                        list(format_dist.items()),
-                        columns=['Format', 'Count']
-                    )
-                    
-                    fig = px.pie(format_df, values='Count', names='Format',
-                               title="Image Format Distribution")
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                modern_formats = format_data.get('modern_format_usage', 0)
-                st.metric("Modern Formats (WebP/AVIF)", modern_formats)
-        else:
-            st.info("No images found to analyze")
-    
-    def _render_security_analysis(self, security_data: Dict[str, Any]):
-        """Render security analysis."""
-        
-        st.markdown("### 🛡️ Security Headers Analysis")
-        
-        if not security_data:
-            st.info("No security analysis data available")
-            return
-        
-        # Security score
-        security_score = security_data.get('security_score', 0)
-        
-        col1, col2 = st.columns([1, 2])
-        
-        with col1:
-            st.metric("Security Score", f"{security_score:.0f}%")
-            
-            if security_score >= 80:
-                st.success("🔒 Good security posture")
-            elif security_score >= 50:
-                st.warning("⚠️ Moderate security")
-            else:
-                st.error("🚨 Poor security posture")
-        
-        with col2:
-            # Security headers status
-            if security_data.get('security_headers_present'):
-                headers_status = security_data['security_headers_present']
-                
-                st.markdown("**Security Headers Status:**")
-                
-                for header, present in headers_status.items():
-                    status = "✅" if present else "❌"
-                    st.write(f"{status} {header}")
-        
-        # Security recommendations
-        if security_data.get('security_recommendations'):
-            recommendations = security_data['security_recommendations']
-            
-            if recommendations:
-                st.markdown("#### 🔧 Security Recommendations")
-                
-                for rec in recommendations:
-                    st.write(f"• {rec}")
-            else:
-                st.success("✅ All security headers properly configured")
-    
-    def _render_ai_recommendations(self, ai_data: Dict[str, Any]):
-        """Render AI-generated recommendations."""
-        
-        st.markdown("### 🤖 AI-Powered Technical Recommendations")
-        
-        if not ai_data:
-            st.info("No AI recommendations available")
-            return
-        
-        # Critical Issues
-        if ai_data.get('critical_issues'):
-            st.markdown("#### 🚨 Critical Issues (Fix Immediately)")
-            
-            critical_issues = ai_data['critical_issues']
-            for issue in critical_issues:
-                st.error(f"🚨 {issue}")
-        
-        # High Priority
-        if ai_data.get('high_priority'):
-            st.markdown("#### 🔥 High Priority Optimizations")
-            
-            high_priority = ai_data['high_priority']
-            for item in high_priority:
-                st.warning(f"⚡ {item}")
-        
-        # Medium Priority
-        if ai_data.get('medium_priority'):
-            st.markdown("#### 📈 Medium Priority Improvements")
-            
-            medium_priority = ai_data['medium_priority']
-            for item in medium_priority:
-                st.info(f"📊 {item}")
-        
-        # Implementation Steps
-        if ai_data.get('implementation_steps'):
-            st.markdown("#### 🛠️ Implementation Steps")
-            
-            steps = ai_data['implementation_steps']
-            for i, step in enumerate(steps, 1):
-                st.write(f"{i}. {step}")
-        
-        # Expected Impact
-        if ai_data.get('expected_impact'):
-            st.markdown("#### 📈 Expected Impact Assessment")
-            
-            impact = ai_data['expected_impact']
-            st.markdown(impact)
-    
-    def _render_export_options(self, results: Dict[str, Any]):
-        """Render export options for analysis results."""
-        
-        st.markdown("---")
-        st.markdown("### 📥 Export Technical SEO Audit")
-        
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            # JSON export
-            if st.button("📄 Export Full Report (JSON)", use_container_width=True):
-                json_data = json.dumps(results, indent=2, default=str)
-                
-                st.download_button(
-                    label="⬇️ Download JSON Report",
-                    data=json_data,
-                    file_name=f"technical_seo_audit_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                    mime="application/json",
-                    use_container_width=True
-                )
-        
-        with col2:
-            # CSV export for issues
-            if st.button("📊 Export Issues CSV", use_container_width=True):
-                issues_data = self._prepare_issues_csv(results)
-                
-                if issues_data:
-                    st.download_button(
-                        label="⬇️ Download Issues CSV",
-                        data=issues_data,
-                        file_name=f"technical_issues_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                        mime="text/csv",
-                        use_container_width=True
-                    )
-                else:
-                    st.info("No issues found to export")
-        
-        with col3:
-            # Executive summary
-            if st.button("📋 Executive Summary", use_container_width=True):
-                summary = self._generate_executive_summary(results)
-                
-                st.download_button(
-                    label="⬇️ Download Summary",
-                    data=summary,
-                    file_name=f"technical_seo_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
-                    mime="text/plain",
-                    use_container_width=True
-                )
-    
-    def _prepare_issues_csv(self, results: Dict[str, Any]) -> str:
-        """Prepare CSV data for technical issues."""
-        
-        issues_list = []
-        
-        # HTTP errors
-        http_errors = results.get('technical_issues', {}).get('http_errors', {})
-        if http_errors.get('error_pages'):
-            for error in http_errors['error_pages']:
-                issues_list.append({
-                    'Issue Type': 'HTTP Error',
-                    'Severity': 'High',
-                    'URL': error.get('url', ''),
-                    'Status Code': error.get('status', ''),
-                    'Description': f"HTTP {error.get('status', '')} error"
-                })
-        
-        # Missing elements
-        missing_elements = results.get('technical_issues', {}).get('missing_elements', {})
-        
-        # Add more issue types as needed...
-        
-        if issues_list:
-            issues_df = pd.DataFrame(issues_list)
-            return issues_df.to_csv(index=False)
-        
-        return ""
-    
-    def _generate_executive_summary(self, results: Dict[str, Any]) -> str:
-        """Generate executive summary report."""
-        
-        website_url = results.get('website_url', 'Unknown')
-        timestamp = results.get('analysis_timestamp', datetime.now().isoformat())
-        
-        summary = f"""
-TECHNICAL SEO AUDIT - EXECUTIVE SUMMARY
-======================================
-
-Website: {website_url}
-Audit Date: {timestamp}
-
-AUDIT OVERVIEW
--------------
-Pages Crawled: {results.get('crawl_overview', {}).get('pages_crawled', 0)}
-HTTP Errors: {results.get('technical_issues', {}).get('http_errors', {}).get('total_errors', 0)}
-Average Load Time: {results.get('performance_analysis', {}).get('load_time_analysis', {}).get('avg_load_time', 0):.2f}s
-Security Score: {results.get('security_headers', {}).get('security_score', 0):.0f}%
-
-CRITICAL FINDINGS
-----------------
-"""
-        
-        # Add critical findings
-        error_count = results.get('technical_issues', {}).get('http_errors', {}).get('total_errors', 0)
-        if error_count > 0:
-            summary += f"• {error_count} pages have HTTP errors requiring immediate attention\n"
-        
-        avg_load_time = results.get('performance_analysis', {}).get('load_time_analysis', {}).get('avg_load_time', 0)
-        if avg_load_time > 3:
-            summary += f"• Page load times are slow (avg: {avg_load_time:.2f}s), impacting user experience\n"
-        
-        security_score = results.get('security_headers', {}).get('security_score', 0)
-        if security_score < 80:
-            summary += f"• Security headers need improvement (current score: {security_score:.0f}%)\n"
-        
-        summary += f"\n\nDetailed technical audit completed by ALwrity Technical SEO Crawler\nGenerated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
-        
-        return summary
-
-# Render function for integration with main dashboard
-def render_technical_seo_crawler():
-    """Render the Technical SEO Crawler UI."""
-    ui = TechnicalSEOCrawlerUI()
-    ui.render() 
--- a/ToBeMigrated/ai_seo_tools/textstaty.py
+++ b/ToBeMigrated/ai_seo_tools/textstaty.py
@@ -1,58 +0,0 @@
-"""Text analysis tools using textstat."""
-
-import streamlit as st
-from textstat import textstat
-
-def analyze_text(text):
-    """Analyze text using textstat metrics."""
-    if not text:
-        st.warning("Please enter some text to analyze.")
-        return
-    
-    # Calculate various metrics
-    metrics = {
-        "Flesch Reading Ease": textstat.flesch_reading_ease(text),
-        "Flesch-Kincaid Grade Level": textstat.flesch_kincaid_grade(text),
-        "Gunning Fog Index": textstat.gunning_fog(text),
-        "SMOG Index": textstat.smog_index(text),
-        "Automated Readability Index": textstat.automated_readability_index(text),
-        "Coleman-Liau Index": textstat.coleman_liau_index(text),
-        "Linsear Write Formula": textstat.linsear_write_formula(text),
-        "Dale-Chall Readability Score": textstat.dale_chall_readability_score(text),
-        "Readability Consensus": textstat.readability_consensus(text)
-    }
-    
-    # Display metrics in a clean format
-    st.subheader("Text Analysis Results")
-    for metric, value in metrics.items():
-        st.metric(metric, f"{value:.2f}")
-    
-    # Add visualizations
-    st.subheader("Visualization")
-    st.bar_chart(metrics)
-
-st.title("📖  Text Readability Analyzer:  Making Your Content Easy to Read")
-
-st.write("""
-    This tool is your guide to writing content that's easy for your audience to understand.
-    Just paste in a sample of your text, and we'll break down the readability scores and offer actionable tips! 
-""")
-
-text_input = st.text_area("Paste your text here:", height=200)
-
-if st.button("Analyze!"):
-    with st.spinner("Analyzing your text..."):
-        test_data = text_input
-        if not test_data.strip():
-            st.error("Please enter text to analyze.")
-        else:
-            analyze_text(test_data)
-
-            st.subheader("Key Takeaways:")
-            st.write("---")
-            st.markdown("""
-                * **Don't Be Afraid to Simplify!** Often, simpler language makes content more impactful and easier to digest.
-                * **Aim for a Reading Level Appropriate for Your Audience:** Consider the education level, background, and familiarity of your readers.
-                * **Use Short Sentences:** This makes your content more scannable and easier to read.
-                * **Write for Everyone:** Accessibility should always be a priority. When in doubt, aim for clear, concise language!
-            """)
--- a/ToBeMigrated/ai_web_researcher/TBD
+++ b/ToBeMigrated/ai_web_researcher/TBD
@@ -1,2 +0,0 @@
-1). Replace Firecrawl with scrapy or crawlee : https://crawlee.dev/python/docs/introduction
-
--- a/ToBeMigrated/ai_web_researcher/arxiv_schlorly_research.py
+++ b/ToBeMigrated/ai_web_researcher/arxiv_schlorly_research.py
@@ -1,980 +0,0 @@
-####################################################
-#
-# FIXME: Gotta use this lib: https://github.com/monk1337/resp/tree/main
-# https://github.com/danielnsilva/semanticscholar
-# https://github.com/shauryr/S2QA
-#
-####################################################
-
-
-import os
-import sys
-import re
-import pandas as pd
-import arxiv
-import PyPDF2
-import requests
-import networkx as nx
-from bs4 import BeautifulSoup
-from urllib.parse import urlparse
-from loguru import logger
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-import bibtexparser
-from pylatexenc.latex2text import LatexNodes2Text
-from matplotlib import pyplot as plt
-from collections import defaultdict
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.cluster import KMeans
-import numpy as np
-
-logger.remove()
-logger.add(sys.stdout, colorize=True, format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
-
-def create_arxiv_client(page_size=100, delay_seconds=3.0, num_retries=3):
-    """
-    Creates a reusable arXiv API client with custom configuration.
-
-    Args:
-        page_size (int): Number of results per page (default: 100)
-        delay_seconds (float): Delay between API requests (default: 3.0)
-        num_retries (int): Number of retries for failed requests (default: 3)
-
-    Returns:
-        arxiv.Client: Configured arXiv API client
-    """
-    try:
-        client = arxiv.Client(
-            page_size=page_size,
-            delay_seconds=delay_seconds,
-            num_retries=num_retries
-        )
-        return client
-    except Exception as e:
-        logger.error(f"Error creating arXiv client: {e}")
-        raise e
-
-def expand_search_query(query, research_interests=None):
-    """
-    Uses AI to expand the search query based on user's research interests.
-
-    Args:
-        query (str): Original search query
-        research_interests (list): List of user's research interests
-
-    Returns:
-        str: Expanded search query
-    """
-    try:
-        interests_context = "\n".join(research_interests) if research_interests else ""
-        prompt = f"""Given the original arXiv search query: '{query}'
-        {f'And considering these research interests:\n{interests_context}' if interests_context else ''}
-        Generate an expanded arXiv search query that:
-        1. Includes relevant synonyms and related concepts
-        2. Uses appropriate arXiv search operators (AND, OR, etc.)
-        3. Incorporates field-specific tags (ti:, abs:, au:, etc.)
-        4. Maintains focus on the core topic
-        Return only the expanded query without any explanation."""
-        
-        expanded_query = llm_text_gen(prompt)
-        logger.info(f"Expanded query: {expanded_query}")
-        return expanded_query
-    except Exception as e:
-        logger.error(f"Error expanding search query: {e}")
-        return query
-
-def analyze_citation_network(papers):
-    """
-    Analyzes citation relationships between papers using DOIs and references.
-
-    Args:
-        papers (list): List of paper metadata dictionaries
-
-    Returns:
-        dict: Citation network analysis results
-    """
-    try:
-        # Create a directed graph for citations
-        G = nx.DiGraph()
-        
-        # Add nodes and edges
-        for paper in papers:
-            paper_id = paper['entry_id']
-            G.add_node(paper_id, title=paper['title'])
-            
-            # Add edges based on DOIs and references
-            if paper['doi']:
-                for other_paper in papers:
-                    if other_paper['doi'] and other_paper['doi'] in paper['summary']:
-                        G.add_edge(paper_id, other_paper['entry_id'])
-        
-        # Calculate network metrics
-        analysis = {
-            'influential_papers': sorted(nx.pagerank(G).items(), key=lambda x: x[1], reverse=True),
-            'citation_clusters': list(nx.connected_components(G.to_undirected())),
-            'citation_paths': dict(nx.all_pairs_shortest_path_length(G))
-        }
-        return analysis
-    except Exception as e:
-        logger.error(f"Error analyzing citation network: {e}")
-        return {}
-
-def categorize_papers(papers):
-    """
-    Uses AI to categorize papers based on their metadata and content.
-
-    Args:
-        papers (list): List of paper metadata dictionaries
-
-    Returns:
-        dict: Paper categorization results
-    """
-    try:
-        categorized_papers = {}
-        for paper in papers:
-            prompt = f"""Analyze this research paper and provide detailed categorization:
-            Title: {paper['title']}
-            Abstract: {paper['summary']}
-            Primary Category: {paper['primary_category']}
-            Categories: {', '.join(paper['categories'])}
-            
-            Provide a JSON response with these fields:
-            1. main_theme: Primary research theme
-            2. sub_themes: List of related sub-themes
-            3. methodology: Research methodology used
-            4. application_domains: Potential application areas
-            5. technical_complexity: Level (Basic/Intermediate/Advanced)"""
-            
-            categorization = llm_text_gen(prompt)
-            categorized_papers[paper['entry_id']] = categorization
-        
-        return categorized_papers
-    except Exception as e:
-        logger.error(f"Error categorizing papers: {e}")
-        return {}
-
-def get_paper_recommendations(papers, research_interests):
-    """
-    Generates personalized paper recommendations based on user's research interests.
-
-    Args:
-        papers (list): List of paper metadata dictionaries
-        research_interests (list): User's research interests
-
-    Returns:
-        dict: Personalized paper recommendations
-    """
-    try:
-        interests_text = "\n".join(research_interests)
-        recommendations = {}
-        
-        for paper in papers:
-            prompt = f"""Evaluate this paper's relevance to the user's research interests:
-            Paper:
-            - Title: {paper['title']}
-            - Abstract: {paper['summary']}
-            - Categories: {', '.join(paper['categories'])}
-            
-            User's Research Interests:
-            {interests_text}
-            
-            Provide a JSON response with:
-            1. relevance_score: 0-100
-            2. relevance_aspects: List of matching aspects
-            3. potential_value: How this paper could benefit the user's research"""
-            
-            evaluation = llm_text_gen(prompt)
-            recommendations[paper['entry_id']] = evaluation
-        
-        return recommendations
-    except Exception as e:
-        logger.error(f"Error generating paper recommendations: {e}")
-        return {}
-
-def fetch_arxiv_data(query, max_results=10, sort_by=arxiv.SortCriterion.SubmittedDate, sort_order=None, client=None, research_interests=None):
-    """
-    Fetches arXiv data based on a query with advanced search options.
-
-    Args:
-        query (str): The search query (supports advanced syntax, e.g., 'au:einstein AND cat:physics')
-        max_results (int): The maximum number of results to fetch
-        sort_by (arxiv.SortCriterion): Sorting criterion (default: SubmittedDate)
-        sort_order (str): Sort order ('ascending' or 'descending', default: None)
-        client (arxiv.Client): Optional custom client (default: None, creates new client)
-
-    Returns:
-        list: A list of arXiv data with extended metadata
-    """
-    try:
-        if client is None:
-            client = create_arxiv_client()
-
-        # Expand search query using AI if research interests are provided
-        expanded_query = expand_search_query(query, research_interests) if research_interests else query
-        logger.info(f"Using expanded query: {expanded_query}")
-
-        search = arxiv.Search(
-            query=expanded_query,
-            max_results=max_results,
-            sort_by=sort_by,
-            sort_order=sort_order
-        )
-
-        results = list(client.results(search))
-        all_data = [
-            {
-                'title': result.title,
-                'published': result.published,
-                'updated': result.updated,
-                'entry_id': result.entry_id,
-                'summary': result.summary,
-                'authors': [str(author) for author in result.authors],
-                'pdf_url': result.pdf_url,
-                'journal_ref': getattr(result, 'journal_ref', None),
-                'doi': getattr(result, 'doi', None),
-                'primary_category': getattr(result, 'primary_category', None),
-                'categories': getattr(result, 'categories', []),
-                'links': [link.href for link in getattr(result, 'links', [])]
-            }
-            for result in results
-        ]
-
-        # Enhance results with AI-powered analysis
-        if all_data:
-            # Analyze citation network
-            citation_analysis = analyze_citation_network(all_data)
-            
-            # Categorize papers using AI
-            paper_categories = categorize_papers(all_data)
-            
-            # Generate recommendations if research interests are provided
-            recommendations = get_paper_recommendations(all_data, research_interests) if research_interests else {}
-            
-            # Perform content analysis
-            content_analyses = [analyze_paper_content(paper['entry_id']) for paper in all_data]
-            trend_analysis = analyze_research_trends(all_data)
-            concept_mapping = map_cross_paper_concepts(all_data)
-            
-            # Generate bibliography data
-            bibliography_data = {
-                'bibtex_entries': [generate_bibtex_entry(paper) for paper in all_data],
-                'citations': {
-                    'apa': [convert_citation_format(generate_bibtex_entry(paper), 'apa') for paper in all_data],
-                    'mla': [convert_citation_format(generate_bibtex_entry(paper), 'mla') for paper in all_data],
-                    'chicago': [convert_citation_format(generate_bibtex_entry(paper), 'chicago') for paper in all_data]
-                },
-                'reference_graph': visualize_reference_graph(all_data),
-                'citation_impact': analyze_citation_impact(all_data)
-            }
-            
-            # Add enhanced data to results
-            enhanced_data = {
-                'papers': all_data,
-                'citation_analysis': citation_analysis,
-                'paper_categories': paper_categories,
-                'recommendations': recommendations,
-                'content_analyses': content_analyses,
-                'trend_analysis': trend_analysis,
-                'concept_mapping': concept_mapping,
-                'bibliography': bibliography_data
-            }
-            return enhanced_data
-        
-        return {'papers': all_data}
-    except Exception as e:
-        logger.error(f"An error occurred while fetching data from arXiv: {e}")
-        raise e
-
-def create_dataframe(data, column_names):
-    """
-    Creates a DataFrame from the provided data.
-
-    Args:
-        data (list): The data to convert to a DataFrame.
-        column_names (list): The column names for the DataFrame.
-
-    Returns:
-        DataFrame: The created DataFrame.
-    """
-    try:
-        df = pd.DataFrame(data, columns=column_names)
-        return df
-    except Exception as e:
-        logger.error(f"An error occurred while creating DataFrame: {e}")
-        return pd.DataFrame()
-
-def get_arxiv_main_content(url):
-    """
-    Returns the main content of an arXiv paper.
-
-    Args:
-        url (str): The URL of the arXiv paper.
-
-    Returns:
-        str: The main content of the paper as a string.
-    """
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.content, "html.parser")
-        main_content = soup.find('div', class_='ltx_page_content')
-        if not main_content:
-            logger.warning("Main content not found in the page.")
-            return "Main content not found."
-        alert_section = main_content.find('div', class_='package-alerts ltx_document')
-        if (alert_section):
-            alert_section.decompose()
-        for element_id in ["abs", "authors"]:
-            element = main_content.find(id=element_id)
-            if (element):
-                element.decompose()
-        return main_content.text.strip()
-    except Exception as html_error:
-        logger.warning(f"HTML content not accessible, trying PDF: {html_error}")
-        return get_pdf_content(url)
-
-def download_paper(paper_id, output_dir="downloads", filename=None, get_source=False):
-    """
-    Downloads a paper's PDF or source files with enhanced error handling.
-
-    Args:
-        paper_id (str): The arXiv ID of the paper
-        output_dir (str): Directory to save the downloaded file (default: 'downloads')
-        filename (str): Custom filename (default: None, uses paper ID)
-        get_source (bool): If True, downloads source files instead of PDF (default: False)
-
-    Returns:
-        str: Path to the downloaded file or None if download fails
-    """
-    try:
-        # Create output directory if it doesn't exist
-        os.makedirs(output_dir, exist_ok=True)
-
-        # Get paper metadata
-        client = create_arxiv_client()
-        paper = next(client.results(arxiv.Search(id_list=[paper_id])))
-
-        # Set filename if not provided
-        if not filename:
-            safe_title = re.sub(r'[^\w\-_.]', '_', paper.title[:50])
-            filename = f"{paper_id}_{safe_title}"
-            filename += ".tar.gz" if get_source else ".pdf"
-
-        # Full path for the downloaded file
-        file_path = os.path.join(output_dir, filename)
-
-        # Download the file
-        if get_source:
-            paper.download_source(dirpath=output_dir, filename=filename)
-        else:
-            paper.download_pdf(dirpath=output_dir, filename=filename)
-
-        logger.info(f"Successfully downloaded {'source' if get_source else 'PDF'} to {file_path}")
-        return file_path
-
-    except Exception as e:
-        logger.error(f"Error downloading {'source' if get_source else 'PDF'} for {paper_id}: {e}")
-        return None
-
-def analyze_paper_content(url_or_id, cleanup=True):
-    """
-    Analyzes paper content using AI to extract key information and insights.
-
-    Args:
-        url_or_id (str): The arXiv URL or ID of the paper
-        cleanup (bool): Whether to delete the PDF after extraction (default: True)
-
-    Returns:
-        dict: Analysis results including summary, key findings, and concepts
-    """
-    try:
-        # Get paper content
-        content = get_pdf_content(url_or_id, cleanup)
-        if not content or 'Failed to' in content:
-            return {'error': content}
-
-        # Generate paper summary
-        summary_prompt = f"""Analyze this research paper and provide a comprehensive summary:
-        {content[:8000]}  # Limit content length for API
-        
-        Provide a JSON response with:
-        1. executive_summary: Brief overview (2-3 sentences)
-        2. key_findings: List of main research findings
-        3. methodology: Research methods used
-        4. implications: Practical implications of the research
-        5. limitations: Study limitations and constraints"""
-        
-        summary_analysis = llm_text_gen(summary_prompt)
-
-        # Extract key concepts and relationships
-        concepts_prompt = f"""Analyze this research paper and identify key concepts and relationships:
-        {content[:8000]}
-        
-        Provide a JSON response with:
-        1. main_concepts: List of key technical concepts
-        2. concept_relationships: How concepts are related
-        3. novel_contributions: New ideas or approaches introduced
-        4. technical_requirements: Required technologies or methods
-        5. future_directions: Suggested future research"""
-        
-        concept_analysis = llm_text_gen(concepts_prompt)
-
-        return {
-            'summary_analysis': summary_analysis,
-            'concept_analysis': concept_analysis,
-            'full_text': content
-        }
-    except Exception as e:
-        logger.error(f"Error analyzing paper content: {e}")
-        return {'error': str(e)}
-
-def analyze_research_trends(papers):
-    """
-    Analyzes research trends across multiple papers.
-
-    Args:
-        papers (list): List of paper metadata and content
-
-    Returns:
-        dict: Trend analysis results
-    """
-    try:
-        # Collect paper information
-        papers_info = []
-        for paper in papers:
-            content = get_pdf_content(paper['entry_id'], cleanup=True)
-            if content and 'Failed to' not in content:
-                papers_info.append({
-                    'title': paper['title'],
-                    'abstract': paper['summary'],
-                    'content': content[:8000],  # Limit content length
-                    'year': paper['published'].year
-                })
-
-        if not papers_info:
-            return {'error': 'No valid paper content found for analysis'}
-
-        # Analyze trends
-        trends_prompt = f"""Analyze these research papers and identify key trends:
-        Papers:
-        {str(papers_info)}
-        
-        Provide a JSON response with:
-        1. temporal_trends: How research focus evolved over time
-        2. emerging_themes: New and growing research areas
-        3. declining_themes: Decreasing research focus areas
-        4. methodology_trends: Evolution of research methods
-        5. technology_trends: Trends in technology usage
-        6. research_gaps: Identified gaps and opportunities"""
-
-        trend_analysis = llm_text_gen(trends_prompt)
-        return {'trend_analysis': trend_analysis}
-
-    except Exception as e:
-        logger.error(f"Error analyzing research trends: {e}")
-        return {'error': str(e)}
-
-def map_cross_paper_concepts(papers):
-    """
-    Maps concepts and relationships across multiple papers.
-
-    Args:
-        papers (list): List of paper metadata and content
-
-    Returns:
-        dict: Concept mapping results
-    """
-    try:
-        # Analyze each paper
-        paper_analyses = []
-        for paper in papers:
-            analysis = analyze_paper_content(paper['entry_id'])
-            if 'error' not in analysis:
-                paper_analyses.append({
-                    'paper_id': paper['entry_id'],
-                    'title': paper['title'],
-                    'analysis': analysis
-                })
-
-        if not paper_analyses:
-            return {'error': 'No valid paper analyses for concept mapping'}
-
-        # Generate cross-paper concept map
-        mapping_prompt = f"""Analyze relationships between concepts across these papers:
-        {str(paper_analyses)}
-        
-        Provide a JSON response with:
-        1. shared_concepts: Concepts appearing in multiple papers
-        2. concept_evolution: How concepts developed across papers
-        3. conflicting_views: Different interpretations of same concepts
-        4. complementary_findings: How papers complement each other
-        5. knowledge_gaps: Areas needing more research"""
-
-        concept_mapping = llm_text_gen(mapping_prompt)
-        return {'concept_mapping': concept_mapping}
-
-    except Exception as e:
-        logger.error(f"Error mapping cross-paper concepts: {e}")
-        return {'error': str(e)}
-
-def generate_bibtex_entry(paper):
-    """
-    Generates a BibTeX entry for a paper with complete metadata.
-
-    Args:
-        paper (dict): Paper metadata dictionary
-
-    Returns:
-        str: BibTeX entry string
-    """
-    try:
-        # Generate a unique citation key
-        first_author = paper['authors'][0].split()[-1] if paper['authors'] else 'Unknown'
-        year = paper['published'].year if paper['published'] else '0000'
-        citation_key = f"{first_author}{year}{paper['entry_id'].split('/')[-1]}"
-
-        # Format authors for BibTeX
-        authors = ' and '.join(paper['authors'])
-
-        # Create BibTeX entry
-        bibtex = f"@article{{{citation_key},\n"
-        bibtex += f"  title = {{{paper['title']}}},\n"
-        bibtex += f"  author = {{{authors}}},\n"
-        bibtex += f"  year = {{{year}}},\n"
-        bibtex += f"  journal = {{arXiv preprint}},\n"
-        bibtex += f"  archivePrefix = {{arXiv}},\n"
-        bibtex += f"  eprint = {{{paper['entry_id'].split('/')[-1]}}},\n"
-        if paper['doi']:
-            bibtex += f"  doi = {{{paper['doi']}}},\n"
-        bibtex += f"  url = {{{paper['entry_id']}}},\n"
-        bibtex += f"  abstract = {{{paper['summary']}}}\n"
-        bibtex += "}"
-
-        return bibtex
-    except Exception as e:
-        logger.error(f"Error generating BibTeX entry: {e}")
-        return ""
-
-def convert_citation_format(bibtex_str, target_format):
-    """
-    Converts BibTeX citations to other formats and validates the output.
-
-    Args:
-        bibtex_str (str): BibTeX entry string
-        target_format (str): Target citation format ('apa', 'mla', 'chicago', etc.)
-
-    Returns:
-        str: Formatted citation string
-    """
-    try:
-        # Parse BibTeX entry
-        bib_database = bibtexparser.loads(bibtex_str)
-        entry = bib_database.entries[0]
-
-        # Generate citation format prompt
-        prompt = f"""Convert this bibliographic information to {target_format} format:
-        Title: {entry.get('title', '')}
-        Authors: {entry.get('author', '')}
-        Year: {entry.get('year', '')}
-        Journal: {entry.get('journal', '')}
-        DOI: {entry.get('doi', '')}
-        URL: {entry.get('url', '')}
-        
-        Return only the formatted citation without any explanation."""
-
-        # Use AI to generate formatted citation
-        formatted_citation = llm_text_gen(prompt)
-        return formatted_citation.strip()
-    except Exception as e:
-        logger.error(f"Error converting citation format: {e}")
-        return ""
-
-def visualize_reference_graph(papers):
-    """
-    Creates a visual representation of the citation network.
-
-    Args:
-        papers (list): List of paper metadata dictionaries
-
-    Returns:
-        str: Path to the saved visualization file
-    """
-    try:
-        # Create directed graph
-        G = nx.DiGraph()
-        
-        # Add nodes and edges
-        for paper in papers:
-            paper_id = paper['entry_id']
-            G.add_node(paper_id, title=paper['title'])
-            
-            # Add citation edges
-            if paper['doi']:
-                for other_paper in papers:
-                    if other_paper['doi'] and other_paper['doi'] in paper['summary']:
-                        G.add_edge(paper_id, other_paper['entry_id'])
-        
-        # Set up the visualization
-        plt.figure(figsize=(12, 8))
-        pos = nx.spring_layout(G)
-        
-        # Draw the graph
-        nx.draw(G, pos, with_labels=False, node_color='lightblue', 
-                node_size=1000, arrowsize=20)
-        
-        # Add labels
-        labels = nx.get_node_attributes(G, 'title')
-        nx.draw_networkx_labels(G, pos, labels, font_size=8)
-        
-        # Save the visualization
-        output_path = 'reference_graph.png'
-        plt.savefig(output_path, dpi=300, bbox_inches='tight')
-        plt.close()
-        
-        return output_path
-    except Exception as e:
-        logger.error(f"Error visualizing reference graph: {e}")
-        return ""
-
-def analyze_citation_impact(papers):
-    """
-    Analyzes citation impact and influence patterns.
-
-    Args:
-        papers (list): List of paper metadata dictionaries
-
-    Returns:
-        dict: Citation impact analysis results
-    """
-    try:
-        # Create citation network
-        G = nx.DiGraph()
-        for paper in papers:
-            G.add_node(paper['entry_id'], **paper)
-            if paper['doi']:
-                for other_paper in papers:
-                    if other_paper['doi'] and other_paper['doi'] in paper['summary']:
-                        G.add_edge(paper_id, other_paper['entry_id'])
-
-        # Calculate impact metrics
-        impact_analysis = {
-            'citation_counts': dict(G.in_degree()),
-            'influence_scores': nx.pagerank(G),
-            'authority_scores': nx.authority_matrix(G).diagonal(),
-            'hub_scores': nx.hub_matrix(G).diagonal(),
-            'citation_paths': dict(nx.all_pairs_shortest_path_length(G))
-        }
-
-        # Add temporal analysis
-        year_citations = defaultdict(int)
-        for paper in papers:
-            if paper['published']:
-                year = paper['published'].year
-                year_citations[year] += G.in_degree(paper['entry_id'])
-        impact_analysis['temporal_trends'] = dict(year_citations)
-
-        return impact_analysis
-    except Exception as e:
-        logger.error(f"Error analyzing citation impact: {e}")
-        return {}
-
-def get_pdf_content(url_or_id, cleanup=True):
-    """
-    Extracts text content from a paper's PDF with improved error handling.
-
-    Args:
-        url_or_id (str): The arXiv URL or ID of the paper
-        cleanup (bool): Whether to delete the PDF after extraction (default: True)
-
-    Returns:
-        str: The extracted text content or error message
-    """
-    try:
-        # Extract arxiv ID from URL if needed
-        arxiv_id = url_or_id.split('/')[-1] if '/' in url_or_id else url_or_id
-        
-        # Download PDF
-        pdf_path = download_paper(arxiv_id)
-        if not pdf_path:
-            return "Failed to download PDF."
-
-        # Extract text from PDF
-        pdf_text = ''
-        with open(pdf_path, 'rb') as f:
-            pdf_reader = PyPDF2.PdfReader(f)
-            for page_num, page in enumerate(pdf_reader.pages, 1):
-                try:
-                    page_text = page.extract_text()
-                    if page_text:
-                        pdf_text += f"\n--- Page {page_num} ---\n{page_text}"
-                except Exception as err:
-                    logger.error(f"Error extracting text from page {page_num}: {err}")
-                    continue
-
-        # Clean up
-        if cleanup:
-            try:
-                os.remove(pdf_path)
-                logger.debug(f"Cleaned up temporary PDF file: {pdf_path}")
-            except Exception as e:
-                logger.warning(f"Failed to cleanup PDF file {pdf_path}: {e}")
-
-        # Process and return text
-        if not pdf_text.strip():
-            return "No text content could be extracted from the PDF."
-            
-        return clean_pdf_text(pdf_text)
-
-    except Exception as e:
-        logger.error(f"Failed to process PDF: {e}")
-        return f"Failed to retrieve content: {str(e)}"
-
-def clean_pdf_text(text):
-    """
-    Helper function to clean the text extracted from a PDF.
-
-    Args:
-        text (str): The text to clean.
-
-    Returns:
-        str: The cleaned text.
-    """
-    pattern = r'References\s*.*'
-    text = re.sub(pattern, '', text, flags=re.IGNORECASE | re.DOTALL)
-    sections_to_remove = ['Acknowledgements', 'References', 'Bibliography']
-    for section in sections_to_remove:
-        pattern = r'(' + re.escape(section) + r'\s*.*?)(?=\n[A-Z]{2,}|$)'
-        text = re.sub(pattern, '', text, flags=re.DOTALL | re.IGNORECASE)
-    return text
-
-def download_image(image_url, base_url, folder="images"):
-    """
-    Downloads an image from a URL.
-
-    Args:
-        image_url (str): The URL of the image.
-        base_url (str): The base URL of the website.
-        folder (str): The folder to save the image.
-
-    Returns:
-        bool: True if the image was downloaded successfully, False otherwise.
-    """
-    if image_url.startswith('data:image'):
-        logger.info(f"Skipping download of data URI image: {image_url}")
-        return False
-    if not os.path.exists(folder):
-        os.makedirs(folder)
-    if not urlparse(image_url).scheme:
-        if not base_url.endswith('/'):
-            base_url += '/'
-        image_url = base_url + image_url
-    try:
-        response = requests.get(image_url)
-        response.raise_for_status()
-        image_name = image_url.split("/")[-1]
-        with open(os.path.join(folder, image_name), 'wb') as file:
-            file.write(response.content)
-        return True
-    except requests.RequestException as e:
-        logger.error(f"Error downloading {image_url}: {e}")
-        return False
-
-def scrape_images_from_arxiv(url):
-    """
-    Scrapes images from an arXiv page.
-
-    Args:
-        url (str): The URL of the arXiv page.
-
-    Returns:
-        list: A list of image URLs.
-    """
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.text, 'html.parser')
-        images = soup.find_all('img')
-        image_urls = [img['src'] for img in images if 'src' in img.attrs]
-        return image_urls
-    except requests.RequestException as e:
-        logger.error(f"Error fetching page {url}: {e}")
-        return []
-
-def generate_bibtex(paper_id, client=None):
-    """
-    Generate a BibTeX entry for an arXiv paper with enhanced metadata.
-
-    Args:
-        paper_id (str): The arXiv ID of the paper
-        client (arxiv.Client): Optional custom client (default: None)
-
-    Returns:
-        str: BibTeX entry as a string
-    """
-    try:
-        if client is None:
-            client = create_arxiv_client()
-
-        # Fetch paper metadata
-        paper = next(client.results(arxiv.Search(id_list=[paper_id])))
-        
-        # Extract author information
-        authors = [str(author) for author in paper.authors]
-        first_author = authors[0].split(', ')[0] if authors else 'Unknown'
-        
-        # Format year
-        year = paper.published.year if paper.published else 'Unknown'
-        
-        # Create citation key
-        citation_key = f"{first_author}{str(year)[-2:]}"
-        
-        # Build BibTeX entry
-        bibtex = [
-            f"@article{{{citation_key},",
-            f"  author = {{{' and '.join(authors)}}},",
-            f"  title = {{{paper.title}}},",
-            f"  year = {{{year}}},",
-            f"  eprint = {{{paper_id}}},",
-            f"  archivePrefix = {{arXiv}},"
-        ]
-        
-        # Add optional fields if available
-        if paper.doi:
-            bibtex.append(f"  doi = {{{paper.doi}}},")
-        if getattr(paper, 'journal_ref', None):
-            bibtex.append(f"  journal = {{{paper.journal_ref}}},")
-        if getattr(paper, 'primary_category', None):
-            bibtex.append(f"  primaryClass = {{{paper.primary_category}}},")
-            
-        # Add URL and close entry
-        bibtex.extend([
-            f"  url = {{https://arxiv.org/abs/{paper_id}}}",
-            "}"
-        ])
-        
-        return '\n'.join(bibtex)
-        
-    except Exception as e:
-        logger.error(f"Error generating BibTeX for {paper_id}: {e}")
-        return ""
-
-def batch_download_papers(paper_ids, output_dir="downloads", get_source=False):
-    """
-    Download multiple papers in batch with progress tracking.
-
-    Args:
-        paper_ids (list): List of arXiv IDs to download
-        output_dir (str): Directory to save downloaded files (default: 'downloads')
-        get_source (bool): If True, downloads source files instead of PDFs (default: False)
-
-    Returns:
-        dict: Mapping of paper IDs to their download status and paths
-    """
-    results = {}
-    client = create_arxiv_client()
-
-    for paper_id in paper_ids:
-        try:
-            file_path = download_paper(paper_id, output_dir, get_source=get_source)
-            results[paper_id] = {
-                'success': bool(file_path),
-                'path': file_path,
-                'error': None
-            }
-        except Exception as e:
-            results[paper_id] = {
-                'success': False,
-                'path': None,
-                'error': str(e)
-            }
-            logger.error(f"Failed to download {paper_id}: {e}")
-
-    return results
-
-def batch_generate_bibtex(paper_ids):
-    """
-    Generate BibTeX entries for multiple papers.
-
-    Args:
-        paper_ids (list): List of arXiv IDs
-
-    Returns:
-        dict: Mapping of paper IDs to their BibTeX entries
-    """
-    results = {}
-    client = create_arxiv_client()
-
-    for paper_id in paper_ids:
-        try:
-            bibtex = generate_bibtex(paper_id, client)
-            results[paper_id] = {
-                'success': bool(bibtex),
-                'bibtex': bibtex,
-                'error': None
-            }
-        except Exception as e:
-            results[paper_id] = {
-                'success': False,
-                'bibtex': '',
-                'error': str(e)
-            }
-            logger.error(f"Failed to generate BibTeX for {paper_id}: {e}")
-
-    return results
-
-def extract_arxiv_ids_from_line(line):
-    """
-    Extract the arXiv ID from a given line of text.
-
-    Args:
-        line (str): A line of text potentially containing an arXiv URL.
-
-    Returns:
-        str: The extracted arXiv ID, or None if not found.
-    """
-    arxiv_id_pattern = re.compile(r'arxiv\.org\/abs\/(\d+\.\d+)(v\d+)?')
-    match = arxiv_id_pattern.search(line)
-    if match:
-        return match.group(1) + (match.group(2) if match.group(2) else '')
-    return None
-
-def read_written_ids(file_path):
-    """
-    Read already written arXiv IDs from a file.
-
-    Args:
-        file_path (str): Path to the file containing written IDs.
-
-    Returns:
-        set: A set of arXiv IDs.
-    """
-    written_ids = set()
-    try:
-        with open(file_path, 'r', encoding="utf-8") as file:
-            for line in file:
-                written_ids.add(line.strip())
-    except FileNotFoundError:
-        logger.error(f"File not found: {file_path}")
-    except Exception as e:
-        logger.error(f"Error while reading the file: {e}")
-    return written_ids
-
-def append_id_to_file(arxiv_id, output_file_path):
-    """
-    Append a single arXiv ID to a file. Checks if the file exists and creates it if not.
-
-    Args:
-        arxiv_id (str): The arXiv ID to append.
-        output_file_path (str): Path to the output file.
-    """
-    try:
-        if not os.path.exists(output_file_path):
-            logger.info(f"File does not exist. Creating new file: {output_file_path}")
-            with open(output_file_path, 'a', encoding="utf-8") as outfile:
-                outfile.write(arxiv_id + '\n')
-        else:
-            logger.info(f"Appending to existing file: {output_file_path}")
-            with open(output_file_path, 'a', encoding="utf-8") as outfile:
-                outfile.write(arxiv_id + '\n')
-    except Exception as e:
-        logger.error(f"Error while appending to file: {e}")
--- a/ToBeMigrated/ai_web_researcher/common_utils.py
+++ b/ToBeMigrated/ai_web_researcher/common_utils.py
@@ -1,100 +0,0 @@
-# Common utils for web_researcher
-import os
-import sys
-import re
-import json
-from pathlib import Path
-from datetime import datetime, timedelta
-from pathlib import Path
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-
-def cfg_search_param(flag):
-    """
-    Read values from the main_config.json file and return them as variables and a dictionary.
-
-    Args:
-        flag (str): A flag to determine which configuration values to return.
-
-    Returns:
-        various: The values read from the config file based on the flag.
-    """
-    try:
-        file_path = Path(os.environ.get("ALWRITY_CONFIG", ""))
-        if not file_path.is_file():
-            raise FileNotFoundError(f"Configuration file not found: {file_path}")
-        logger.info(f"Reading search config params from {file_path}")
-        
-        with open(file_path, 'r', encoding='utf-8') as file:
-            config = json.load(file)
-        web_research_section = config["Search Engine Parameters"]
-
-        if 'serperdev' in flag:
-            # Get values as variables
-            geo_location = web_research_section.get("Geographic Location")
-            search_language = web_research_section.get("Search Language")
-            num_results = web_research_section.get("Number of Results")
-            return geo_location, search_language, num_results
-
-        elif 'tavily' in flag:
-            include_urls = web_research_section.get("Include Domains")
-            pattern = re.compile(r"^(https?://[^\s,]+)(,\s*https?://[^\s,]+)*$")
-            if pattern.match(include_urls):
-                include_urls = [url.strip() for url in include_urls.split(',')]
-            else:
-                include_urls = None
-            return include_urls
-
-        elif 'exa' in flag:
-            include_urls = web_research_section.get("Include Domains")
-            pattern = re.compile(r"^(https?://\w+)(,\s*https?://\w+)*$")
-            if pattern.match(include_urls) is not None:
-                include_urls = include_urls.split(',')
-            elif re.match(r"^http?://\w+$", include_urls) is not None:
-                include_urls = include_urls.split(" ")
-            else:
-                include_urls = None
-
-            num_results = web_research_section.get("Number of Results")
-            similar_url = web_research_section.get("Similar URL")
-            time_range = web_research_section.get("Time Range")
-            if time_range == "past day":
-                start_published_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
-            elif time_range == "past week":
-                start_published_date = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
-            elif time_range == "past month":
-                start_published_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
-            elif time_range == "past year":
-                start_published_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
-            elif time_range == "anytime" or not time_range:
-                start_published_date = None
-            time_range = start_published_date
-            return include_urls, time_range, num_results, similar_url
-
-    except FileNotFoundError:
-        logger.error(f"Error: Config file '{file_path}' not found.")
-        return {}, None, None, None
-    except KeyError as e:
-        logger.error(f"Error: Missing section or option in config file: {e}")
-        return {}, None, None, None
-    except ValueError as e:
-        logger.error(f"Error: Invalid value in config file: {e}")
-        return {}, None, None, None
-
-def save_in_file(table_content):
-    """ Helper function to save search analysis in a file. """
-    file_path = os.environ.get('SEARCH_SAVE_FILE')
-    try:
-        # Save the content to the file
-        with open(file_path, "a+", encoding="utf-8") as file:
-            file.write(table_content)
-            file.write("\n" * 3)  # Add three newlines at the end
-        logger.info(f"Search content saved to {file_path}")
-        return file_path
-    except Exception as e:
-        logger.error(f"Error occurred while writing to the file: {e}")
--- a/ToBeMigrated/ai_web_researcher/finance_data_researcher.py
+++ b/ToBeMigrated/ai_web_researcher/finance_data_researcher.py
@@ -1,256 +0,0 @@
-import matplotlib.pyplot as plt
-import pandas as pd
-import yfinance as yf
-import pandas_ta as ta
-import matplotlib.dates as mdates
-from datetime import datetime, timedelta
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def calculate_technical_indicators(data: pd.DataFrame) -> pd.DataFrame:
-    """
-    Calculates a suite of technical indicators using pandas_ta.
-
-    Args:
-        data (pd.DataFrame): DataFrame containing historical stock price data.
-
-    Returns:
-        pd.DataFrame: DataFrame with added technical indicators.
-    """
-    try:
-        # Moving Averages
-        data.ta.macd(append=True)
-        data.ta.sma(length=20, append=True)
-        data.ta.ema(length=50, append=True)
-
-        # Momentum Indicators
-        data.ta.rsi(append=True)
-        data.ta.stoch(append=True)
-
-        # Volatility Indicators
-        data.ta.bbands(append=True)
-        data.ta.adx(append=True)
-
-        # Other Indicators
-        data.ta.obv(append=True)
-        data.ta.willr(append=True)
-        data.ta.cmf(append=True)
-        data.ta.psar(append=True)
-
-        # Custom Calculations
-        data['OBV_in_million'] = data['OBV'] / 1e6 
-        data['MACD_histogram_12_26_9'] = data['MACDh_12_26_9']
-
-        logging.info("Technical indicators calculated successfully.")
-        return data
-    except KeyError as e:
-        logging.error(f"Missing key in data: {e}")
-    except ValueError as e:
-        logging.error(f"Value error: {e}")
-    except Exception as e:
-        logging.error(f"Error during technical indicator calculation: {e}")
-    return None
-
-def get_last_day_summary(data: pd.DataFrame) -> pd.Series:
-    """
-    Extracts and summarizes technical indicators for the last trading day.
-
-    Args:
-        data (pd.DataFrame): DataFrame with calculated technical indicators.
-
-    Returns:
-        pd.Series: Summary of technical indicators for the last day.
-    """
-    try:
-        last_day_summary = data.iloc[-1][[
-            'Adj Close', 'MACD_12_26_9', 'MACD_histogram_12_26_9', 'RSI_14', 
-            'BBL_5_2.0', 'BBM_5_2.0', 'BBU_5_2.0', 'SMA_20', 'EMA_50',
-            'OBV_in_million', 'STOCHk_14_3_3', 'STOCHd_14_3_3', 'ADX_14', 
-            'WILLR_14', 'CMF_20', 'PSARl_0.02_0.2', 'PSARs_0.02_0.2'
-        ]]
-        logging.info("Last day summary extracted.")
-        return last_day_summary
-    except KeyError as e:
-        logging.error(f"Missing columns in data: {e}")
-    except Exception as e:
-        logging.error(f"Error extracting last day summary: {e}")
-    return None
-
-def analyze_stock(ticker_symbol: str, start_date: datetime, end_date: datetime) -> pd.Series:
-    """
-    Fetches stock data, calculates technical indicators, and provides a summary.
-
-    Args:
-        ticker_symbol (str): The stock symbol.
-        start_date (datetime): Start date for data retrieval.
-        end_date (datetime): End date for data retrieval.
-
-    Returns:
-        pd.Series: Summary of technical indicators for the last day.
-    """
-    try:
-        # Fetch stock data
-        stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
-        logging.info(f"Stock data fetched for {ticker_symbol} from {start_date} to {end_date}")
-
-        # Calculate technical indicators
-        stock_data = calculate_technical_indicators(stock_data)
-
-        # Get last day summary
-        if stock_data is not None:
-            last_day_summary = get_last_day_summary(stock_data)
-            if last_day_summary is not None:
-                print("Summary of Technical Indicators for the Last Day:")
-                print(last_day_summary)
-                return last_day_summary
-        else:
-            logging.error("Stock data is None, unable to calculate indicators.")
-    except Exception as e:
-        logging.error(f"Error during analysis: {e}")
-    return None
-
-def get_finance_data(symbol: str) -> pd.Series:
-    """
-    Fetches financial data for a given stock symbol.
-
-    Args:
-        symbol (str): The stock symbol.
-
-    Returns:
-        pd.Series: Summary of technical indicators for the last day.
-    """
-    end_date = datetime.today()
-    start_date = end_date - timedelta(days=120)
-
-    # Perform analysis
-    last_day_summary = analyze_stock(symbol, start_date, end_date)
-    return last_day_summary
-
-def analyze_options_data(ticker: str, expiry_date: str) -> tuple:
-    """
-    Analyzes option data for a given ticker and expiry date.
-
-    Args:
-        ticker (str): The stock ticker symbol.
-        expiry_date (str): The option expiry date.
-
-    Returns:
-        tuple: A tuple containing calculated metrics for call and put options.
-    """
-    call_df = options.get_calls(ticker, expiry_date)
-    put_df = options.get_puts(ticker, expiry_date)
-
-    # Implied Volatility Analysis:
-    avg_call_iv = call_df["Implied Volatility"].str.rstrip("%").astype(float).mean()
-    avg_put_iv = put_df["Implied Volatility"].str.rstrip("%").astype(float).mean()
-    logging.info(f"Average Implied Volatility for Call Options: {avg_call_iv}%")
-    logging.info(f"Average Implied Volatility for Put Options: {avg_put_iv}%")
-
-    # Option Prices Analysis:
-    avg_call_last_price = call_df["Last Price"].mean()
-    avg_put_last_price = put_df["Last Price"].mean()
-    logging.info(f"Average Last Price for Call Options: {avg_call_last_price}")
-    logging.info(f"Average Last Price for Put Options: {avg_put_last_price}")
-
-    # Strike Price Analysis:
-    min_call_strike = call_df["Strike"].min()
-    max_call_strike = call_df["Strike"].max()
-    min_put_strike = put_df["Strike"].min()
-    max_put_strike = put_df["Strike"].max()
-    logging.info(f"Minimum Strike Price for Call Options: {min_call_strike}")
-    logging.info(f"Maximum Strike Price for Call Options: {max_call_strike}")
-    logging.info(f"Minimum Strike Price for Put Options: {min_put_strike}")
-    logging.info(f"Maximum Strike Price for Put Options: {max_put_strike}")
-
-    # Volume Analysis:
-    total_call_volume = call_df["Volume"].str.replace('-', '0').astype(float).sum()
-    total_put_volume = put_df["Volume"].str.replace('-', '0').astype(float).sum()
-    logging.info(f"Total Volume for Call Options: {total_call_volume}")
-    logging.info(f"Total Volume for Put Options: {total_put_volume}")
-
-    # Open Interest Analysis:
-    call_df['Open Interest'] = call_df['Open Interest'].str.replace('-', '0').astype(float)
-    put_df['Open Interest'] = put_df['Open Interest'].str.replace('-', '0').astype(float)
-    total_call_open_interest = call_df["Open Interest"].sum()
-    total_put_open_interest = put_df["Open Interest"].sum()
-    logging.info(f"Total Open Interest for Call Options: {total_call_open_interest}")
-    logging.info(f"Total Open Interest for Put Options: {total_put_open_interest}")
-
-    # Convert Implied Volatility to float
-    call_df['Implied Volatility'] = call_df['Implied Volatility'].str.replace('%', '').astype(float)
-    put_df['Implied Volatility'] = put_df['Implied Volatility'].str.replace('%', '').astype(float)
-
-    # Calculate Put-Call Ratio
-    put_call_ratio = total_put_volume / total_call_volume
-    logging.info(f"Put-Call Ratio: {put_call_ratio}")
-
-    # Calculate Implied Volatility Percentile
-    call_iv_percentile = (call_df['Implied Volatility'] > call_df['Implied Volatility'].mean()).mean() * 100
-    put_iv_percentile = (put_df['Implied Volatility'] > put_df['Implied Volatility'].mean()).mean() * 100
-    logging.info(f"Call Option Implied Volatility Percentile: {call_iv_percentile}")
-    logging.info(f"Put Option Implied Volatility Percentile: {put_iv_percentile}")
-
-    # Calculate Implied Volatility Skew
-    implied_vol_skew = call_df['Implied Volatility'].mean() - put_df['Implied Volatility'].mean()
-    logging.info(f"Implied Volatility Skew: {implied_vol_skew}")
-
-    # Determine market sentiment
-    is_bullish_sentiment = call_df['Implied Volatility'].mean() > put_df['Implied Volatility'].mean()
-    sentiment = "bullish" if is_bullish_sentiment else "bearish"
-    logging.info(f"The overall sentiment of {ticker} is {sentiment}.")
-
-    return (avg_call_iv, avg_put_iv, avg_call_last_price, avg_put_last_price,
-            min_call_strike, max_call_strike, min_put_strike, max_put_strike,
-            total_call_volume, total_put_volume, total_call_open_interest, total_put_open_interest,
-            put_call_ratio, call_iv_percentile, put_iv_percentile, implied_vol_skew, sentiment)
-
-def get_fin_options_data(ticker: str) -> list:
-    """
-    Fetches and analyzes options data for a given stock ticker.
-
-    Args:
-        ticker (str): The stock ticker symbol.
-
-    Returns:
-        list: A list of sentences summarizing the options data.
-    """
-    current_price = round(stock_info.get_live_price(ticker), 3)
-    option_expiry_dates = options.get_expiration_dates(ticker)
-    nearest_expiry = option_expiry_dates[0]
-
-    results = analyze_options_data(ticker, nearest_expiry)
-
-    # Unpack the results tuple
-    (avg_call_iv, avg_put_iv, avg_call_last_price, avg_put_last_price,
-    min_call_strike, max_call_strike, min_put_strike, max_put_strike,
-    total_call_volume, total_put_volume, total_call_open_interest, total_put_open_interest,
-    put_call_ratio, call_iv_percentile, put_iv_percentile, implied_vol_skew, sentiment) = results
-
-    # Create a list of complete sentences with the results
-    results_sentences = [
-        f"Average Implied Volatility for Call Options: {avg_call_iv}%",
-        f"Average Implied Volatility for Put Options: {avg_put_iv}%",
-        f"Average Last Price for Call Options: {avg_call_last_price}",
-        f"Average Last Price for Put Options: {avg_put_last_price}",
-        f"Minimum Strike Price for Call Options: {min_call_strike}",
-        f"Maximum Strike Price for Call Options: {max_call_strike}",
-        f"Minimum Strike Price for Put Options: {min_put_strike}",
-        f"Maximum Strike Price for Put Options: {max_put_strike}",
-        f"Total Volume for Call Options: {total_call_volume}",
-        f"Total Volume for Put Options: {total_put_volume}",
-        f"Total Open Interest for Call Options: {total_call_open_interest}",
-        f"Total Open Interest for Put Options: {total_put_open_interest}",
-        f"Put-Call Ratio: {put_call_ratio}",
-        f"Call Option Implied Volatility Percentile: {call_iv_percentile}",
-        f"Put Option Implied Volatility Percentile: {put_iv_percentile}",
-        f"Implied Volatility Skew: {implied_vol_skew}",
-        f"The overall sentiment of {ticker} is {sentiment}."
-    ]
-
-    # Print each sentence
-    for sentence in results_sentences:
-        logging.info(sentence)
-
-    return results_sentences
--- a/ToBeMigrated/ai_web_researcher/firecrawl_web_crawler.py
+++ b/ToBeMigrated/ai_web_researcher/firecrawl_web_crawler.py
@@ -1,96 +0,0 @@
-import os
-from pathlib import Path
-from firecrawl import FirecrawlApp
-import logging
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv(Path('../../.env'))
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def initialize_client() -> FirecrawlApp:
-    """
-    Initialize and return a Firecrawl client.
-
-    Returns:
-        FirecrawlApp: An instance of the Firecrawl client.
-    """
-    return FirecrawlApp(api_key=os.getenv("FIRECRAWL_API_KEY"))
-
-def scrape_website(website_url: str, depth: int = 1, max_pages: int = 10) -> dict:
-    """
-    Scrape a website starting from the given URL.
-
-    Args:
-        website_url (str): The URL of the website to scrape.
-        depth (int, optional): The depth of crawling. Default is 1.
-        max_pages (int, optional): The maximum number of pages to scrape. Default is 10.
-
-    Returns:
-        dict: The result of the website scraping, or None if an error occurred.
-    """
-    client = initialize_client()
-    try:
-        result = client.crawl_url({
-            'url': website_url,
-            'depth': depth,
-            'max_pages': max_pages
-        })
-        return result
-    except KeyError as e:
-        logging.error(f"Missing key in data: {e}")
-    except ValueError as e:
-        logging.error(f"Value error: {e}")
-    except Exception as e:
-        logging.error(f"Error scraping website: {e}")
-    return None
-
-def scrape_url(url: str) -> dict:
-    """
-    Scrape a specific URL.
-
-    Args:
-        url (str): The URL to scrape.
-
-    Returns:
-        dict: The result of the URL scraping, or None if an error occurred.
-    """
-    client = initialize_client()
-    try:
-        result = client.scrape_url(url)
-        return result
-    except KeyError as e:
-        logging.error(f"Missing key in data: {e}")
-    except ValueError as e:
-        logging.error(f"Value error: {e}")
-    except Exception as e:
-        logging.error(f"Error scraping URL: {e}")
-    return None
-
-def extract_data(url: str, schema: dict) -> dict:
-    """
-    Extract structured data from a URL using the provided schema.
-
-    Args:
-        url (str): The URL to extract data from.
-        schema (dict): The schema to use for data extraction.
-
-    Returns:
-        dict: The extracted data, or None if an error occurred.
-    """
-    client = initialize_client()
-    try:
-        result = client.extract({
-            'url': url,
-            'schema': schema
-        })
-        return result
-    except KeyError as e:
-        logging.error(f"Missing key in data: {e}")
-    except ValueError as e:
-        logging.error(f"Value error: {e}")
-    except Exception as e:
-        logging.error(f"Error extracting data: {e}")
-    return None
--- a/ToBeMigrated/ai_web_researcher/google_serp_search.py
+++ b/ToBeMigrated/ai_web_researcher/google_serp_search.py
@@ -1,339 +0,0 @@
-"""
-This Python script performs Google searches using various services such as SerpApi, Serper.dev, and more. It displays the search results, including organic results, People Also Ask, and Related Searches, in formatted tables. The script also utilizes GPT to generate titles and FAQs for the Google search results.
-
-Features:
- Utilizes SerpApi, Serper.dev, and other services for Google searches.
- Displays organic search results, including position, title, link, and snippet.
- Presents People Also Ask questions and snippets in a formatted table.
- Includes Related Searches in the combined table with People Also Ask.
- Configures logging with Loguru for informative messages.
- Uses Rich and Tabulate for visually appealing and formatted tables.
-
-Usage:
- Ensure the necessary API keys are set in the .env file.
- Run the script to perform a Google search with the specified query.
- View the displayed tables with organic results, People Also Ask, and Related Searches.
- Additional information, such as generated titles and FAQs using GPT, is presented.
-
-Modifications:
- Update the environment variables in the .env file with the required API keys.
- Customize the search parameters, such as location and language, in the functions as needed.
- Adjust logging configurations, table formatting, and other aspects based on preferences.
-
-"""
-
-import os
-from pathlib import Path
-import sys
-import configparser
-from pathlib import Path
-import pandas as pd
-import json
-import requests
-from clint.textui import progress
-import streamlit as st
-
-#from serpapi import GoogleSearch
-from loguru import logger
-from tabulate import tabulate
-#from GoogleNews import GoogleNews
-# Configure logger
-logger.remove()
-from dotenv import load_dotenv
-# Load environment variables from .env file
-load_dotenv(Path('../../.env'))
-logger.add(
-    sys.stdout,
-    colorize=True,
-    format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-           )
-
-from .common_utils import save_in_file, cfg_search_param
-from tenacity import retry, stop_after_attempt, wait_random_exponential
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def google_search(query):
-    """
-    Perform a Google search for the given query.
-
-    Args:
-        query (str): The search query.
-        flag (str, optional): The search flag (default is "faq").
-
-    Returns:
-        list: List of search results based on the specified flag.
-    """
-    #try:
-    #    perform_serpapi_google_search(query)
-    #    logger.info(f"FIXME: Google serapi: {query}")
-    #    #return process_search_results(search_result)
-    #except Exception as err:
-    #    logger.error(f"ERROR: Check Here: https://serpapi.com/. Your requests may be over. {err}")
-
-    # Retry with serper.dev
-    try:
-        logger.info("Trying Google search with Serper.dev: https://serper.dev/api-key")
-        search_result = perform_serperdev_google_search(query)
-        if search_result:
-            process_search_results(search_result)
-            return(search_result)
-    except Exception as err:
-        logger.error(f"Failed Google search with serper.dev: {err}")
-        return None
-
- 
-#    # Retry with BROWSERLESS API
-#    try:
-#        search_result = perform_browserless_google_search(query)
-#        #return process_search_results(search_result, flag)
-#    except Exception as err:
-#        logger.error("FIXME: Failed to do Google search with BROWSERLESS API.")
-#        logger.debug("FIXME: Trying with dataforSEO API.")
-
-
-
-def perform_serpapi_google_search(query):
-    """
-    Perform a Google search using the SerpApi service.
-
-    Args:
-        query (str): The search query.
-        location (str, optional): The location for the search (default is "Austin, Texas").
-        api_key (str, optional): Your secret API key for SerpApi.
-
-    Returns:
-        dict: A dictionary containing the search results.
-    """
-    try:
-        logger.info("Reading Web search config values from main_config")
-        geo_location, search_language, num_results, time_range, include_domains, similar_url = read_return_config_section('web_research')
-    except Exception as err:
-        logger.error(f"Failed to read web research params: {err}")
-        return
-    try:
-        # Check if API key is provided
-        if not os.getenv("SERPAPI_KEY"):
-            #raise ValueError("SERPAPI_KEY key is required for SerpApi")
-            logger.error("SERPAPI_KEY key is required for SerpApi")
-            return
-            
-
-        # Create a GoogleSearch instance
-        search = GoogleSearch({
-            "q": query,
-            "location": location,
-            "api_key": api_key
-        })
-        # Get search results as a dictionary
-        result = search.get_dict()
-        return result
-
-    except ValueError as ve:
-        # Handle missing API key error
-        logger.info(f"SERPAPI ValueError: {ve}")
-    except Exception as e:
-        # Handle other exceptions
-        logger.info(f"SERPAPI An error occurred: {e}")
-
-
-def perform_serperdev_google_search(query):
-    """
-    Perform a Google search using the Serper API.
-
-    Args:
-        query (str): The search query.
-
-    Returns:
-        dict: The JSON response from the Serper API.
-    """
-    # Get the Serper API key from environment variables
-    logger.info("Doing serper.dev google search.")
-    serper_api_key = os.getenv('SERPER_API_KEY')
-
-    # Check if the API key is available
-    if not serper_api_key:
-        raise ValueError("SERPER_API_KEY is missing. Set it in the .env file.")
-
-    # Serper API endpoint URL
-    url = "https://google.serper.dev/search"
-
-    try:
-        geo_loc, lang, num_results = cfg_search_param('serperdev') 
-    except Exception as err:
-        logger.error(f"Failed to read config {err}")
-
-    # Build payload as end user or main_config
-    payload = json.dumps({
-        "q": query,
-        "gl": geo_loc,
-        "hl": lang,
-        "num": num_results,
-        "autocorrect": True,
-    })
-
-    # Request headers with API key
-    headers = {
-        'X-API-KEY': serper_api_key,
-        'Content-Type': 'application/json'
-    }
-
-    # Send a POST request to the Serper API with progress bar
-    with progress.Bar(label="Searching", expected_size=100) as bar:
-        response = requests.post(url, headers=headers, data=payload, stream=True)
-        # Check if the request was successful
-        if response.status_code == 200:
-            # Parse and return the JSON response
-            return response.json()
-        else:
-            # Print an error message if the request fails
-            logger.error(f"Error: {response.status_code}, {response.text}")
-            return None
-
-
-def perform_serper_news_search(news_keywords, news_country, news_language):
-    """ Function for Serper.dev News google search """
-        # Get the Serper API key from environment variables
-    logger.info(f"Doing serper.dev google search. {news_keywords} - {news_country} - {news_language}")
-    serper_api_key = os.getenv('SERPER_API_KEY')
-
-    # Check if the API key is available
-    if not serper_api_key:
-        raise ValueError("SERPER_API_KEY is missing. Set it in the .env file.")
-
-    # Serper API endpoint URL
-    url = "https://google.serper.dev/news"
-    payload = json.dumps({
-        "q": news_keywords,
-        "gl": news_country,
-        "hl": news_language,
-    })
-    # Request headers with API key
-    headers = {
-        'X-API-KEY': serper_api_key,
-        'Content-Type': 'application/json'
-    }
-    # Send a POST request to the Serper API with progress bar
-    with progress.Bar(label="Searching News", expected_size=100) as bar:
-        response = requests.post(url, headers=headers, data=payload, stream=True)
-        # Check if the request was successful
-        if response.status_code == 200:
-            # Parse and return the JSON response
-            #process_search_results(response, "news")
-            return response.json()
-        else:
-            # Print an error message if the request fails
-            logger.error(f"Error: {response.status_code}, {response.text}")
-            return None
-
-
-
-def perform_browserless_google_search():
-    return
-
-def perform_dataforseo_google_search():
-    return
-
-
-def google_news(search_keywords, news_period="7d", region="IN"):
-    """ Get news articles from google_news"""
-    googlenews = GoogleNews()
-    googlenews.enableException(True)
-    googlenews = GoogleNews(lang='en', region=region)
-    googlenews = GoogleNews(period=news_period)
-    print(googlenews.get_news('APPLE'))
-    print(googlenews.search('APPLE'))
-
-
-def process_search_results(search_results, search_type="general"):
-    """
-    Create a Pandas DataFrame from the search results.
-
-    Args:
-        search_results (dict): The search results JSON.
-
-    Returns:
-        pd.DataFrame: Pandas DataFrame containing the search results.
-    """
-    data = []
-    logger.info(f"Google Search Parameters: {search_results.get('searchParameters', {})}")
-    if 'general' in search_type:
-        organic_results = search_results.get("organic", [])
-    if 'news' in search_type:
-        organic_results = search_results.get("news", [])
-
-    # Displaying Organic Results
-    organic_data = []
-    for result in search_results["organic"]:
-        position = result.get("position", "")
-        title = result.get("title", "")
-        link = result.get("link", "")
-        snippet = result.get("snippet", "")
-        organic_data.append([position, title, link, snippet])
-    
-    organic_headers = ["Rank", "Title", "Link", "Snippet"]
-    organic_table = tabulate(organic_data, 
-            headers=organic_headers, 
-            tablefmt="fancy_grid", 
-            colalign=["center", "left", "left", "left"], 
-            maxcolwidths=[5, 25, 35, 50])
-
-    # Print the tables
-    print("\n\n📢❗🚨 Google search Organic Results:")
-    print(organic_table)
-
-    # Displaying People Also Ask and Related Searches combined
-    combined_data = []
-    try:
-        people_also_ask_data = []
-        if "peopleAlsoAsk" in search_results:
-            for question in search_results["peopleAlsoAsk"]:
-                title = question.get("title", "")
-                snippet = question.get("snippet", "")
-                link = question.get("link", "")
-                people_also_ask_data.append([title, snippet, link])
-    except Exception as people_also_ask_err:
-        logger.error(f"Error processing 'peopleAlsoAsk': {people_also_ask_err}")
-        people_also_ask_data = []
-
-    related_searches_data = []
-    for query in search_results.get("relatedSearches", []):
-        related_searches_data.append([query.get("query", "")]) 
-    related_searches_headers = ["Related Search"]
-
-    if people_also_ask_data:
-        # Add Related Searches as a column to People Also Ask
-        combined_data = [
-            row + [related_searches_data[i][0] if i < len(related_searches_data) else ""]
-            for i, row in enumerate(people_also_ask_data)
-        ]
-        combined_headers = ["Question", "Snippet", "Link", "Related Search"]
-        # Display the combined table
-        combined_table = tabulate(
-            combined_data,
-            headers=combined_headers,
-            tablefmt="fancy_grid",
-            colalign=["left", "left", "left", "left"],
-            maxcolwidths=[20, 50, 20, 30]
-        )
-    else:
-        combined_table = tabulate(
-            related_searches_data,
-            headers=related_searches_headers,
-            tablefmt="fancy_grid",
-            colalign=["left"],
-            maxcolwidths=[60]
-        )
-
-    print("\n\n📢❗🚨 People Also Ask & Related Searches:")
-    print(combined_table)
-    # Save the combined table to a file
-    try:
-        # Display on Alwrity UI
-        st.write(organic_table)
-        st.write(combined_table)
-        save_in_file(organic_table)
-        save_in_file(combined_table)
-    except Exception as save_results_err:
-        logger.error(f"Failed to save search results: {save_results_err}")
-    return search_results
--- a/ToBeMigrated/ai_web_researcher/google_trends_researcher.py
+++ b/ToBeMigrated/ai_web_researcher/google_trends_researcher.py
@@ -1,500 +0,0 @@
-"""
-This Python script analyzes Google search keywords by fetching auto-suggestions, performing keyword clustering, and visualizing Google Trends data. It uses various libraries such as pytrends, requests_html, tqdm, and more.
-
-Features:
- Fetches auto-suggestions for a given search keyword from Google.
- Performs keyword clustering using K-means algorithm based on TF-IDF vectors.
- Visualizes Google Trends data, including interest over time and interest by region.
- Retrieves related queries and topics for a set of search keywords.
- Utilizes visualization libraries such as Matplotlib, Plotly, and Rich for displaying results.
- Incorporates logger.for error handling and informative messages.
-
-Usage:
- Provide a search term or a list of search terms for analysis.
- Run the script to fetch auto-suggestions, perform clustering, and visualize Google Trends data.
- Explore the displayed results, including top keywords in each cluster and related topics.
-
-Modifications:
- Customize the search terms in the 'do_google_trends_analysis' function.
- Adjust the number of clusters for keyword clustering and other parameters as needed.
- Explore further visualizations and analyses based on the generated data.
-
-Note: Ensure that the required libraries are installed using 'pip install pytrends requests_html tqdm tabulate plotly rich'.
-"""
-
-import os
-import time # I wish
-import random
-import requests
-import numpy as np
-import sys
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-from sklearn.metrics import silhouette_score, silhouette_samples
-from rich.console import Console
-from rich.progress import Progress
-import urllib
-import json
-import pandas as pd
-import matplotlib.pyplot as plt
-import plotly.express as px
-import plotly.io as pio
-from requests_html import HTML, HTMLSession
-from urllib.parse import quote_plus
-from tqdm import tqdm
-from tabulate import tabulate
-from pytrends.request import TrendReq
-from loguru import logger
-
-# Configure logger
-logger.remove()
-logger.add(sys.stdout,
-           colorize=True,
-           format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-           )
-
-
-def fetch_google_trends_interest_overtime(keyword):
-    try:
-        pytrends = TrendReq(hl='en-US', tz=360)
-        pytrends.build_payload([keyword], timeframe='today 1-y', geo='US')
-        
-        # 1. Interest Over Time
-        data = pytrends.interest_over_time()
-        data = data.reset_index()
-
-        # Visualization using Matplotlib
-        plt.figure(figsize=(10, 6))
-        plt.plot(data['date'], data[keyword], label=keyword)
-        plt.title(f'Interest Over Time for "{keyword}"')
-        plt.xlabel('Date')
-        plt.ylabel('Interest')
-        plt.legend()
-        plt.show()
-
-        return data
-    except Exception as e:
-        logger.error(f"Error in fetch_google_trends_data: {e}")
-        return pd.DataFrame()
-
-
-def plot_interest_by_region(kw_list):
-    try:
-        from pytrends.request import TrendReq
-        import matplotlib.pyplot as plt
-        trends = TrendReq()
-        trends.build_payload(kw_list=kw_list)
-        kw_list = ' '.join(kw_list)
-        data = trends.interest_by_region() #sorting by region
-        data = data.sort_values(by=f"{kw_list}", ascending=False)
-        print("\n📢❗🚨 ")
-        print(f"Top 10 regions with highest interest for keyword: {kw_list}")
-        data = data.head(10) #Top 10
-        print(data)
-        data.reset_index().plot(x="geoName", y=f"{kw_list}",
-                        figsize=(20,15), kind="bar")
-        plt.style.use('fivethirtyeight')
-        plt.show()
-        # FIXME: Send this image to vision GPT for analysis.
-
-    except Exception as e:
-        print(f"Error plotting interest by region: {e}")
-        return None
-
-
-
-
-def get_related_topics_and_save_csv(search_keywords):
-    search_keywords = [f"{search_keywords}"]
-    try:
-        pytrends = TrendReq(hl='en-US', tz=360)
-        pytrends.build_payload(kw_list=search_keywords, timeframe='today 12-m')
-        
-        # Get related topics - this returns a dictionary
-        topics_data = pytrends.related_topics()
-        
-        # Extract data for the first keyword
-        if topics_data and search_keywords[0] in topics_data:
-            keyword_data = topics_data[search_keywords[0]]
-            
-            # Create two separate dataframes for top and rising
-            top_df = keyword_data.get('top', pd.DataFrame())
-            rising_df = keyword_data.get('rising', pd.DataFrame())
-            
-            return {
-                'top': top_df[['topic_title', 'value']] if not top_df.empty else pd.DataFrame(),
-                'rising': rising_df[['topic_title', 'value']] if not rising_df.empty else pd.DataFrame()
-            }
-    except Exception as e:
-        logger.error(f"Error in related topics: {e}")
-        return {'top': pd.DataFrame(), 'rising': pd.DataFrame()}
-
-def get_related_queries_and_save_csv(search_keywords):
-    search_keywords = [f"{search_keywords}"]
-    try:
-        pytrends = TrendReq(hl='en-US', tz=360)
-        pytrends.build_payload(kw_list=search_keywords, timeframe='today 12-m')
-        
-        # Get related queries - this returns a dictionary
-        queries_data = pytrends.related_queries()
-        
-        # Extract data for the first keyword
-        if queries_data and search_keywords[0] in queries_data:
-            keyword_data = queries_data[search_keywords[0]]
-            
-            # Create two separate dataframes for top and rising
-            top_df = keyword_data.get('top', pd.DataFrame())
-            rising_df = keyword_data.get('rising', pd.DataFrame())
-            
-            return {
-                'top': top_df if not top_df.empty else pd.DataFrame(),
-                'rising': rising_df if not rising_df.empty else pd.DataFrame()
-            }
-    except Exception as e:
-        logger.error(f"Error in related queries: {e}")
-        return {'top': pd.DataFrame(), 'rising': pd.DataFrame()}
-
-
-def get_source(url):
-    try:
-        session = HTMLSession()
-        response = session.get(url)
-        response.raise_for_status()  # Raise an HTTPError for bad responses
-        return response
-    except requests.exceptions.RequestException as e:
-        logger.error(f"Error during HTTP request: {e}")
-        return None
-
-
-
-def get_results(query):
-    try:
-        query = urllib.parse.quote_plus(query)
-        response = get_source(f"https://suggestqueries.google.com/complete/search?output=chrome&hl=en&q={query}")
-        time.sleep(random.uniform(0.1, 0.6))
-
-        if response:
-            response.raise_for_status()
-            results = json.loads(response.text)
-            return results
-        else:
-            return None
-    except json.JSONDecodeError as e:
-        logger.error(f"Error decoding JSON response: {e}")
-        return None
-    except requests.exceptions.RequestException as e:
-        logger.error(f"Error during HTTP request: {e}")
-        return None
-
-
-
-def format_results(results):
-    try:
-        suggestions = []
-        for index, value in enumerate(results[1]):
-            suggestion = {'term': value, 'relevance': results[4]['google:suggestrelevance'][index]}
-            suggestions.append(suggestion)
-        return suggestions
-    except (KeyError, IndexError) as e:
-        logger.error(f"Error parsing search results: {e}")
-        return []
-
-
-
-def get_expanded_term_suffixes():
-    return ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm','n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
-
-
-
-def get_expanded_term_prefixes():
-    # For shopping, review type blogs.
-    #return ['discount *', 'pricing *', 'cheap', 'best price *', 'lowest price', 'best value', 'sale', 'affordable', 'promo', 'budget''what *', 'where *', 'how to *', 'why *', 'buy*', 'how much*','best *', 'worse *', 'rent*', 'sale*', 'offer*','vs*','or*']
-    return ['what *', 'where *', 'how to *', 'why *','best *', 'vs*', 'or*']
-
-
-
-def get_expanded_terms(query):
-    try:
-        expanded_term_prefixes = get_expanded_term_prefixes()
-        expanded_term_suffixes = get_expanded_term_suffixes()
-
-        terms = [query]
-
-        for term in expanded_term_prefixes:
-            terms.append(f"{term} {query}")
-
-        for term in expanded_term_suffixes:
-            terms.append(f"{query} {term}")
-
-        return terms
-    except Exception as e:
-        logger.error(f"Error in get_expanded_terms: {e}")
-        return []
-
-
-
-def get_expanded_suggestions(query):
-    try:
-        all_results = []
-
-        expanded_terms = get_expanded_terms(query)
-        for term in tqdm(expanded_terms, desc="📢❗🚨 Fetching Google AutoSuggestions", unit="term"):
-            results = get_results(term)
-            if results:
-                formatted_results = format_results(results)
-                all_results += formatted_results
-                all_results = sorted(all_results, key=lambda k: k.get('relevance', 0), reverse=True)
-
-        return all_results
-    except Exception as e:
-        logger.error(f"Error in get_expanded_suggestions: {e}")
-        return []
-
-
-
-def get_suggestions_for_keyword(search_term):
-    """ """
-    try:
-        expanded_results = get_expanded_suggestions(search_term)
-        expanded_results_df = pd.DataFrame(expanded_results)
-        expanded_results_df.columns = ['Keywords', 'Relevance']
-        #expanded_results_df.to_csv('results.csv', index=False)
-        pd.set_option('display.max_rows', expanded_results_df.shape[0]+1)
-        expanded_results_df.drop_duplicates('Keywords', inplace=True)
-        table = tabulate(expanded_results_df, headers=['Keywords', 'Relevance'], tablefmt='fancy_grid')
-        # FIXME: Too much data for LLM context window. We will need to embed it.
-        #try:
-        #    save_in_file(table)
-        #except Exception as save_results_err:
-        #    logger.error(f"Failed to save search results: {save_results_err}")
-        return expanded_results_df
-    except Exception as e:
-        logger.error(f"get_suggestions_for_keyword: Error in main: {e}")
-
-
-
-def perform_keyword_clustering(expanded_results_df, num_clusters=5):
-    try:
-        # Preprocessing: Convert the keywords to lowercase
-        expanded_results_df['Keywords'] = expanded_results_df['Keywords'].str.lower()
-
-        # Vectorization: Create a TF-IDF vectorizer
-        vectorizer = TfidfVectorizer()
-
-        # Fit the vectorizer to the keywords
-        tfidf_vectors = vectorizer.fit_transform(expanded_results_df['Keywords'])
-
-        # Applying K-means clustering
-        kmeans = KMeans(n_clusters=num_clusters, random_state=42)
-        cluster_labels = kmeans.fit_predict(tfidf_vectors)
-
-        # Add cluster labels to the DataFrame
-        expanded_results_df['cluster_label'] = cluster_labels
-
-        # Assessing cluster quality through silhouette score
-        silhouette_avg = silhouette_score(tfidf_vectors, cluster_labels)
-        print(f"Silhouette Score: {silhouette_avg}")
-
-        # Visualize cluster quality using a silhouette plot
-        #visualize_silhouette(tfidf_vectors, cluster_labels)
-
-        return expanded_results_df
-    except Exception as e:
-        logger.error(f"Error in perform_keyword_clustering: {e}")
-        return pd.DataFrame()
-
-
-
-def visualize_silhouette(X, labels):
-    try:
-        silhouette_avg = silhouette_score(X, labels)
-        print(f"Silhouette Score: {silhouette_avg}")
-
-        # Create a subplot with 1 row and 2 columns
-        fig, ax1 = plt.subplots(1, 1, figsize=(8, 6))
-
-        # The 1st subplot is the silhouette plot
-        ax1.set_xlim([-0.1, 1])
-        ax1.set_ylim([0, X.shape[0] + (len(set(labels)) + 1) * 10])
-
-        # Compute the silhouette scores for each sample
-        sample_silhouette_values = silhouette_samples(X, labels)
-
-        y_lower = 10
-        for i in set(labels):
-            # Aggregate the silhouette scores for samples belonging to the cluster
-            ith_cluster_silhouette_values = sample_silhouette_values[labels == i]
-            ith_cluster_silhouette_values.sort()
-
-            size_cluster_i = ith_cluster_silhouette_values.shape[0]
-            y_upper = y_lower + size_cluster_i
-
-            color = plt.cm.nipy_spectral(float(i) / len(set(labels)))
-            ax1.fill_betweenx(np.arange(y_lower, y_upper),
-                              0, ith_cluster_silhouette_values,
-                              facecolor=color, edgecolor=color, alpha=0.7)
-
-            # Label the silhouette plots with their cluster numbers at the middle
-            ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
-
-            # Compute the new y_lower for the next plot
-            y_lower = y_upper + 10  # 10 for the 0 samples
-
-        ax1.set_title("Silhouette plot for KMeans clustering")
-        ax1.set_xlabel("Silhouette coefficient values")
-        ax1.set_ylabel("Cluster label")
-
-        # The vertical line for the average silhouette score of all the values
-        ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
-
-        plt.show()
-    except Exception as e:
-        logger.error(f"Error in visualize_silhouette: {e}")
-
-
-
-def print_and_return_top_keywords(expanded_results_df, num_clusters=5):
-    """
-    Display and return top keywords in each cluster.
-
-    Args:
-        expanded_results_df (pd.DataFrame): DataFrame containing expanded keywords, relevance, and cluster labels.
-        num_clusters (int or str): Number of clusters or 'all'.
-
-    Returns:
-        pd.DataFrame: DataFrame with top keywords for each cluster.
-    """
-    top_keywords_df = pd.DataFrame()
-
-    if num_clusters == 'all':
-        unique_clusters = expanded_results_df['cluster_label'].unique()
-    else:
-        unique_clusters = range(int(num_clusters))
-
-    for i in unique_clusters:
-        cluster_df = expanded_results_df[expanded_results_df['cluster_label'] == i]
-        top_keywords = cluster_df.sort_values(by='Relevance', ascending=False).head(5)
-        top_keywords_df = pd.concat([top_keywords_df, top_keywords])
-
-    print(f"\n📢❗🚨 GTop Keywords for All Clusters:")
-    table = tabulate(top_keywords_df, headers='keys', tablefmt='fancy_grid')
-    # Save the combined table to a file
-    try:
-        save_in_file(table)
-    except Exception as save_results_err:
-        logger.error(f"🚨 Failed to save search results: {save_results_err}")
-    print(table)
-    return top_keywords_df
-
-
-def generate_wordcloud(keywords):
-    """
-    Generate and display a word cloud from a list of keywords.
-
-    Args:
-        keywords (list): List of keywords.
-    """
-    # Convert the list of keywords to a string
-    text = ' '.join(keywords)
-
-    # Generate word cloud
-    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
-
-    # Display the word cloud using matplotlib
-    plt.figure(figsize=(600, 200))
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.axis('off')
-    plt.show()
-
-
-
-def save_in_file(table_content):
-    """ Helper function to save search analysis in a file. """
-    file_path = os.environ.get('SEARCH_SAVE_FILE')
-    try:
-        # Save the content to the file
-        with open(file_path, "a+", encoding="utf-8") as file:
-            file.write(table_content)
-            file.write("\n" * 3)  # Add three newlines at the end
-        logger.info(f"Search content saved to {file_path}")
-    except Exception as e:
-        logger.error(f"Error occurred while writing to the file: {e}")
-
-
-def do_google_trends_analysis(search_term):
-    """ Get a google search keywords, get its stats."""
-    search_term = [f"{search_term}"]
-    all_the_keywords = []
-    try:
-        for asearch_term in search_term:
-            #FIXME: Lets work with a single root keyword.
-            suggestions_df = get_suggestions_for_keyword(asearch_term)
-            if len(suggestions_df['Keywords']) > 10:
-                result_df = perform_keyword_clustering(suggestions_df)
-                # Display top keywords in each cluster
-                top_keywords = print_and_return_top_keywords(result_df)
-                all_the_keywords.append(top_keywords['Keywords'].tolist())
-            else:
-                all_the_keywords.append(suggestions_df['Keywords'].tolist())
-            all_the_keywords = ','.join([', '.join(filter(None, map(str, sublist))) for sublist in all_the_keywords])
-            
-            # Generate a random sleep time between 2 and 3 seconds 
-            time.sleep(random.uniform(2, 3))
-        
-        # Display additional information
-        try:
-            result_df = get_related_topics_and_save_csv(search_term)
-            logger.info(f"Related topics:: result_df: {result_df}")
-            # Extract 'Top' topic_title
-            if result_df:
-                top_topic_title = result_df['top']['topic_title'].values.tolist()
-                # Join each sublist into one string separated by comma
-                #top_topic_title = [','.join(filter(None, map(str, sublist))) for sublist in top_topic_title]
-                top_topic_title = ','.join([', '.join(filter(None, map(str, sublist))) for sublist in top_topic_title])
-        except Exception as err:
-            logger.error(f"Failed to get results from google trends related topics: {err}")
-
-        # TBD: Not getting great results OR unable to understand them.
-        #all_the_keywords += top_topic_title
-        all_the_keywords = all_the_keywords.split(',')
-        # Split the list into chunks of 5 keywords
-        chunk_size = 4
-        chunks = [all_the_keywords[i:i + chunk_size] for i in range(0, len(all_the_keywords), chunk_size)]
-        # Create a DataFrame with columns named 'Keyword 1', 'Keyword 2', etc.
-        combined_df = pd.DataFrame(chunks, columns=[f'K📢eyword Col{i + 1}' for i in range(chunk_size)])
-        
-        # Print the table
-        table = tabulate(combined_df, headers='keys', tablefmt='fancy_grid')
-        # Save the combined table to a file
-        try:
-            save_in_file(table)
-        except Exception as save_results_err:                 
-            logger.error(f"Failed to save search results: {save_results_err}")
-        print(table)
-        
-        #generate_wordcloud(all_the_keywords)
-        return(all_the_keywords)
-    except Exception as e:
-        logger.error(f"Error in Google Trends Analysis: {e}")
-
-
-def get_trending_searches(country='united_states'):
-    """Get trending searches for a specific country."""
-    try:
-        pytrends = TrendReq(hl='en-US', tz=360)
-        trending_searches = pytrends.trending_searches(pn=country)
-        return trending_searches
-    except Exception as e:
-        logger.error(f"Error getting trending searches: {e}")
-        return pd.DataFrame()
-
-def get_realtime_trends(country='US'):
-    """Get realtime trending searches for a specific country."""
-    try:
-        pytrends = TrendReq(hl='en-US', tz=360)
-        realtime_trends = pytrends.realtime_trending_searches(pn=country)
-        return realtime_trends
-    except Exception as e:
-        logger.error(f"Error getting realtime trends: {e}")
-        return pd.DataFrame()
--- a/ToBeMigrated/ai_web_researcher/gpt_online_researcher.py
+++ b/ToBeMigrated/ai_web_researcher/gpt_online_researcher.py
@@ -1,803 +0,0 @@
-################################################################
-# 
-# ## Features
-#
-# - **Web Research**: Alwrity enables users to conduct web research efficiently. 
-# By providing keywords or topics of interest, users can initiate searches across multiple platforms simultaneously.
-#
-# - **Google SERP Search**: The tool integrates with Google Search Engine Results Pages (SERP) 
-# to retrieve relevant information based on user queries. It offers insights into organic search results, 
-# People Also Ask, and related searches.
-#
-# - **Tavily AI Integration**: Alwrity leverages Tavily AI's capabilities to enhance web research. 
-# It utilizes advanced algorithms to search for information and extract relevant data from various sources.
-#
-# - **Metaphor AI Semantic Search**: Alwrity employs Metaphor AI's semantic search technology to find related articles and content. 
-# By analyzing context and meaning, it delivers precise and accurate results.
-#
-# - **Google Trends Analysis**: The tool provides Google Trends analysis for user-defined keywords. 
-# It helps users understand the popularity and trends associated with specific topics over time.
-# 
-##############################################################
-
-import os
-import json
-import time
-from pathlib import Path
-import sys
-from datetime import datetime
-import streamlit as st
-import pandas as pd
-import random
-import numpy as np
-
-from lib.alwrity_ui.display_google_serp_results import (
-    process_research_results,
-    process_search_results,
-    display_research_results
-)
-from lib.alwrity_ui.google_trends_ui import display_google_trends_data, process_trends_data
-
-from .tavily_ai_search import do_tavily_ai_search
-from .metaphor_basic_neural_web_search import metaphor_search_articles, streamlit_display_metaphor_results
-from .google_serp_search import google_search
-from .google_trends_researcher import do_google_trends_analysis
-#from .google_gemini_web_researcher import do_gemini_web_research
-
-from loguru import logger
-# Configure logger
-logger.remove()
-logger.add(sys.stdout,
-           colorize=True,
-           format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-           )
-
-
-def gpt_web_researcher(search_keywords, search_mode, **kwargs):
-    """Keyword based web researcher with progress tracking."""
-    
-    logger.info(f"Starting web research - Keywords: {search_keywords}, Mode: {search_mode}")
-    logger.debug(f"Additional parameters: {kwargs}")
-    
-    try:
-        # Reset session state variables for this research operation
-        if 'metaphor_results_displayed' in st.session_state:
-            del st.session_state.metaphor_results_displayed
-        
-        # Initialize result container
-        research_results = None
-        
-        # Create status containers
-        status_container = st.empty()
-        progress_bar = st.progress(0)
-        
-        def update_progress(message, progress=None, level="info"):
-            if progress is not None:
-                progress_bar.progress(progress)
-            if level == "error":
-                status_container.error(f"🚫 {message}")
-            elif level == "warning":
-                status_container.warning(f"⚠️ {message}")
-            else:
-                status_container.info(f"🔄 {message}")
-            logger.debug(f"Progress update [{level}]: {message}")
-
-        if search_mode == "google":
-            logger.info("Starting Google research pipeline")
-            
-            try:
-                # First try Google SERP
-                update_progress("Initiating SERP search...", progress=10)
-                serp_results = do_google_serp_search(search_keywords, **kwargs)
-                
-                if serp_results and serp_results.get('organic'):
-                    logger.info("SERP search successful")
-                    update_progress("SERP search completed", progress=40)
-                    research_results = serp_results
-                else:
-                    logger.warning("SERP search returned no results, falling back to Gemini")
-                    update_progress("No SERP results, trying Gemini...", progress=45)
-                    
-                    # Keep it commented. Fallback to Gemini
-                    #try:
-                    #    gemini_results = do_gemini_web_research(search_keywords)
-                    #    if gemini_results:
-                    #        logger.info("Gemini research successful")
-                    #        update_progress("Gemini research completed", progress=80)
-                    #        research_results = {
-                    #            'source': 'gemini',
-                    #            'results': gemini_results
-                    #        }
-                    #except Exception as gemini_err:
-                    #    logger.error(f"Gemini research failed: {gemini_err}")
-                    #    update_progress("Gemini research failed", level="warning")
-                
-                if research_results:
-                    update_progress("Processing final results...", progress=90)
-                    processed_results = process_research_results(research_results)
-                    
-                    if processed_results:
-                        update_progress("Research completed!", progress=100, level="success")
-                        display_research_results(processed_results)
-                        return processed_results
-                    else:
-                        error_msg = "Failed to process research results"
-                        logger.warning(error_msg)
-                        update_progress(error_msg, level="warning")
-                        return None
-                else:
-                    error_msg = "No results from either SERP or Gemini"
-                    logger.warning(error_msg)
-                    update_progress(error_msg, level="warning")
-                    return None
-                    
-            except Exception as search_err:
-                error_msg = f"Research pipeline failed: {str(search_err)}"
-                logger.error(error_msg, exc_info=True)
-                update_progress(error_msg, level="error")
-                raise
-
-        elif search_mode == "ai":
-            logger.info("Starting AI research pipeline")
-            
-            try:
-                # Do Tavily AI Search
-                update_progress("Initiating Tavily AI search...", progress=10)
-                
-                # Extract relevant parameters for Tavily search
-                include_domains = kwargs.pop('include_domains', None)
-                search_depth = kwargs.pop('search_depth', 'advanced')
-                
-                # Pass the parameters to do_tavily_ai_search
-                t_results = do_tavily_ai_search(
-                    search_keywords,  # Pass as positional argument
-                    max_results=kwargs.get('num_results', 10),
-                    include_domains=include_domains,
-                    search_depth=search_depth,
-                    **kwargs
-                )
-                
-                # Do Metaphor AI Search
-                update_progress("Initiating Metaphor AI search...", progress=50)
-                metaphor_results, metaphor_titles = do_metaphor_ai_research(search_keywords)
-                
-                if metaphor_results is None:
-                    update_progress("Metaphor AI search failed, continuing with Tavily results only...", level="warning")
-                else:
-                    update_progress("Metaphor AI search completed successfully", progress=75)
-                    # Add debug logging to check the structure of metaphor_results
-                    logger.debug(f"Metaphor results structure: {type(metaphor_results)}")
-                    if isinstance(metaphor_results, dict):
-                        logger.debug(f"Metaphor results keys: {metaphor_results.keys()}")
-                        if 'data' in metaphor_results:
-                            logger.debug(f"Metaphor data keys: {metaphor_results['data'].keys()}")
-                            if 'results' in metaphor_results['data']:
-                                logger.debug(f"Number of results: {len(metaphor_results['data']['results'])}")
-                    
-                    # Display Metaphor results only if not already displayed
-                    if 'metaphor_results_displayed' not in st.session_state:
-                        st.session_state.metaphor_results_displayed = True
-                        # Make sure to pass the correct parameters to streamlit_display_metaphor_results
-                        streamlit_display_metaphor_results(metaphor_results, search_keywords)
-                
-                # Add Google Trends Analysis
-                update_progress("Initiating Google Trends analysis...", progress=80)
-                try:
-                    # Add an informative message about Google Trends
-                    with st.expander("ℹ️ About Google Trends Analysis", expanded=False):
-                        st.markdown("""
-                        **What is Google Trends Analysis?**
-                        
-                        Google Trends Analysis provides insights into how often a particular search-term is entered relative to the total search-volume across various regions of the world, and in various languages.
-                        
-                        **What data will be shown?**
-                        
-                        - **Related Keywords**: Terms that are frequently searched together with your keyword
-                        - **Interest Over Time**: How interest in your keyword has changed over the past 12 months
-                        - **Regional Interest**: Where in the world your keyword is most popular
-                        - **Related Queries**: What people search for before and after searching for your keyword
-                        - **Related Topics**: Topics that are closely related to your keyword
-                        
-                        **How to use this data:**
-                        
-                        - Identify trending topics in your industry
-                        - Understand seasonal patterns in search behavior
-                        - Discover related keywords for content planning
-                        - Target content to specific regions with high interest
-                        """)
-                    
-                    trends_results = do_google_pytrends_analysis(search_keywords)
-                    if trends_results:
-                        update_progress("Google Trends analysis completed successfully", progress=90)
-                        # Store trends results in the research_results
-                        if metaphor_results:
-                            metaphor_results['trends_data'] = trends_results
-                        else:
-                            # If metaphor_results is None, create a new container for results
-                            metaphor_results = {'trends_data': trends_results}
-                        
-                        # Display Google Trends data using the new UI module
-                        display_google_trends_data(trends_results, search_keywords)
-                    else:
-                        update_progress("Google Trends analysis returned no results", level="warning")
-                except Exception as trends_err:
-                    logger.error(f"Google Trends analysis failed: {trends_err}")
-                    update_progress("Google Trends analysis failed", level="warning")
-                    st.error(f"Error in Google Trends analysis: {str(trends_err)}")
-                
-                # Return the combined results
-                update_progress("Research completed!", progress=100, level="success")
-                return metaphor_results or t_results
-                    
-            except Exception as ai_err:
-                error_msg = f"AI research pipeline failed: {str(ai_err)}"
-                logger.error(error_msg, exc_info=True)
-                update_progress(error_msg, level="error")
-                raise
-
-        else:
-            error_msg = f"Unsupported search mode: {search_mode}"
-            logger.error(error_msg)
-            update_progress(error_msg, level="error")
-            raise ValueError(error_msg)
-            
-    except Exception as err:
-        error_msg = f"Failed in gpt_web_researcher: {str(err)}"
-        logger.error(error_msg, exc_info=True)
-        if 'update_progress' in locals():
-            update_progress(error_msg, level="error")
-        raise
-
-
-def do_google_serp_search(search_keywords, status_container, update_progress, **kwargs):
-    """Perform Google SERP analysis with sidebar progress tracking."""
-    
-    logger.info("="*50)
-    logger.info("Starting Google SERP Search")
-    logger.info("="*50)
-    
-    try:
-        # Validate parameters
-        update_progress("Validating search parameters", progress=0.1)
-        status_container.info("📝 Validating parameters...")
-        
-        if not search_keywords or not isinstance(search_keywords, str):
-            logger.error(f"Invalid search keywords: {search_keywords}")
-            raise ValueError("Search keywords must be a non-empty string")
-        
-        # Update search initiation
-        update_progress(f"Initiating search for: '{search_keywords}'", progress=0.2)
-        status_container.info("🌐 Querying search API...")
-        logger.info(f"Search params: {kwargs}")
-        
-        # Execute search
-        g_results = google_search(search_keywords)
-        
-        if g_results:
-            # Log success
-            update_progress("Search completed successfully", progress=0.8, level="success")
-            
-            # Update statistics
-            stats = f"""Found:
-                - {len(g_results.get('organic', []))} organic results
-                - {len(g_results.get('peopleAlsoAsk', []))} related questions
-                - {len(g_results.get('relatedSearches', []))} related searches"""
-            update_progress(stats, progress=0.9)
-            
-            # Process results
-            update_progress("Processing search results", progress=0.95)
-            status_container.info("⚡ Processing results...")
-            processed_results = process_search_results(g_results)
-            
-            # Extract titles
-            update_progress("Extracting information", progress=0.98)
-            g_titles = extract_info(g_results, 'titles')
-            
-            # Final success
-            update_progress("Analysis completed successfully", progress=1.0, level="success")
-            status_container.success("✨ Research completed!")
-            
-            # Clear main status after delay
-            time.sleep(1)
-            status_container.empty()
-            
-            return {
-                'results': g_results,
-                'titles': g_titles,
-                'summary': processed_results,
-                'stats': {
-                    'organic_count': len(g_results.get('organic', [])),
-                    'questions_count': len(g_results.get('peopleAlsoAsk', [])),
-                    'related_count': len(g_results.get('relatedSearches', []))
-                }
-            }
-            
-        else:
-            update_progress("No results found", progress=0.5, level="warning")
-            status_container.warning("⚠️ No results found")
-            return None
-            
-    except Exception as err:
-        error_msg = f"Search failed: {str(err)}"
-        update_progress(error_msg, progress=0.5, level="error")
-        logger.error(error_msg)
-        logger.debug("Stack trace:", exc_info=True)
-        raise
-        
-    finally:
-        logger.info("="*50)
-        logger.info("Google SERP Search function completed")
-        logger.info("="*50)
-
-
-def do_tavily_ai_search(search_keywords, max_results=10, **kwargs):
-    """ Common function to do Tavily AI web research."""
-    try:
-        logger.info(f"Doing Tavily AI search for: {search_keywords}")
-        
-        # Prepare Tavily search parameters
-        tavily_params = {
-            'max_results': max_results,
-            'search_depth': 'advanced' if kwargs.get('search_depth', 3) > 2 else 'basic',
-            'time_range': kwargs.get('time_range', 'year'),
-            'include_domains': kwargs.get('include_domains', [""]) if kwargs.get('include_domains') else [""]
-        }
-        
-        # Import the Tavily search function directly
-        from .tavily_ai_search import do_tavily_ai_search as tavily_search
-        
-        # Call the actual Tavily search function
-        t_results = tavily_search(
-            keywords=search_keywords,
-            **tavily_params
-        )
-        
-        if t_results:
-            t_titles = tavily_extract_information(t_results, 'titles')
-            t_answer = tavily_extract_information(t_results, 'answer')
-            return(t_results, t_titles, t_answer)
-        else:
-            logger.warning("No results returned from Tavily AI search")
-            return None, None, None
-    except Exception as err:
-        logger.error(f"Failed to do Tavily AI Search: {err}")
-        return None, None, None
-
-
-def do_metaphor_ai_research(search_keywords):
-    """
-    Perform Metaphor AI research and return results with titles.
-    
-    Args:
-        search_keywords (str): Keywords to search for
-        
-    Returns:
-        tuple: (response_articles, titles) or (None, None) if search fails
-    """
-    try:
-        logger.info(f"Start Semantic/Neural web search with Metaphor: {search_keywords}")
-        response_articles = metaphor_search_articles(search_keywords)
-        
-        if response_articles and 'data' in response_articles:
-            m_titles = [result.get('title', '') for result in response_articles['data'].get('results', [])]
-            return response_articles, m_titles
-        else:
-            logger.warning("No valid results from Metaphor search")
-            return None, None
-            
-    except Exception as err:
-        logger.error(f"Failed to do Metaphor search: {err}")
-        return None, None
-
-
-def do_google_pytrends_analysis(keywords):
-    """
-    Perform Google Trends analysis for the given keywords.
-    
-    Args:
-        keywords (str): The search keywords to analyze
-        
-    Returns:
-        dict: A dictionary containing formatted Google Trends data with the following keys:
-            - related_keywords: List of related keywords
-            - interest_over_time: DataFrame with date and interest columns
-            - regional_interest: DataFrame with country_code, country, and interest columns
-            - related_queries: DataFrame with query and value columns
-            - related_topics: DataFrame with topic and value columns
-    """
-    logger.info(f"Performing Google Trends analysis for keywords: {keywords}")
-    
-    # Create a progress container for Streamlit
-    progress_container = st.empty()
-    progress_bar = st.progress(0)
-    
-    def update_progress(message, progress=None, level="info"):
-        """Helper function to update progress in Streamlit UI"""
-        if progress is not None:
-            progress_bar.progress(progress)
-        
-        if level == "error":
-            progress_container.error(f"🚫 {message}")
-        elif level == "warning":
-            progress_container.warning(f"⚠️ {message}")
-        else:
-            progress_container.info(f"🔄 {message}")
-        logger.debug(f"Progress update [{level}]: {message}")
-    
-    try:
-        # Initialize the formatted data dictionary
-        formatted_data = {
-            'related_keywords': [],
-            'interest_over_time': pd.DataFrame(),
-            'regional_interest': pd.DataFrame(),
-            'related_queries': pd.DataFrame(),
-            'related_topics': pd.DataFrame()
-        }
-        
-        # Get raw trends data from google_trends_researcher
-        update_progress("Fetching Google Trends data...", progress=10)
-        raw_trends_data = do_google_trends_analysis(keywords)
-        
-        if not raw_trends_data:
-            logger.warning("No Google Trends data returned")
-            update_progress("No Google Trends data returned", level="warning", progress=20)
-            return formatted_data
-        
-        # Process related keywords from the raw data
-        update_progress("Processing related keywords...", progress=30)
-        if isinstance(raw_trends_data, list):
-            formatted_data['related_keywords'] = raw_trends_data
-        elif isinstance(raw_trends_data, dict):
-            if 'keywords' in raw_trends_data:
-                formatted_data['related_keywords'] = raw_trends_data['keywords']
-            if 'interest_over_time' in raw_trends_data:
-                formatted_data['interest_over_time'] = raw_trends_data['interest_over_time']
-            if 'regional_interest' in raw_trends_data:
-                formatted_data['regional_interest'] = raw_trends_data['regional_interest']
-            if 'related_queries' in raw_trends_data:
-                formatted_data['related_queries'] = raw_trends_data['related_queries']
-            if 'related_topics' in raw_trends_data:
-                formatted_data['related_topics'] = raw_trends_data['related_topics']
-        
-        # If we have keywords but missing other data, try to fetch them using pytrends directly
-        if formatted_data['related_keywords'] and (
-            formatted_data['interest_over_time'].empty or 
-            formatted_data['regional_interest'].empty or 
-            formatted_data['related_queries'].empty or 
-            formatted_data['related_topics'].empty
-        ):
-            try:
-                update_progress("Fetching additional data from Google Trends API...", progress=40)
-                from pytrends.request import TrendReq
-                pytrends = TrendReq(hl='en-US', tz=360)
-                
-                # Build payload with the main keyword
-                update_progress("Building search payload...", progress=45)
-                pytrends.build_payload([keywords], timeframe='today 12-m', geo='')
-                
-                # Get interest over time if missing
-                if formatted_data['interest_over_time'].empty:
-                    try:
-                        update_progress("Fetching interest over time data...", progress=50)
-                        interest_df = pytrends.interest_over_time()
-                        if not interest_df.empty:
-                            formatted_data['interest_over_time'] = interest_df.reset_index()
-                            update_progress(f"Successfully fetched interest over time data with {len(formatted_data['interest_over_time'])} data points", progress=55)
-                        else:
-                            update_progress("No interest over time data available", level="warning", progress=55)
-                    except Exception as e:
-                        logger.error(f"Error fetching interest over time: {e}")
-                        update_progress(f"Error fetching interest over time: {str(e)}", level="warning", progress=55)
-                
-                # Get regional interest if missing
-                if formatted_data['regional_interest'].empty:
-                    try:
-                        update_progress("Fetching regional interest data...", progress=60)
-                        regional_df = pytrends.interest_by_region()
-                        if not regional_df.empty:
-                            formatted_data['regional_interest'] = regional_df.reset_index()
-                            update_progress(f"Successfully fetched regional interest data for {len(formatted_data['regional_interest'])} regions", progress=65)
-                        else:
-                            update_progress("No regional interest data available", level="warning", progress=65)
-                    except Exception as e:
-                        logger.error(f"Error fetching regional interest: {e}")
-                        update_progress(f"Error fetching regional interest: {str(e)}", level="warning", progress=65)
-                
-                # Get related queries if missing
-                if formatted_data['related_queries'].empty:
-                    try:
-                        update_progress("Fetching related queries data...", progress=70)
-                        # Get related queries data
-                        related_queries = pytrends.related_queries()
-                        
-                        # Create empty DataFrame as fallback
-                        formatted_data['related_queries'] = pd.DataFrame(columns=['query', 'value'])
-                        
-                        # Simple direct approach to avoid list index errors
-                        if related_queries and isinstance(related_queries, dict):
-                            # Check if our keyword exists in the results
-                            if keywords in related_queries:
-                                keyword_data = related_queries[keywords]
-                                
-                                # Process top queries if available
-                                if 'top' in keyword_data and keyword_data['top'] is not None:
-                                    try:
-                                        update_progress("Processing top related queries...", progress=75)
-                                        # Convert to DataFrame if it's not already
-                                        if isinstance(keyword_data['top'], pd.DataFrame):
-                                            top_df = keyword_data['top']
-                                        else:
-                                            # Try to convert to DataFrame
-                                            top_df = pd.DataFrame(keyword_data['top'])
-                                        
-                                        # Ensure it has the right columns
-                                        if not top_df.empty:
-                                            # Rename columns if needed
-                                            if 'query' in top_df.columns:
-                                                # Already has the right column name
-                                                pass
-                                            elif len(top_df.columns) > 0:
-                                                # Use first column as query
-                                                top_df = top_df.rename(columns={top_df.columns[0]: 'query'})
-                                            
-                                            # Add to our results
-                                            formatted_data['related_queries'] = top_df
-                                            update_progress(f"Successfully processed {len(top_df)} top related queries", progress=80)
-                                    except Exception as e:
-                                        logger.warning(f"Error processing top queries: {e}")
-                                        update_progress(f"Error processing top queries: {str(e)}", level="warning", progress=80)
-                                
-                                # Process rising queries if available
-                                if 'rising' in keyword_data and keyword_data['rising'] is not None:
-                                    try:
-                                        update_progress("Processing rising related queries...", progress=85)
-                                        # Convert to DataFrame if it's not already
-                                        if isinstance(keyword_data['rising'], pd.DataFrame):
-                                            rising_df = keyword_data['rising']
-                                        else:
-                                            # Try to convert to DataFrame
-                                            rising_df = pd.DataFrame(keyword_data['rising'])
-                                        
-                                        # Ensure it has the right columns
-                                        if not rising_df.empty:
-                                            # Rename columns if needed
-                                            if 'query' in rising_df.columns:
-                                                # Already has the right column name
-                                                pass
-                                            elif len(rising_df.columns) > 0:
-                                                # Use first column as query
-                                                rising_df = rising_df.rename(columns={rising_df.columns[0]: 'query'})
-                                            
-                                            # Combine with existing data if we have any
-                                            if not formatted_data['related_queries'].empty:
-                                                formatted_data['related_queries'] = pd.concat([formatted_data['related_queries'], rising_df])
-                                                update_progress(f"Successfully processed {len(rising_df)} rising related queries", progress=90)
-                                            else:
-                                                formatted_data['related_queries'] = rising_df
-                                                update_progress(f"Successfully processed {len(rising_df)} rising related queries", progress=90)
-                                    except Exception as e:
-                                        logger.warning(f"Error processing rising queries: {e}")
-                                        update_progress(f"Error processing rising queries: {str(e)}", level="warning", progress=90)
-                    except Exception as e:
-                        logger.error(f"Error fetching related queries: {e}")
-                        update_progress(f"Error fetching related queries: {str(e)}", level="warning", progress=90)
-                        # Ensure we have an empty DataFrame with the right columns
-                        formatted_data['related_queries'] = pd.DataFrame(columns=['query', 'value'])
-                
-                # Get related topics if missing
-                if formatted_data['related_topics'].empty:
-                    try:
-                        update_progress("Fetching related topics data...", progress=95)
-                        # Get related topics data
-                        related_topics = pytrends.related_topics()
-                        
-                        # Create empty DataFrame as fallback
-                        formatted_data['related_topics'] = pd.DataFrame(columns=['topic', 'value'])
-                        
-                        # Simple direct approach to avoid list index errors
-                        if related_topics and isinstance(related_topics, dict):
-                            # Check if our keyword exists in the results
-                            if keywords in related_topics:
-                                keyword_data = related_topics[keywords]
-                                
-                                # Process top topics if available
-                                if 'top' in keyword_data and keyword_data['top'] is not None:
-                                    try:
-                                        update_progress("Processing top related topics...", progress=97)
-                                        # Convert to DataFrame if it's not already
-                                        if isinstance(keyword_data['top'], pd.DataFrame):
-                                            top_df = keyword_data['top']
-                                        else:
-                                            # Try to convert to DataFrame
-                                            top_df = pd.DataFrame(keyword_data['top'])
-                                        
-                                        # Ensure it has the right columns
-                                        if not top_df.empty:
-                                            # Rename columns if needed
-                                            if 'topic_title' in top_df.columns:
-                                                top_df = top_df.rename(columns={'topic_title': 'topic'})
-                                            elif len(top_df.columns) > 0 and 'topic' not in top_df.columns:
-                                                # Use first column as topic
-                                                top_df = top_df.rename(columns={top_df.columns[0]: 'topic'})
-                                            
-                                            # Add to our results
-                                            formatted_data['related_topics'] = top_df
-                                            update_progress(f"Successfully processed {len(top_df)} top related topics", progress=98)
-                                    except Exception as e:
-                                        logger.warning(f"Error processing top topics: {e}")
-                                        update_progress(f"Error processing top topics: {str(e)}", level="warning", progress=98)
-                                
-                                # Process rising topics if available
-                                if 'rising' in keyword_data and keyword_data['rising'] is not None:
-                                    try:
-                                        update_progress("Processing rising related topics...", progress=99)
-                                        # Convert to DataFrame if it's not already
-                                        if isinstance(keyword_data['rising'], pd.DataFrame):
-                                            rising_df = keyword_data['rising']
-                                        else:
-                                            # Try to convert to DataFrame
-                                            rising_df = pd.DataFrame(keyword_data['rising'])
-                                        
-                                        # Ensure it has the right columns
-                                        if not rising_df.empty:
-                                            # Rename columns if needed
-                                            if 'topic_title' in rising_df.columns:
-                                                rising_df = rising_df.rename(columns={'topic_title': 'topic'})
-                                            elif len(rising_df.columns) > 0 and 'topic' not in rising_df.columns:
-                                                # Use first column as topic
-                                                rising_df = rising_df.rename(columns={rising_df.columns[0]: 'topic'})
-                                            
-                                            # Combine with existing data if we have any
-                                            if not formatted_data['related_topics'].empty:
-                                                formatted_data['related_topics'] = pd.concat([formatted_data['related_topics'], rising_df])
-                                                update_progress(f"Successfully processed {len(rising_df)} rising related topics", progress=100)
-                                            else:
-                                                formatted_data['related_topics'] = rising_df
-                                                update_progress(f"Successfully processed {len(rising_df)} rising related topics", progress=100)
-                                    except Exception as e:
-                                        logger.warning(f"Error processing rising topics: {e}")
-                                        update_progress(f"Error processing rising topics: {str(e)}", level="warning", progress=100)
-                    except Exception as e:
-                        logger.error(f"Error fetching related topics: {e}")
-                        update_progress(f"Error fetching related topics: {str(e)}", level="warning", progress=100)
-                        # Ensure we have an empty DataFrame with the right columns
-                        formatted_data['related_topics'] = pd.DataFrame(columns=['topic', 'value'])
-                
-            except Exception as e:
-                logger.error(f"Error fetching additional trends data: {e}")
-                update_progress(f"Error fetching additional trends data: {str(e)}", level="warning", progress=100)
-        
-        # Ensure all DataFrames have the correct column names for the UI
-        update_progress("Finalizing data formatting...", progress=100)
-        
-        if not formatted_data['interest_over_time'].empty:
-            if 'date' not in formatted_data['interest_over_time'].columns:
-                formatted_data['interest_over_time'] = formatted_data['interest_over_time'].reset_index()
-            if 'interest' not in formatted_data['interest_over_time'].columns and keywords in formatted_data['interest_over_time'].columns:
-                formatted_data['interest_over_time'] = formatted_data['interest_over_time'].rename(columns={keywords: 'interest'})
-        
-        if not formatted_data['regional_interest'].empty:
-            if 'country_code' not in formatted_data['regional_interest'].columns and 'geoName' in formatted_data['regional_interest'].columns:
-                formatted_data['regional_interest'] = formatted_data['regional_interest'].rename(columns={'geoName': 'country_code'})
-            if 'interest' not in formatted_data['regional_interest'].columns and keywords in formatted_data['regional_interest'].columns:
-                formatted_data['regional_interest'] = formatted_data['regional_interest'].rename(columns={keywords: 'interest'})
-        
-        if not formatted_data['related_queries'].empty:
-            # Handle different column names that might be present in the related queries DataFrame
-            if 'query' not in formatted_data['related_queries'].columns:
-                if 'Top query' in formatted_data['related_queries'].columns:
-                    formatted_data['related_queries'] = formatted_data['related_queries'].rename(columns={'Top query': 'query'})
-                elif 'Rising query' in formatted_data['related_queries'].columns:
-                    formatted_data['related_queries'] = formatted_data['related_queries'].rename(columns={'Rising query': 'query'})
-                elif 'query' not in formatted_data['related_queries'].columns and len(formatted_data['related_queries'].columns) > 0:
-                    # If we have a DataFrame but no 'query' column, use the first column as 'query'
-                    first_col = formatted_data['related_queries'].columns[0]
-                    formatted_data['related_queries'] = formatted_data['related_queries'].rename(columns={first_col: 'query'})
-            
-            if 'value' not in formatted_data['related_queries'].columns and len(formatted_data['related_queries'].columns) > 1:
-                # If we have a second column, use it as 'value'
-                second_col = formatted_data['related_queries'].columns[1]
-                formatted_data['related_queries'] = formatted_data['related_queries'].rename(columns={second_col: 'value'})
-            elif 'value' not in formatted_data['related_queries'].columns:
-                # If no 'value' column exists, add one with default values
-                formatted_data['related_queries']['value'] = 0
-        
-        if not formatted_data['related_topics'].empty:
-            # Handle different column names that might be present in the related topics DataFrame
-            if 'topic' not in formatted_data['related_topics'].columns:
-                if 'topic_title' in formatted_data['related_topics'].columns:
-                    formatted_data['related_topics'] = formatted_data['related_topics'].rename(columns={'topic_title': 'topic'})
-                elif 'topic' not in formatted_data['related_topics'].columns and len(formatted_data['related_topics'].columns) > 0:
-                    # If we have a DataFrame but no 'topic' column, use the first column as 'topic'
-                    first_col = formatted_data['related_topics'].columns[0]
-                    formatted_data['related_topics'] = formatted_data['related_topics'].rename(columns={first_col: 'topic'})
-            
-            if 'value' not in formatted_data['related_topics'].columns and len(formatted_data['related_topics'].columns) > 1:
-                # If we have a second column, use it as 'value'
-                second_col = formatted_data['related_topics'].columns[1]
-                formatted_data['related_topics'] = formatted_data['related_topics'].rename(columns={second_col: 'value'})
-            elif 'value' not in formatted_data['related_topics'].columns:
-                # If no 'value' column exists, add one with default values
-                formatted_data['related_topics']['value'] = 0
-        
-        # Clear the progress container after completion
-        progress_container.empty()
-        progress_bar.empty()
-        
-        return formatted_data
-        
-    except Exception as e:
-        logger.error(f"Error in Google Trends analysis: {e}")
-        update_progress(f"Error in Google Trends analysis: {str(e)}", level="error", progress=100)
-        # Clear the progress container after error
-        progress_container.empty()
-        progress_bar.empty()
-        return {
-            'related_keywords': [],
-            'interest_over_time': pd.DataFrame(),
-            'regional_interest': pd.DataFrame(),
-            'related_queries': pd.DataFrame(),
-            'related_topics': pd.DataFrame()
-        }
-
-
-def metaphor_extract_titles_or_text(json_data, return_titles=True):
-    """
-    Extract either titles or text from the given JSON structure.
-
-    Args:
-        json_data (list): List of Result objects in JSON format.
-        return_titles (bool): If True, return titles. If False, return text.
-
-    Returns:
-        list: List of titles or text.
-    """
-    if return_titles:
-        return [(result.title) for result in json_data]
-    else:
-        return [result.text for result in json_data]
-
-
-def extract_info(json_data, info_type):
-    """
-    Extract information (titles, peopleAlsoAsk, or relatedSearches) from the given JSON.
-
-    Args:
-        json_data (dict): The JSON data.
-        info_type (str): The type of information to extract (titles, peopleAlsoAsk, relatedSearches).
-
-    Returns:
-        list or None: A list containing the requested information, or None if the type is invalid.
-    """
-    if info_type == "titles":
-        return [result.get("title") for result in json_data.get("organic", [])]
-    elif info_type == "peopleAlsoAsk":
-        return [item.get("question") for item in json_data.get("peopleAlsoAsk", [])]
-    elif info_type == "relatedSearches":
-        return [item.get("query") for item in json_data.get("relatedSearches", [])]
-    else:
-        print("Invalid info_type. Please use 'titles', 'peopleAlsoAsk', or 'relatedSearches'.")
-        return None
-
-
-def tavily_extract_information(json_data, keyword):
-    """
-    Extract information from the given JSON based on the specified keyword.
-
-    Args:
-        json_data (dict): The JSON data.
-        keyword (str): The keyword (title, content, answer, follow-query).
-
-    Returns:
-        list or str: The extracted information based on the keyword.
-    """
-    if keyword == 'titles':
-        return [result['title'] for result in json_data['results']]
-    elif keyword == 'content':
-        return [result['content'] for result in json_data['results']]
-    elif keyword == 'answer':
-        return json_data['answer']
-    elif keyword == 'follow-query':
-        return json_data['follow_up_questions']
-    else:
-        return f"Invalid keyword: {keyword}"
--- a/ToBeMigrated/ai_web_researcher/metaphor_basic_neural_web_search.py
+++ b/ToBeMigrated/ai_web_researcher/metaphor_basic_neural_web_search.py
@@ -1,623 +0,0 @@
-import os
-import sys
-import pandas as pd
-from io import StringIO
-from pathlib import Path
-
-from metaphor_python import Metaphor
-from datetime import datetime, timedelta
-
-import streamlit as st 
-from loguru import logger
-from tqdm import tqdm
-from tabulate import tabulate
-from collections import namedtuple
-import textwrap
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from dotenv import load_dotenv
-load_dotenv(Path('../../.env'))
-
-from exa_py import Exa
-
-from tenacity import (retry, stop_after_attempt, wait_random_exponential,)# for exponential backoff
-from .gpt_summarize_web_content import summarize_web_content
-from .gpt_competitor_analysis import summarize_competitor_content
-from .common_utils import save_in_file, cfg_search_param
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def get_metaphor_client():
-    """
-    Get the Metaphor client.
-
-    Returns:
-        Metaphor: An instance of the Metaphor client.
-    """
-    METAPHOR_API_KEY = os.environ.get('METAPHOR_API_KEY')
-    if not METAPHOR_API_KEY:
-        logger.error("METAPHOR_API_KEY environment variable not set!")
-        st.error("METAPHOR_API_KEY environment variable not set!")
-        raise ValueError("METAPHOR_API_KEY environment variable not set!")
-    return Exa(METAPHOR_API_KEY)
-
-
-def metaphor_rag_search():
-    """ Mainly used for researching blog sections. """
-    metaphor = get_metaphor_client()
-    query = "blog research"  # Example query, this can be parameterized as needed
-    results = metaphor.search(query)
-    if not results:
-        logger.error("No results found for the query.")
-        st.error("No results found for the query.")
-        return None
-    
-    # Process the results (this is a placeholder, actual processing logic will depend on requirements)
-    processed_results = [result['title'] for result in results]
-    
-    # Display the results
-    st.write("Search Results:")
-    st.write(processed_results)
-    
-    return processed_results
-
-def metaphor_find_similar(similar_url, usecase, num_results=5, start_published_date=None, end_published_date=None,
-                         include_domains=None, exclude_domains=None, include_text=None, exclude_text=None,
-                         summary_query=None, progress_bar=None):
-    """Find similar content using Metaphor API."""
-    
-    try:
-        # Initialize progress if not provided
-        if progress_bar is None:
-            progress_bar = st.progress(0.0)
-        
-        # Update progress
-        progress_bar.progress(0.1, text="Initializing search...")
-        
-        # Get Metaphor client
-        metaphor = get_metaphor_client()
-        logger.info(f"Initialized Metaphor client for URL: {similar_url}")
-        
-        # Prepare search parameters
-        search_params = {
-            "highlights": True,
-            "num_results": num_results,
-        }
-        
-        # Add optional parameters if provided
-        if start_published_date:
-            search_params["start_published_date"] = start_published_date
-        if end_published_date:
-            search_params["end_published_date"] = end_published_date
-        if include_domains:
-            search_params["include_domains"] = include_domains
-        if exclude_domains:
-            search_params["exclude_domains"] = exclude_domains
-        if include_text:
-            search_params["include_text"] = include_text
-        if exclude_text:
-            search_params["exclude_text"] = exclude_text
-            
-        # Add summary query
-        if summary_query:
-            search_params["summary"] = summary_query
-        else:
-            search_params["summary"] = {"query": f"Find {usecase} similar to the given URL."}
-        
-        logger.debug(f"Search parameters: {search_params}")
-        
-        # Update progress
-        progress_bar.progress(0.2, text="Preparing search parameters...")
-        
-        # Make API call
-        logger.info("Calling Metaphor API find_similar_and_contents...")
-        search_response = metaphor.find_similar_and_contents(
-            similar_url,
-            **search_params
-        )
-        
-        if search_response and hasattr(search_response, 'results'):
-            competitors = search_response.results
-            total_results = len(competitors)
-            
-            # Update progress
-            progress_bar.progress(0.3, text=f"Found {total_results} results...")
-            
-            # Process results
-            processed_results = []
-            for i, result in enumerate(competitors):
-                # Calculate progress as decimal (0.0-1.0)
-                progress = 0.3 + (0.6 * (i / total_results))
-                progress_text = f"Processing result {i+1}/{total_results}..."
-                progress_bar.progress(progress, text=progress_text)
-                
-                # Process each result
-                processed_result = {
-                    "Title": result.title,
-                    "URL": result.url,
-                    "Content Summary": result.text if hasattr(result, 'text') else "No content available"
-                }
-                processed_results.append(processed_result)
-            
-            # Update progress
-            progress_bar.progress(0.9, text="Finalizing results...")
-            
-            # Create DataFrame
-            df = pd.DataFrame(processed_results)
-            
-            # Update progress
-            progress_bar.progress(1.0, text="Analysis completed!")
-            
-            return df, search_response
-            
-        else:
-            logger.warning("No results found in search response")
-            progress_bar.progress(1.0, text="No results found")
-            return pd.DataFrame(), search_response
-            
-    except Exception as e:
-        logger.error(f"Error in metaphor_find_similar: {str(e)}", exc_info=True)
-        if progress_bar:
-            progress_bar.progress(1.0, text="Error occurred during analysis")
-        raise
-
-
-def calculate_date_range(time_range: str) -> tuple:
-    """
-    Calculate start and end dates based on time range selection.
-    
-    Args:
-        time_range (str): One of 'past_day', 'past_week', 'past_month', 'past_year', 'anytime'
-        
-    Returns:
-        tuple: (start_date, end_date) in ISO format with milliseconds
-    """
-    now = datetime.utcnow()
-    end_date = now.strftime('%Y-%m-%dT%H:%M:%S.999Z')
-    
-    if time_range == 'past_day':
-        start_date = (now - timedelta(days=1)).strftime('%Y-%m-%dT%H:%M:%S.000Z')
-    elif time_range == 'past_week':
-        start_date = (now - timedelta(weeks=1)).strftime('%Y-%m-%dT%H:%M:%S.000Z')
-    elif time_range == 'past_month':
-        start_date = (now - timedelta(days=30)).strftime('%Y-%m-%dT%H:%M:%S.000Z')
-    elif time_range == 'past_year':
-        start_date = (now - timedelta(days=365)).strftime('%Y-%m-%dT%H:%M:%S.000Z')
-    else:  # anytime
-        start_date = None
-        end_date = None
-    
-    return start_date, end_date
-
-def metaphor_search_articles(query, search_options: dict = None):
-    """
-    Search for articles using the Metaphor/Exa API.
-
-    Args:
-        query (str): The search query.
-        search_options (dict): Search configuration options including:
-            - num_results (int): Number of results to retrieve
-            - use_autoprompt (bool): Whether to use autoprompt
-            - include_domains (list): List of domains to include
-            - time_range (str): One of 'past_day', 'past_week', 'past_month', 'past_year', 'anytime'
-            - exclude_domains (list): List of domains to exclude
-
-    Returns:
-        dict: Search results and metadata
-    """
-    exa = get_metaphor_client()
-    try:
-        # Initialize default search options
-        if search_options is None:
-            search_options = {}
-
-        # Get config parameters or use defaults
-        try:
-            include_domains, _, num_results, _ = cfg_search_param('exa')
-        except Exception as cfg_err:
-            logger.warning(f"Failed to load config parameters: {cfg_err}. Using defaults.")
-            include_domains = None
-            num_results = 10
-
-        # Calculate date range based on time_range option
-        time_range = search_options.get('time_range', 'anytime')
-        start_published_date, end_published_date = calculate_date_range(time_range)
-
-        # Prepare search parameters
-        search_params = {
-            'num_results': search_options.get('num_results', num_results),
-            'summary': True,  # Always get summaries
-            'include_domains': search_options.get('include_domains', include_domains),
-            'use_autoprompt': search_options.get('use_autoprompt', True),
-        }
-
-        # Add date parameters only if they are not None
-        if start_published_date:
-            search_params['start_published_date'] = start_published_date
-        if end_published_date:
-            search_params['end_published_date'] = end_published_date
-
-        logger.info(f"Exa web search with params: {search_params} and Query: {query}")
-        
-        # Execute search
-        search_response = exa.search_and_contents(
-            query,
-            **search_params
-        )
-            
-        if not search_response or not hasattr(search_response, 'results'):
-            logger.warning("No results returned from Exa search")
-            return None
-
-        # Get cost information safely
-        try:
-            cost_dollars = {
-                'total': float(search_response.cost_dollars['total']),
-            } if hasattr(search_response, 'cost_dollars') else None
-        except Exception as cost_err:
-            logger.warning(f"Error processing cost information: {cost_err}")
-            cost_dollars = None
-
-        # Format response to match expected structure
-        formatted_response = {
-            "data": {
-                "requestId": getattr(search_response, 'request_id', None),
-                "resolvedSearchType": "neural",
-                "results": [
-                    {
-                        "id": result.url,
-                        "title": result.title,
-                        "url": result.url,
-                        "publishedDate": result.published_date if hasattr(result, 'published_date') else None,
-                        "author": getattr(result, 'author', None),
-                        "score": getattr(result, 'score', 0),
-                        "summary": result.summary if hasattr(result, 'summary') else None,
-                        "text": result.text if hasattr(result, 'text') else None,
-                        "image": getattr(result, 'image', None),
-                        "favicon": getattr(result, 'favicon', None)
-                    }
-                    for result in search_response.results
-                ],
-                "costDollars": cost_dollars
-            }
-        }
-
-        # Get AI-generated answer from Metaphor
-        try:
-            exa_answer = get_exa_answer(query)
-            if exa_answer:
-                formatted_response.update(exa_answer)
-        except Exception as exa_err:
-            logger.warning(f"Error getting Exa answer: {exa_err}")
-        
-        # Get AI-generated answer from Tavily
-        try:
-            # Import the function directly from the module
-            import importlib
-            tavily_module = importlib.import_module('lib.ai_web_researcher.tavily_ai_search')
-            if hasattr(tavily_module, 'do_tavily_ai_search'):
-                tavily_response = tavily_module.do_tavily_ai_search(query)
-                if tavily_response and 'answer' in tavily_response:
-                    formatted_response.update({
-                        "tavily_answer": tavily_response.get("answer"),
-                        "tavily_citations": tavily_response.get("citations", []),
-                        "tavily_cost_dollars": tavily_response.get("costDollars", {"total": 0})
-                    })
-            else:
-                logger.warning("do_tavily_ai_search function not found in tavily_ai_search module")
-        except Exception as tavily_err:
-            logger.warning(f"Error getting Tavily answer: {tavily_err}")
-        
-        # Return the formatted response without displaying it
-        # The display will be handled by gpt_web_researcher
-        return formatted_response
-
-    except Exception as e:
-        logger.error(f"Error in Exa searching articles: {e}")
-        return None
-
-def streamlit_display_metaphor_results(metaphor_response, search_keywords=None):
-    """Display Metaphor search results in Streamlit."""
-    
-    if not metaphor_response:
-        st.error("No search results found.")
-        return
-    
-    # Add debug logging
-    logger.debug(f"Displaying Metaphor results. Type: {type(metaphor_response)}")
-    if isinstance(metaphor_response, dict):
-        logger.debug(f"Metaphor response keys: {metaphor_response.keys()}")
-    
-    # Initialize session state variables if they don't exist
-    if 'search_insights' not in st.session_state:
-        st.session_state.search_insights = None
-    if 'metaphor_response' not in st.session_state:
-        st.session_state.metaphor_response = None
-    if 'insights_generated' not in st.session_state:
-        st.session_state.insights_generated = False
-    
-    # Store the current response in session state
-    st.session_state.metaphor_response = metaphor_response
-    
-    # Display search results
-    st.subheader("🔍 Search Results")
-    
-    # Calculate metrics - handle different data structures
-    results = []
-    if isinstance(metaphor_response, dict):
-        if 'data' in metaphor_response and 'results' in metaphor_response['data']:
-            results = metaphor_response['data']['results']
-        elif 'results' in metaphor_response:
-            results = metaphor_response['results']
-    
-    total_results = len(results)
-    avg_relevance = sum(r.get('score', 0) for r in results) / total_results if total_results > 0 else 0
-    
-    # Display metrics
-    col1, col2 = st.columns(2)
-    with col1:
-        st.metric("Total Results", total_results)
-    with col2:
-        st.metric("Average Relevance Score", f"{avg_relevance:.2f}")
-    
-    # Display AI-generated answers if available
-    if 'tavily_answer' in metaphor_response or 'metaphor_answer' in metaphor_response:
-        st.subheader("🤖 AI-Generated Answers")
-        
-        if 'tavily_answer' in metaphor_response:
-            st.markdown("**Tavily AI Answer:**")
-            st.write(metaphor_response['tavily_answer'])
-        
-        if 'metaphor_answer' in metaphor_response:
-            st.markdown("**Metaphor AI Answer:**")
-            st.write(metaphor_response['metaphor_answer'])
-    
-    # Get Search Insights button
-    if st.button("Generate Search Insights", key="metaphor_generate_insights_button"):
-        st.session_state.insights_generated = True
-        st.rerun()
-    
-    # Display insights if they exist in session state
-    if st.session_state.search_insights:
-        st.subheader("🔍 Search Insights")
-        st.write(st.session_state.search_insights)
-    
-    # Display search results in a data editor
-    st.subheader("📊 Detailed Results")
-    
-    # Prepare data for display
-    results_data = []
-    for result in results:
-        result_data = {
-            'Title': result.get('title', ''),
-            'URL': result.get('url', ''),
-            'Snippet': result.get('summary', ''),
-            'Relevance Score': result.get('score', 0),
-            'Published Date': result.get('publishedDate', '')
-        }
-        results_data.append(result_data)
-    
-    # Create DataFrame
-    df = pd.DataFrame(results_data)
-    
-    # Display the DataFrame if it's not empty
-    if not df.empty:
-        # Configure columns
-        st.dataframe(
-            df,
-            column_config={
-                "Title": st.column_config.TextColumn(
-                    "Title",
-                    help="Title of the search result",
-                    width="large",
-                ),
-                "URL": st.column_config.LinkColumn(
-                    "URL",
-                    help="Link to the search result",
-                    width="medium",
-                    display_text="Visit Article",
-                ),
-                "Snippet": st.column_config.TextColumn(
-                    "Snippet",
-                    help="Summary of the search result",
-                    width="large",
-                ),
-                "Relevance Score": st.column_config.NumberColumn(
-                    "Relevance Score",
-                    help="Relevance score of the search result",
-                    format="%.2f",
-                    width="small",
-                ),
-                "Published Date": st.column_config.DateColumn(
-                    "Published Date",
-                    help="Publication date of the search result",
-                    width="medium",
-                ),
-            },
-            hide_index=True,
-        )
-        
-        # Add popover for snippets
-        st.markdown("""
-        <style>
-        .snippet-popover {
-            position: relative;
-            display: inline-block;
-        }
-        .snippet-popover .snippet-content {
-            visibility: hidden;
-            width: 300px;
-            background-color: #f9f9f9;
-            color: #333;
-            text-align: left;
-            border-radius: 6px;
-            padding: 10px;
-            position: absolute;
-            z-index: 1;
-            bottom: 125%;
-            left: 50%;
-            margin-left: -150px;
-            opacity: 0;
-            transition: opacity 0.3s;
-            box-shadow: 0 2px 5px rgba(0,0,0,0.2);
-        }
-        .snippet-popover:hover .snippet-content {
-            visibility: visible;
-            opacity: 1;
-        }
-        </style>
-        """, unsafe_allow_html=True)
-        
-        # Display snippets with popover
-        st.subheader("📝 Snippets")
-        for i, result in enumerate(results):
-            snippet = result.get('summary', '')
-            if snippet:
-                st.markdown(f"""
-                <div class="snippet-popover">
-                    <strong>{result.get('title', '')}</strong>
-                    <div class="snippet-content">
-                        {snippet}
-                    </div>
-                </div>
-                """, unsafe_allow_html=True)
-    else:
-        st.info("No detailed results available.")
-    
-    # Add a collapsible section for the raw JSON data
-    with st.expander("Research Results (JSON)", expanded=False):
-        st.json(metaphor_response)
-
-
-def metaphor_news_summarizer(news_keywords):
-    """ build a LLM-based news summarizer app with the Exa API to keep us up-to-date 
-    with the latest news on a given topic.
-    """
-    exa = get_metaphor_client()
-
-    # FIXME: Needs to be user defined.
-    one_week_ago = (datetime.now() - timedelta(days=7))
-    date_cutoff = one_week_ago.strftime("%Y-%m-%d")
-
-    search_response = exa.search_and_contents(
-        news_keywords, use_autoprompt=True, start_published_date=date_cutoff
-    )
-
-    urls = [result.url for result in search_response.results]
-    print("URLs:")
-    for url in urls:
-        print(url)
-
-
-def print_search_result(contents_response):
-    # Define the Result namedtuple
-    Result = namedtuple("Result", ["url", "title", "text"])
-    # Tabulate the data
-    table_headers = ["URL", "Title", "Summary"]
-    table_data = [(result.url, result.title, result.text) for result in contents_response]
-
-    table = tabulate(table_data,
-        headers=table_headers,
-        tablefmt="fancy_grid",
-        colalign=["left", "left", "left"],
-        maxcolwidths=[20, 20, 70])
-
-    # Convert table_data to DataFrame
-    import pandas as pd
-    df = pd.DataFrame(table_data, columns=["URL", "Title", "Summary"])
-    import streamlit as st
-    st.table(df)
-    print(table)
-    # Save the combined table to a file
-    try:
-        save_in_file(table)
-    except Exception as save_results_err:
-        logger.error(f"Failed to save search results: {save_results_err}")
-
-
-def metaphor_scholar_search(query, include_domains=None, time_range="anytime"):
-    """
-    Search for papers using the Metaphor API.
-
-    Args:
-        query (str): The search query.
-        include_domains (list): List of domains to include.
-        time_range (str): Time range for published articles ("day", "week", "month", "year", "anytime").
-
-    Returns:
-        MetaphorResponse: The response from the Metaphor API.
-    """
-    client = get_metaphor_client()
-    try:
-        if time_range == "day":
-            start_published_date = (datetime.utcnow() - timedelta(days=1)).strftime('%Y-%m-%dT%H:%M:%SZ')
-        elif time_range == "week":
-            start_published_date = (datetime.utcnow() - timedelta(weeks=1)).strftime('%Y-%m-%dT%H:%M:%SZ')
-        elif time_range == "month":
-            start_published_date = (datetime.utcnow() - timedelta(weeks=4)).strftime('%Y-%m-%dT%H:%M:%SZ')
-        elif time_range == "year":
-            start_published_date = (datetime.utcnow() - timedelta(days=365)).strftime('%Y-%m-%dT%H:%M:%SZ')
-        else:
-            start_published_date = None
-
-        response = client.search(query, include_domains=include_domains, start_published_date=start_published_date, use_autoprompt=True)
-        return response
-    except Exception as e:
-        logger.error(f"Error in searching papers: {e}")
-
-def get_exa_answer(query: str, system_prompt: str = None) -> dict:
-    """
-    Get an AI-generated answer for a query using Exa's answer endpoint.
-    
-    Args:
-        query (str): The search query to get an answer for
-        system_prompt (str, optional): Custom system prompt for the LLM. If None, uses default prompt.
-        
-    Returns:
-        dict: Response containing answer, citations, and cost information
-            {
-                "answer": str,
-                "citations": list[dict],
-                "costDollars": dict
-            }
-    """
-    exa = get_metaphor_client()
-    try:
-        # Use default system prompt if none provided
-        if system_prompt is None:
-            system_prompt = (
-                "I am doing research to write factual content. "
-                "Help me find answers for content generation task. "
-                "Provide detailed, well-structured answers with clear citations."
-            )
-
-        logger.info(f"Getting Exa answer for query: {query}")
-        logger.debug(f"Using system prompt: {system_prompt}")
-
-        # Make API call to get answer with system_prompt parameter
-        result = exa.answer(
-            query,
-            model="exa",
-            text=True  # Include full text in citations
-        )
-
-        if not result or not result.get('answer'):
-            logger.warning("No answer received from Exa")
-            return None
-
-        # Format response to match expected structure
-        response = {
-            "answer": result.get('answer'),
-            "citations": result.get('citations', []),
-            "costDollars": result.get('costDollars', {"total": 0})
-        }
-
-        return response
-
-    except Exception as e:
-        logger.error(f"Error getting Exa answer: {e}")
-        return None
--- a/ToBeMigrated/ai_web_researcher/tavily_ai_search.py
+++ b/ToBeMigrated/ai_web_researcher/tavily_ai_search.py
@@ -1,218 +0,0 @@
-"""
-This Python script uses the Tavily AI service to perform advanced searches based on specified keywords and options. It retrieves Tavily AI search results, pretty-prints them using Rich and Tabulate, and provides additional information such as the answer to the search query and follow-up questions.
-
-Features:
- Utilizes the Tavily AI service for advanced searches.
- Retrieves API keys from the environment variables loaded from a .env file.
- Configures logging with Loguru for informative messages.
- Implements a retry mechanism using Tenacity to handle transient failures during Tavily searches.
- Displays search results, including titles, snippets, and links, in a visually appealing table using Tabulate and Rich.
-
-Usage:
- Ensure the necessary API keys are set in the .env file.
- Run the script to perform a Tavily AI search with specified keywords and options.
- The search results, including titles, snippets, and links, are displayed in a formatted table.
- Additional information, such as the answer to the search query and follow-up questions, is presented in separate tables.
-
-Modifications:
- To modify the script, update the environment variables in the .env file with the required API keys.
- Adjust the search parameters, such as keywords and search depth, in the `do_tavily_ai_search` function as needed.
- Customize logging configurations and table formatting according to preferences.
-
-To-Do (TBD):
- Consider adding further enhancements or customization based on specific use cases.
-
-"""
-
-
-import os
-from pathlib import Path
-import sys
-from dotenv import load_dotenv
-from loguru import logger
-from tavily import TavilyClient
-from rich import print
-from tabulate import tabulate
-# Load environment variables from .env file
-load_dotenv(Path('../../.env'))
-from rich import print
-import streamlit as st
-# Configure logger
-logger.remove()
-logger.add(sys.stdout,
-           colorize=True,
-           format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-           )
-
-from .common_utils import save_in_file, cfg_search_param
-from tenacity import retry, stop_after_attempt, wait_random_exponential
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def do_tavily_ai_search(keywords, max_results=5, include_domains=None, search_depth="advanced", **kwargs):
-    """
-    Get Tavily AI search results based on specified keywords and options.
-    """
-    # Run Tavily search
-    logger.info(f"Running Tavily search on: {keywords}")
-
-    # Retrieve API keys
-    api_key = os.getenv('TAVILY_API_KEY')
-    if not api_key:
-        raise ValueError("API keys for Tavily is Not set.")
-
-    # Initialize Tavily client
-    try:
-        client = TavilyClient(api_key=api_key)
-    except Exception as err:
-        logger.error(f"Failed to create Tavily client. Check TAVILY_API_KEY: {err}")
-        raise
-
-    try:
-        # Create search parameters exactly matching Tavily's API format
-        tavily_search_result = client.search(
-            query=keywords,
-            search_depth="advanced",
-            time_range="year",
-            include_answer="advanced",
-            include_domains=[""] if not include_domains else include_domains,
-            max_results=max_results
-        )
-
-        if tavily_search_result:
-            print_result_table(tavily_search_result)
-            streamlit_display_results(tavily_search_result)
-            return tavily_search_result
-        return None
-
-    except Exception as err:
-        logger.error(f"Failed to do Tavily Research: {err}")
-        raise
-
-
-def streamlit_display_results(output_data):
-    """Display Tavily AI search results in Streamlit UI with enhanced visualization."""
-
-    # Display the 'answer' in Streamlit with enhanced styling
-    answer = output_data.get("answer", "No answer available")
-    st.markdown("### 🤖 AI-Generated Answer")
-    st.markdown(f"""
-    <div style="background-color: #f0f2f6; padding: 20px; border-radius: 10px; border-left: 5px solid #4CAF50;">
-        {answer}
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Display follow-up questions if available
-    follow_up_questions = output_data.get("follow_up_questions", [])
-    if follow_up_questions:
-        st.markdown("### ❓ Follow-up Questions")
-        for i, question in enumerate(follow_up_questions, 1):
-            st.markdown(f"**{i}.** {question}")
-    
-    # Prepare data for display with dataeditor
-    st.markdown("### 📊 Search Results")
-    
-    # Create a DataFrame for the results
-    import pandas as pd
-    results_data = []
-    
-    for item in output_data.get("results", []):
-        title = item.get("title", "")
-        snippet = item.get("content", "")
-        link = item.get("url", "")
-        results_data.append({
-            "Title": title,
-            "Content": snippet,
-            "Link": link
-        })
-    
-    if results_data:
-        df = pd.DataFrame(results_data)
-        
-        # Display the data editor
-        st.data_editor(
-            df,
-            column_config={
-                "Title": st.column_config.TextColumn(
-                    "Title",
-                    help="Article title",
-                    width="medium",
-                ),
-                "Content": st.column_config.TextColumn(
-                    "Content",
-                    help="Click the button below to view full content",
-                    width="large",
-                ),
-                "Link": st.column_config.LinkColumn(
-                    "Link",
-                    help="Click to visit the website",
-                    width="small",
-                    display_text="Visit Site"
-                ),
-            },
-            hide_index=True,
-            use_container_width=True,
-        )
-
-        # Add popovers for full content display
-        for item in output_data.get("results", []):
-            with st.popover(f"View content: {item.get('title', '')[:50]}..."):
-                st.markdown(item.get("content", ""))
-    else:
-        st.info("No results found for your search query.")
-
-
-def print_result_table(output_data):
-    """ Pretty print the tavily AI search result. """
-    # Prepare data for tabulate
-    table_data = []
-    for item in output_data.get("results"):
-        title = item.get("title", "")
-        snippet = item.get("content", "")
-        link = item.get("url", "")
-        table_data.append([title, snippet, link])
-    
-    # Define table headers
-    table_headers = ["Title", "Snippet", "Link"] 
-    # Display the table using tabulate
-    table = tabulate(table_data, 
-            headers=table_headers, 
-            tablefmt="fancy_grid", 
-            colalign=["left", "left", "left"], 
-            maxcolwidths=[30, 60, 30])
-    # Print the table
-    print(table)
-
-    # Save the combined table to a file
-    try:
-        save_in_file(table)
-    except Exception as save_results_err:
-        logger.error(f"Failed to save search results: {save_results_err}")
-    
-    # Display the 'answer' in a table
-    table_headers = [f"The answer to search query: {output_data.get('query')}"]
-    table_data = [[output_data.get("answer")]]
-    table = tabulate(table_data, 
-            headers=table_headers, 
-            tablefmt="fancy_grid", 
-            maxcolwidths=[80])
-    print(table)
-    # Save the combined table to a file
-    try:
-        save_in_file(table)
-    except Exception as save_results_err:
-        logger.error(f"Failed to save search results: {save_results_err}")
-    
-    # Display the 'follow_up_questions' in a table
-    if output_data.get("follow_up_questions"):
-        table_headers = [f"Search Engine follow up questions for query: {output_data.get('query')}"]
-        table_data = [[output_data.get("follow_up_questions")]]
-        table = tabulate(table_data, 
-            headers=table_headers, 
-            tablefmt="fancy_grid",
-            maxcolwidths=[80])
-        print(table)
-        try:
-            save_in_file(table)
-        except Exception as save_results_err:
-            logger.error(f"Failed to save search results: {save_results_err}")
--- a/ToBeMigrated/ai_writers/ai_agents_crew_writer.py
+++ b/ToBeMigrated/ai_writers/ai_agents_crew_writer.py
@@ -1,192 +0,0 @@
-import os
-import configparser
-import streamlit as st
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-# Initialize session state variables if not already done
-if 'progress' not in st.session_state:
-    st.session_state.progress = 0
-
-
-def create_agents(search_keywords):
-    """Create agents for content creation."""
-    try:
-        from crewai import Agent
-        from crewai_tools import SerperDevTool
-    except ImportError:
-        raise ImportError("The 'crewai' and/or 'crewai_tools' package is not installed. Please install them to use AI Agents Crew Writer features.")
-    search_tool = SerperDevTool()
-    google_api_key = os.getenv("GEMINI_API_KEY")
-    
-    llm = ChatGoogleGenerativeAI(
-        model="gemini-1.5-flash-latest", verbose=True, temperature=0.6, google_api_key=google_api_key
-    )
-
-    try:
-        role, goal, backstory = read_config("content_researcher")
-        content_researcher = Agent(
-            role=role, goal=goal, backstory=backstory, tools=[search_tool], memory=True, 
-            verbose=True, max_rpm=None, max_iter=10, allow_delegation=False, llm=llm
-        )
-
-        role, goal, backstory = read_config("content_outliner")
-        content_outliner = Agent(
-            role=role, goal=goal, backstory=backstory, memory=True, 
-            verbose=True, tools=[search_tool], max_rpm=10, max_iter=10, allow_delegation=False, llm=llm
-        )
-
-        role, goal, backstory = read_config("content_writer")
-        content_writer = Agent(
-            role=role, goal=goal, backstory=backstory, memory=True, 
-            verbose=True, max_rpm=10, max_iter=15, allow_delegation=False, llm=llm
-        )
-
-        role, goal, backstory = read_config("content_reviewer")
-        content_reviewer = Agent(
-            role=role, goal=goal, backstory=backstory, memory=True, 
-            verbose=True, max_rpm=10, max_iter=10, allow_delegation=False, llm=llm
-        )
-
-    except Exception as err:
-        st.error(f"Error creating agents: {err}")
-        st.stop()
-
-    return [content_researcher, content_outliner, content_writer, content_reviewer]
-
-def create_tasks(agents, search_keywords):
-    """Create tasks for the agents."""
-    try:
-        from crewai import Task
-    except ImportError:
-        raise ImportError("The 'crewai' package is not installed. Please install it to use AI Agents Crew Writer features.")
-    try:
-        task_description, expected_output = read_config("research_task")
-        research_task = Task(
-            description=f"The main focus keywords are: '{search_keywords}'.\n{task_description}.",
-            expected_output=expected_output,
-            agent=agents[0]
-        )
-
-        task_description, expected_output = read_config("outline_task")
-        outline_task = Task(
-            description=f"{task_description}.\nThe main focus keywords are {search_keywords}",
-            expected_output=expected_output,
-            agent=agents[1]
-        )
-
-        task_description, expected_output = read_config("writer_task")
-        writer_task = Task(
-            description=f"{task_description}\nThe main focus keywords are {search_keywords}.",
-            expected_output=expected_output,
-            agent=agents[2]
-        )
-
-        task_description, expected_output = read_config("review_task")
-        proofread_task = Task(
-            description=f"{task_description}.\nThe main focus keywords are: {search_keywords}.",
-            expected_output=expected_output,
-            agent=agents[3]
-        )
-
-    except Exception as err:
-        st.error(f"Error creating tasks: {err}")
-        st.stop()
-
-    return [research_task, outline_task, writer_task, proofread_task]
-
-def execute_tasks(agents, tasks, lang):
-    """Execute tasks with the agents."""
-    try:
-        from crewai import Crew
-    except ImportError:
-        raise ImportError("The 'crewai' package is not installed. Please install it to use AI Agents Crew Writer features.")
-    crew = Crew(
-        agents=agents,
-        tasks=tasks,
-        verbose=2,
-        language=lang
-    )
-    try:
-        result = crew.kickoff()
-    except Exception as err:
-        st.error(f"Error executing tasks: {err}")
-        st.stop()
-    return result
-
-def read_config(which_member):
-    """Reads configuration for the specified agent or task."""
-    team_dir = os.path.join(os.getcwd(), "lib", "workspace", "my_content_team")
-    config_file = None
-    
-    if 'content_researcher' in which_member or 'research_task' in which_member:
-        config_file = os.path.join(team_dir, "content_researcher.txt")
-    elif 'content_writer' in which_member or 'writer_task' in which_member:
-        config_file = os.path.join(team_dir, "content_writer.txt")
-    elif 'content_reviewer' in which_member or 'review_task' in which_member:
-        config_file = os.path.join(team_dir, "content_reviewer.txt")
-    elif 'content_outliner' in which_member or 'outline_task' in which_member:
-        config_file = os.path.join(team_dir, "content_outliner.txt")
-
-    try:
-        config = configparser.ConfigParser()
-        config.read(config_file)
-        role = config.get('main', 'role')
-        goal = config.get('main', 'goal')
-        backstory = config.get('backstory', 'text')
-    except Exception as err:
-        st.error(f"Error reading config: {err}")
-        st.stop()
-
-    if 'task' not in which_member:
-        return role, goal, backstory
-    else:
-        try:
-            task_description = config.get('task', 'task_description')
-            expected_output = config.get('task', 'task_expected_output')
-        except Exception as err:
-            st.error(f"Error reading task config: {err}")
-            st.stop()
-        return task_description, expected_output
-
-
-def ai_agents_writers(search_keywords, lang="en"):
-    """Main function to kickoff AI Agents content team."""
-
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    
-    st.session_state.progress = 0
-    status_text.text("Setting up environment...")
-    status_text.text("Creating Agents team...")
-    try:
-        agents = create_agents(search_keywords)
-        st.session_state.progress += 10
-        progress_bar.progress(st.session_state.progress)
-    except Exception as err:
-        st.error(f"Failed in creating Agents team: {err}")
-        st.stop()
-
-    status_text.text("Creating tasks for Agents team...")
-    try:
-        tasks = create_tasks(agents, search_keywords)
-        st.session_state.progress += 25
-        progress_bar.progress(st.session_state.progress)
-    except Exception as err:
-        st.error(f"Failed in creating tasks for Agents team: {err}")
-        st.stop()
-
-    status_text.text("AI Agents busy writing your content...")
-    try:
-        result = execute_tasks(agents, tasks, lang)
-        st.session_state.progress += 60
-        progress_bar.progress(st.session_state.progress)
-        status_text.text("Tasks executed successfully.")
-        st.success("Successfully executed tasks.")
-        
-        # Display result with an option to copy the content
-        st.markdown("### Result")
-        st.code(result, language='markdown')
-        st.download_button('Download Content', data=result, file_name='alwrity_result.md')
-    except Exception as err:
-        st.error(f"Failed to execute tasks: {err}")
-
--- a/ToBeMigrated/ai_writers/ai_blog_faqs_writer/README.md
+++ b/ToBeMigrated/ai_writers/ai_blog_faqs_writer/README.md
@@ -1,192 +0,0 @@
-# AI-Powered FAQ Generator
-
-A sophisticated FAQ generation system that creates comprehensive, well-researched FAQs from various content sources. This tool leverages AI to analyze content, conduct web research, and generate detailed FAQs with customizable options.
-
-## Features
-
-### Content Processing
- **Multiple Input Sources**
-  - Direct text input
-  - File uploads (DOCX, TXT)
-  - URL content extraction
-  - Support for any content type (general, technical, educational, etc.)
-
-### Research Capabilities
- **Multi-level Search Depth**
-  - **Basic**: Google Search for quick, general information
-  - **Comprehensive**: Tavily AI for detailed, in-depth research
-  - **Expert**: Metaphor AI for specialized, expert-level content
-
-### Customization Options
- **Target Audience**
-  - Beginner
-  - Intermediate
-  - Expert
-
- **FAQ Style**
-  - Technical
-  - Conversational
-  - Professional
-
- **Advanced Features**
-  - Emoji inclusion
-  - Code example generation
-  - Reference integration
-  - Customizable time range for research
-  - Multi-language support
-
-### Output Formats
- Interactive preview
- Markdown
- HTML
- JSON
-
-## Installation
-
-1. Clone the repository
-2. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-
-### Basic Usage
-```python
-from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import FAQGenerator, FAQConfig
-
-# Initialize with default configuration
-generator = FAQGenerator()
-
-# Generate FAQs from content
-faqs = await generator.generate_faqs("Your content here")
-```
-
-### Advanced Configuration
-```python
-from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import (
-    FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
-)
-
-# Custom configuration
-config = FAQConfig(
-    num_faqs=10,
-    target_audience=TargetAudience.INTERMEDIATE,
-    faq_style=FAQStyle.TECHNICAL,
-    include_emojis=True,
-    include_code_examples=True,
-    include_references=True,
-    search_depth=SearchDepth.COMPREHENSIVE,
-    time_range="last_6_months",
-    language="English"
-)
-
-generator = FAQGenerator(config)
-```
-
-### Web Interface
-Run the Streamlit interface:
-```bash
-streamlit run lib/ai_writers/ai_blog_faqs_writer/faqs_ui.py
-```
-
-## Research Process
-
-1. **Content Analysis**
-   - Identifies key topics and concepts
-   - Extracts potential questions
-   - Determines research requirements
-
-2. **Web Research**
-   - Selects appropriate search function based on depth
-   - Gathers relevant information
-   - Validates and cross-references data
-
-3. **FAQ Generation**
-   - Creates comprehensive questions
-   - Provides detailed answers
-   - Includes code examples (if applicable)
-   - Adds references and citations
-
-## Output Structure
-
-Each FAQ item includes:
- Question
- Detailed answer
- Category
- Code example (if applicable)
- References
- Confidence score
- Last updated timestamp
-
-## Configuration Options
-
-### FAQConfig Parameters
- `num_faqs`: Number of FAQs to generate (default: 5)
- `target_audience`: Target audience level (default: INTERMEDIATE)
- `faq_style`: Writing style (default: PROFESSIONAL)
- `include_emojis`: Whether to include emojis (default: True)
- `include_code_examples`: Whether to include code examples (default: True)
- `include_references`: Whether to include references (default: True)
- `search_depth`: Research depth level (default: COMPREHENSIVE)
- `time_range`: Time range for research (default: "last_6_months")
- `language`: Output language (default: "English")
-
-## Research Depth Options
-
-### Basic (Google Search)
- Quick, general information
- Broad coverage
- Suitable for basic topics
-
-### Comprehensive (Tavily AI)
- Detailed, in-depth research
- Multiple source integration
- Best for most use cases
-
-### Expert (Metaphor AI)
- Specialized, expert-level content
- Advanced topic coverage
- Technical and academic focus
-
-## Best Practices
-
-1. **Content Preparation**
-   - Provide clear, well-structured content
-   - Include key terms and concepts
-   - Specify target audience and style
-
-2. **Research Selection**
-   - Use Basic for general topics
-   - Choose Comprehensive for detailed analysis
-   - Select Expert for technical subjects
-
-3. **Output Review**
-   - Verify accuracy of information
-   - Check code examples
-   - Validate references
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Commit your changes
-4. Push to the branch
-5. Create a Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Support
-
-For support, please open an issue in the repository or contact the maintainers.
-
-## Acknowledgments
-
- OpenAI for GPT integration
- Google Search API
- Tavily AI
- Metaphor AI
- BeautifulSoup for web scraping
- Streamlit for UI 
--- a/ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_generator_blog.py
+++ b/ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_generator_blog.py
@@ -1,444 +0,0 @@
-"""
-Enhanced FAQ Generator
-
-This module provides a comprehensive FAQ generation system that can create detailed,
-well-researched FAQs from various content sources with customizable options.
-"""
-
-import sys
-import json
-import re
-from typing import Dict, List, Optional, Union
-from pathlib import Path
-from enum import Enum
-from dataclasses import dataclass
-from loguru import logger
-
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from lib.ai_web_researcher.google_serp_search import google_search
-from lib.ai_web_researcher.tavily_ai_search import do_tavily_ai_search
-from lib.ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
-
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-          format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
-
-class TargetAudience(Enum):
-    BEGINNER = "beginner"
-    INTERMEDIATE = "intermediate"
-    EXPERT = "expert"
-
-class FAQStyle(Enum):
-    TECHNICAL = "technical"
-    CONVERSATIONAL = "conversational"
-    PROFESSIONAL = "professional"
-
-class SearchDepth(Enum):
-    BASIC = "basic"
-    COMPREHENSIVE = "comprehensive"
-    EXPERT = "expert"
-
-@dataclass
-class FAQConfig:
-    """Configuration for FAQ generation."""
-    num_faqs: int = 5
-    target_audience: TargetAudience = TargetAudience.INTERMEDIATE
-    faq_style: FAQStyle = FAQStyle.PROFESSIONAL
-    include_emojis: bool = True
-    include_code_examples: bool = True
-    include_references: bool = True
-    search_depth: SearchDepth = SearchDepth.COMPREHENSIVE
-    time_range: str = "last_6_months"
-    exclude_domains: List[str] = None
-    language: str = "English"
-    selected_search_queries: List[str] = None
-
-@dataclass
-class FAQItem:
-    """Individual FAQ item with metadata."""
-    question: str
-    answer: str
-    category: str
-    code_example: Optional[str] = None
-    references: List[Dict[str, str]] = None
-    confidence_score: float = 0.0
-    last_updated: str = None
-
-class FAQGenerator:
-    """Enhanced FAQ Generator with research capabilities."""
-    
-    def __init__(self, config: Optional[FAQConfig] = None):
-        """Initialize the FAQ generator with optional configuration."""
-        self.config = config or FAQConfig()
-        self.faqs: List[FAQItem] = []
-        self.research_results = {}
-        self.search_queries = []
-        
-    def generate_search_queries(self, content: str) -> List[str]:
-        """Generate search queries based on the content."""
-        try:
-            prompt = f"""Based on the following content, generate 5 specific search queries that would help create comprehensive FAQs.
-            Content: {content}
-            
-            Guidelines for search queries:
-            1. Focus on key concepts and terms
-            2. Include common questions users might have
-            3. Cover technical aspects that need clarification
-            4. Include best practices and recommendations
-            5. Make queries specific and focused
-            
-            Please provide exactly 5 search queries, one per line.
-            Do not include numbers or bullet points in the queries.
-            """
-            
-            response = llm_text_gen(prompt)
-            # Clean up the queries by removing numbers and extra spaces
-            queries = []
-            for line in response.split('\n'):
-                # Remove any leading numbers, dots, or spaces
-                cleaned = re.sub(r'^\d+\.\s*', '', line.strip())
-                if cleaned:
-                    queries.append(cleaned)
-            
-            self.search_queries = queries[:5]  # Ensure we only get 5 queries
-            return self.search_queries
-            
-        except Exception as err:
-            logger.error(f"Failed to generate search queries: {err}")
-            return []
-    
-    def _clean_search_query(self, query: str) -> str:
-        """Clean up a search query by removing numbers and extra formatting."""
-        # Remove any leading numbers, dots, or spaces
-        cleaned = re.sub(r'^\d+\.\s*', '', query.strip())
-        # Remove any quotes
-        cleaned = cleaned.replace('"', '').replace("'", '')
-        # Remove any extra spaces
-        cleaned = ' '.join(cleaned.split())
-        return cleaned
-    
-    def generate_faqs(self, content: str, content_type: str = "general") -> List[FAQItem]:
-        """Generate FAQs from the given content with research integration."""
-        try:
-            if not self.config.selected_search_queries:
-                raise ValueError("No search queries selected. Please select queries to proceed.")
-            
-            # Clean up selected queries
-            cleaned_queries = [self._clean_search_query(q) for q in self.config.selected_search_queries]
-            self.config.selected_search_queries = cleaned_queries
-            
-            # Step 1: Research the topic using selected queries
-            research_results = self._conduct_research(content)
-            
-            # Step 2: Generate initial FAQs
-            initial_faqs = self._generate_initial_faqs(content, research_results)
-            
-            # Step 3: Enhance FAQs with research
-            enhanced_faqs = self._enhance_faqs_with_research(initial_faqs, research_results)
-            
-            # Step 4: Add code examples if requested
-            if self.config.include_code_examples:
-                enhanced_faqs = self._add_code_examples(enhanced_faqs)
-            
-            # Step 5: Add references if requested
-            if self.config.include_references:
-                enhanced_faqs = self._add_references(enhanced_faqs, research_results)
-            
-            self.faqs = enhanced_faqs
-            return enhanced_faqs
-            
-        except Exception as err:
-            logger.error(f"Failed to generate FAQs: {err}")
-            raise
-    
-    def _conduct_research(self, content: str) -> Dict:
-        """Conduct online research based on the selected search queries."""
-        try:
-            research_results = {}
-            
-            for query in self.config.selected_search_queries:
-                try:
-                    # Clean the query before searching
-                    cleaned_query = self._clean_search_query(query)
-                    logger.info(f"Researching query: {cleaned_query}")
-                    
-                    # Select search function based on search depth
-                    if self.config.search_depth == SearchDepth.BASIC:
-                        results = google_search(cleaned_query)
-                    elif self.config.search_depth == SearchDepth.COMPREHENSIVE:
-                        results = do_tavily_ai_search(cleaned_query)
-                    elif self.config.search_depth == SearchDepth.EXPERT:
-                        results = metaphor_search_articles(cleaned_query)
-                    else:
-                        logger.warning(f"Unknown search depth: {self.config.search_depth}, defaulting to Google search")
-                        results = google_search(cleaned_query)
-                    
-                    research_results[query] = results
-                    logger.info(f"Research completed for query: {query}")
-                    
-                except Exception as err:
-                    logger.error(f"Failed to research query '{query}': {err}")
-                    continue
-            
-            return research_results
-            
-        except Exception as err:
-            logger.error(f"Failed to conduct research: {err}")
-            return {}
-    
-    def _generate_initial_faqs(self, content: str, research_results: Dict) -> List[FAQItem]:
-        """Generate initial FAQs using LLM."""
-        try:
-            system_prompt = f"""You are an expert FAQ generator with deep knowledge in content creation and technical writing.
-            Your task is to create comprehensive FAQs based on the given content and research.
-
-            Guidelines:
-            1. Target Audience: {self.config.target_audience.value}
-            2. Style: {self.config.faq_style.value}
-            3. Include emojis: {self.config.include_emojis}
-            4. Language: {self.config.language}
-            5. Number of FAQs: {self.config.num_faqs}
-
-            Create FAQs that are:
-            - Clear and concise
-            - Well-structured
-            - Technically accurate
-            - Engaging and informative
-            - Based on the provided research
-            - Relevant to the target audience
-            - Written in the specified style
-
-            Format each FAQ exactly as follows:
-            Q: [Your question here]
-            A: [Your detailed answer here]
-            Category: [Category name]
-            Confidence: [Score between 0 and 1]
-            ---
-            """
-            
-            prompt = f"""Content to generate FAQs from:
-            {content}
-
-            Research Results:
-            {json.dumps(research_results, indent=2)}
-
-            Please generate {self.config.num_faqs} FAQs following the guidelines above.
-            Each FAQ must be separated by '---' and include all required fields.
-            """
-            
-            response = llm_text_gen(prompt, system_prompt=system_prompt)
-            logger.info(f"LLM Response: {response}")
-            
-            # Parse the response into FAQItem objects
-            faqs = []
-            current_faq = None
-            
-            for line in response.split('\n'):
-                line = line.strip()
-                if not line or line == '---':
-                    if current_faq and current_faq.question and current_faq.answer:
-                        faqs.append(current_faq)
-                        current_faq = None
-                    continue
-                
-                if line.startswith('Q:'):
-                    if current_faq and current_faq.question and current_faq.answer:
-                        faqs.append(current_faq)
-                    current_faq = FAQItem(question=line[2:].strip(), answer="", category="")
-                elif line.startswith('A:'):
-                    if current_faq:
-                        current_faq.answer = line[2:].strip()
-                elif line.startswith('Category:'):
-                    if current_faq:
-                        current_faq.category = line[9:].strip()
-                elif line.startswith('Confidence:'):
-                    if current_faq:
-                        try:
-                            current_faq.confidence_score = float(line[11:].strip())
-                        except ValueError:
-                            current_faq.confidence_score = 0.5
-            
-            # Add the last FAQ if it exists and is complete
-            if current_faq and current_faq.question and current_faq.answer:
-                faqs.append(current_faq)
-            
-            logger.info(f"Generated {len(faqs)} FAQs")
-            return faqs
-            
-        except Exception as err:
-            logger.error(f"Failed to generate initial FAQs: {err}")
-            raise
-    
-    def _enhance_faqs_with_research(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
-        """Enhance FAQs with research findings."""
-        try:
-            enhanced_faqs = []
-            
-            for faq in faqs:
-                # Find relevant research for this FAQ
-                relevant_research = self._find_relevant_research(faq, research_results)
-                
-                if relevant_research:
-                    # Enhance the answer with research findings
-                    enhancement_prompt = f"""Enhance the following FAQ answer with the provided research:
-                    
-                    Question: {faq.question}
-                    Current Answer: {faq.answer}
-                    
-                    Research:
-                    {json.dumps(relevant_research, indent=2)}
-                    
-                    Please enhance the answer while:
-                    1. Maintaining the original style and tone
-                    2. Adding relevant information from the research
-                    3. Ensuring technical accuracy
-                    4. Keeping the answer concise and clear
-                    """
-                    
-                    enhanced_answer = llm_text_gen(enhancement_prompt)
-                    faq.answer = enhanced_answer
-                
-                enhanced_faqs.append(faq)
-            
-            return enhanced_faqs
-            
-        except Exception as err:
-            logger.error(f"Failed to enhance FAQs with research: {err}")
-            return faqs
-    
-    def _add_code_examples(self, faqs: List[FAQItem]) -> List[FAQItem]:
-        """Add code examples to FAQs where applicable."""
-        try:
-            for faq in faqs:
-                if self._is_technical_question(faq.question):
-                    code_prompt = f"""Generate a code example for the following FAQ:
-                    Question: {faq.question}
-                    Answer: {faq.answer}
-                    
-                    Please provide a relevant code example that demonstrates the concept.
-                    Include comments and explanations where necessary.
-                    """
-                    
-                    code_example = llm_text_gen(code_prompt)
-                    faq.code_example = code_example
-            
-            return faqs
-            
-        except Exception as err:
-            logger.error(f"Failed to add code examples: {err}")
-            return faqs
-    
-    def _add_references(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
-        """Add references to FAQs based on research results."""
-        try:
-            for faq in faqs:
-                relevant_research = self._find_relevant_research(faq, research_results)
-                if relevant_research:
-                    references = []
-                    for source, content in relevant_research.items():
-                        references.append({
-                            "source": source,
-                            "content": content
-                        })
-                    faq.references = references
-            
-            return faqs
-            
-        except Exception as err:
-            logger.error(f"Failed to add references: {err}")
-            return faqs
-    
-    def _find_relevant_research(self, faq: FAQItem, research_results: Dict) -> Dict:
-        """Find research results relevant to a specific FAQ."""
-        relevant_research = {}
-        for topic, results in research_results.items():
-            if any(keyword in faq.question.lower() for keyword in topic.lower().split()):
-                relevant_research[topic] = results
-        return relevant_research
-    
-    def _is_technical_question(self, question: str) -> bool:
-        """Determine if a question is technical and might benefit from a code example."""
-        technical_keywords = ["code", "program", "function", "method", "class", "api", "syntax", "error", "debug"]
-        return any(keyword in question.lower() for keyword in technical_keywords)
-    
-    def to_markdown(self) -> str:
-        """Convert FAQs to markdown format."""
-        markdown = "# Frequently Asked Questions\n\n"
-        
-        for faq in self.faqs:
-            markdown += f"## {faq.question}\n\n"
-            markdown += f"{faq.answer}\n\n"
-            
-            if faq.code_example:
-                markdown += "```\n"
-                markdown += f"{faq.code_example}\n"
-                markdown += "```\n\n"
-            
-            if faq.references:
-                markdown += "### References\n"
-                for ref in faq.references:
-                    markdown += f"- {ref['source']}\n"
-                markdown += "\n"
-        
-        return markdown
-    
-    def to_html(self) -> str:
-        """Convert FAQs to HTML format."""
-        html = """
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <title>Frequently Asked Questions</title>
-            <style>
-                body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }
-                .faq { margin-bottom: 30px; }
-                .question { font-weight: bold; font-size: 1.2em; color: #2c3e50; }
-                .answer { margin: 10px 0; }
-                .code-example { background: #f8f9fa; padding: 15px; border-radius: 4px; }
-                .references { margin-top: 15px; font-size: 0.9em; }
-            </style>
-        </head>
-        <body>
-            <h1>Frequently Asked Questions</h1>
-        """
-        
-        for faq in self.faqs:
-            html += f"""
-            <div class="faq">
-                <div class="question">{faq.question}</div>
-                <div class="answer">{faq.answer}</div>
-            """
-            
-            if faq.code_example:
-                html += f"""
-                <div class="code-example">
-                    <pre><code>{faq.code_example}</code></pre>
-                </div>
-                """
-            
-            if faq.references:
-                html += """
-                <div class="references">
-                    <h3>References</h3>
-                    <ul>
-                """
-                for ref in faq.references:
-                    html += f"""
-                        <li>{ref['source']}</li>
-                    """
-                html += """
-                    </ul>
-                </div>
-                """
-            
-            html += """
-            </div>
-            """
-        
-        html += """
-        </body>
-        </html>
-        """
-        
-        return html
--- a/ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_ui.py
+++ b/ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_ui.py
@@ -1,312 +0,0 @@
-"""
-Streamlit UI for FAQ Generator
-
-This module provides a user-friendly interface for generating FAQs from various content sources.
-"""
-
-import streamlit as st
-from pathlib import Path
-from typing import Optional
-import json
-import requests
-from bs4 import BeautifulSoup
-import logging
-import pyperclip
-
-from .faqs_generator_blog import FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def copy_to_clipboard(text: str) -> None:
-    """Copy text to clipboard and show success message."""
-    try:
-        pyperclip.copy(text)
-        st.success("Copied to clipboard!")
-    except Exception as e:
-        st.error(f"Failed to copy to clipboard: {str(e)}")
-
-def fetch_url_content(url):
-    """Fetch and extract content from a URL."""
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Remove script and style elements
-        for script in soup(["script", "style"]):
-            script.decompose()
-            
-        # Get text
-        text = soup.get_text()
-        
-        # Break into lines and remove leading and trailing space
-        lines = (line.strip() for line in text.splitlines())
-        # Break multi-headlines into a line each
-        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-        # Drop blank lines
-        text = '\n'.join(chunk for chunk in chunks if chunk)
-        
-        return text
-    except Exception as e:
-        st.error(f"Error fetching URL content: {str(e)}")
-        return None
-
-def main():
-    st.title("FAQ Generator")
-    st.markdown("Generate comprehensive FAQs from your content with research integration.")
-    
-    # Initialize session state variables if they don't exist
-    if 'search_queries' not in st.session_state:
-        st.session_state.search_queries = []
-    if 'selected_queries' not in st.session_state:
-        st.session_state.selected_queries = []
-    if 'research_completed' not in st.session_state:
-        st.session_state.research_completed = False
-    if 'research_results' not in st.session_state:
-        st.session_state.research_results = {}
-    if 'faq_config' not in st.session_state:
-        st.session_state.faq_config = None
-    if 'generator' not in st.session_state:
-        st.session_state.generator = FAQGenerator()
-    if 'generated_faqs' not in st.session_state:
-        st.session_state.generated_faqs = None
-    if 'output_format' not in st.session_state:
-        st.session_state.output_format = "Preview"
-    
-    # Sidebar for configuration
-    with st.sidebar:
-        st.header("Configuration")
-        
-        # Basic settings
-        num_faqs = st.slider("Number of FAQs", 1, 20, 5)
-        target_audience = st.selectbox(
-            "Target Audience",
-            [audience.value for audience in TargetAudience]
-        )
-        faq_style = st.selectbox(
-            "FAQ Style",
-            [style.value for style in FAQStyle]
-        )
-        
-        # Advanced settings
-        with st.expander("Advanced Settings"):
-            include_emojis = st.checkbox("Include Emojis", value=True)
-            include_code_examples = st.checkbox("Include Code Examples", value=True)
-            include_references = st.checkbox("Include References", value=True)
-            
-            search_depth = st.selectbox(
-                "Search Depth",
-                [depth.value for depth in SearchDepth]
-            )
-            time_range = st.selectbox(
-                "Time Range",
-                ["last_month", "last_6_months", "last_year", "all_time"]
-            )
-            language = st.text_input("Language", value="English")
-    
-    # Main content area
-    content_type = st.radio(
-        "Content Source",
-        ["Direct Input", "File Upload", "URL"]
-    )
-    
-    content = ""
-    if content_type == "Direct Input":
-        content = st.text_area("Enter your content", height=300)
-    
-    elif content_type == "URL":
-        url = st.text_input("Enter URL")
-        if url:
-            content = fetch_url_content(url)
-            if content:
-                st.text_area("Extracted Content", content, height=300)
-    
-    # Step 1: Generate search queries
-    if content and not st.session_state.search_queries:
-        if st.button("Generate Search Queries"):
-            with st.spinner("Generating search queries..."):
-                search_queries = st.session_state.generator.generate_search_queries(content)
-                if search_queries:
-                    st.session_state.search_queries = search_queries
-                    st.session_state.selected_queries = []  # Reset selected queries
-                    st.session_state.research_completed = False  # Reset research status
-                    st.session_state.research_results = {}  # Reset research results
-                    st.session_state.faq_config = None  # Reset config
-                    st.session_state.generated_faqs = None  # Reset generated FAQs
-                    st.success("Search queries generated successfully!")
-    
-    # Step 2: Display and select search queries
-    if st.session_state.search_queries:
-        st.subheader("Select Search Queries")
-        st.info("Select the queries you want to use for web research. You can select multiple queries.")
-        
-        # Create checkboxes for each search query
-        selected_queries = []
-        for query in st.session_state.search_queries:
-            if st.checkbox(query, key=f"query_{query}", value=query in st.session_state.selected_queries):
-                selected_queries.append(query)
-        
-        # Update selected queries in session state
-        st.session_state.selected_queries = selected_queries
-        
-        # Step 3: Do web research
-        if st.session_state.selected_queries and not st.session_state.research_completed:
-            if st.button("Do Web Research"):
-                try:
-                    # Create config with selected queries
-                    config = FAQConfig(
-                        num_faqs=num_faqs,
-                        target_audience=TargetAudience(target_audience),
-                        faq_style=FAQStyle(faq_style),
-                        include_emojis=include_emojis,
-                        include_code_examples=include_code_examples,
-                        include_references=include_references,
-                        search_depth=SearchDepth(search_depth),
-                        time_range=time_range,
-                        language=language,
-                        selected_search_queries=selected_queries
-                    )
-                    
-                    # Store config in session state
-                    st.session_state.faq_config = config
-                    
-                    # Update generator with config
-                    st.session_state.generator.config = config
-                    
-                    # Do research
-                    with st.spinner("Conducting web research..."):
-                        research_results = st.session_state.generator._conduct_research(content)
-                        st.session_state.research_completed = True
-                        st.session_state.research_results = research_results
-                        st.success("Web research completed successfully!")
-                        
-                        # Display research results
-                        st.subheader("Research Results")
-                        for query, results in research_results.items():
-                            with st.expander(f"Results for: {query}"):
-                                if isinstance(results, dict):
-                                    st.json(results)
-                                else:
-                                    st.text(results)
-                
-                except Exception as e:
-                    st.error(f"Error during web research: {str(e)}")
-                    st.error("Please try again with different search queries or adjust the search depth.")
-        
-        # Step 4: Generate FAQs
-        if st.session_state.research_completed and st.session_state.research_results and st.session_state.faq_config:
-            if st.button("Generate FAQs"):
-                try:
-                    # Update generator with stored config
-                    st.session_state.generator.config = st.session_state.faq_config
-                    
-                    # Generate FAQs
-                    with st.spinner("Generating FAQs..."):
-                        logger.info("Starting FAQ generation...")
-                        faqs = st.session_state.generator.generate_faqs(content)
-                        logger.info(f"Generated {len(faqs) if faqs else 0} FAQs")
-                        
-                        if not faqs:
-                            st.error("No FAQs were generated. Please try again.")
-                            return
-                            
-                        st.session_state.generated_faqs = faqs
-                        st.success("FAQs generated successfully!")
-                
-                except Exception as e:
-                    logger.error(f"Error generating FAQs: {str(e)}")
-                    st.error(f"Error generating FAQs: {str(e)}")
-                    st.error("Please try again or adjust your settings.")
-        
-        # Display generated FAQs if they exist
-        if st.session_state.generated_faqs:
-            st.subheader("Generated FAQs")
-            
-            # Output format selection
-            output_format = st.radio(
-                "Output Format",
-                ["Preview", "Markdown", "HTML", "JSON"],
-                key="output_format"
-            )
-            
-            # Create columns for copy and download buttons
-            col1, col2 = st.columns(2)
-            
-            if output_format == "Preview":
-                # Create a formatted text for copying
-                preview_text = ""
-                for i, faq in enumerate(st.session_state.generated_faqs, 1):
-                    preview_text += f"{i}. {faq.question}\n"
-                    preview_text += f"{faq.answer}\n\n"
-                    if faq.code_example:
-                        preview_text += f"Code Example:\n{faq.code_example}\n\n"
-                    if faq.references:
-                        preview_text += "References:\n"
-                        for ref in faq.references:
-                            preview_text += f"- {ref['source']}\n"
-                    preview_text += "\n"
-                
-                with col1:
-                    if st.button("Copy to Clipboard", key="copy_preview"):
-                        copy_to_clipboard(preview_text)
-                
-                # Display the FAQs
-                for i, faq in enumerate(st.session_state.generated_faqs, 1):
-                    with st.expander(f"{i}. {faq.question}"):
-                        st.markdown(faq.answer)
-                        if faq.code_example:
-                            st.code(faq.code_example)
-                        if faq.references:
-                            st.markdown("**References:**")
-                            for ref in faq.references:
-                                st.markdown(f"- {ref['source']}")
-            
-            elif output_format == "Markdown":
-                markdown_output = st.session_state.generator.to_markdown()
-                st.code(markdown_output, language="markdown")
-                
-                with col1:
-                    if st.button("Copy to Clipboard", key="copy_markdown"):
-                        copy_to_clipboard(markdown_output)
-                with col2:
-                    st.download_button(
-                        "Download Markdown",
-                        markdown_output,
-                        file_name="faqs.md",
-                        mime="text/markdown"
-                    )
-            
-            elif output_format == "HTML":
-                html_output = st.session_state.generator.to_html()
-                st.code(html_output, language="html")
-                
-                with col1:
-                    if st.button("Copy to Clipboard", key="copy_html"):
-                        copy_to_clipboard(html_output)
-                with col2:
-                    st.download_button(
-                        "Download HTML",
-                        html_output,
-                        file_name="faqs.html",
-                        mime="text/html"
-                    )
-            
-            elif output_format == "JSON":
-                json_output = json.dumps([faq.__dict__ for faq in st.session_state.generated_faqs], indent=2)
-                st.code(json_output, language="json")
-                
-                with col1:
-                    if st.button("Copy to Clipboard", key="copy_json"):
-                        copy_to_clipboard(json_output)
-                with col2:
-                    st.download_button(
-                        "Download JSON",
-                        json_output,
-                        file_name="faqs.json",
-                        mime="application/json"
-                    )
-
-if __name__ == "__main__":
-    main() 
--- a/ToBeMigrated/ai_writers/ai_copywriter/4c_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/4c_copywriter.py
@@ -1,226 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 4C Copywriting Generator</h2>
-        <p>Create compelling copy that follows the 4C (Clear, Concise, Credible, Compelling) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about 4C copywriting
-    with st.expander("📚 What is 4C Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the 4C Copywriting Framework
-        
-        The 4C framework is a powerful copywriting approach that ensures your message is effective and persuasive:
-        
-        - **Clear**: Your message is easy to understand, with no ambiguity or confusion
-        - **Concise**: Your copy is brief and to the point, without unnecessary words
-        - **Credible**: Your claims are backed by evidence, testimonials, or authority
-        - **Compelling**: Your message is interesting and persuasive, motivating action
-        
-        ### Why 4C Copywriting Works
-        
-        The 4C framework works because it:
-        
-        - Improves readability and comprehension
-        - Respects the reader's time and attention
-        - Builds trust and credibility
-        - Increases the likelihood of conversion
-        - Creates a professional, polished impression
-        - Works across all marketing channels and platforms
-        
-        ### When to Use 4C Copywriting
-        
-        The 4C framework is particularly effective for:
-        
-        - Email marketing campaigns
-        - Landing pages and sales pages
-        - Social media posts and ads
-        - Product descriptions
-        - Service offerings
-        - Any marketing content where clarity and persuasion are essential
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your 4C Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                     placeholder="e.g., Alwrity AI Writer",
-                                     help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Content marketers",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            campaign_description = st.text_input('**📝 Campaign Description** (In 3-4 words)', 
-                                               placeholder="e.g., AI writing assistant",
-                                               help="Describe your campaign briefly.")
-            
-            clear_message = st.text_area('**🔍 Clear Message**', 
-                                       placeholder="e.g., Our AI writing assistant helps you create high-quality content in minutes",
-                                       help="What is the main message you want to convey? Make it easy to understand.")
-            
-        with col2:
-            brand_description = st.text_input('**📋 Brand Description** (In 2-3 words)', 
-                                            placeholder="e.g., AI writing platform",
-                                            help="Describe what your company does briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., All-in-one AI copywriting platform",
-                                               help="What makes your product/service different from competitors?")
-            
-            concise_content = st.text_area('**📏 Concise Content**', 
-                                         placeholder="e.g., Create content 10x faster with our AI assistant",
-                                         help="How can you express your message in the fewest words possible?")
-            
-            credible_elements = st.text_area('**✅ Credible Elements**', 
-                                           placeholder="e.g., Trusted by 10,000+ businesses, 4.8/5 star rating, 30-day money-back guarantee",
-                                           help="What evidence, testimonials, or authority can you use to build credibility?")
-            
-            compelling_hook = st.text_area('**🎣 Compelling Hook**', 
-                                         placeholder="e.g., Stop struggling with writer's block. Our AI assistant helps you create engaging content in minutes.",
-                                         help="What will grab attention and motivate action?")
-            
-            call_to_action = st.text_area('**🚀 Call to Action**', 
-                                        placeholder="e.g., Start creating high-converting content today with our 14-day free trial...",
-                                        help="Prompt your audience to take action with a strong call to action.")
-            
-            landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)', 
-                                           placeholder="e.g., https://alwrity.com",
-                                           help="Provide a URL to include in your call to action.")
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            platform = st.selectbox(
-                '**📱 Content Platform**',
-                options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
-                help="Select the platform where your copy will be used."
-            )
-            
-        with col2:
-            language = st.selectbox(
-                '**🌍 Language**',
-                options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
-                help="Select the language for your copy."
-            )
-            
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate 4C Copy**', type="primary"):
-            if not brand_name or not brand_description or not campaign_description or not clear_message or not concise_content or not credible_elements or not compelling_hook:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Campaign Description, Clear Message, Concise Content, Credible Elements, and Compelling Hook)!")
-            else:
-                with st.spinner("✨ Crafting compelling 4C copy..."):
-                    four_cs_copy = generate_four_cs_copy(
-                        brand_name, 
-                        brand_description, 
-                        campaign_description,
-                        clear_message,
-                        concise_content,
-                        credible_elements,
-                        compelling_hook,
-                        target_audience,
-                        unique_selling_point,
-                        call_to_action,
-                        landing_page_url,
-                        platform,
-                        language,
-                        tone_style
-                    )
-                    
-                    if four_cs_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your 4C Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(four_cs_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your 4C Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your 4C Copy Effectively
-                            
-                            1. **Test for clarity**: Ask someone unfamiliar with your product to read your copy and explain what they understand
-                            
-                            2. **Edit ruthlessly**: Review your copy to eliminate unnecessary words and phrases
-                            
-                            3. **Add specific details**: Include concrete numbers, statistics, and examples to enhance credibility
-                            
-                            4. **Create urgency**: Add time-sensitive elements to make your compelling hook even more effective
-                            
-                            5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            6. **Measure results**: Track conversion metrics to see how your 4C copy performs
-                            
-                            7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate 4C Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_four_cs_copy(brand_name, brand_description, campaign_description, clear_message, 
-                         concise_content, credible_elements, compelling_hook, target_audience, 
-                         unique_selling_point, call_to_action, landing_page_url, platform, 
-                         language, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the 4C (Clear, Concise, Credible, Compelling) framework. 
-    Your expertise is in creating effective, persuasive marketing copy that communicates clearly, builds credibility, and drives action. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {brand_description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    PLATFORM: {platform}
-    LANGUAGE: {language}
-    TONE & STYLE: {tone_style}
-    
-    Use the 4C framework with these elements:
-    - **Clear Message**: {clear_message}
-    - **Concise Content**: {concise_content}
-    - **Credible Elements**: {credible_elements}
-    - **Compelling Hook**: {compelling_hook}
-    - **Call to Action**: {call_to_action}
-    """
-    
-    if landing_page_url:
-        prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
-    
-    prompt += """
-    For each campaign:
-    1. Start with a compelling hook that grabs attention
-    2. Present your clear message in a concise way
-    3. Support your claims with credible elements
-    4. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/4r_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/4r_copywriter.py
@@ -1,214 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 4R Copywriting Generator</h2>
-        <p>Create compelling copy that follows the 4R (Relevance, Resonance, Response, Results) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about 4R copywriting
-    with st.expander("📚 What is 4R Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the 4R Copywriting Framework
-        
-        The 4R framework is a powerful copywriting approach that ensures your message connects with your audience and drives action:
-        
-        - **Relevance**: Your message addresses the specific needs, interests, or pain points of your target audience
-        - **Resonance**: Your copy creates an emotional connection with the audience, making them feel understood
-        - **Response**: Your message prompts the audience to take a specific action
-        - **Results**: Your copy clearly communicates the positive outcomes or benefits the audience will experience
-        
-        ### Why 4R Copywriting Works
-        
-        The 4R framework works because it:
-        
-        - Ensures your message is targeted to the right audience
-        - Creates emotional connections that build trust and loyalty
-        - Drives specific actions that lead to conversions
-        - Focuses on the outcomes that matter most to your audience
-        - Creates a complete journey from awareness to action
-        - Works across all marketing channels and platforms
-        
-        ### When to Use 4R Copywriting
-        
-        The 4R framework is particularly effective for:
-        
-        - Email marketing campaigns
-        - Landing pages and sales pages
-        - Social media posts and ads
-        - Product descriptions
-        - Service offerings
-        - Any marketing content where audience connection and action are essential
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your 4R Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                     placeholder="e.g., Alwrity AI Writer",
-                                     help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Content marketers",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            relevance = st.text_area('**🎯 Relevance**', 
-                                   placeholder="e.g., Struggling with writer's block? Our AI assistant helps you create high-quality content in minutes",
-                                   help="How does your product/service address the specific needs or pain points of your target audience?")
-            
-        with col2:
-            brand_description = st.text_input('**📋 Brand Description** (In 2-3 words)', 
-                                            placeholder="e.g., AI writing platform",
-                                            help="Describe what your company does briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., All-in-one AI copywriting platform",
-                                               help="What makes your product/service different from competitors?")
-            
-            resonance = st.text_area('**💖 Resonance**', 
-                                   placeholder="e.g., We understand the frustration of staring at a blank page. Our AI assistant feels like having a professional writer by your side",
-                                   help="How can you create an emotional connection with your audience? What language or imagery will resonate with them?")
-            
-            response = st.text_area('**🚀 Response**', 
-                                  placeholder="e.g., Start creating high-converting content today with our 14-day free trial",
-                                  help="What specific action do you want your audience to take?")
-            
-            results = st.text_area('**✨ Results**', 
-                                 placeholder="e.g., Save 20+ hours per week on content creation, increase conversion rates by 35%, improve SEO rankings",
-                                 help="What positive outcomes or benefits will your audience experience?")
-            
-            landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)', 
-                                           placeholder="e.g., https://alwrity.com",
-                                           help="Provide a URL to include in your call to action.")
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            platform = st.selectbox(
-                '**📱 Content Platform**',
-                options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
-                help="Select the platform where your copy will be used."
-            )
-            
-        with col2:
-            language = st.selectbox(
-                '**🌍 Language**',
-                options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
-                help="Select the language for your copy."
-            )
-            
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate 4R Copy**', type="primary"):
-            if not brand_name or not brand_description or not relevance or not resonance or not response or not results:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Relevance, Resonance, Response, and Results)!")
-            else:
-                with st.spinner("✨ Crafting compelling 4R copy..."):
-                    four_r_copy = generate_four_r_copy(
-                        brand_name, 
-                        brand_description, 
-                        relevance,
-                        resonance,
-                        response,
-                        results,
-                        target_audience,
-                        unique_selling_point,
-                        landing_page_url,
-                        platform,
-                        language,
-                        tone_style
-                    )
-                    
-                    if four_r_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your 4R Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(four_r_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your 4R Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your 4R Copy Effectively
-                            
-                            1. **Test for relevance**: Ensure your copy speaks directly to your target audience's needs and interests
-                            
-                            2. **Enhance emotional resonance**: Use language and imagery that creates a deeper connection with your audience
-                            
-                            3. **Clarify the response**: Make sure your call to action is clear, specific, and compelling
-                            
-                            4. **Quantify results**: Use specific numbers, statistics, and examples to make your results more tangible
-                            
-                            5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            6. **Measure performance**: Track conversion metrics to see how your 4R copy performs
-                            
-                            7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate 4R Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_four_r_copy(brand_name, brand_description, relevance, resonance, response, results, 
-                        target_audience, unique_selling_point, landing_page_url, platform, 
-                        language, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the 4R (Relevance, Resonance, Response, Results) framework. 
-    Your expertise is in creating compelling marketing copy that connects with audiences on a deep level and drives specific actions. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {brand_description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    PLATFORM: {platform}
-    LANGUAGE: {language}
-    TONE & STYLE: {tone_style}
-    
-    Use the 4R framework with these elements:
-    - **Relevance**: {relevance}
-    - **Resonance**: {resonance}
-    - **Response**: {response}
-    - **Results**: {results}
-    """
-    
-    if landing_page_url:
-        prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
-    
-    prompt += """
-    For each campaign:
-    1. Start by establishing relevance to your target audience's needs or pain points
-    2. Create emotional resonance by connecting with your audience's feelings and experiences
-    3. Clearly communicate the specific action you want your audience to take
-    4. End by highlighting the positive results or benefits they will experience
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None 
--- a/ToBeMigrated/ai_writers/ai_copywriter/README.md
+++ b/ToBeMigrated/ai_writers/ai_copywriter/README.md
@@ -1,141 +0,0 @@
-# AI Copywriting Tools
-
-A comprehensive collection of AI-powered copywriting tools designed to help create compelling, conversion-focused content using various proven frameworks and approaches.
-
-## Available Copywriting Tools
-
-### 1. AIDA Copywriter
-The AIDA (Attention-Interest-Desire-Action) framework is a classic copywriting approach that guides your audience through a complete journey:
- **Attention**: Captures attention with compelling headlines
- **Interest**: Generates interest through benefits and pain points
- **Desire**: Creates desire by showcasing solutions
- **Action**: Prompts specific actions with strong CTAs
-
-Best for: Landing pages, sales pages, email campaigns, and direct response advertising.
-
-### 2. 4C Copywriter
-The 4C framework ensures your message is effective and persuasive through:
- **Clear**: Easy to understand messaging
- **Concise**: Brief and to-the-point content
- **Credible**: Evidence-backed claims
- **Compelling**: Interesting and persuasive messaging
-
-Best for: Email marketing, landing pages, social media, and product descriptions.
-
-### 3. 4R Copywriter
-The 4R framework focuses on building relationships with your audience through:
- **Relevance**: Content that matters to your audience
- **Receptivity**: Timing and context optimization
- **Response**: Clear calls to action
- **Return**: Value-driven content
-
-Best for: Content marketing, blog posts, and relationship-building campaigns.
-
-### 4. PAS Copywriter
-The PAS (Problem-Agitation-Solution) framework addresses customer pain points:
- **Problem**: Identifies the customer's issue
- **Agitation**: Amplifies the problem's impact
- **Solution**: Presents your offering as the answer
-
-Best for: Problem-solving content, product launches, and service offerings.
-
-### 5. FAB Copywriter
-The FAB (Features-Advantages-Benefits) framework focuses on product value:
- **Features**: Product characteristics
- **Advantages**: How features stand out
- **Benefits**: Customer value proposition
-
-Best for: Product descriptions, sales pages, and feature highlights.
-
-### 6. QUEST Copywriter
-The QUEST framework creates engaging storytelling:
- **Qualify**: Identify the right audience
- **Understand**: Show empathy
- **Educate**: Provide value
- **Stimulate**: Create desire
- **Transition**: Guide to action
-
-Best for: Story-based marketing, brand storytelling, and content marketing.
-
-### 7. STAR Copywriter
-The STAR framework focuses on social proof and testimonials:
- **Situation**: Context of the problem
- **Task**: Challenge faced
- **Action**: Solution implemented
- **Result**: Outcome achieved
-
-Best for: Case studies, testimonials, and success stories.
-
-### 8. OATH Copywriter
-The OATH framework addresses customer objections:
- **Objection**: Identify common concerns
- **Acknowledge**: Show understanding
- **Transform**: Turn negatives to positives
- **Handle**: Provide solutions
-
-Best for: Sales pages, product launches, and objection handling.
-
-### 9. AIDPPC Copywriter
-The AIDPPC framework extends AIDA with additional elements:
- **Attention**: Initial hook
- **Interest**: Generate curiosity
- **Desire**: Create want
- **Proof**: Provide evidence
- **Push**: Create urgency
- **Close**: Final call to action
-
-Best for: Long-form sales pages and comprehensive marketing materials.
-
-### 10. Emotional Copywriter
-Focuses on creating emotional connections through:
- Emotional triggers (FOMO, trust, joy, urgency)
- Personal connections
- Pain point addressing
- Trust building
- Community creation
-
-Best for: Brand storytelling, emotional marketing, and relationship building.
-
-## Features
-
-All copywriting tools include:
- User-friendly interface with Streamlit
- Educational content about each framework
- Customizable input parameters
- Multiple language support
- Tone and style options
- Target audience customization
- Brand-specific content generation
- Retry mechanism for reliable API calls
-
-## Usage
-
-1. Select your desired copywriting framework
-2. Fill in the required information:
-   - Brand/Company details
-   - Target audience
-   - Unique selling points
-   - Desired tone and style
-   - Platform-specific requirements
-3. Generate your copy
-4. Review and refine the output
-
-## Best Practices
-
-1. **Know Your Audience**: Always provide detailed target audience information
-2. **Be Specific**: Include clear unique selling points and value propositions
-3. **Choose the Right Framework**: Match the framework to your content goals
-4. **Maintain Consistency**: Keep brand voice and messaging consistent
-5. **Test and Optimize**: Use different frameworks for A/B testing
-6. **Review and Edit**: Always review AI-generated content for accuracy and tone
-
-## Technical Requirements
-
- Python 3.7+
- Streamlit
- GPT API access
- Required Python packages (see requirements.txt)
-
-## Support
-
-For technical support or questions about specific frameworks, please refer to the documentation or contact the development team. 
--- a/ToBeMigrated/ai_writers/ai_copywriter/README_TBD.md
+++ b/ToBeMigrated/ai_writers/ai_copywriter/README_TBD.md
@@ -1,97 +0,0 @@
-# Brainstorming for Copywriting Tools UI and Features (TBD)
-
-## Showing All Copywriting Tools in a Single UI
-
-1. **Unified Dashboard Approach**
-   - Create a central dashboard with cards/tiles for each copywriting formula
-   - Use visual icons and brief descriptions to distinguish each formula
-   - Implement a consistent color scheme and design language across all tools
-
-2. **Categorization System**
-   - Group formulas by purpose (e.g., "Emotional Appeal," "Problem-Solution," "Storytelling")
-   - Allow users to filter by category or search by keyword
-   - Include a "Featured" or "Popular" section for commonly used formulas
-
-3. **Interactive Selection Interface**
-   - Create a decision tree or guided selection process
-   - Ask users a few key questions to recommend the most appropriate formula
-   - Show a comparison view of multiple formulas side-by-side
-
-4. **Progressive Disclosure**
-   - Start with a simplified view showing just the formula names and basic descriptions
-   - Allow users to expand each formula for more details and to start using it
-   - Implement a "Recently Used" section for quick access to frequently used formulas
-
-## Presenting the Right Formula for User Needs
-
-1. **Guided Selection Wizard**
-   - Create a multi-step wizard that asks about the user's marketing goals
-   - Include questions about target audience, industry, content type, and desired outcome
-   - Provide recommendations based on user responses with explanations
-
-2. **Formula Comparison Tool**
-   - Create a comparison matrix showing strengths of each formula
-   - Include use cases and examples for each formula
-   - Allow users to see side-by-side comparisons of different formulas
-
-3. **Educational Content Integration**
-   - Add a "Learn More" section for each formula with detailed explanations
-   - Include case studies showing successful applications of each formula
-   - Provide templates and examples for common use cases
-
-4. **Contextual Recommendations**
-   - Analyze the user's input and automatically suggest the most appropriate formula
-   - Show a confidence score for each recommendation
-   - Allow users to easily switch between formulas if the recommendation isn't right
-
-## Using AI to Pre-fill Inputs Based on Brief Requirements
-
-1. **Smart Input Generation**
-   - Create an initial input field where users can describe their copywriting needs in natural language
-   - Use AI to analyze this input and extract key information (brand, audience, goals, etc.)
-   - Pre-fill the formula-specific fields with AI-generated content
-   - Allow users to edit and refine the pre-filled content
-
-2. **Contextual Understanding**
-   - Implement industry-specific templates and prompts
-   - Use AI to recognize industry terminology and adapt suggestions accordingly
-   - Provide multiple options for each field based on the user's brief description
-
-3. **Progressive Refinement**
-   - Start with AI-generated suggestions for all fields
-   - Allow users to focus on refining specific fields while keeping others
-   - Implement a "regenerate" option for individual fields if the AI suggestion isn't suitable
-
-4. **Learning from User Edits**
-   - Track which AI-generated suggestions users keep vs. modify
-   - Use this data to improve future suggestions
-   - Implement a feedback mechanism for users to rate the quality of AI suggestions
-
-## AI-Generated Images as a Feature
-
-1. **Complementary Visual Content**
-   - Generate images that match the tone and message of the copy
-   - Create multiple image options for different platforms (social media, email, website)
-   - Ensure images align with the copywriting formula being used
-
-2. **Integrated Workflow**
-   - Add an "Generate Matching Images" button after copy is created
-   - Allow users to specify image style, mood, and key elements
-   - Provide options to customize generated images further
-
-3. **Platform-Specific Optimization**
-   - Automatically size and format images for different platforms
-   - Generate variations optimized for different aspect ratios
-   - Include text overlay options that complement the copy
-
-4. **Brand Consistency**
-   - Allow users to upload brand assets (logos, colors, fonts)
-   - Generate images that maintain brand identity
-   - Create a visual style guide based on user preferences
-
-5. **Enhanced Engagement**
-   - A/B test different image options with the same copy
-   - Provide analytics on which image-copy combinations perform best
-   - Suggest image improvements based on performance data
-
-These enhancements would create a more comprehensive, user-friendly copywriting platform that guides users to the right formula, simplifies the input process, and delivers complete marketing assets ready for deployment.
--- a/ToBeMigrated/ai_writers/ai_copywriter/acca_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/acca_copywriter.py
@@ -1,182 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🚀 ACCA Copywriting Generator</h2>
-        <p>Create persuasive marketing copy using the proven ACCA (Awareness-Curiosity-Conviction-Action) formula.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about ACCA copywriting
-    with st.expander("📚 What is ACCA Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the ACCA Copywriting Formula
-        
-        The ACCA formula is a powerful copywriting framework that guides your audience through a journey from problem recognition to action:
-        
-        - **Awareness**: Highlight the problem or pain point your audience faces
-        - **Curiosity**: Agitate the problem by emphasizing its negative impact
-        - **Conviction**: Present your solution and build confidence in it
-        - **Action**: Provide a clear, compelling call to action
-        
-        ### Why ACCA Copywriting Works
-        
-        The ACCA formula works because it:
-        
-        - Follows the natural decision-making process of your audience
-        - Creates a logical progression from problem to solution
-        - Builds emotional investment before asking for commitment
-        - Addresses objections before they arise
-        - Ends with a clear next step
-        
-        ### When to Use ACCA Copywriting
-        
-        The ACCA formula is particularly effective for:
-        
-        - Product launches
-        - Service promotions
-        - Problem-solving offers
-        - Educational content
-        - Sales pages
-        - Email marketing sequences
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your ACCA Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            awareness = st.text_input('❓ **Awareness (Problem)**', 
-                                    placeholder="e.g., Struggling to manage finances",
-                                    help="What problem or pain point does your audience face?")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 5-6 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            curiosity = st.text_input('🔥 **Curiosity (Agitation)**', 
-                                    placeholder="e.g., Leads to financial instability and stress",
-                                    help="Why is this problem serious for your audience? Highlight the negative impact.")
-        
-        conviction = st.text_input('💡 **Conviction (Solution)**', 
-                                 placeholder="e.g., Provides easy-to-use budgeting tools with AI insights",
-                                 help="How does your product/service solve this problem? Explain the benefits.")
-        
-        call_to_action = st.text_input('🎯 **Action (Call to Action)**', 
-                                      placeholder="e.g., Start your free trial today",
-                                      help="What specific action do you want your audience to take?")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate ACCA Copy**', type="primary"):
-            if not brand_name or not description or not awareness or not curiosity or not conviction:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Awareness, Curiosity, and Conviction)!")
-            else:
-                with st.spinner("✨ Crafting persuasive ACCA copy..."):
-                    acca_copy = generate_acca_copy(
-                        brand_name, 
-                        description, 
-                        awareness,
-                        curiosity,
-                        conviction,
-                        target_audience,
-                        unique_selling_point,
-                        call_to_action,
-                        tone_style
-                    )
-                    
-                    if acca_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>✨ Your ACCA Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(acca_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy - using a container instead of an expander
-                        st.markdown("""
-                        <div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #333;'>💡 Tips for Using Your ACCA Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.markdown("""
-                        ### How to Use Your ACCA Copy Effectively
-                        
-                        1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
-                        
-                        2. **Pair with visuals**: Combine your copy with images that reinforce each stage of the ACCA formula
-                        
-                        3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
-                        
-                        4. **Measure results**: Track conversion metrics to see how your ACCA copy performs
-                        
-                        5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                        """)
-                    else:
-                        st.error("💥 **Failed to generate ACCA Copy. Please try again!**")
-
-
-def generate_acca_copy(brand_name, description, awareness, curiosity, conviction, target_audience, 
-                      unique_selling_point, call_to_action, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the ACCA (Awareness-Curiosity-Conviction-Action) formula. 
-    Your expertise is in creating compelling, persuasive marketing copy that guides audiences through a journey from problem 
-    recognition to taking action. Your copy is authentic, specific to the brand, and focused on the target audience's needs."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the ACCA formula with these elements:
-    - **Awareness**: {awareness}
-    - **Curiosity**: {curiosity}
-    - **Conviction**: {conviction}
-    - **Action**: {call_to_action}
-
-    For each campaign:
-    1. Create a compelling headline that captures attention
-    2. Write 2-3 paragraphs that follow the ACCA formula
-    3. End with a strong call to action
-    4. Explain how each element of the ACCA formula is used in the copy
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/ai_emotional_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/ai_emotional_copywriter.py
@@ -1,168 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎭 Emotional Copywriting Generator</h2>
-        <p>Create compelling copy that resonates with your audience's emotions and drives action.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about emotional copywriting
-    with st.expander("📚 What is Emotional Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding Emotional Copywriting
-        
-        Emotional copywriting is a powerful marketing technique that connects with your audience on a deeper level by:
-        
-        - **Triggering specific emotions** (joy, fear, urgency, trust, etc.)
-        - **Creating personal connections** with your audience
-        - **Addressing pain points** and offering solutions
-        - **Building trust and credibility**
-        - **Creating a sense of belonging** or exclusivity
-        
-        ### Why Emotional Copywriting Works
-        
-        Research shows that people make purchasing decisions based on emotions first, then justify with logic. By tapping into the right emotions, you can:
-        
-        - Increase engagement and response rates
-        - Build stronger brand loyalty
-        - Drive more conversions
-        - Create memorable brand experiences
-        
-        ### Common Emotional Triggers
-        
-        - **Fear of Missing Out (FOMO)**: Limited time offers, exclusive access
-        - **Trust**: Testimonials, guarantees, social proof
-        - **Joy/Happiness**: Benefits, positive outcomes, aspirational messaging
-        - **Urgency**: Time-sensitive offers, countdown timers
-        - **Belonging**: Community, exclusivity, shared values
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your Emotional Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**Brand/Company Name**', 
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**Target Audience**', 
-                                          help="Who is your ideal customer? (e.g., 'busy moms', 'tech-savvy millennials')")
-            
-            emotional_trigger = st.selectbox(
-                '**Primary Emotional Trigger**',
-                options=['Trust', 'Fear of Missing Out', 'Joy/Happiness', 'Urgency', 'Belonging', 'Exclusivity'],
-                help="Select the primary emotion you want to evoke in your audience."
-            )
-            
-        with col2:
-            description = st.text_input('**Brand Description** (In 5-6 words)', 
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**Unique Selling Point**', 
-                                               help="What makes your product/service different from competitors?")
-            
-            call_to_action = st.text_input('**Desired Call to Action**', 
-                                         help="What action do you want your audience to take? (e.g., 'Sign up now', 'Buy today')")
-        
-        trust_elements = st.text_area('**Trust Elements**',
-                                    help="Build trust and credibility by showcasing testimonials, guarantees, or endorsements.",
-                                    placeholder="Testimonials from satisfied customers...\nOur guarantee that...\nIndustry certifications...")
-        
-        tone_style = st.selectbox(
-            '**Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**Generate Emotional Copy**', type="primary"):
-            if not brand_name or not description or not trust_elements:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, and Trust Elements)!")
-            else:
-                with st.spinner("✨ Crafting emotionally compelling copy..."):
-                    emotional_copy = generate_emotional_copy(
-                        brand_name, 
-                        description, 
-                        trust_elements,
-                        target_audience,
-                        emotional_trigger,
-                        unique_selling_point,
-                        call_to_action,
-                        tone_style
-                    )
-                    
-                    if emotional_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your Emotional Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(emotional_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy - using a container instead of an expander
-                        st.markdown("""
-                        <div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #333;'>💡 Tips for Using Your Emotional Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.markdown("""
-                        ### How to Use Your Emotional Copy Effectively
-                        
-                        1. **Test different versions**: A/B test your copy to see which emotional triggers resonate most with your audience
-                        
-                        2. **Pair with visuals**: Combine your copy with images that reinforce the emotional message
-                        
-                        3. **Consider the context**: Adapt the copy based on where it will appear (social media, email, website, etc.)
-                        
-                        4. **Measure results**: Track engagement metrics to see how your emotional copy performs
-                        
-                        5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                        """)
-                    else:
-                        st.error("💥 **Failed to generate Emotional Copy. Please try again!**")
-
-
-def generate_emotional_copy(brand_name, description, trust_elements, target_audience, emotional_trigger, 
-                           unique_selling_point, call_to_action, tone_style):
-    system_prompt = """You are an expert emotional copywriter with years of experience in creating compelling marketing copy 
-    that resonates with audiences on a deep emotional level. Your specialty is crafting copy that triggers specific emotions 
-    and drives action while maintaining authenticity and credibility."""
-    
-    prompt = f"""Create 3 different emotional marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    PRIMARY EMOTIONAL TRIGGER: {emotional_trigger}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    DESIRED CALL TO ACTION: {call_to_action}
-    TONE & STYLE: {tone_style}
-    TRUST ELEMENTS: {trust_elements}
-
-    For each campaign:
-    1. Create a compelling headline that captures attention
-    2. Write 2-3 paragraphs of body copy that builds emotional connection
-    3. End with a strong call to action
-    4. Explain which emotional triggers you used and why they're effective for this audience
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/aida_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/aida_copywriter.py
@@ -1,211 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 AIDA Copywriting Generator</h2>
-        <p>Create compelling copy that follows the AIDA (Attention-Interest-Desire-Action) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about AIDA copywriting
-    with st.expander("📚 What is AIDA Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the AIDA Copywriting Framework
-        
-        AIDA is an acronym for Attention-Interest-Desire-Action. It's a classic copywriting framework that guides your audience through a complete journey:
-        
-        - **Attention**: Capturing the audience's attention with a compelling headline or hook
-        - **Interest**: Generating interest by highlighting benefits or addressing pain points
-        - **Desire**: Creating desire by showcasing how the product/service solves problems or fulfills needs
-        - **Action**: Prompting the audience to take a specific action with a strong call to action
-        
-        ### Why AIDA Copywriting Works
-        
-        The AIDA framework works because it:
-        
-        - Follows the natural decision-making process of consumers
-        - Addresses all key elements needed for conversion
-        - Creates a complete journey from awareness to action
-        - Balances emotional and rational appeals
-        - Focuses on the customer's journey rather than just product features
-        
-        ### When to Use AIDA Copywriting
-        
-        The AIDA framework is particularly effective for:
-        
-        - Landing pages and sales pages
-        - Email marketing campaigns
-        - Product descriptions
-        - Direct response advertising
-        - Content that needs to drive specific actions
-        - Marketing materials that need to address objections
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your AIDA Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            attention = st.text_area('**🔔 Attention-Grabbing Hook**', 
-                                   placeholder="e.g., Tired of spending hours writing content that doesn't convert?",
-                                   help="Create a compelling headline or hook that captures attention.")
-            
-            interest = st.text_area('**💡 Generate Interest**', 
-                                  placeholder="e.g., Imagine creating high-quality content in minutes instead of hours...",
-                                  help="Highlight benefits or address pain points to generate interest.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 5-6 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            desire = st.text_area('**❤️ Create Desire**', 
-                                placeholder="e.g., Our AI analyzes top-performing content to ensure your copy resonates with your target audience...",
-                                help="Showcase how your product/service solves problems or fulfills needs.")
-            
-            action = st.text_area('**🚀 Call to Action**', 
-                                placeholder="e.g., Start creating converting content today with our 14-day free trial...",
-                                help="Prompt your audience to take action with a strong call to action.")
-            
-            landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)', 
-                                           placeholder="e.g., https://alwrity.com",
-                                           help="Provide a URL to include in your call to action.")
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            platform = st.selectbox(
-                '**📱 Content Platform**',
-                options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
-                help="Select the platform where your copy will be used."
-            )
-            
-        with col2:
-            language = st.selectbox(
-                '**🌍 Language**',
-                options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
-                help="Select the language for your copy."
-            )
-            
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate AIDA Copy**', type="primary"):
-            if not brand_name or not description or not attention or not interest or not desire or not action:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all AIDA elements)!")
-            else:
-                with st.spinner("✨ Crafting compelling AIDA copy..."):
-                    aida_copy = generate_aida_copy(
-                        brand_name, 
-                        description, 
-                        attention,
-                        interest,
-                        desire,
-                        action,
-                        target_audience,
-                        unique_selling_point,
-                        landing_page_url,
-                        platform,
-                        language,
-                        tone_style
-                    )
-                    
-                    if aida_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your AIDA Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(aida_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your AIDA Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your AIDA Copy Effectively
-                            
-                            1. **Follow the sequence**: The AIDA framework creates a natural progression - make sure your copy maintains this flow
-                            
-                            2. **Test different hooks**: A/B test different attention-grabbing headlines to see which resonates most with your audience
-                            
-                            3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the AIDA journey
-                            
-                            4. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            5. **Measure results**: Track conversion metrics to see how your AIDA copy performs
-                            
-                            6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate AIDA Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_aida_copy(brand_name, description, attention, interest, desire, action, 
-                      target_audience, unique_selling_point, landing_page_url, 
-                      platform, language, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the AIDA (Attention-Interest-Desire-Action) framework. 
-    Your expertise is in creating compelling, conversion-focused marketing copy that guides readers through a complete journey from awareness to action. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    PLATFORM: {platform}
-    LANGUAGE: {language}
-    TONE & STYLE: {tone_style}
-    
-    Use the AIDA framework with these elements:
-    - **Attention**: {attention}
-    - **Interest**: {interest}
-    - **Desire**: {desire}
-    - **Action**: {action}
-    """
-    
-    if landing_page_url:
-        prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
-    
-    prompt += """
-    For each campaign:
-    1. Start with the attention-grabbing hook to capture the audience's attention
-    2. Generate interest by highlighting benefits or addressing pain points
-    3. Create desire by showcasing how the product/service solves problems or fulfills needs
-    4. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/aidppc_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/aidppc_copywriter.py
@@ -1,191 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 AIDPPC Copywriting Generator</h2>
-        <p>Create compelling copy that follows the AIDPPC (Attention-Interest-Description-Persuasion-Proof-Close) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about AIDPPC copywriting
-    with st.expander("📚 What is AIDPPC Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the AIDPPC Copywriting Framework
-        
-        AIDPPC is an acronym for Attention-Interest-Description-Persuasion-Proof-Close. It's a comprehensive copywriting framework that guides your audience through a complete journey:
-        
-        - **Attention**: Capturing the audience's attention with a compelling headline or hook
-        - **Interest**: Generating interest by highlighting benefits or addressing pain points
-        - **Description**: Describing your product or service in detail
-        - **Persuasion**: Presenting compelling arguments or incentives to persuade
-        - **Proof**: Providing social proof, testimonials, or guarantees to build credibility
-        - **Close**: Prompting the audience to take action with a strong call to action
-        
-        ### Why AIDPPC Copywriting Works
-        
-        The AIDPPC framework works because it:
-        
-        - Follows the natural decision-making process of consumers
-        - Addresses all key elements needed for conversion
-        - Builds credibility through multiple stages
-        - Creates a complete journey from awareness to action
-        - Balances emotional and rational appeals
-        
-        ### When to Use AIDPPC Copywriting
-        
-        The AIDPPC framework is particularly effective for:
-        
-        - Landing pages and sales pages
-        - Email marketing campaigns
-        - Product descriptions
-        - Direct response advertising
-        - Content that needs to drive specific actions
-        - Marketing materials that need to address objections
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your AIDPPC Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            attention = st.text_area('**🔔 Attention-Grabbing Hook**', 
-                                   placeholder="e.g., Tired of spending hours writing content that doesn't convert?",
-                                   help="Create a compelling headline or hook that captures attention.")
-            
-            interest = st.text_area('**💡 Generate Interest**', 
-                                  placeholder="e.g., Imagine creating high-quality content in minutes instead of hours...",
-                                  help="Highlight benefits or address pain points to generate interest.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 2-3 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            persuasion = st.text_area('**💪 Persuasive Arguments**', 
-                                    placeholder="e.g., Our AI analyzes top-performing content to ensure your copy resonates with your target audience...",
-                                    help="Present compelling arguments or incentives to persuade your audience.")
-            
-            proof = st.text_area('**✅ Social Proof**', 
-                                placeholder="e.g., Join 10,000+ satisfied customers who have transformed their content strategy...",
-                                help="Provide testimonials, statistics, or guarantees to build credibility.")
-            
-            close = st.text_area('**🚀 Call to Action**', 
-                                placeholder="e.g., Start creating converting content today with our 14-day free trial...",
-                                help="Prompt your audience to take action with a strong call to action.")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate AIDPPC Copy**', type="primary"):
-            if not brand_name or not description or not attention or not interest or not persuasion or not proof or not close:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all AIDPPC elements)!")
-            else:
-                with st.spinner("✨ Crafting compelling AIDPPC copy..."):
-                    aidppc_copy = generate_aidppc_copy(
-                        brand_name, 
-                        description, 
-                        attention,
-                        interest,
-                        persuasion,
-                        proof,
-                        close,
-                        target_audience,
-                        unique_selling_point,
-                        tone_style
-                    )
-                    
-                    if aidppc_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your AIDPPC Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(aidppc_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your AIDPPC Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your AIDPPC Copy Effectively
-                            
-                            1. **Follow the sequence**: The AIDPPC framework creates a natural progression - make sure your copy maintains this flow
-                            
-                            2. **Test different hooks**: A/B test different attention-grabbing headlines to see which resonates most with your audience
-                            
-                            3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the AIDPPC journey
-                            
-                            4. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            5. **Measure results**: Track conversion metrics to see how your AIDPPC copy performs
-                            
-                            6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate AIDPPC Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_aidppc_copy(brand_name, description, attention, interest, persuasion, proof, close, 
-                        target_audience, unique_selling_point, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the AIDPPC (Attention-Interest-Description-Persuasion-Proof-Close) framework. 
-    Your expertise is in creating compelling, conversion-focused marketing copy that guides readers through a complete journey from awareness to action. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the AIDPPC framework with these elements:
-    - **Attention**: {attention}
-    - **Interest**: {interest}
-    - **Persuasion**: {persuasion}
-    - **Proof**: {proof}
-    - **Close**: {close}
-
-    For each campaign:
-    1. Start with the attention-grabbing hook to capture the audience's attention
-    2. Generate interest by highlighting benefits or addressing pain points
-    3. Describe your product or service in detail
-    4. Present persuasive arguments or incentives
-    5. Provide social proof, testimonials, or guarantees
-    6. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/app_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/app_copywriter.py
@@ -1,176 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🔍 APP Copywriting Generator</h2>
-        <p>Create compelling marketing copy using the proven APP (Agree-Promise-Preview) formula.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about APP copywriting
-    with st.expander("📚 What is APP Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the APP Copywriting Formula
-        
-        The APP formula is a powerful copywriting framework that creates a natural connection with your audience:
-        
-        - **Agree**: Acknowledge a shared problem or pain point your audience faces
-        - **Promise**: Make a compelling promise or offer a solution to that problem
-        - **Preview**: Provide a preview of how your solution will deliver on that promise
-        
-        ### Why APP Copywriting Works
-        
-        The APP formula works because it:
-        
-        - Creates immediate rapport by showing you understand your audience's challenges
-        - Builds trust by acknowledging problems before selling solutions
-        - Reduces resistance by connecting on a human level first
-        - Demonstrates empathy and understanding
-        - Follows a natural conversation flow that feels authentic
-        
-        ### When to Use APP Copywriting
-        
-        The APP formula is particularly effective for:
-        
-        - Building trust with new audiences
-        - Introducing new products or services
-        - Addressing common objections
-        - Creating relatable content
-        - Establishing your brand as a solution provider
-        - Email marketing sequences
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your APP Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            agree = st.text_area('**🤝 Agree (Shared Problem)**', 
-                               placeholder="We all face..., Like you, I've..., Safety, Unprofessionalism..",
-                               help="Connect with the audience by acknowledging a shared problem or pain point they face.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 2-3 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            promise = st.text_area('**✨ Promise (Solution)**', 
-                                 placeholder="We guarantee..., Our solution ensures..., You'll never have to worry about...",
-                                 help="Make a compelling promise or offer a solution to the problem.")
-        
-        preview = st.text_area('**🔮 Preview (Proof)**', 
-                              placeholder="Here's how..., Our customers have experienced..., You'll see results like...",
-                              help="Provide a preview of how your solution will deliver on the promise.")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate APP Copy**', type="primary"):
-            if not brand_name or not description or not agree or not promise or not preview:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Agree, Promise, and Preview)!")
-            else:
-                with st.spinner("✨ Crafting compelling APP copy..."):
-                    app_copy = generate_app_copy(
-                        brand_name, 
-                        description, 
-                        agree,
-                        target_audience,
-                        unique_selling_point,
-                        promise,
-                        preview,
-                        tone_style
-                    )
-                    
-                    if app_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>✨ Your APP Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(app_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy - using a container instead of an expander
-                        st.markdown("""
-                        <div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #333;'>💡 Tips for Using Your APP Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.markdown("""
-                        ### How to Use Your APP Copy Effectively
-                        
-                        1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
-                        
-                        2. **Pair with visuals**: Combine your copy with images that reinforce each stage of the APP formula
-                        
-                        3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
-                        
-                        4. **Measure results**: Track engagement metrics to see how your APP copy performs
-                        
-                        5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                        """)
-                    else:
-                        st.error("💥 **Failed to generate APP Copy. Please try again!**")
-
-
-def generate_app_copy(brand_name, description, agree, target_audience, unique_selling_point, 
-                     promise, preview, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the APP (Agree-Promise-Preview) formula. 
-    Your expertise is in creating compelling, persuasive marketing copy that builds rapport with audiences by 
-    acknowledging their problems, making promises, and providing previews of solutions. Your copy is authentic, 
-    specific to the brand, and focused on the target audience's needs."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the APP formula with these elements:
-    - **Agree**: {agree}
-    - **Promise**: {promise}
-    - **Preview**: {preview}
-
-    For each campaign:
-    1. Create a compelling headline that captures attention
-    2. Write 2-3 paragraphs that follow the APP formula
-    3. End with a strong call to action
-    4. Explain how each element of the APP formula is used in the copy
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/copywriter_dashboard.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/copywriter_dashboard.py
@@ -1,674 +0,0 @@
-import streamlit as st
-import importlib
-import sys
-import os
-from pathlib import Path
-import time
-import json
-from typing import Dict, List, Callable, Optional, Tuple
-
-# Add the parent directory to the path to allow importing from lib
-current_dir = Path(__file__).parent
-root_dir = current_dir.parent.parent.parent
-sys.path.append(str(root_dir))
-
-# Dictionary to store the input section functions
-input_sections = {}
-
-# List of copywriter modules to import
-copywriter_modules = [
-    "ai_emotional_copywriter",
-    "acca_copywriter",
-    "app_copywriter",
-    "star_copywriter",
-    "oath_copywriter",
-    "quest_copywriter",
-    "aidppc_copywriter",
-    "aida_copywriter",
-    "pas_copywriter",
-    "fab_copywriter",
-    "4c_copywriter",
-    "4r_copywriter"
-]
-
-# Define formula categories for better organization
-formula_categories = {
-    "Emotional Appeal": ["ai_emotional_copywriter", "oath_copywriter"],
-    "Structured Framework": ["acca_copywriter", "app_copywriter", "star_copywriter", "quest_copywriter"],
-    "Sales Funnel": ["aidppc_copywriter", "aida_copywriter"],
-    "Problem-Solution": ["pas_copywriter"],
-    "Feature-Benefit": ["fab_copywriter"],
-    "Messaging Framework": ["4c_copywriter", "4r_copywriter"]
-}
-
-# Define formula metadata for better display and filtering
-formula_metadata = {
-    "ai_emotional_copywriter": {
-        "name": "Emotional Copywriter",
-        "icon": "🎭",
-        "description": "Create copy that resonates with your audience's emotions and drives action.",
-        "color": "#FF6B6B",
-        "difficulty": "Intermediate",
-        "best_for": ["Landing Pages", "Email", "Social Media"],
-        "tags": ["emotional", "persuasive", "engagement"]
-    },
-    "acca_copywriter": {
-        "name": "ACCA Copywriter",
-        "icon": "🎯",
-        "description": "Use the ACCA (Attention, Context, Content, Action) framework to create compelling copy.",
-        "color": "#4ECDC4",
-        "difficulty": "Beginner",
-        "best_for": ["Ads", "Email", "Landing Pages"],
-        "tags": ["structured", "conversion", "clear"]
-    },
-    "app_copywriter": {
-        "name": "APP Copywriter",
-        "icon": "🤝",
-        "description": "Implement the APP (Agree, Promise, Preview) formula to create persuasive copy.",
-        "color": "#45B7D1",
-        "difficulty": "Beginner",
-        "best_for": ["Blog Posts", "Sales Pages", "Email"],
-        "tags": ["persuasive", "agreement", "preview"]
-    },
-    "star_copywriter": {
-        "name": "STAR Copywriter",
-        "icon": "⭐",
-        "description": "Use the STAR (Situation, Task, Action, Result) framework to tell compelling stories.",
-        "color": "#FFD166",
-        "difficulty": "Intermediate",
-        "best_for": ["Case Studies", "Testimonials", "About Pages"],
-        "tags": ["storytelling", "results", "case-study"]
-    },
-    "oath_copywriter": {
-        "name": "OATH Copywriter",
-        "icon": "📜",
-        "description": "Apply the OATH (Oblivious, Apathetic, Thinking, Hurting) framework to target specific audience mindsets.",
-        "color": "#06D6A0",
-        "difficulty": "Advanced",
-        "best_for": ["Ads", "Landing Pages", "Email Sequences"],
-        "tags": ["audience", "mindset", "targeting"]
-    },
-    "quest_copywriter": {
-        "name": "QUEST Copywriter",
-        "icon": "🔍",
-        "description": "Use the QUEST (Question, Unpack, Emphasize, Solution, Transform) framework for narrative-driven copy.",
-        "color": "#118AB2",
-        "difficulty": "Intermediate",
-        "best_for": ["Long-form Content", "Sales Pages", "Video Scripts"],
-        "tags": ["narrative", "transformation", "solution"]
-    },
-    "aidppc_copywriter": {
-        "name": "AIDPPC Copywriter",
-        "icon": "💰",
-        "description": "Implement the AIDPPC (Attention, Interest, Desire, Proof, Persuasion, Call to Action) framework for PPC ads.",
-        "color": "#073B4C",
-        "difficulty": "Advanced",
-        "best_for": ["PPC Ads", "Social Ads", "Display Ads"],
-        "tags": ["advertising", "ppc", "conversion"]
-    },
-    "aida_copywriter": {
-        "name": "AIDA Copywriter",
-        "icon": "🎬",
-        "description": "Use the AIDA (Attention, Interest, Desire, Action) framework to guide customers through the sales funnel.",
-        "color": "#EF476F",
-        "difficulty": "Beginner",
-        "best_for": ["Sales Pages", "Email", "Product Descriptions"],
-        "tags": ["sales", "funnel", "conversion"]
-    },
-    "pas_copywriter": {
-        "name": "PAS Copywriter",
-        "icon": "🔧",
-        "description": "Apply the PAS (Problem, Agitate, Solution) formula to address pain points and offer solutions.",
-        "color": "#7209B7",
-        "difficulty": "Beginner",
-        "best_for": ["Ads", "Email", "Landing Pages"],
-        "tags": ["problem-solving", "pain-points", "solutions"]
-    },
-    "fab_copywriter": {
-        "name": "FAB Copywriter",
-        "icon": "💎",
-        "description": "Use the FAB (Features, Advantages, Benefits) framework to highlight product value.",
-        "color": "#3A0CA3",
-        "difficulty": "Beginner",
-        "best_for": ["Product Descriptions", "Sales Pages", "Brochures"],
-        "tags": ["product", "features", "benefits"]
-    },
-    "4c_copywriter": {
-        "name": "4C Copywriter",
-        "icon": "📝",
-        "description": "Implement the 4C (Clear, Concise, Credible, Compelling) framework for effective messaging.",
-        "color": "#4361EE",
-        "difficulty": "Intermediate",
-        "best_for": ["Brand Messaging", "Mission Statements", "Value Propositions"],
-        "tags": ["clarity", "concise", "credibility"]
-    },
-    "4r_copywriter": {
-        "name": "4R Copywriter",
-        "icon": "🔄",
-        "description": "Use the 4R (Relevance, Resonance, Response, Results) framework to connect with your audience.",
-        "color": "#F72585",
-        "difficulty": "Intermediate",
-        "best_for": ["Content Marketing", "Email", "Social Media"],
-        "tags": ["relevance", "resonance", "results"]
-    }
-}
-
-def load_user_preferences() -> Dict:
-    """Load user preferences from session state or initialize if not present."""
-    if "copywriter_preferences" not in st.session_state:
-        st.session_state.copywriter_preferences = {
-            "recent_formulas": [],
-            "favorite_formulas": [],
-            "comparison_formulas": [],
-            "view_mode": "grid"  # or "list"
-        }
-    return st.session_state.copywriter_preferences
-
-def save_user_preferences(preferences: Dict) -> None:
-    """Save user preferences to session state."""
-    st.session_state.copywriter_preferences = preferences
-
-def add_recent_formula(module_name: str) -> None:
-    """Add a formula to the recent formulas list."""
-    preferences = load_user_preferences()
-    
-    # Remove if already exists
-    if module_name in preferences["recent_formulas"]:
-        preferences["recent_formulas"].remove(module_name)
-    
-    # Add to the beginning of the list
-    preferences["recent_formulas"].insert(0, module_name)
-    
-    # Keep only the 5 most recent
-    preferences["recent_formulas"] = preferences["recent_formulas"][:5]
-    
-    save_user_preferences(preferences)
-
-def toggle_favorite_formula(module_name: str) -> bool:
-    """Toggle a formula as favorite and return the new state."""
-    preferences = load_user_preferences()
-    
-    if module_name in preferences["favorite_formulas"]:
-        preferences["favorite_formulas"].remove(module_name)
-        is_favorite = False
-    else:
-        preferences["favorite_formulas"].append(module_name)
-        is_favorite = True
-    
-    save_user_preferences(preferences)
-    return is_favorite
-
-def is_favorite_formula(module_name: str) -> bool:
-    """Check if a formula is in the favorites list."""
-    preferences = load_user_preferences()
-    return module_name in preferences["favorite_formulas"]
-
-def add_to_comparison(module_name: str) -> None:
-    """Add a formula to the comparison list."""
-    preferences = load_user_preferences()
-    
-    if module_name not in preferences["comparison_formulas"]:
-        preferences["comparison_formulas"].append(module_name)
-        
-    # Keep only up to 3 formulas for comparison
-    preferences["comparison_formulas"] = preferences["comparison_formulas"][:3]
-    
-    save_user_preferences(preferences)
-
-def remove_from_comparison(module_name: str) -> None:
-    """Remove a formula from the comparison list."""
-    preferences = load_user_preferences()
-    
-    if module_name in preferences["comparison_formulas"]:
-        preferences["comparison_formulas"].remove(module_name)
-    
-    save_user_preferences(preferences)
-
-def clear_comparison() -> None:
-    """Clear the comparison list."""
-    preferences = load_user_preferences()
-    preferences["comparison_formulas"] = []
-    save_user_preferences(preferences)
-
-def lazy_load_module(module_name: str) -> Optional[Callable]:
-    """Lazily load a module and return its input_section function."""
-    if module_name in input_sections:
-        return input_sections[module_name]
-    
-    try:
-        module_path = f"lib.ai_writers.ai_copywriter.{module_name}"
-        module = importlib.import_module(module_path)
-        if hasattr(module, "input_section"):
-            input_sections[module_name] = module.input_section
-            return module.input_section
-        else:
-            st.warning(f"Module {module_name} does not have an input_section function.")
-            return None
-    except Exception as e:
-        st.error(f"Error loading module {module_name}: {str(e)}")
-        return None
-
-def render_formula_card(module_name: str, index: int, view_mode: str = "grid") -> None:
-    """Render a formula card with its details."""
-    metadata = formula_metadata.get(module_name, {})
-    
-    if not metadata:
-        return
-    
-    is_favorite = is_favorite_formula(module_name)
-    favorite_icon = "★" if is_favorite else "☆"
-    favorite_tooltip = "Remove from favorites" if is_favorite else "Add to favorites"
-    
-    if view_mode == "grid":
-        with st.container():
-            st.markdown(f"""
-            <div style='background-color: {metadata["color"]}; padding: 20px; border-radius: 10px; margin-bottom: 20px; color: white; position: relative;'>
-                <div style='position: absolute; top: 10px; right: 10px; font-size: 1.5em;'>{favorite_icon}</div>
-                <h2 style='color: white;'>{metadata["icon"]} {metadata["name"]}</h2>
-                <p>{metadata["description"]}</p>
-                <div style='margin-top: 10px;'>
-                    <span style='background-color: rgba(255,255,255,0.2); padding: 3px 8px; border-radius: 10px; margin-right: 5px; font-size: 0.8em;'>
-                        {metadata["difficulty"]}
-                    </span>
-                </div>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                if st.button(f"Use {metadata['name']}", key=f"use_btn_{index}", use_container_width=True):
-                    add_recent_formula(module_name)
-                    st.session_state.selected_formula = {
-                        "module": module_name,
-                        "name": metadata["name"],
-                        "icon": metadata["icon"],
-                        "function": lazy_load_module(module_name)
-                    }
-                    st.rerun()
-            
-            with col2:
-                if st.button(f"{favorite_icon} Favorite", key=f"fav_btn_{index}", help=favorite_tooltip, use_container_width=True):
-                    toggle_favorite_formula(module_name)
-                    st.rerun()
-            
-            with col3:
-                if module_name in load_user_preferences()["comparison_formulas"]:
-                    if st.button("Remove from Compare", key=f"comp_btn_{index}", use_container_width=True):
-                        remove_from_comparison(module_name)
-                        st.rerun()
-                else:
-                    if st.button("Add to Compare", key=f"comp_btn_{index}", use_container_width=True):
-                        add_to_comparison(module_name)
-                        st.rerun()
-    else:  # list view
-        with st.container():
-            col1, col2 = st.columns([3, 1])
-            
-            with col1:
-                st.markdown(f"""
-                <div style='padding: 10px; border-left: 5px solid {metadata["color"]}; margin-bottom: 10px;'>
-                    <h3>{metadata["icon"]} {metadata["name"]} {favorite_icon}</h3>
-                    <p>{metadata["description"]}</p>
-                    <div>
-                        <span style='background-color: #f0f2f6; padding: 3px 8px; border-radius: 10px; margin-right: 5px; font-size: 0.8em;'>
-                            {metadata["difficulty"]}
-                        </span>
-                        <span style='font-size: 0.8em;'>Best for: {", ".join(metadata["best_for"][:2])}</span>
-                    </div>
-                </div>
-                """, unsafe_allow_html=True)
-            
-            with col2:
-                if st.button(f"Use", key=f"use_list_btn_{index}", use_container_width=True):
-                    add_recent_formula(module_name)
-                    st.session_state.selected_formula = {
-                        "module": module_name,
-                        "name": metadata["name"],
-                        "icon": metadata["icon"],
-                        "function": lazy_load_module(module_name)
-                    }
-                    st.rerun()
-                
-                if st.button(f"{favorite_icon}", key=f"fav_list_btn_{index}", help=favorite_tooltip):
-                    toggle_favorite_formula(module_name)
-                    st.rerun()
-                
-                if module_name in load_user_preferences()["comparison_formulas"]:
-                    if st.button("- Compare", key=f"comp_list_btn_{index}"):
-                        remove_from_comparison(module_name)
-                        st.rerun()
-                else:
-                    if st.button("+ Compare", key=f"comp_list_btn_{index}"):
-                        add_to_comparison(module_name)
-                        st.rerun()
-
-def render_formula_comparison() -> None:
-    """Render a comparison of selected formulas."""
-    preferences = load_user_preferences()
-    comparison_formulas = preferences["comparison_formulas"]
-    
-    if not comparison_formulas:
-        st.info("Add formulas to compare them side by side.")
-        return
-    
-    # Create a table for comparison
-    comparison_data = []
-    for module_name in comparison_formulas:
-        metadata = formula_metadata.get(module_name, {})
-        if metadata:
-            comparison_data.append({
-                "Name": f"{metadata['icon']} {metadata['name']}",
-                "Description": metadata["description"],
-                "Difficulty": metadata["difficulty"],
-                "Best For": ", ".join(metadata["best_for"][:3]),
-                "Tags": ", ".join(metadata["tags"])
-            })
-    
-    # Display the comparison table
-    st.markdown("### Formula Comparison")
-    
-    # Create columns for each formula
-    cols = st.columns(len(comparison_data))
-    
-    # Display headers
-    for i, col in enumerate(cols):
-        with col:
-            st.markdown(f"#### {comparison_data[i]['Name']}")
-    
-    # Display description
-    st.markdown("##### Description")
-    for i, col in enumerate(cols):
-        with col:
-            st.write(comparison_data[i]["Description"])
-    
-    # Display difficulty
-    st.markdown("##### Difficulty")
-    for i, col in enumerate(cols):
-        with col:
-            st.write(comparison_data[i]["Difficulty"])
-    
-    # Display best for
-    st.markdown("##### Best For")
-    for i, col in enumerate(cols):
-        with col:
-            st.write(comparison_data[i]["Best For"])
-    
-    # Display tags
-    st.markdown("##### Tags")
-    for i, col in enumerate(cols):
-        with col:
-            st.write(comparison_data[i]["Tags"])
-    
-    # Add buttons to use each formula
-    st.markdown("##### Actions")
-    for i, col in enumerate(cols):
-        with col:
-            module_name = comparison_formulas[i]
-            if st.button(f"Use {formula_metadata[module_name]['name']}", key=f"use_comp_btn_{i}"):
-                add_recent_formula(module_name)
-                st.session_state.selected_formula = {
-                    "module": module_name,
-                    "name": formula_metadata[module_name]["name"],
-                    "icon": formula_metadata[module_name]["icon"],
-                    "function": lazy_load_module(module_name)
-                }
-                st.rerun()
-    
-    # Add a button to clear the comparison
-    if st.button("Clear Comparison", key="clear_comparison"):
-        clear_comparison()
-        st.rerun()
-
-def filter_formulas(formulas: List[str], search_term: str, category: str, difficulty: str) -> List[str]:
-    """Filter formulas based on search term, category, and difficulty."""
-    filtered_formulas = []
-    
-    for module_name in formulas:
-        metadata = formula_metadata.get(module_name, {})
-        if not metadata:
-            continue
-        
-        # Check if the formula matches the search term
-        name_match = search_term.lower() in metadata["name"].lower()
-        desc_match = search_term.lower() in metadata["description"].lower()
-        tags_match = any(search_term.lower() in tag.lower() for tag in metadata.get("tags", []))
-        
-        # Check if the formula matches the category
-        category_match = True
-        if category != "All Categories":
-            category_match = module_name in formula_categories.get(category, [])
-        
-        # Check if the formula matches the difficulty
-        difficulty_match = True
-        if difficulty != "All Difficulties":
-            difficulty_match = metadata.get("difficulty", "") == difficulty
-        
-        # Add the formula if it matches all criteria
-        if (name_match or desc_match or tags_match) and category_match and difficulty_match:
-            filtered_formulas.append(module_name)
-    
-    return filtered_formulas
-
-def copywriter_dashboard():
-    """
-    Main function to display the copywriting dashboard.
-    This function can be called from content_generator.py when the user selects "AI Copywriter".
-    """
-    # Load user preferences
-    preferences = load_user_preferences()
-    
-    # Initialize session state for selected formula if it doesn't exist
-    if "selected_formula" not in st.session_state:
-        st.session_state.selected_formula = None
-    
-    # Initialize session state for search and filter options
-    if "search_term" not in st.session_state:
-        st.session_state.search_term = ""
-    if "selected_category" not in st.session_state:
-        st.session_state.selected_category = "All Categories"
-    if "selected_difficulty" not in st.session_state:
-        st.session_state.selected_difficulty = "All Difficulties"
-    if "view_mode" not in st.session_state:
-        st.session_state.view_mode = preferences["view_mode"]
-    
-    # Create a container for the formula input section
-    formula_container = st.container()
-    
-    # If a formula is selected, show its input section
-    if st.session_state.selected_formula is not None:
-        with formula_container:
-            # Display the selected formula's input section
-            st.markdown("---")
-            st.markdown(f"# {st.session_state.selected_formula['icon']} {st.session_state.selected_formula['name']}")
-            
-            # Add a back button
-            if st.button("← Back to Dashboard", key="back_to_dashboard"):
-                # Clear the selected formula from session state
-                st.session_state.selected_formula = None
-                st.rerun()
-            
-            # Call the input section function for the selected formula
-            if st.session_state.selected_formula["function"]:
-                st.session_state.selected_formula["function"]()
-            else:
-                st.error(f"The {st.session_state.selected_formula['name']} module is not available.")
-    else:
-        # Create a container for the dashboard
-        dashboard_container = st.container()
-        
-        with dashboard_container:
-            # Display the dashboard
-            # Header
-            st.markdown("""
-            <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-                <h1 style='color: #1E88E5; text-align: center;'>✍️ AI Copywriting Tools</h1>
-                <p style='text-align: center;'>Choose the perfect copywriting formula for your marketing needs</p>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            # Create tabs for different sections
-            tab1, tab2, tab3, tab4 = st.tabs(["All Formulas", "Recent & Favorites", "Compare Formulas", "Help & Guide"])
-            
-            with tab1:
-                # Search and filter options
-                col1, col2, col3, col4 = st.columns([3, 2, 2, 1])
-                
-                with col1:
-                    search_term = st.text_input("🔍 Search formulas", value=st.session_state.search_term)
-                    if search_term != st.session_state.search_term:
-                        st.session_state.search_term = search_term
-                
-                with col2:
-                    categories = ["All Categories"] + list(formula_categories.keys())
-                    selected_category = st.selectbox("Category", categories, index=categories.index(st.session_state.selected_category))
-                    if selected_category != st.session_state.selected_category:
-                        st.session_state.selected_category = selected_category
-                
-                with col3:
-                    difficulties = ["All Difficulties", "Beginner", "Intermediate", "Advanced"]
-                    selected_difficulty = st.selectbox("Difficulty", difficulties, index=difficulties.index(st.session_state.selected_difficulty))
-                    if selected_difficulty != st.session_state.selected_difficulty:
-                        st.session_state.selected_difficulty = selected_difficulty
-                
-                with col4:
-                    view_options = {"Grid": "grid", "List": "list"}
-                    view_mode = st.selectbox("View", list(view_options.keys()), index=list(view_options.values()).index(st.session_state.view_mode))
-                    st.session_state.view_mode = view_options[view_mode]
-                    preferences["view_mode"] = st.session_state.view_mode
-                    save_user_preferences(preferences)
-                
-                # Filter formulas based on search and filter options
-                filtered_formulas = filter_formulas(
-                    copywriter_modules,
-                    st.session_state.search_term,
-                    st.session_state.selected_category,
-                    st.session_state.selected_difficulty
-                )
-                
-                if not filtered_formulas:
-                    st.info("No formulas match your search criteria. Try adjusting your filters.")
-                else:
-                    # Display the formula cards
-                    if st.session_state.view_mode == "grid":
-                        # Create a 3-column layout for the formula cards
-                        col1, col2, col3 = st.columns(3)
-                        
-                        # Display the formula cards
-                        for i, module_name in enumerate(filtered_formulas):
-                            # Determine which column to use
-                            col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
-                            
-                            with col:
-                                render_formula_card(module_name, i, st.session_state.view_mode)
-                    else:  # list view
-                        for i, module_name in enumerate(filtered_formulas):
-                            render_formula_card(module_name, i, st.session_state.view_mode)
-            
-            with tab2:
-                # Recent formulas
-                st.subheader("Recently Used Formulas")
-                recent_formulas = preferences["recent_formulas"]
-                
-                if not recent_formulas:
-                    st.info("You haven't used any formulas yet. Start by selecting a formula from the 'All Formulas' tab.")
-                else:
-                    # Create a 3-column layout for the recent formula cards
-                    col1, col2, col3 = st.columns(3)
-                    
-                    # Display the recent formula cards
-                    for i, module_name in enumerate(recent_formulas):
-                        # Determine which column to use
-                        col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
-                        
-                        with col:
-                            render_formula_card(module_name, i + 100, "grid")  # Use a different index to avoid key conflicts
-                
-                # Favorite formulas
-                st.subheader("Favorite Formulas")
-                favorite_formulas = preferences["favorite_formulas"]
-                
-                if not favorite_formulas:
-                    st.info("You haven't added any formulas to your favorites yet. Click the star icon on a formula card to add it to your favorites.")
-                else:
-                    # Create a 3-column layout for the favorite formula cards
-                    col1, col2, col3 = st.columns(3)
-                    
-                    # Display the favorite formula cards
-                    for i, module_name in enumerate(favorite_formulas):
-                        # Determine which column to use
-                        col = col1 if i % 3 == 0 else col2 if i % 3 == 1 else col3
-                        
-                        with col:
-                            render_formula_card(module_name, i + 200, "grid")  # Use a different index to avoid key conflicts
-            
-            with tab3:
-                # Formula comparison
-                render_formula_comparison()
-            
-            with tab4:
-                # Help and guide
-                st.subheader("Copywriting Formula Guide")
-                st.write("""
-                This dashboard provides access to a variety of copywriting formulas, each designed for specific marketing needs.
-                Here's how to make the most of these powerful tools:
-                """)
-                
-                st.markdown("""
-                #### How to Use This Dashboard
-                
-                1. **Browse Formulas**: Explore the available copywriting formulas in the "All Formulas" tab
-                2. **Search & Filter**: Use the search box and filters to find the perfect formula for your needs
-                3. **Compare Formulas**: Add up to 3 formulas to the comparison tab to see them side by side
-                4. **Save Favorites**: Click the star icon to save formulas you use frequently
-                5. **Access Recent**: Quickly access your recently used formulas in the "Recent & Favorites" tab
-                
-                #### Choosing the Right Formula
-                
-                Different formulas work best for different marketing goals:
-                
-                - **Emotional Appeal**: Use when you want to connect with your audience on an emotional level
-                - **Structured Framework**: Great for organizing complex information in a compelling way
-                - **Sales Funnel**: Designed to guide prospects through the buying journey
-                - **Problem-Solution**: Effective for highlighting pain points and positioning your solution
-                - **Feature-Benefit**: Perfect for product descriptions and technical offerings
-                - **Messaging Framework**: Helps create clear, consistent messaging across channels
-                
-                #### Formula Difficulty Levels
-                
-                - **Beginner**: Easy to use with minimal copywriting experience
-                - **Intermediate**: Requires some understanding of copywriting principles
-                - **Advanced**: Most effective when used by experienced copywriters
-                """)
-                
-                # Add a section about how to use the generated copy
-                st.subheader("Using Your Generated Copy")
-                st.write("""
-                After generating copy with your chosen formula:
-                
-                1. **Review & Edit**: Always review and personalize the generated content
-                2. **Test Different Versions**: Try multiple formulas for the same product/service
-                3. **A/B Test**: Use different versions in your marketing to see which performs best
-                4. **Adapt for Channels**: Modify the copy as needed for different marketing channels
-                """)
-                
-                # Add a feedback section
-                st.subheader("Feedback & Suggestions")
-                st.write("We're constantly improving our copywriting tools. If you have feedback or suggestions, please let us know!")
-                
-                feedback = st.text_area("Your feedback", placeholder="Share your thoughts, suggestions, or report any issues...")
-                if st.button("Submit Feedback"):
-                    if feedback:
-                        st.success("Thank you for your feedback! We'll use it to improve our tools.")
-                        # In a real implementation, you would save this feedback somewhere
-                    else:
-                        st.warning("Please enter your feedback before submitting.")
-
-# For standalone execution
-if __name__ == "__main__":
-    st.set_page_config(
-        page_title="AI Copywriting Tools",
-        page_icon="✍️",
-        layout="wide",
-        initial_sidebar_state="expanded"
-    )
-    copywriter_dashboard()
--- a/ToBeMigrated/ai_writers/ai_copywriter/fab_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/fab_copywriter.py
@@ -1,212 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 FAB Copywriting Generator</h2>
-        <p>Create compelling copy that follows the FAB (Features-Advantages-Benefits) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about FAB copywriting
-    with st.expander("📚 What is FAB Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the FAB Copywriting Framework
-        
-        FAB is an acronym for Features-Advantages-Benefits. It's a powerful copywriting framework that focuses on translating product features into customer benefits:
-        
-        - **Features**: The specific characteristics, attributes, or capabilities of your product or service
-        - **Advantages**: How these features compare to or outperform competitors
-        - **Benefits**: The positive outcomes or results that customers will experience when using your product or service
-        
-        ### Why FAB Copywriting Works
-        
-        The FAB framework works because it:
-        
-        - Focuses on customer value rather than just product specifications
-        - Translates technical features into meaningful benefits
-        - Addresses the "what's in it for me" question that customers ask
-        - Creates a clear connection between product capabilities and customer outcomes
-        - Helps customers understand why they should choose your product over alternatives
-        
-        ### When to Use FAB Copywriting
-        
-        The FAB framework is particularly effective for:
-        
-        - Product descriptions and specifications
-        - Technical products with complex features
-        - Comparison marketing
-        - B2B marketing where features matter
-        - Content that needs to explain product capabilities
-        - Marketing materials that need to address feature-based objections
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your FAB Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            product_name = st.text_input('**🏢 Product/Service Name**', 
-                                       placeholder="e.g., Alwrity AI Writer",
-                                       help="Enter the name of your product or service.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Content marketers",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            features = st.text_area('**🔧 Features**', 
-                                  placeholder="e.g., AI-powered content generation, Multiple copywriting frameworks, SEO optimization",
-                                  help="List the specific characteristics, attributes, or capabilities of your product or service.")
-            
-            advantages = st.text_area('**💪 Advantages**', 
-                                    placeholder="e.g., 10x faster than manual writing, Supports 12+ copywriting frameworks, Built-in SEO analysis",
-                                    help="How do these features compare to or outperform competitors?")
-            
-        with col2:
-            product_description = st.text_input('**📝 Product Description** (In 5-6 words)', 
-                                              placeholder="e.g., AI writing assistant",
-                                              help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., All-in-one AI copywriting platform",
-                                               help="What makes your product/service different from competitors?")
-            
-            benefits = st.text_area('**✨ Benefits**', 
-                                  placeholder="e.g., Save 20+ hours per week on content creation, Increase conversion rates by 35%, Improve SEO rankings",
-                                  help="What positive outcomes or results will customers experience when using your product or service?")
-            
-            call_to_action = st.text_area('**🚀 Call to Action**', 
-                                        placeholder="e.g., Start creating high-converting content today with our 14-day free trial...",
-                                        help="Prompt your audience to take action with a strong call to action.")
-            
-            landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)', 
-                                           placeholder="e.g., https://alwrity.com",
-                                           help="Provide a URL to include in your call to action.")
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            platform = st.selectbox(
-                '**📱 Content Platform**',
-                options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
-                help="Select the platform where your copy will be used."
-            )
-            
-        with col2:
-            language = st.selectbox(
-                '**🌍 Language**',
-                options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
-                help="Select the language for your copy."
-            )
-            
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate FAB Copy**', type="primary"):
-            if not product_name or not product_description or not features or not advantages or not benefits:
-                st.error("⚠️ Please fill in all required fields (Product Name, Description, Features, Advantages, and Benefits)!")
-            else:
-                with st.spinner("✨ Crafting compelling FAB copy..."):
-                    fab_copy = generate_fab_copy(
-                        product_name, 
-                        product_description, 
-                        features,
-                        advantages,
-                        benefits,
-                        target_audience,
-                        unique_selling_point,
-                        call_to_action,
-                        landing_page_url,
-                        platform,
-                        language,
-                        tone_style
-                    )
-                    
-                    if fab_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your FAB Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(fab_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your FAB Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your FAB Copy Effectively
-                            
-                            1. **Follow the sequence**: The FAB framework creates a natural progression - make sure your copy maintains this flow
-                            
-                            2. **Balance features and benefits**: While benefits are most important, don't neglect features for technical audiences
-                            
-                            3. **Be specific**: Use concrete numbers, statistics, and examples to make your advantages and benefits more compelling
-                            
-                            4. **Pair with visuals**: Combine your copy with images that showcase your product features and the resulting benefits
-                            
-                            5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            6. **Measure results**: Track conversion metrics to see how your FAB copy performs
-                            
-                            7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate FAB Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_fab_copy(product_name, product_description, features, advantages, benefits, 
-                     target_audience, unique_selling_point, call_to_action, 
-                     landing_page_url, platform, language, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the FAB (Features-Advantages-Benefits) framework. 
-    Your expertise is in creating compelling, conversion-focused marketing copy that translates product features into meaningful customer benefits. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {product_name}, which is a {product_description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    PLATFORM: {platform}
-    LANGUAGE: {language}
-    TONE & STYLE: {tone_style}
-    
-    Use the FAB framework with these elements:
-    - **Features**: {features}
-    - **Advantages**: {advantages}
-    - **Benefits**: {benefits}
-    - **Call to Action**: {call_to_action}
-    """
-    
-    if landing_page_url:
-        prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
-    
-    prompt += """
-    For each campaign:
-    1. Start by highlighting the key features of the product or service
-    2. Explain the advantages these features provide compared to alternatives
-    3. Connect these advantages to specific benefits that customers will experience
-    4. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/oath_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/oath_copywriter.py
@@ -1,186 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>📋 OATH Copywriting Generator</h2>
-        <p>Create compelling copy that addresses different audience mindsets using the OATH (Oblivious-Apathetic-Thinking-Hurting) framework.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about OATH copywriting
-    with st.expander("📚 What is OATH Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the OATH Copywriting Framework
-        
-        The OATH framework is a powerful copywriting approach that recognizes different audience mindsets:
-        
-        - **Oblivious**: People who don't know they have a problem or need
-        - **Apathetic**: People who know about the problem but don't care enough to act
-        - **Thinking**: People who are actively considering solutions
-        - **Hurting**: People who are experiencing pain and urgently need a solution
-        
-        ### Why OATH Copywriting Works
-        
-        The OATH framework works because it:
-        
-        - Addresses the full spectrum of audience awareness
-        - Creates targeted messaging for each mindset
-        - Increases conversion rates by meeting people where they are
-        - Helps you craft the right message for the right audience
-        - Allows for more personalized and effective marketing campaigns
-        
-        ### When to Use OATH Copywriting
-        
-        The OATH framework is particularly effective for:
-        
-        - New product launches
-        - Educational content
-        - Problem-solution marketing
-        - Awareness campaigns
-        - Multi-channel marketing strategies
-        - Content that needs to address different audience segments
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your OATH Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            oblivious = st.text_area('**🔍 Oblivious Audience**', 
-                                   placeholder="People who don't know they have this problem...",
-                                   help="Describe the audience who doesn't know they have a problem or need your solution.")
-            
-            apathetic = st.text_area('**😐 Apathetic Audience**', 
-                                   placeholder="People who know about the problem but don't care enough to act...",
-                                   help="Describe the audience who knows about the problem but isn't motivated to solve it.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 2-3 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            thinking = st.text_area('**🤔 Thinking Audience**', 
-                                  placeholder="People who are actively considering solutions...",
-                                  help="Describe the audience who is actively researching solutions to their problem.")
-            
-            hurting = st.text_area('**😫 Hurting Audience**', 
-                                 placeholder="People who are experiencing pain and urgently need a solution...",
-                                 help="Describe the audience who is experiencing significant pain and urgently needs a solution.")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate OATH Copy**', type="primary"):
-            if not brand_name or not description or not oblivious or not apathetic or not thinking or not hurting:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all audience segments)!")
-            else:
-                with st.spinner("✨ Crafting compelling OATH copy..."):
-                    oath_copy = generate_oath_copy(
-                        brand_name, 
-                        description, 
-                        oblivious,
-                        apathetic,
-                        thinking,
-                        hurting,
-                        target_audience,
-                        unique_selling_point,
-                        tone_style
-                    )
-                    
-                    if oath_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>📋 Your OATH Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(oath_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy - using a container instead of an expander
-                        st.markdown("""
-                        <div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #333;'>💡 Tips for Using Your OATH Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.markdown("""
-                        ### How to Use Your OATH Copy Effectively
-                        
-                        1. **Target the right audience**: Use the appropriate OATH segment copy based on your target audience's current mindset
-                        
-                        2. **Create a journey**: Consider how to move audiences from one mindset to another (e.g., from Oblivious to Thinking)
-                        
-                        3. **Test different versions**: A/B test your copy to see which OATH segment resonates most with your audience
-                        
-                        4. **Pair with visuals**: Combine your copy with images that reinforce the message for each audience segment
-                        
-                        5. **Measure results**: Track engagement metrics to see how your OATH copy performs across different audience segments
-                        
-                        6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                        """)
-                    else:
-                        st.error("💥 **Failed to generate OATH Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_oath_copy(brand_name, description, oblivious, apathetic, thinking, hurting, 
-                      target_audience, unique_selling_point, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the OATH (Oblivious-Apathetic-Thinking-Hurting) framework. 
-    Your expertise is in creating compelling, targeted marketing copy that addresses different audience mindsets and awareness levels. 
-    Your copy is authentic, specific to the brand, and focused on meeting audiences where they are in their journey."""
-    
-    prompt = f"""Create 4 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the OATH framework with these audience segments:
-    - **Oblivious**: {oblivious}
-    - **Apathetic**: {apathetic}
-    - **Thinking**: {thinking}
-    - **Hurting**: {hurting}
-
-    For each campaign:
-    1. Create a compelling headline that captures attention
-    2. Write 2-3 paragraphs that address the specific audience mindset
-    3. End with a strong call to action
-    4. Explain how the copy is tailored to that specific audience mindset
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/pas_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/pas_copywriter.py
@@ -1,213 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🎯 PAS Copywriting Generator</h2>
-        <p>Create compelling copy that follows the PAS (Problem-Agitate-Solution) framework to drive conversions.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about PAS copywriting
-    with st.expander("📚 What is PAS Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the PAS Copywriting Framework
-        
-        PAS is an acronym for Problem-Agitate-Solution. It's a powerful copywriting framework that focuses on identifying and solving customer pain points:
-        
-        - **Problem**: Identifying a specific problem or pain point that your target audience faces
-        - **Agitate**: Amplifying the problem by highlighting its negative consequences and emotional impact
-        - **Solution**: Presenting your product or service as the ideal solution to the problem
-        
-        ### Why PAS Copywriting Works
-        
-        The PAS framework works because it:
-        
-        - Addresses real customer pain points and needs
-        - Creates emotional resonance by highlighting the consequences of inaction
-        - Positions your product/service as the hero that solves the problem
-        - Follows a natural problem-solving narrative that readers can relate to
-        - Focuses on the customer's journey rather than just product features
-        
-        ### When to Use PAS Copywriting
-        
-        The PAS framework is particularly effective for:
-        
-        - Products or services that solve specific problems
-        - Marketing to audiences with clear pain points
-        - Content that needs to drive specific actions
-        - Landing pages and sales pages
-        - Email marketing campaigns
-        - Direct response advertising
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your PAS Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            problem = st.text_area('**❌ Problem**', 
-                                 placeholder="e.g., Struggling to create high-quality content that converts",
-                                 help="Identify a specific problem or pain point that your target audience faces.")
-            
-            agitate = st.text_area('**😫 Agitate**', 
-                                 placeholder="e.g., Without effective content, you're losing potential customers and revenue every day...",
-                                 help="Amplify the problem by highlighting its negative consequences and emotional impact.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 5-6 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            solution = st.text_area('**✨ Solution**', 
-                                  placeholder="e.g., Our AI-powered platform creates high-converting content in minutes...",
-                                  help="Present your product or service as the ideal solution to the problem.")
-            
-            call_to_action = st.text_area('**🚀 Call to Action**', 
-                                        placeholder="e.g., Start creating converting content today with our 14-day free trial...",
-                                        help="Prompt your audience to take action with a strong call to action.")
-            
-            landing_page_url = st.text_input('**🌐 Landing Page URL** (Optional)', 
-                                           placeholder="e.g., https://alwrity.com",
-                                           help="Provide a URL to include in your call to action.")
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            platform = st.selectbox(
-                '**📱 Content Platform**',
-                options=['Social media copy', 'Email copy', 'Website copy', 'Ad copy', 'Product copy'],
-                help="Select the platform where your copy will be used."
-            )
-            
-        with col2:
-            language = st.selectbox(
-                '**🌍 Language**',
-                options=['English', 'Hindustani', 'Chinese', 'Hindi', 'Spanish'],
-                help="Select the language for your copy."
-            )
-            
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate PAS Copy**', type="primary"):
-            if not brand_name or not description or not problem or not agitate or not solution:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Problem, Agitate, and Solution)!")
-            else:
-                with st.spinner("✨ Crafting compelling PAS copy..."):
-                    pas_copy = generate_pas_copy(
-                        brand_name, 
-                        description, 
-                        problem,
-                        agitate,
-                        solution,
-                        target_audience,
-                        unique_selling_point,
-                        call_to_action,
-                        landing_page_url,
-                        platform,
-                        language,
-                        tone_style
-                    )
-                    
-                    if pas_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🎯 Your PAS Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(pas_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your PAS Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your PAS Copy Effectively
-                            
-                            1. **Follow the sequence**: The PAS framework creates a natural progression - make sure your copy maintains this flow
-                            
-                            2. **Be specific about the problem**: The more specific and relatable the problem, the more effective your copy will be
-                            
-                            3. **Balance agitation**: Don't over-agitate to the point of creating anxiety; find the right balance to motivate action
-                            
-                            4. **Pair with visuals**: Combine your copy with images that reinforce each stage of the PAS journey
-                            
-                            5. **Consider the context**: Adapt the copy based on where it will appear (landing page, email, social media, etc.)
-                            
-                            6. **Measure results**: Track conversion metrics to see how your PAS copy performs
-                            
-                            7. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate PAS Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_pas_copy(brand_name, description, problem, agitate, solution, 
-                     target_audience, unique_selling_point, call_to_action, 
-                     landing_page_url, platform, language, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the PAS (Problem-Agitate-Solution) framework. 
-    Your expertise is in creating compelling, conversion-focused marketing copy that identifies customer pain points, 
-    amplifies their impact, and positions your product or service as the ideal solution. 
-    Your copy is authentic, specific to the brand, and focused on driving measurable results."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    PLATFORM: {platform}
-    LANGUAGE: {language}
-    TONE & STYLE: {tone_style}
-    
-    Use the PAS framework with these elements:
-    - **Problem**: {problem}
-    - **Agitate**: {agitate}
-    - **Solution**: {solution}
-    - **Call to Action**: {call_to_action}
-    """
-    
-    if landing_page_url:
-        prompt += f"\nInclude the landing page URL ({landing_page_url}) in your call to action."
-    
-    prompt += """
-    For each campaign:
-    1. Start by identifying the specific problem or pain point
-    2. Amplify the problem by highlighting its negative consequences and emotional impact
-    3. Present your product or service as the ideal solution to the problem
-    4. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/quest_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/quest_copywriter.py
@@ -1,191 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-
-def title_and_description():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>🔍 QUEST Copywriting Generator</h2>
-        <p>Create compelling copy that guides your audience through a journey using the QUEST (Question-Unpack-Emphasize-Solution-Transform) framework.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about QUEST copywriting
-    with st.expander("📚 What is QUEST Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the QUEST Copywriting Framework
-        
-        QUEST is an acronym for Question-Unpack-Emphasize-Solution-Transform. It's a copywriting framework that focuses on guiding the audience through different stages:
-        
-        - **Question**: Presenting a thought-provoking question to engage the audience
-        - **Unpack**: Unpacking the question by elaborating on its implications and relevance
-        - **Emphasize**: Emphasizing the importance or significance of the topic
-        - **Solution**: Presenting your product or service as the solution to the question
-        - **Transform**: Describing the transformation or improvement your solution offers
-        
-        ### Why QUEST Copywriting Works
-        
-        The QUEST framework works because it:
-        
-        - Creates a natural flow that guides readers through a journey
-        - Engages readers by starting with a question they care about
-        - Builds credibility by showing deep understanding of the problem
-        - Demonstrates value by clearly connecting the solution to the problem
-        - Inspires action by showing the transformation that's possible
-        
-        ### When to Use QUEST Copywriting
-        
-        The QUEST framework is particularly effective for:
-        
-        - Educational content and blog posts
-        - Product launches and feature announcements
-        - Problem-solution marketing
-        - Thought leadership content
-        - Content that needs to guide readers through a journey
-        - Marketing materials that need to explain complex solutions
-        """)
-
-def input_section():
-    # Main input form
-    with st.expander("✍️ Create Your QUEST Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            question = st.text_area('**❓ Thought-Provoking Question**', 
-                                  placeholder="e.g., What if you could create content 10x faster without sacrificing quality?",
-                                  help="Pose a question that resonates with your audience and highlights a problem they face.")
-            
-            unpack = st.text_area('**📦 Unpack the Question**', 
-                                placeholder="e.g., Content creation is time-consuming and often results in inconsistent quality...",
-                                help="Elaborate on the implications of the question and provide context that your audience can relate to.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 2-3 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            emphasize = st.text_area('**💪 Emphasize Importance**', 
-                                   placeholder="e.g., In today's fast-paced digital world, efficient content creation is essential for business growth...",
-                                   help="Highlight the relevance and impact of addressing this problem.")
-            
-            solution = st.text_area('**🔧 Present Your Solution**', 
-                                  placeholder="e.g., Our AI-powered writing assistant helps you create high-quality content in a fraction of the time...",
-                                  help="Introduce your product or service as the solution to the question.")
-            
-            transform = st.text_area('**✨ Describe the Transformation**', 
-                                   placeholder="e.g., Imagine having more time to focus on strategy while maintaining consistent, high-quality content...",
-                                   help="Describe the transformation or improvement your solution offers to your audience.")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate QUEST Copy**', type="primary"):
-            if not brand_name or not description or not question or not unpack or not emphasize or not solution or not transform:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, and all QUEST elements)!")
-            else:
-                with st.spinner("✨ Crafting compelling QUEST copy..."):
-                    quest_copy = generate_quest_copy(
-                        brand_name, 
-                        description, 
-                        question,
-                        unpack,
-                        emphasize,
-                        solution,
-                        transform,
-                        target_audience,
-                        unique_selling_point,
-                        tone_style
-                    )
-                    
-                    if quest_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>🔍 Your QUEST Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(quest_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy
-                        with st.expander("💡 Tips for Using Your QUEST Copy", expanded=False):
-                            st.markdown("""
-                            ### How to Use Your QUEST Copy Effectively
-                            
-                            1. **Follow the journey**: The QUEST framework creates a natural flow - make sure your copy maintains this progression
-                            
-                            2. **Test different questions**: A/B test different opening questions to see which resonates most with your audience
-                            
-                            3. **Pair with visuals**: Combine your copy with images that reinforce each stage of the QUEST journey
-                            
-                            4. **Consider the context**: Adapt the copy based on where it will appear (blog post, landing page, email, etc.)
-                            
-                            5. **Measure results**: Track engagement metrics to see how your QUEST copy performs
-                            
-                            6. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                            """)
-                    else:
-                        st.error("💥 **Failed to generate QUEST Copy. Please try again!**")
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def generate_quest_copy(brand_name, description, question, unpack, emphasize, solution, transform, 
-                       target_audience, unique_selling_point, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the QUEST (Question-Unpack-Emphasize-Solution-Transform) framework. 
-    Your expertise is in creating compelling, narrative-driven marketing copy that guides readers through a journey. 
-    Your copy is authentic, specific to the brand, and focused on connecting with the audience's needs and desires."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the QUEST framework with these elements:
-    - **Question**: {question}
-    - **Unpack**: {unpack}
-    - **Emphasize**: {emphasize}
-    - **Solution**: {solution}
-    - **Transform**: {transform}
-
-    For each campaign:
-    1. Start with the thought-provoking question to engage the audience
-    2. Unpack the question by elaborating on its implications
-    3. Emphasize the importance of addressing this issue
-    4. Present your solution clearly and convincingly
-    5. Describe the transformation that your solution offers
-    6. End with a strong call to action
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_copywriter/star_copywriter.py
+++ b/ToBeMigrated/ai_writers/ai_copywriter/star_copywriter.py
@@ -1,182 +0,0 @@
-import streamlit as st
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-def input_section():
-    st.markdown("""
-    <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
-        <h2 style='color: #1E88E5;'>⭐ STAR Copywriting Generator</h2>
-        <p>Create compelling marketing copy using the proven STAR (Situation-Task-Action-Result) framework.</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Educational content about STAR copywriting
-    with st.expander("📚 What is STAR Copywriting?", expanded=False):
-        st.markdown("""
-        ### Understanding the STAR Copywriting Framework
-        
-        The STAR framework is a powerful storytelling structure that creates compelling narratives:
-        
-        - **Situation**: Set the context and background for the problem or need
-        - **Task**: Describe the specific challenge or objective that needs to be addressed
-        - **Action**: Explain the specific actions taken to address the challenge
-        - **Result**: Highlight the positive outcomes and benefits achieved
-        
-        ### Why STAR Copywriting Works
-        
-        The STAR framework works because it:
-        
-        - Creates a complete narrative arc that engages readers
-        - Demonstrates problem-solving capabilities
-        - Shows concrete results and benefits
-        - Builds credibility through specific examples
-        - Makes abstract benefits tangible through storytelling
-        
-        ### When to Use STAR Copywriting
-        
-        The STAR framework is particularly effective for:
-        
-        - Case studies and success stories
-        - Product or service demonstrations
-        - Customer testimonials
-        - Company achievements and milestones
-        - Problem-solution marketing
-        - Portfolio showcases
-        """)
-    
-    # Main input form
-    with st.expander("✍️ Create Your STAR Copy", expanded=True):
-        col1, col2 = st.columns([1, 1])
-        
-        with col1:
-            brand_name = st.text_input('**🏢 Brand/Company Name**', 
-                                      placeholder="e.g., Alwrity",
-                                      help="Enter the name of your brand or company.")
-            
-            target_audience = st.text_input('**👥 Target Audience**', 
-                                          placeholder="e.g., Small business owners, Tech professionals",
-                                          help="Who is your ideal customer? Be specific about demographics and psychographics.")
-            
-            situation = st.text_area('**🌍 Situation (Context)**', 
-                                   placeholder="In a busy city, Late Delivery, Unsafe Activities, Unprofessional Service..",
-                                   help="Describe the background context or problem that needs to be addressed.")
-            
-            action = st.text_area('**⚡ Action (Solution)**', 
-                                placeholder="New strategy, launched campaign, better service, New product...",
-                                help="Describe the specific actions taken to address the challenge or objective.")
-            
-        with col2:
-            description = st.text_input('**📝 Brand Description** (In 2-3 words)', 
-                                      placeholder="e.g., AI writing tools",
-                                      help="Describe your product or service briefly.")
-            
-            unique_selling_point = st.text_input('**💎 Unique Selling Point**', 
-                                               placeholder="e.g., 10x faster content creation",
-                                               help="What makes your product/service different from competitors?")
-            
-            task = st.text_area('**🎯 Task (Challenge)**', 
-                              placeholder="Increase website traffic by 30%, improve customer satisfaction, Safe Travels...",
-                              help="Describe the specific challenge or objective that needs to be addressed.")
-            
-            result = st.text_area('**✨ Result (Outcome)**', 
-                                placeholder="Improved customer engagement, sales revenue, Happy customers, Improved Service X...",
-                                help="Highlight the positive outcomes and benefits achieved from the actions taken.")
-        
-        tone_style = st.selectbox(
-            '**🎭 Copy Tone & Style**',
-            options=['Professional', 'Conversational', 'Humorous', 'Authoritative', 'Empathetic', 'Aspirational'],
-            help="Select the tone and style for your copy."
-        )
-        
-        if st.button('**🚀 Generate STAR Copy**', type="primary"):
-            if not brand_name or not description or not situation or not task or not action or not result:
-                st.error("⚠️ Please fill in all required fields (Brand Name, Description, Situation, Task, Action, and Result)!")
-            else:
-                with st.spinner("✨ Crafting compelling STAR copy..."):
-                    star_copy = generate_star_copy(
-                        brand_name, 
-                        description, 
-                        situation,
-                        task,
-                        action,
-                        result,
-                        target_audience,
-                        unique_selling_point,
-                        tone_style
-                    )
-                    
-                    if star_copy:
-                        st.markdown("""
-                        <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #0066cc;'>⭐ Your STAR Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Display the copy with a nice format
-                        st.markdown(star_copy)
-                        
-                        # Add copy button
-                        st.markdown("""
-                        <div style='margin-top: 20px;'>
-                            <button style='background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;'>
-                                Copy to Clipboard
-                            </button>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Add tips for using the copy - using a container instead of an expander
-                        st.markdown("""
-                        <div style='background-color: #f9f9f9; padding: 15px; border-radius: 10px; margin-top: 20px;'>
-                            <h3 style='color: #333;'>💡 Tips for Using Your STAR Copy</h3>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.markdown("""
-                        ### How to Use Your STAR Copy Effectively
-                        
-                        1. **Test different versions**: A/B test your copy to see which version resonates most with your audience
-                        
-                        2. **Pair with visuals**: Combine your copy with images that illustrate each stage of the STAR framework
-                        
-                        3. **Consider the platform**: Adapt your copy based on where it will appear (social media, email, website, etc.)
-                        
-                        4. **Measure results**: Track engagement metrics to see how your STAR copy performs
-                        
-                        5. **Refine over time**: Continuously improve your copy based on audience feedback and performance data
-                        """)
-                    else:
-                        st.error("💥 **Failed to generate STAR Copy. Please try again!**")
-
-
-def generate_star_copy(brand_name, description, situation, task, action, result, target_audience, 
-                      unique_selling_point, tone_style):
-    system_prompt = """You are an expert copywriter specializing in the STAR (Situation-Task-Action-Result) framework. 
-    Your expertise is in creating compelling, narrative-driven marketing copy that tells a complete story from problem to solution. 
-    Your copy is authentic, specific to the brand, and focused on demonstrating concrete results and benefits."""
-    
-    prompt = f"""Create 3 different marketing campaigns for {brand_name}, which is a {description}.
-
-    TARGET AUDIENCE: {target_audience}
-    UNIQUE SELLING POINT: {unique_selling_point}
-    TONE & STYLE: {tone_style}
-    
-    Use the STAR framework with these elements:
-    - **Situation**: {situation}
-    - **Task**: {task}
-    - **Action**: {action}
-    - **Result**: {result}
-
-    For each campaign:
-    1. Create a compelling headline that captures attention
-    2. Write 2-3 paragraphs that follow the STAR framework
-    3. End with a strong call to action
-    4. Explain how each element of the STAR framework is used in the copy
-
-    Format each campaign clearly with "CAMPAIGN 1:", "CAMPAIGN 2:", etc. as headers.
-    Make the copy authentic, specific to the brand, and focused on the target audience's needs and desires.
-    """
-    
-    try:
-        return llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as e:
-        st.error(f"Error generating copy: {str(e)}")
-        return None
--- a/ToBeMigrated/ai_writers/ai_essay_writer.py
+++ b/ToBeMigrated/ai_writers/ai_essay_writer.py
@@ -1,184 +0,0 @@
-#####################################################
-#
-# Alwrity, AI essay writer - Essay_Writing_with_Prompt_Chaining
-#
-#####################################################
-
-import os
-from pathlib import Path
-from dotenv import load_dotenv
-from pprint import pprint
-from loguru import logger
-import sys
-
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def generate_with_retry(prompt, system_prompt=None):
-    """
-    Generates content using the llm_text_gen function with retry handling for errors.
-
-    Parameters:
-        prompt (str): The prompt to generate content from.
-        system_prompt (str, optional): Custom system prompt to use instead of the default one.
-
-    Returns:
-        str: The generated content.
-    """
-    try:
-        # Use llm_text_gen instead of directly calling the model
-        return llm_text_gen(prompt, system_prompt)
-    except Exception as e:
-        logger.error(f"Error generating content: {e}")
-        return ""
-
-
-def ai_essay_generator(essay_title, selected_essay_type, selected_education_level, selected_num_pages):
-    """
-    Write an Essay using prompt chaining and iterative generation.
-
-    Parameters:
-        essay_title (str): The title or topic of the essay.
-        selected_essay_type (str): The type of essay to write.
-        selected_education_level (str): The education level of the target audience.
-        selected_num_pages (int): The number of pages or words for the essay.
-    """
-    logger.info(f"Starting to write Essay on {essay_title}..")
-    try:
-        # Define persona and writing guidelines
-        guidelines = f'''\
-        Writing Guidelines
-
-        As an expert Essay writer and academic researcher, demostrate your world class essay writing skills.
-        
-        Follow the below writing guidelines for writing your essay:
-        1). You specialize in {selected_essay_type} essay writing.
-        2). Your target audiences include readers from {selected_education_level} level.
-        3). The title of the essay is {essay_title}.
-        5). The final essay should of {selected_num_pages} words/pages.
-        3). Plant the seeds of subplots or potential character arc shifts that can be expanded later.
-
-        Remember, your main goal is to write as much as you can. If you get through
-        the story too fast, that is bad. Expand, never summarize.
-        '''
-        # Generate prompts
-        premise_prompt = f'''\
-        As an expert essay writer, specilizing in {selected_essay_type} essay writing.
-
-        Write an Essay title for given keywords {essay_title}. 
-        The title should appeal to audience level of {selected_education_level}.
-        '''
-
-        outline_prompt = f'''\
-        As an expert essay writer, specilizing in {selected_essay_type} essay writing.
-
-        Your Essay title is:
-
-        {{premise}}
-
-        Write an outline for the essay.
-        '''
-
-        starting_prompt = f'''\
-        As an expert essay writer, specilizing in {selected_essay_type} essay writing.
-
-        Your essay title is:
-
-        {{premise}}
-
-        The outline of the Essay is:
-
-        {{outline}}
-
-        First, silently review the outline and the essay title. Consider how to start the Essay.
-        Start to write the very beginning of the Essay. You are not expected to finish
-        the whole Essay now. Your writing should be detailed enough that you are only
-        scratching the surface of the first bullet of your outline. Try to write AT
-        MINIMUM 1000 WORDS.
-
-        {guidelines}
-        '''
-
-        continuation_prompt = f'''\
-        As an expert essay writer, specilizing in {selected_essay_type} essay writing.
-
-        Your essay title is:
-
-        {{premise}}
-
-        The outline of the Essay is:
-
-        {{outline}}
-
-        You've begun to write the essay and continue to do so.
-        Here's what you've written so far:
-
-        {{story_text}}
-
-        =====
-
-        First, silently review the outline and essay so far. 
-        Identify what the single next part of your outline you should write.
-
-        Your task is to continue where you left off and write the next part of the Essay.
-        You are not expected to finish the whole essay now. Your writing should be
-        detailed enough that you are only scratching the surface of the next part of
-        your outline. Try to write AT MINIMUM 1000 WORDS. However, only once the essay
-        is COMPLETELY finished, write IAMDONE. Remember, do NOT write a whole chapter
-        right now.
-
-        {guidelines}
-        '''
-
-        # Generate prompts
-        try:
-            premise = generate_with_retry(premise_prompt)
-            logger.info(f"The title of the Essay is: {premise}")
-        except Exception as err:
-            logger.error(f"Essay title Generation Error: {err}")
-            return
-
-        outline = generate_with_retry(outline_prompt.format(premise=premise))
-        logger.info(f"The Outline of the essay is: {outline}\n\n")
-        if not outline:
-            logger.error("Failed to generate Essay outline. Exiting...")
-            return
-
-        try:
-            starting_draft = generate_with_retry(
-                    starting_prompt.format(premise=premise, outline=outline))
-            pprint(starting_draft)
-        except Exception as err:
-            logger.error(f"Failed to Generate Essay draft: {err}")
-            return
-
-        try:
-            draft = starting_draft
-            continuation = generate_with_retry(
-                    continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
-            pprint(continuation)
-        except Exception as err:
-            logger.error(f"Failed to write the initial draft: {err}")
-
-        # Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
-        try:
-            draft += '\n\n' + continuation
-        except Exception as err:
-            logger.error(f"Failed as: {err} and {continuation}")
-        while 'IAMDONE' not in continuation:
-            try:
-                continuation = generate_with_retry(
-                        continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
-                draft += '\n\n' + continuation
-            except Exception as err:
-                logger.error(f"Failed to continually write the Essay: {err}")
-                return
-
-        # Remove 'IAMDONE' and print the final story
-        final = draft.replace('IAMDONE', '').strip()
-        pprint(final)
-        return final
-
-    except Exception as e:
-        logger.error(f"Main Essay writing: An error occurred: {e}")
-        return ""
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/README.md
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/README.md
@@ -1,190 +0,0 @@
-# AI Finance Report Generator
-
-An advanced AI-powered financial analysis and report generation system that combines data collection, technical analysis, visualization, and automated report generation.
-
-## Project Structure
-
-```
-ai_finance_report_generator/
-├── ai_financial_dashboard.py    # Main dashboard interface
-├── utils/                       # Utility functions
-│   ├── __init__.py
-│   └── storage.py              # Data persistence
-├── reports/                     # Report generation modules
-│   ├── technical_analysis/     # Technical analysis reports
-│   ├── fundamental_analysis/   # Fundamental analysis reports
-│   ├── options_analysis/      # Options analysis reports
-│   ├── portfolio_analysis/    # Portfolio analysis reports
-│   ├── market_research/       # Market research reports
-│   └── news_analysis/         # News analysis reports
-└── README.md                   # This file
-```
-
-## Features
-
-### Current Features
- Unified dashboard interface for all financial analysis tools
- Technical Analysis report generation
- Options analysis report generation
- User preferences management
- Recent reports tracking
- Data persistence with JSON storage
- Financial data collection from various sources
- Integration with LLM for report generation
-
-### Planned Features
-
-#### 1. Data Collection Module
- Web scraping for financial news and data
- API integrations (Yahoo Finance, Alpha Vantage, Financial Modeling Prep)
- Real-time market data collection
- Historical data retrieval
- Company financial statements
- Market sentiment data
- Economic indicators
- Sector analysis data
-
-#### 2. Technical Analysis Module
- Moving averages (SMA, EMA, WMA)
- RSI, MACD, Bollinger Bands
- Volume analysis
- Support/Resistance levels
- Trend analysis
- Pattern recognition
- Fibonacci retracements
- Momentum indicators
-
-#### 3. Fundamental Analysis Module
- Financial ratios calculation
- Company valuation metrics
- Growth analysis
- Profitability analysis
- Debt analysis
- Cash flow analysis
- Industry comparison
- Peer analysis
-
-#### 4. Data Visualization Module
- Candlestick charts
- Technical indicator overlays
- Volume charts
- Price action patterns
- Correlation matrices
- Heat maps
- Interactive charts
- Custom chart templates
-
-#### 5. Report Generation Module
- Technical analysis reports
- Fundamental analysis reports
- Market research reports
- Investment recommendations
- Risk assessment reports
- Sector analysis reports
- News impact analysis
- Custom report templates
-
-#### 6. News and Sentiment Analysis Module
- News aggregation
- Sentiment scoring
- Social media analysis
- Market sentiment indicators
- News impact analysis
- Event correlation
- Trend detection
- Sentiment visualization
-
-#### 7. Portfolio Analysis Module
- Portfolio performance analysis
- Risk assessment
- Asset allocation
- Correlation analysis
- Diversification metrics
- Performance attribution
- Portfolio optimization
- Rebalancing suggestions
-
-## Usage
-
-### Basic Usage
-
-```python
-from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
-
-# Get dashboard instance
-dashboard = get_dashboard()
-
-# Generate technical analysis report
-ta_report = dashboard.generate_technical_analysis("AAPL")
-
-# Generate options analysis report
-options_report = dashboard.generate_options_analysis("AAPL")
-
-# Get recent reports
-recent_reports = dashboard.get_recent_reports()
-```
-
-### User Preferences
-
-```python
-# Update user preferences
-dashboard.update_preferences({
-    "report_format": "markdown",
-    "include_charts": True,
-    "chart_style": "dark",
-    "language": "en"
-})
-
-# Get current preferences
-preferences = dashboard.get_preferences()
-```
-
-### Portfolio Analysis
-
-```python
-# Create portfolio
-portfolio = [
-    {"symbol": "AAPL", "shares": 100},
-    {"symbol": "GOOGL", "shares": 50}
-]
-
-# Generate portfolio report
-portfolio_report = dashboard.generate_portfolio_analysis(portfolio)
-```
-
-## Installation
-
-```bash
-pip install -r requirements.txt
-```
-
-## Dependencies
-
-1. **Data Collection**
-   - `finance_data_researcher`
-   - `web_scraping_tools`
-
-2. **Analysis Tools**
-   - `pandas_ta`
-   - `numpy`
-   - `scipy`
-
-3. **Visualization**
-   - `matplotlib`
-   - `plotly`
-
-4. **Text Generation**
-   - `llm_text_gen`
-   - `gpt_providers`
-
-## Contributing
-
-1. Fork the repository
-2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details. 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/ai_financial_dashboard.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/ai_financial_dashboard.py
@@ -1,358 +0,0 @@
-"""
-AI Financial Dashboard Module
-
-This module combines the financial dashboard interface with financial report generation capabilities.
-It provides a unified interface for managing financial analysis tools and generating reports.
-"""
-
-import sys
-import os
-from textwrap import dedent
-from pathlib import Path
-from datetime import datetime
-from typing import Dict, List, Any, Optional, Union
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from ...ai_web_researcher.finance_data_researcher import get_finance_data, get_fin_options_data
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-from .utils import get_feature_status
-from .utils.storage import get_storage_manager
-
-class UserPreferences:
-    """Class to manage user preferences and settings."""
-    
-    def __init__(self):
-        self.default_settings = {
-            "theme": "light",
-            "currency": "USD",
-            "timezone": "UTC",
-            "date_format": "%Y-%m-%d",
-            "default_symbols": [],
-            "notifications": True,
-            "auto_refresh": False,
-            "refresh_interval": 300,  # 5 minutes
-            "report_format": "markdown",
-            "include_charts": True,
-            "chart_style": "default",
-            "language": "en"
-        }
-        self.settings = self.default_settings.copy()
-        self.storage = get_storage_manager()
-        self.load_settings()
-    
-    def update_setting(self, key: str, value: Any) -> None:
-        """Update a specific setting."""
-        if key in self.default_settings:
-            self.settings[key] = value
-            self.save_settings()
-    
-    def get_setting(self, key: str) -> Any:
-        """Get a specific setting value."""
-        return self.settings.get(key, self.default_settings.get(key))
-    
-    def reset_settings(self) -> None:
-        """Reset all settings to default values."""
-        self.settings = self.default_settings.copy()
-        self.save_settings()
-    
-    def save_settings(self) -> None:
-        """Save current settings to storage."""
-        self.storage.save_user_preferences(self.settings)
-    
-    def load_settings(self) -> None:
-        """Load settings from storage."""
-        stored_settings = self.storage.load_user_preferences()
-        if stored_settings:
-            self.settings.update(stored_settings)
-
-class RecentReport:
-    """Class to represent a recently generated report."""
-    
-    def __init__(self, report_type: str, symbol: Optional[str], timestamp: datetime, content: Optional[str] = None):
-        self.report_type = report_type
-        self.symbol = symbol
-        self.timestamp = timestamp
-        self.content = content
-        self.id = f"{report_type}_{symbol}_{timestamp.strftime('%Y%m%d%H%M%S')}"
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert report to dictionary format."""
-        return {
-            "id": self.id,
-            "type": self.report_type,
-            "symbol": self.symbol,
-            "timestamp": self.timestamp.isoformat(),
-            "content": self.content
-        }
-    
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> 'RecentReport':
-        """Create report from dictionary format."""
-        return cls(
-            report_type=data["type"],
-            symbol=data["symbol"],
-            timestamp=datetime.fromisoformat(data["timestamp"]),
-            content=data.get("content")
-        )
-
-class FinancialDashboard:
-    """Main dashboard class for managing financial analysis tools and generating reports."""
-    
-    def __init__(self):
-        self.features = {
-            "technical_analysis": {
-                "name": "Technical Analysis",
-                "description": "Generate technical analysis reports with indicators and patterns",
-                "icon": "📊",
-                "route": "/technical-analysis",
-                "category": "analysis",
-                "dependencies": ["data_collection"],
-                "version": "1.0.0"
-            },
-            "fundamental_analysis": {
-                "name": "Fundamental Analysis",
-                "description": "Analyze company financials and valuation metrics",
-                "icon": "📈",
-                "route": "/fundamental-analysis",
-                "category": "analysis",
-                "dependencies": ["data_collection"],
-                "version": "0.1.0"
-            },
-            "options_analysis": {
-                "name": "Options Analysis",
-                "description": "Analyze options chains and generate trading strategies",
-                "icon": "⚡",
-                "route": "/options-analysis",
-                "category": "analysis",
-                "dependencies": ["data_collection", "options_data"],
-                "version": "1.0.0"
-            },
-            "portfolio_analysis": {
-                "name": "Portfolio Analysis",
-                "description": "Analyze portfolio performance and risk metrics",
-                "icon": "📑",
-                "route": "/portfolio-analysis",
-                "category": "portfolio",
-                "dependencies": ["data_collection", "portfolio_data"],
-                "version": "0.1.0"
-            },
-            "market_research": {
-                "name": "Market Research",
-                "description": "Generate market research reports and sector analysis",
-                "icon": "🔍",
-                "route": "/market-research",
-                "category": "research",
-                "dependencies": ["data_collection", "news_data"],
-                "version": "0.1.0"
-            },
-            "news_analysis": {
-                "name": "News Analysis",
-                "description": "Analyze news impact and market sentiment",
-                "icon": "📰",
-                "route": "/news-analysis",
-                "category": "research",
-                "dependencies": ["data_collection", "news_data"],
-                "version": "0.1.0"
-            }
-        }
-        
-        self.user_preferences = UserPreferences()
-        self.storage = get_storage_manager()
-        self.recent_reports: List[RecentReport] = []
-        self.max_recent_reports = 10
-        self.load_recent_reports()
-    
-    def get_all_features(self) -> List[Dict[str, Any]]:
-        """Get all available features with their status."""
-        features_list = []
-        for feature_id, feature_info in self.features.items():
-            status = get_feature_status(feature_id)
-            feature_info.update(status)
-            features_list.append(feature_info)
-        return features_list
-    
-    def get_feature(self, feature_id: str) -> Dict[str, Any]:
-        """Get information about a specific feature."""
-        if feature_id not in self.features:
-            raise ValueError(f"Feature {feature_id} not found")
-            
-        feature_info = self.features[feature_id].copy()
-        status = get_feature_status(feature_id)
-        feature_info.update(status)
-        return feature_info
-    
-    def get_implemented_features(self) -> List[Dict[str, Any]]:
-        """Get only the implemented features."""
-        return [f for f in self.get_all_features() if f["implemented"]]
-    
-    def get_coming_soon_features(self) -> List[Dict[str, Any]]:
-        """Get features that are coming soon."""
-        return [f for f in self.get_all_features() if f["coming_soon"]]
-    
-    def get_features_by_category(self, category: str) -> List[Dict[str, Any]]:
-        """Get features filtered by category."""
-        return [f for f in self.get_all_features() if f["category"] == category]
-    
-    def add_recent_report(self, report_type: str, symbol: Optional[str] = None, content: Optional[str] = None) -> None:
-        """Add a report to the recent reports list."""
-        report = RecentReport(report_type, symbol, datetime.now(), content)
-        self.recent_reports.insert(0, report)
-        if len(self.recent_reports) > self.max_recent_reports:
-            self.recent_reports.pop()
-        self.save_recent_reports()
-    
-    def get_recent_reports(self, limit: Optional[int] = None) -> List[Dict[str, Any]]:
-        """Get recent reports."""
-        reports = self.recent_reports[:limit] if limit else self.recent_reports
-        return [{
-            **r.to_dict(),
-            "feature_info": self.get_feature(r.report_type)
-        } for r in reports]
-    
-    def save_recent_reports(self) -> None:
-        """Save recent reports to storage."""
-        reports_data = [r.to_dict() for r in self.recent_reports]
-        self.storage.save_recent_reports(reports_data)
-    
-    def load_recent_reports(self) -> None:
-        """Load recent reports from storage."""
-        reports_data = self.storage.load_recent_reports()
-        self.recent_reports = [RecentReport.from_dict(r) for r in reports_data]
-    
-    def get_dashboard_summary(self) -> Dict[str, Any]:
-        """Get a summary of the dashboard state."""
-        return {
-            "total_features": len(self.features),
-            "implemented_features": len(self.get_implemented_features()),
-            "coming_soon_features": len(self.get_coming_soon_features()),
-            "recent_reports": len(self.recent_reports),
-            "categories": list(set(f["category"] for f in self.features.values())),
-            "user_preferences": self.user_preferences.settings
-        }
-    
-    def check_feature_dependencies(self, feature_id: str) -> Dict[str, bool]:
-        """Check if all dependencies for a feature are met."""
-        if feature_id not in self.features:
-            raise ValueError(f"Feature {feature_id} not found")
-            
-        feature = self.features[feature_id]
-        dependencies = feature.get("dependencies", [])
-        
-        return {
-            dep: get_feature_status(dep)["implemented"]
-            for dep in dependencies
-        }
-    
-    def backup_data(self, backup_dir: Optional[str] = None) -> None:
-        """Create a backup of all dashboard data."""
-        self.storage.backup_storage(backup_dir)
-    
-    def restore_from_backup(self, backup_file: str) -> None:
-        """Restore dashboard data from a backup file."""
-        self.storage.restore_from_backup(backup_file)
-        self.user_preferences.load_settings()
-        self.load_recent_reports()
-    
-    def generate_technical_analysis(self, symbol: str) -> str:
-        """Generate a technical analysis report for the given symbol."""
-        try:
-            # Get financial data
-            symbol_fin_data = get_finance_data(symbol)
-            
-            # Generate report
-            report_content = self._generate_ta_report(symbol_fin_data, symbol)
-            
-            # Add to recent reports
-            self.add_recent_report("technical_analysis", symbol, report_content)
-            
-            logger.info(f"Done: Final Technical Analysis for {symbol}")
-            return report_content
-            
-        except Exception as err:
-            logger.error(f"Error: Failed to generate Technical Analysis report: {err}")
-            raise
-    
-    def generate_options_analysis(self, symbol: str) -> str:
-        """Generate an options analysis report for the given symbol."""
-        try:
-            # Get options data
-            options_data = get_fin_options_data(symbol)
-            
-            # Generate report
-            report_content = self._generate_options_report(options_data, symbol)
-            
-            # Add to recent reports
-            self.add_recent_report("options_analysis", symbol, report_content)
-            
-            logger.info(f"Done: Options Analysis for {symbol}")
-            return report_content
-            
-        except Exception as err:
-            logger.error(f"Error: Failed to generate Options Analysis report: {err}")
-            raise
-    
-    def _generate_ta_report(self, last_day_summary: str, symbol: str) -> str:
-        """Generate technical analysis report using LLM."""
-        prompt = f"""
-            You are a seasoned Technical Analysis (TA) expert, rivaling legends like Charles Dow, John Bollinger, and Alan Andrews. 
-            Your deep understanding of market dynamics, coupled with mastery of technical indicators, 
-            allows you to decipher complex patterns and offer precise predictions.
-
-            Your expertise extends to practical tools like the pandas_ta module, enabling you to extract valuable insights from raw data.
-
-            **Objective:**
-            Analyze the provided technical indicators for {symbol} on its last trading day and predict its price movement over the next few trading sessions.
-
-            **Instructions:**
-            1. **Identify Potential Trading Signals:** Highlight specific indicators suggesting bullish, bearish, or neutral signals. Explain the rationale behind each signal, referencing historical patterns or comparable market scenarios.
-            2. **Detect Patterns and Divergences:** Analyze the interplay between different indicators. Detect patterns like moving average crossovers, candlestick formations, or divergences between price action and indicators. Explain the significance of each pattern.
-            3. **Price Movement Prediction:** Based on your analysis, provide a clear prediction for {symbol}'s price movement in the next few days. State the expected direction (up, down, sideways) and potential price targets if identifiable.
-            4. **Risk Assessment:** Briefly discuss any potential risks or factors that could invalidate your predictions, promoting a balanced and informed perspective.
-
-            **Technical Indicators for {symbol} on the Last Trading Day:**
-            {last_day_summary}
-
-            Remember, your analysis should be detailed, insightful, and actionable for traders seeking to capitalize on market movements.
-        """
-        
-        try:
-            return llm_text_gen(prompt)
-        except Exception as err:
-            logger.error(f"Failed to generate TA report: {err}")
-            raise
-    
-    def _generate_options_report(self, results_sentences: List[str], ticker: str) -> str:
-        """Generate options analysis report using LLM."""
-        prompt = f"""
-            You are a financial expert specializing in options trading and market sentiment analysis. 
-            You have been provided with the following technical analysis of options data for the ticker symbol {ticker} with the nearest expiry date:
-
-            {chr(10).join(results_sentences)}
-
-            Based on this data, provide a comprehensive analysis of the options market for {ticker}.
-
-            Your analysis should include:
-
-            1. **Implied Volatility Interpretation:** Discuss the significance of the average implied volatility for both call and put options. What does it suggest about market expectations of future price movements?
-            2. **Volume and Open Interest Insights:** Analyze the volume and open interest for call and put options. What does this data reveal about current market positioning and potential future trading activity?
-            3. **Sentiment Analysis:** Evaluate the put-call ratio, implied volatility skew, and overall market sentiment. What do these indicators suggest about trader sentiment and potential future price direction?
-            4. **Potential Trading Strategies:** Based on your analysis, suggest potential options trading strategies that could be employed for {ticker}, considering the current market conditions and sentiment.
-
-            Please provide your analysis in a clear and concise manner, suitable for someone with a good understanding of options trading.
-        """
-        
-        try:
-            return llm_text_gen(prompt)
-        except Exception as err:
-            logger.error(f"Failed to generate options report: {err}")
-            raise
-
-def get_dashboard() -> FinancialDashboard:
-    """Get the financial dashboard instance."""
-    return FinancialDashboard() 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/README.md
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/README.md
@@ -1,265 +0,0 @@
-# Financial Reports Module
-
-This directory contains the core report generation modules for different types of financial analysis. Each module is designed to handle a specific type of financial report and can be accessed through the main dashboard interface.
-
-## Directory Structure
-
-```
-reports/
-├── technical_analysis/     # Technical analysis reports
-├── fundamental_analysis/   # Fundamental analysis reports
-├── options_analysis/      # Options analysis reports
-├── portfolio_analysis/    # Portfolio analysis reports
-├── market_research/       # Market research reports
-└── news_analysis/         # News analysis reports
-```
-
-## Report Types
-
-### 1. Technical Analysis Reports
-Location: `technical_analysis/`
-
-Generates technical analysis reports including:
- Moving averages (SMA, EMA, WMA)
- RSI, MACD, Bollinger Bands
- Volume analysis
- Support/Resistance levels
- Trend analysis
- Pattern recognition
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.technical_analysis import generate_ta_report
-
-report = generate_ta_report("AAPL")
-```
-
-### 2. Fundamental Analysis Reports
-Location: `fundamental_analysis/`
-
-Generates fundamental analysis reports including:
- Financial ratios
- Company valuation metrics
- Growth analysis
- Profitability analysis
- Debt analysis
- Cash flow analysis
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.fundamental_analysis import generate_fa_report
-
-report = generate_fa_report("AAPL")
-```
-
-### 3. Options Analysis Reports
-Location: `options_analysis/`
-
-Generates options analysis reports including:
- Options chain analysis
- Implied volatility analysis
- Options strategies
- Risk metrics
- Greeks analysis
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.options_analysis import generate_options_report
-
-report = generate_options_report("AAPL")
-```
-
-### 4. Portfolio Analysis Reports
-Location: `portfolio_analysis/`
-
-Generates portfolio analysis reports including:
- Portfolio performance analysis
- Risk assessment
- Asset allocation
- Correlation analysis
- Diversification metrics
- Performance attribution
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.portfolio_analysis import generate_portfolio_report
-
-portfolio = [
-    {"symbol": "AAPL", "shares": 100},
-    {"symbol": "GOOGL", "shares": 50}
-]
-report = generate_portfolio_report(portfolio)
-```
-
-### 5. Market Research Reports
-Location: `market_research/`
-
-Generates market research reports including:
- Sector analysis
- Industry trends
- Market overview
- Competitive analysis
- Market opportunities
- Risk factors
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.market_research import generate_market_research_report
-
-report = generate_market_research_report(sectors=["Technology", "Healthcare"])
-```
-
-### 6. News Analysis Reports
-Location: `news_analysis/`
-
-Generates news analysis reports including:
- News sentiment analysis
- Market impact analysis
- Event correlation
- Trend detection
- Social media analysis
- News aggregation
-
-Usage:
-```python
-from lib.ai_writers.ai_finance_report_generator.reports.news_analysis import generate_news_analysis_report
-
-report = generate_news_analysis_report("AAPL")
-```
-
-## Common Features
-
-All report modules share the following features:
-
-1. **Data Validation**
-   - Input validation for symbols and parameters
-   - Error handling for invalid inputs
-   - Data type checking
-
-2. **Report Formatting**
-   - Markdown formatting
-   - Chart generation (when applicable)
-   - Customizable templates
-
-3. **Storage Integration**
-   - Automatic report storage
-   - Recent reports tracking
-   - Report versioning
-
-4. **User Preferences**
-   - Customizable report formats
-   - Language selection
-   - Chart style preferences
-
-## Integration with Dashboard
-
-All report modules are integrated with the main dashboard and can be accessed through the `FinancialDashboard` class:
-
-```python
-from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
-
-dashboard = get_dashboard()
-
-# Generate reports through dashboard
-ta_report = dashboard.generate_technical_analysis("AAPL")
-options_report = dashboard.generate_options_analysis("AAPL")
-
-# Get recent reports
-recent_reports = dashboard.get_recent_reports()
-```
-
-## Adding New Report Types
-
-To add a new report type:
-
-1. Create a new directory in the `reports/` folder
-2. Create an `__init__.py` file with the report generation function
-3. Add the report type to the dashboard features
-4. Implement the report generation logic
-5. Add appropriate error handling and validation
-
-Example:
-```python
-# reports/new_analysis/__init__.py
-from typing import Dict, Any
-from ...utils import validate_symbol
-
-def generate_new_analysis_report(symbol: str) -> Dict[str, Any]:
-    """
-    Generate a new type of analysis report.
-    
-    Args:
-        symbol (str): Stock symbol to analyze
-        
-    Returns:
-        Dict[str, Any]: Analysis report
-    """
-    if not validate_symbol(symbol):
-        raise ValueError("Invalid symbol provided")
-        
-    # Implement report generation logic
-    return {
-        "symbol": symbol,
-        "analysis": "Report content"
-    }
-```
-
-## Error Handling
-
-All report modules implement consistent error handling:
-
-1. **Input Validation**
-   - Symbol validation
-   - Parameter validation
-   - Data type checking
-
-2. **Data Collection Errors**
-   - API errors
-   - Network errors
-   - Data format errors
-
-3. **Report Generation Errors**
-   - LLM errors
-   - Template errors
-   - Formatting errors
-
-4. **Storage Errors**
-   - File system errors
-   - Database errors
-   - Backup errors
-
-## Contributing
-
-When contributing to the reports module:
-
-1. Follow the existing code structure
-2. Add appropriate type hints
-3. Include comprehensive docstrings
-4. Add error handling
-5. Update the dashboard integration
-6. Add tests for new functionality
-
-## Dependencies
-
-The reports module depends on:
-
-1. **Data Collection**
-   - `finance_data_researcher`
-   - `web_scraping_tools`
-
-2. **Analysis Tools**
-   - `pandas_ta`
-   - `numpy`
-   - `scipy`
-
-3. **Visualization**
-   - `matplotlib`
-   - `plotly`
-
-4. **Text Generation**
-   - `llm_text_gen`
-   - `gpt_providers`
-
-## License
-
-This module is part of the AI Finance Report Generator project and is licensed under the MIT License. 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/fundamental_analysis/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/fundamental_analysis/init.py
@@ -1,34 +0,0 @@
-"""
-Fundamental Analysis Reports Module
-
-This module handles the generation of fundamental analysis reports including:
- Financial ratios
- Company valuation metrics
- Growth analysis
- Profitability analysis
- Debt analysis
- Cash flow analysis
-"""
-
-from typing import Dict, Any
-from ...utils import validate_symbol
-
-def generate_fa_report(symbol: str) -> Dict[str, Any]:
-    """
-    Generate a fundamental analysis report for the given symbol.
-    
-    Args:
-        symbol (str): Stock symbol to analyze
-        
-    Returns:
-        Dict[str, Any]: Fundamental analysis report
-    """
-    if not validate_symbol(symbol):
-        raise ValueError("Invalid symbol provided")
-        
-    # TODO: Implement fundamental analysis report generation
-    return {
-        "symbol": symbol,
-        "status": "coming_soon",
-        "message": "Fundamental analysis report generation is coming soon"
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/market_research/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/market_research/init.py
@@ -1,29 +0,0 @@
-"""
-Market Research Reports Module
-
-This module handles the generation of market research reports including:
- Sector analysis
- Industry trends
- Market overview
- Competitive analysis
- Market opportunities
- Risk factors
-"""
-
-from typing import Dict, Any, List
-
-def generate_market_research_report(sectors: List[str] = None) -> Dict[str, Any]:
-    """
-    Generate a market research report.
-    
-    Args:
-        sectors (List[str], optional): List of sectors to analyze
-        
-    Returns:
-        Dict[str, Any]: Market research report
-    """
-    # TODO: Implement market research report generation
-    return {
-        "status": "coming_soon",
-        "message": "Market research report generation is coming soon"
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/news_analysis/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/news_analysis/init.py
@@ -1,33 +0,0 @@
-"""
-News Analysis Reports Module
-
-This module handles the generation of news analysis reports including:
- News sentiment analysis
- Market impact analysis
- Event correlation
- Trend detection
- Social media analysis
- News aggregation
-"""
-
-from typing import Dict, Any, List
-from ...utils import validate_symbol
-
-def generate_news_analysis_report(symbol: str = None) -> Dict[str, Any]:
-    """
-    Generate a news analysis report.
-    
-    Args:
-        symbol (str, optional): Stock symbol to analyze news for
-        
-    Returns:
-        Dict[str, Any]: News analysis report
-    """
-    if symbol and not validate_symbol(symbol):
-        raise ValueError("Invalid symbol provided")
-        
-    # TODO: Implement news analysis report generation
-    return {
-        "status": "coming_soon",
-        "message": "News analysis report generation is coming soon"
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/options_analysis/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/options_analysis/init.py
@@ -1,33 +0,0 @@
-"""
-Options Analysis Reports Module
-
-This module handles the generation of options analysis reports including:
- Options chain analysis
- Implied volatility analysis
- Options strategies
- Risk metrics
- Greeks analysis
-"""
-
-from typing import Dict, Any
-from ...utils import validate_symbol
-
-def generate_options_report(symbol: str) -> Dict[str, Any]:
-    """
-    Generate an options analysis report for the given symbol.
-    
-    Args:
-        symbol (str): Stock symbol to analyze
-        
-    Returns:
-        Dict[str, Any]: Options analysis report
-    """
-    if not validate_symbol(symbol):
-        raise ValueError("Invalid symbol provided")
-        
-    # TODO: Implement options analysis report generation
-    return {
-        "symbol": symbol,
-        "status": "coming_soon",
-        "message": "Options analysis report generation is coming soon"
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/portfolio_analysis/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/portfolio_analysis/init.py
@@ -1,32 +0,0 @@
-"""
-Portfolio Analysis Reports Module
-
-This module handles the generation of portfolio analysis reports including:
- Portfolio performance analysis
- Risk assessment
- Asset allocation
- Correlation analysis
- Diversification metrics
- Performance attribution
-"""
-
-from typing import Dict, Any, List
-
-def generate_portfolio_report(portfolio: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """
-    Generate a portfolio analysis report.
-    
-    Args:
-        portfolio (List[Dict[str, Any]]): List of portfolio positions
-        
-    Returns:
-        Dict[str, Any]: Portfolio analysis report
-    """
-    if not portfolio:
-        raise ValueError("Portfolio cannot be empty")
-        
-    # TODO: Implement portfolio analysis report generation
-    return {
-        "status": "coming_soon",
-        "message": "Portfolio analysis report generation is coming soon"
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/technical_analysis/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/reports/technical_analysis/init.py
@@ -1,314 +0,0 @@
-"""
-Technical Analysis Reports Module
-
-This module handles the generation of technical analysis reports using yfinance data and pandas_ta for indicators.
-"""
-
-from typing import Dict, Any, List, Optional
-import yfinance as yf
-import pandas as pd
-import pandas_ta as ta
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
-from loguru import logger
-from ...utils import validate_symbol
-from ...ai_financial_dashboard import get_dashboard
-
-class TechnicalAnalysis:
-    def __init__(self, symbol: str, timeframe: str = "1d", period: str = "1y"):
-        """
-        Initialize Technical Analysis.
-        
-        Args:
-            symbol (str): Stock symbol to analyze
-            timeframe (str): Data timeframe (1m, 5m, 15m, 30m, 1h, 1d, 1wk, 1mo)
-            period (str): Data period (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
-        """
-        logger.info(f"Initializing Technical Analysis for {symbol} with timeframe {timeframe} and period {period}")
-        self.symbol = symbol
-        self.timeframe = timeframe
-        self.period = period
-        self.data = None
-        self.indicators = {}
-        self.stock = yf.Ticker(symbol)
-        
-    def fetch_data(self) -> None:
-        """Fetch historical price data using yfinance"""
-        try:
-            logger.info(f"Fetching historical data for {self.symbol}")
-            # Get historical data
-            self.data = self.stock.history(period=self.period, interval=self.timeframe)
-            logger.debug(f"Retrieved {len(self.data)} data points")
-            
-            # Get additional info
-            logger.info("Fetching company information")
-            self.info = self.stock.info
-            
-            # Calculate basic metrics
-            logger.debug("Calculating basic metrics")
-            self.data['Returns'] = self.data['Close'].pct_change()
-            self.data['Volatility'] = self.data['Returns'].rolling(window=20).std()
-            
-            logger.success(f"Successfully fetched data for {self.symbol}")
-            
-        except Exception as e:
-            logger.error(f"Error fetching data for {self.symbol}: {str(e)}")
-            raise ValueError(f"Error fetching data for {self.symbol}: {str(e)}")
-        
-    def calculate_indicators(self) -> None:
-        """Calculate technical indicators using pandas_ta"""
-        if self.data is None:
-            logger.error("Data not fetched. Call fetch_data() first.")
-            raise ValueError("Data not fetched. Call fetch_data() first.")
-            
-        logger.info("Calculating technical indicators")
-        
-        # Moving Averages
-        logger.debug("Calculating Moving Averages")
-        self.indicators['sma_20'] = self.data.ta.sma(length=20)
-        self.indicators['sma_50'] = self.data.ta.sma(length=50)
-        self.indicators['sma_200'] = self.data.ta.sma(length=200)
-        self.indicators['ema_20'] = self.data.ta.ema(length=20)
-        
-        # RSI
-        logger.debug("Calculating RSI")
-        self.indicators['rsi'] = self.data.ta.rsi()
-        
-        # MACD
-        logger.debug("Calculating MACD")
-        macd = self.data.ta.macd()
-        self.indicators['macd'] = macd['MACD_12_26_9']
-        self.indicators['macd_signal'] = macd['MACDs_12_26_9']
-        self.indicators['macd_hist'] = macd['MACDh_12_26_9']
-        
-        # Bollinger Bands
-        logger.debug("Calculating Bollinger Bands")
-        bbands = self.data.ta.bbands()
-        self.indicators['bb_upper'] = bbands['BBU_20_2.0']
-        self.indicators['bb_middle'] = bbands['BBM_20_2.0']
-        self.indicators['bb_lower'] = bbands['BBL_20_2.0']
-        
-        # Volume Analysis
-        logger.debug("Calculating Volume indicators")
-        self.indicators['volume_sma'] = self.data['Volume'].rolling(window=20).mean()
-        self.indicators['obv'] = self.data.ta.obv()
-        
-        # Additional Indicators
-        logger.debug("Calculating additional indicators")
-        self.indicators['stoch'] = self.data.ta.stoch()
-        self.indicators['adx'] = self.data.ta.adx()
-        self.indicators['atr'] = self.data.ta.atr()
-        
-        logger.success("Successfully calculated all technical indicators")
-        
-    def identify_patterns(self) -> List[Dict[str, Any]]:
-        """Identify chart patterns"""
-        logger.info("Identifying chart patterns")
-        patterns = []
-        
-        # Candlestick Patterns
-        if len(self.data) >= 3:
-            logger.debug("Analyzing candlestick patterns")
-            # Doji
-            doji = self.data.ta.cdl_doji()
-            if doji['CDL_DOJI'].iloc[-1] != 0:
-                logger.debug("Doji pattern detected")
-                patterns.append({
-                    'type': 'doji',
-                    'date': self.data.index[-1],
-                    'significance': 'neutral'
-                })
-            
-            # Engulfing
-            engulfing = self.data.ta.cdl_engulfing()
-            if engulfing['CDL_ENGULFING'].iloc[-1] != 0:
-                logger.debug("Engulfing pattern detected")
-                patterns.append({
-                    'type': 'engulfing',
-                    'date': self.data.index[-1],
-                    'significance': 'bullish' if engulfing['CDL_ENGULFING'].iloc[-1] > 0 else 'bearish'
-                })
-        
-        logger.info(f"Identified {len(patterns)} patterns")
-        return patterns
-        
-    def find_support_resistance(self) -> Dict[str, List[float]]:
-        """Find support and resistance levels using price action"""
-        logger.info("Finding support and resistance levels")
-        levels = {
-            'support': [],
-            'resistance': []
-        }
-        
-        # Use recent price action to identify levels
-        recent_data = self.data.tail(100)
-        logger.debug(f"Analyzing {len(recent_data)} recent data points for S/R levels")
-        
-        # Find local minima and maxima
-        for i in range(2, len(recent_data) - 2):
-            # Support level
-            if (recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-1] and 
-                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-2] and
-                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+1] and
-                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+2]):
-                levels['support'].append(recent_data['Low'].iloc[i])
-            
-            # Resistance level
-            if (recent_data['High'].iloc[i] > recent_data['High'].iloc[i-1] and 
-                recent_data['High'].iloc[i] > recent_data['High'].iloc[i-2] and
-                recent_data['High'].iloc[i] > recent_data['High'].iloc[i+1] and
-                recent_data['High'].iloc[i] > recent_data['High'].iloc[i+2]):
-                levels['resistance'].append(recent_data['High'].iloc[i])
-        
-        # Remove duplicates and sort
-        levels['support'] = sorted(list(set(levels['support'])))
-        levels['resistance'] = sorted(list(set(levels['resistance'])))
-        
-        logger.info(f"Found {len(levels['support'])} support and {len(levels['resistance'])} resistance levels")
-        return levels
-        
-    def generate_chart(self) -> go.Figure:
-        """Generate interactive chart using plotly"""
-        logger.info("Generating interactive chart")
-        fig = go.Figure()
-        
-        # Candlestick chart
-        logger.debug("Adding candlestick chart")
-        fig.add_trace(go.Candlestick(
-            x=self.data.index,
-            open=self.data['Open'],
-            high=self.data['High'],
-            low=self.data['Low'],
-            close=self.data['Close'],
-            name='Price'
-        ))
-        
-        # Moving Averages
-        logger.debug("Adding moving averages")
-        fig.add_trace(go.Scatter(
-            x=self.data.index,
-            y=self.indicators['sma_20'],
-            name='SMA 20',
-            line=dict(color='blue')
-        ))
-        
-        fig.add_trace(go.Scatter(
-            x=self.data.index,
-            y=self.indicators['sma_50'],
-            name='SMA 50',
-            line=dict(color='orange')
-        ))
-        
-        # Bollinger Bands
-        logger.debug("Adding Bollinger Bands")
-        fig.add_trace(go.Scatter(
-            x=self.data.index,
-            y=self.indicators['bb_upper'],
-            name='BB Upper',
-            line=dict(color='gray', dash='dash')
-        ))
-        
-        fig.add_trace(go.Scatter(
-            x=self.data.index,
-            y=self.indicators['bb_lower'],
-            name='BB Lower',
-            line=dict(color='gray', dash='dash'),
-            fill='tonexty'
-        ))
-        
-        # Volume
-        logger.debug("Adding volume bars")
-        fig.add_trace(go.Bar(
-            x=self.data.index,
-            y=self.data['Volume'],
-            name='Volume',
-            marker_color='rgba(0,0,255,0.3)'
-        ))
-        
-        # Layout
-        logger.debug("Setting chart layout")
-        fig.update_layout(
-            title=f'{self.symbol} Technical Analysis',
-            yaxis_title='Price',
-            xaxis_title='Date',
-            template='plotly_dark'
-        )
-        
-        logger.success("Successfully generated chart")
-        return fig
-        
-    def _generate_summary(self) -> Dict[str, Any]:
-        """Generate summary of technical analysis"""
-        logger.info("Generating analysis summary")
-        current_price = self.data['Close'].iloc[-1]
-        sma_20 = self.indicators['sma_20'].iloc[-1]
-        sma_50 = self.indicators['sma_50'].iloc[-1]
-        rsi = self.indicators['rsi'].iloc[-1]
-        
-        summary = {
-            'current_price': current_price,
-            'price_change': self.data['Returns'].iloc[-1] * 100,
-            'trend': 'bullish' if current_price > sma_20 > sma_50 else 'bearish',
-            'rsi_signal': 'overbought' if rsi > 70 else 'oversold' if rsi < 30 else 'neutral',
-            'volatility': self.data['Volatility'].iloc[-1],
-            'volume_trend': 'increasing' if self.data['Volume'].iloc[-1] > self.indicators['volume_sma'].iloc[-1] else 'decreasing'
-        }
-        
-        logger.debug(f"Analysis summary: {summary}")
-        return summary
-        
-    def generate_report(self) -> Dict[str, Any]:
-        """Generate comprehensive technical analysis report"""
-        logger.info(f"Generating comprehensive report for {self.symbol}")
-        
-        self.fetch_data()
-        self.calculate_indicators()
-        patterns = self.identify_patterns()
-        levels = self.find_support_resistance()
-        chart = self.generate_chart()
-        summary = self._generate_summary()
-        
-        report = {
-            'symbol': self.symbol,
-            'timestamp': datetime.now(),
-            'company_info': self.info,
-            'indicators': self.indicators,
-            'patterns': patterns,
-            'levels': levels,
-            'chart': chart,
-            'summary': summary
-        }
-        
-        logger.success(f"Successfully generated report for {self.symbol}")
-        return report
-
-def generate_ta_report(symbol: str) -> Dict[str, Any]:
-    """
-    Generate a technical analysis report for the given symbol.
-    
-    Args:
-        symbol (str): Stock symbol to analyze
-        
-    Returns:
-        Dict[str, Any]: Technical analysis report
-    """
-    logger.info(f"Generating technical analysis report for {symbol}")
-    
-    if not validate_symbol(symbol):
-        logger.error(f"Invalid symbol provided: {symbol}")
-        raise ValueError("Invalid symbol provided")
-        
-    try:
-        analysis = TechnicalAnalysis(symbol)
-        report = analysis.generate_report()
-        
-        # Add to dashboard's recent reports
-        dashboard = get_dashboard()
-        dashboard.add_recent_report("technical_analysis", symbol, report)
-        
-        logger.success(f"Successfully completed technical analysis for {symbol}")
-        return report
-        
-    except Exception as e:
-        logger.error(f"Error generating technical analysis report for {symbol}: {str(e)}")
-        raise 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/utils/init.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/utils/init.py
@@ -1,62 +0,0 @@
-"""
-Utility functions and helpers for the AI Finance Report Generator.
-"""
-
-from typing import Dict, List, Any
-import logging
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-
-logger = logging.getLogger(__name__)
-
-def validate_symbol(symbol: str) -> bool:
-    """
-    Validate if the given symbol is in correct format.
-    
-    Args:
-        symbol (str): Stock symbol to validate
-        
-    Returns:
-        bool: True if valid, False otherwise
-    """
-    if not isinstance(symbol, str):
-        return False
-    return len(symbol.strip()) > 0
-
-def format_currency(value: float) -> str:
-    """
-    Format number as currency.
-    
-    Args:
-        value (float): Number to format
-        
-    Returns:
-        str: Formatted currency string
-    """
-    return f"${value:,.2f}"
-
-def get_feature_status(feature_name: str) -> Dict[str, Any]:
-    """
-    Get the status of a feature.
-    
-    Args:
-        feature_name (str): Name of the feature
-        
-    Returns:
-        Dict[str, Any]: Feature status information
-    """
-    # This will be expanded as we implement more features
-    implemented_features = {
-        "technical_analysis": True,
-        "options_analysis": True,
-    }
-    
-    return {
-        "name": feature_name,
-        "implemented": implemented_features.get(feature_name, False),
-        "coming_soon": not implemented_features.get(feature_name, False)
-    } 
--- a/ToBeMigrated/ai_writers/ai_finance_report_generator/utils/storage.py
+++ b/ToBeMigrated/ai_writers/ai_finance_report_generator/utils/storage.py
@@ -1,208 +0,0 @@
-"""
-Storage Module for AI Finance Report Generator
-
-This module handles the persistence of user preferences and recent reports using JSON files.
-"""
-
-import json
-import os
-from typing import Dict, List, Any, Optional
-from datetime import datetime
-from pathlib import Path
-
-class StorageManager:
-    """Manages storage operations for user preferences and recent reports."""
-    
-    def __init__(self, base_dir: Optional[str] = None):
-        """
-        Initialize the storage manager.
-        
-        Args:
-            base_dir (Optional[str]): Base directory for storage files
-        """
-        if base_dir is None:
-            # Use user's home directory by default
-            self.base_dir = Path.home() / ".ai_finance"
-        else:
-            self.base_dir = Path(base_dir)
-            
-        # Create storage directory if it doesn't exist
-        self.base_dir.mkdir(parents=True, exist_ok=True)
-        
-        # Define file paths
-        self.prefs_file = self.base_dir / "preferences.json"
-        self.reports_file = self.base_dir / "recent_reports.json"
-        
-        # Initialize files if they don't exist
-        self._initialize_storage()
-    
-    def _initialize_storage(self) -> None:
-        """Initialize storage files if they don't exist."""
-        if not self.prefs_file.exists():
-            self._save_preferences({})
-            
-        if not self.reports_file.exists():
-            self._save_reports([])
-    
-    def _save_preferences(self, preferences: Dict[str, Any]) -> None:
-        """
-        Save user preferences to file.
-        
-        Args:
-            preferences (Dict[str, Any]): User preferences to save
-        """
-        with open(self.prefs_file, 'w') as f:
-            json.dump(preferences, f, indent=4)
-    
-    def _load_preferences(self) -> Dict[str, Any]:
-        """
-        Load user preferences from file.
-        
-        Returns:
-            Dict[str, Any]: User preferences
-        """
-        try:
-            with open(self.prefs_file, 'r') as f:
-                return json.load(f)
-        except (json.JSONDecodeError, FileNotFoundError):
-            return {}
-    
-    def _save_reports(self, reports: List[Dict[str, Any]]) -> None:
-        """
-        Save recent reports to file.
-        
-        Args:
-            reports (List[Dict[str, Any]]): Recent reports to save
-        """
-        with open(self.reports_file, 'w') as f:
-            json.dump(reports, f, indent=4)
-    
-    def _load_reports(self) -> List[Dict[str, Any]]:
-        """
-        Load recent reports from file.
-        
-        Returns:
-            List[Dict[str, Any]]: Recent reports
-        """
-        try:
-            with open(self.reports_file, 'r') as f:
-                return json.load(f)
-        except (json.JSONDecodeError, FileNotFoundError):
-            return []
-    
-    def save_user_preferences(self, preferences: Dict[str, Any]) -> None:
-        """
-        Save user preferences.
-        
-        Args:
-            preferences (Dict[str, Any]): User preferences to save
-        """
-        self._save_preferences(preferences)
-    
-    def load_user_preferences(self) -> Dict[str, Any]:
-        """
-        Load user preferences.
-        
-        Returns:
-            Dict[str, Any]: User preferences
-        """
-        return self._load_preferences()
-    
-    def save_recent_reports(self, reports: List[Dict[str, Any]]) -> None:
-        """
-        Save recent reports.
-        
-        Args:
-            reports (List[Dict[str, Any]]): Recent reports to save
-        """
-        # Convert datetime objects to ISO format strings
-        serialized_reports = []
-        for report in reports:
-            serialized_report = report.copy()
-            if isinstance(report.get('timestamp'), datetime):
-                serialized_report['timestamp'] = report['timestamp'].isoformat()
-            serialized_reports.append(serialized_report)
-        
-        self._save_reports(serialized_reports)
-    
-    def load_recent_reports(self) -> List[Dict[str, Any]]:
-        """
-        Load recent reports.
-        
-        Returns:
-            List[Dict[str, Any]]: Recent reports with datetime objects
-        """
-        reports = self._load_reports()
-        
-        # Convert ISO format strings back to datetime objects
-        for report in reports:
-            if isinstance(report.get('timestamp'), str):
-                report['timestamp'] = datetime.fromisoformat(report['timestamp'])
-        
-        return reports
-    
-    def clear_storage(self) -> None:
-        """Clear all stored data."""
-        self._save_preferences({})
-        self._save_reports([])
-    
-    def backup_storage(self, backup_dir: Optional[str] = None) -> None:
-        """
-        Create a backup of the storage files.
-        
-        Args:
-            backup_dir (Optional[str]): Directory to store backup files
-        """
-        if backup_dir is None:
-            backup_dir = self.base_dir / "backups"
-        else:
-            backup_dir = Path(backup_dir)
-            
-        backup_dir.mkdir(parents=True, exist_ok=True)
-        
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        
-        # Backup preferences
-        if self.prefs_file.exists():
-            backup_prefs = backup_dir / f"preferences_{timestamp}.json"
-            with open(self.prefs_file, 'r') as src, open(backup_prefs, 'w') as dst:
-                dst.write(src.read())
-        
-        # Backup reports
-        if self.reports_file.exists():
-            backup_reports = backup_dir / f"recent_reports_{timestamp}.json"
-            with open(self.reports_file, 'r') as src, open(backup_reports, 'w') as dst:
-                dst.write(src.read())
-    
-    def restore_from_backup(self, backup_file: str) -> None:
-        """
-        Restore storage from a backup file.
-        
-        Args:
-            backup_file (str): Path to the backup file
-        """
-        backup_path = Path(backup_file)
-        if not backup_path.exists():
-            raise FileNotFoundError(f"Backup file not found: {backup_file}")
-        
-        # Determine which type of backup file it is
-        if "preferences" in backup_path.name:
-            with open(backup_path, 'r') as src, open(self.prefs_file, 'w') as dst:
-                dst.write(src.read())
-        elif "recent_reports" in backup_path.name:
-            with open(backup_path, 'r') as src, open(self.reports_file, 'w') as dst:
-                dst.write(src.read())
-        else:
-            raise ValueError(f"Unknown backup file type: {backup_file}")
-
-def get_storage_manager(base_dir: Optional[str] = None) -> StorageManager:
-    """
-    Get a storage manager instance.
-    
-    Args:
-        base_dir (Optional[str]): Base directory for storage files
-        
-    Returns:
-        StorageManager: Storage manager instance
-    """
-    return StorageManager(base_dir) 
--- a/ToBeMigrated/ai_writers/ai_news_article_writer.py
+++ b/ToBeMigrated/ai_writers/ai_news_article_writer.py
@@ -1,102 +0,0 @@
-######################################################
-#
-# Alwrity, as an AI news writer, will have to be factually correct. 
-# We will do multiple rounds of web research and cite our sources. 
-# 'include_urls' will focus news articles only from well known sources. 
-# Choosing a country will help us get better results.
-#
-######################################################
-
-import sys
-import os
-import json
-from textwrap import dedent
-from pathlib import Path
-from datetime import datetime
-
-from dotenv import load_dotenv
-load_dotenv(Path('../../.env'))
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ..ai_web_researcher.google_serp_search import perform_serper_news_search
-
-
-def ai_news_generation(news_keywords, news_country, news_language):
-    """ Generate news aritcle based on given keywords. """
-    # Use to store the blog in a string, to save in a *.md file.
-    blog_markdown_str = ""
-
-    logger.info(f"Researching and Writing News Article on keywords: {news_keywords}")
-    # Call on the got-researcher, tavily apis for this. Do google search for organic competition.
-    try:
-        google_news_result = perform_serper_news_search(news_keywords, news_country, news_language)
-        blog_markdown_str = write_news_google_search(news_keywords, news_country, news_language, google_news_result)
-        #print(blog_markdown_str)
-    except Exception as err:
-        logger.error(f"Failed in Google News web research: {err}")
-    logger.info("\n######### Draft1: Finished News article from Google web search: ###########\n\n")
-    return blog_markdown_str
-
-
-def write_news_google_search(news_keywords, news_country, news_language, search_results):
-    """Combine the given online research and gpt blog content"""
-    news_language = get_language_name(news_language)
-    news_country = get_country_name(news_country)
-    
-    prompt = f"""
-        As an experienced {news_language} news journalist and editor, 
-        I will provide you with my 'News keywords' and its 'google search results'.
-        Your goal is to write a News report, backed by given google search results.
-        Important, as a news report, its imperative that your content is factually correct and cited.
-        
-        Follow below guidelines:
-        1). Understand and utilize the provided google search result json.
-        2). Always provide in-line citations and provide referance links. 
-        3). Understand the given news item and adapt your tone accordingly.
-        4). Always include the dates when then news was reported.
-        6). Do not explain, describe your response.
-        7). Your blog should be highly formatted in markdown style and highly readable.
-        8). Important: Please read the entire prompt before writing anything. Follow the prompt exactly as I instructed.
-
-        \n\nNews Keywords: "{news_keywords}"\n\n
-        Google search Result: "{search_results}"
-        """
-    logger.info("Generating blog and FAQs from Google web search results.")
-    try:
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        logger.error(f"Exit: Failed to get response from LLM: {err}")
-        exit(1)
-
-
-def get_language_name(language_code):
-    languages = {
-        "es": "Spanish",
-        "vn": "Vietnamese",
-        "en": "English",
-        "ar": "Arabic",
-        "hi": "Hindi",
-        "de": "German",
-        "zh-cn": "Chinese (Simplified)"
-        # Add more language codes and corresponding names as needed
-    }
-    return languages.get(language_code, "Unknown")
-
-def get_country_name(country_code):
-    countries = {
-            "es": "Spain",
-            "vn": "Vietnam",
-            "pk": "Pakistan",
-            "in": "India",
-            "de": "Germany",
-            "cn": "China"
-        # Add more country codes and corresponding names as needed
-        }
-    return countries.get(country_code, "Unknown")
--- a/ToBeMigrated/ai_writers/ai_product_description_writer.py
+++ b/ToBeMigrated/ai_writers/ai_product_description_writer.py
@@ -1,115 +0,0 @@
-import streamlit as st
-import json
-
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def generate_product_description(title, details, audience, tone, length, keywords):
-    """
-    Generates a product description using OpenAI's API.
-
-    Args:
-        title (str): The title of the product.
-        details (list): A list of product details (features, benefits, etc.).
-        audience (list): A list of target audience segments.
-        tone (str): The desired tone of the description (e.g., "Formal", "Informal").
-        length (str): The desired length of the description (e.g., "short", "medium", "long").
-        keywords (str): Keywords related to the product (comma-separated).
-
-    Returns:
-        str: The generated product description.
-    """
-    prompt = f"""
-        Write a compelling product description for {title}.
-
-        Highlight these key features: {', '.join(details)} 
-
-        Emphasize the benefits of these features for the target audience ({audience}). 
-        Maintain a {tone} tone and aim for a length of approximately {length} words.
-
-        Use these keywords naturally throughout the description: {', '.join(keywords)}.
-
-        Remember to be persuasive and focus on the value proposition.
-    """
-    
-    try:
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        logger.error(f"Exit: Failed to get response from LLM: {err}")
-        exit(1)
-
-
-def display_inputs():
-    st.title("📝 AI Product Description Writer 🚀")
-    st.markdown("**Generate compelling and accurate product descriptions with AI.**")
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        product_title = st.text_input("🏷️ **Product Title**", placeholder="Enter the product title (e.g., Wireless Bluetooth Headphones)")
-    with col2:
-        product_details = st.text_area("📄 **Product Details**", placeholder="Enter features, benefits, specifications, materials, etc. (e.g., Noise Cancellation, Long Battery Life, Water Resistant, Comfortable Design)")
-
-    col3, col4 = st.columns(2)
-
-    with col3:
-        keywords = st.text_input("🔑 **Keywords**", placeholder="Enter keywords, comma-separated (e.g., wireless headphones, noise cancelling, Bluetooth 5.0)")
-    with col4:
-        target_audience = st.multiselect(
-            "🎯 **Target Audience**",
-            ["Teens", "Adults", "Seniors", "Music Lovers", "Fitness Enthusiasts", "Tech Savvy", "Busy Professionals", "Travelers", "Casual Users"],
-            placeholder="Select target audience (optional)"
-        )
-
-    col5, col6 = st.columns(2)
-
-    with col5:
-        description_length = st.selectbox(
-            "📏 **Desired Description Length**",
-            ["Short (1-2 sentences)", "Medium (3-5 sentences)", "Long (6+ sentences)"],
-            help="Select the desired length of the product description"
-        )
-    with col6:
-        brand_tone = st.selectbox(
-            "🎨 **Brand Tone**",
-            ["Formal", "Informal", "Fun & Energetic"],
-            help="Select the desired tone for the description"
-        )
-
-    return product_title, product_details, target_audience, brand_tone, description_length, keywords
-
-
-def display_output(description):
-    if description:
-        st.subheader("✨ Generated Product Description:")
-        st.write(description)
-
-        json_ld = {
-            "@context": "https://schema.org",
-            "@type": "Product",
-            "name": product_title,
-            "description": description,
-            "audience": target_audience,
-            "brand": {
-                "@type": "Brand",
-                "name": "Your Brand Name"
-            },
-            "keywords": keywords.split(", ")
-        }
-
-
-def write_ai_prod_desc():
-    product_title, product_details, target_audience, brand_tone, description_length, keywords = display_inputs()
-
-    if st.button("Generate Product Description 🚀"):
-        with st.spinner("Generating description..."):
-            description = generate_product_description(
-                product_title,
-                product_details.split(", "),  # Split details into a list
-                target_audience,
-                brand_tone,
-                description_length.split(" ")[0].lower(),  # Extract length from selectbox
-                keywords
-            )
-            display_output(description)
--- a/ToBeMigrated/ai_writers/ai_story_illustrator/README.md
+++ b/ToBeMigrated/ai_writers/ai_story_illustrator/README.md
@@ -1,75 +0,0 @@
-# AI Story Illustrator
-
-The AI Story Illustrator is a powerful tool that generates beautiful illustrations for stories using Google's Gemini AI. This module allows users to input stories via text, file upload, or URL, and automatically generates appropriate illustrations for different scenes in the story.
-
-## Features
-
- **Multiple Input Methods**: Input stories via direct text entry, file upload, or URL extraction
- **Intelligent Scene Segmentation**: Automatically divides stories into logical segments for illustration
- **Customizable Illustration Styles**: Choose from various artistic styles or define your own
- **Scene Element Extraction**: Analyzes story segments to identify key visual elements
- **Multiple Export Options**: Export as PDF storybook or ZIP archive of individual images
- **Customizable Aspect Ratios**: Support for different image dimensions (16:9, 4:3, 1:1)
- **Advanced Settings**: Control the number of segments to illustrate and other parameters
-
-## Usage
-
-The Story Illustrator is integrated into the Alwrity platform and can be accessed through the main interface. The workflow consists of three main steps:
-
-1. **Story Input**: Enter your story text, upload a file, or provide a URL
-2. **Illustration Settings**: Configure the style, aspect ratio, and other parameters
-3. **Generate & Export**: Generate illustrations for all or individual segments and export the results
-
-## Technical Details
-
-### Dependencies
-
- Streamlit: For the user interface
- Gemini AI: For image generation
- BeautifulSoup: For URL text extraction
- ReportLab: For PDF generation (optional)
- PIL: For image processing
-
-### Key Functions
-
- `segment_story()`: Divides a story into logical segments for illustration
- `extract_scene_elements()`: Analyzes story segments to identify key visual elements
- `generate_illustration_prompt()`: Creates detailed prompts for the AI image generator
- `create_illustration()`: Generates an illustration for a story segment
- `create_storybook_pdf()`: Combines story text and illustrations into a PDF
- `create_zip_archive()`: Creates a ZIP archive of individual illustrations
-
-## Example
-
-```python
-from lib.ai_writers.ai_story_illustrator.story_illustrator import write_story_illustrator
-
-# Run the Story Illustrator app
-write_story_illustrator()
-```
-
-## Best Practices
-
- **Provide Clear Segments**: The system works best with stories that have clear scene transitions
- **Be Specific with Styles**: More specific style descriptions yield better results
- **Balance Text and Images**: For best results, aim for segments of 100-500 words per illustration
- **Review and Regenerate**: If an illustration doesn't capture the scene well, use the regenerate option
-
-## Future Enhancements
-
- Support for more export formats (EPUB, HTML)
- Enhanced character consistency across illustrations
- Animation options for digital storytelling
- Voice narration integration
- Custom character design options
-
-## Troubleshooting
-
- If illustrations are not generating, check your internet connection and API access
- If PDF export fails, ensure ReportLab is installed (`pip install reportlab`)
- If URL extraction fails, try copying the text manually
- For large stories, consider processing in smaller batches
-
-## Credits
-
-This module uses Google's Gemini AI for image generation and leverages various open-source libraries for text processing and document generation.
--- a/ToBeMigrated/ai_writers/ai_story_illustrator/init.py
+++ b/ToBeMigrated/ai_writers/ai_story_illustrator/init.py
@@ -1,7 +0,0 @@
-"""
-AI Story Illustrator module for generating illustrations for stories using AI.
-"""
-
-from .story_illustrator import write_story_illustrator
-
-__all__ = ['write_story_illustrator']
--- a/ToBeMigrated/ai_writers/ai_story_illustrator/story_illustrator.py
+++ b/ToBeMigrated/ai_writers/ai_story_illustrator/story_illustrator.py
@@ -1,727 +0,0 @@
-"""
-AI Story Illustrator - Generate illustrations for stories using Gemini AI
-
-This module provides functionality to generate illustrations for stories using Google's Gemini AI.
-Users can input stories via text, file upload, or URL, and the system will generate appropriate
-illustrations for different scenes in the story.
-
-Based on: https://github.com/google-gemini/cookbook/blob/main/examples/Book_illustration.ipynb
-"""
-
-import streamlit as st
-import os
-import re
-import time
-import tempfile
-import requests
-from pathlib import Path
-import io
-import base64
-import json
-import uuid
-import logging
-from urllib.parse import urlparse
-from bs4 import BeautifulSoup
-import zipfile
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-logger = logging.getLogger('story_illustrator')
-
-# Constants
-MAX_STORY_LENGTH = 10000  # Maximum story length in characters
-MIN_SEGMENT_LENGTH = 100  # Minimum segment length for illustration
-MAX_SEGMENTS = 20  # Maximum number of segments to illustrate
-DEFAULT_STYLE = "digital art"  # Default illustration style
-DEFAULT_ASPECT_RATIO = "16:9"  # Default aspect ratio
-
-
-def extract_text_from_url(url):
-    """Extract text content from a URL."""
-    try:
-        headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
-        }
-        response = requests.get(url, headers=headers, timeout=10)
-        response.raise_for_status()
-        
-        soup = BeautifulSoup(response.content, 'html.parser')
-        
-        # Remove script and style elements
-        for script in soup(["script", "style"]):
-            script.extract()
-        
-        # Get text
-        text = soup.get_text(separator='\\n')
-        
-        # Break into lines and remove leading and trailing space on each
-        lines = (line.strip() for line in text.splitlines())
-        # Break multi-headlines into a line each
-        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-        # Drop blank lines
-        text = '\\n'.join(chunk for chunk in chunks if chunk)
-        
-        return text
-    except Exception as e:
-        logger.error(f"Error extracting text from URL: {e}")
-        return None
-
-
-def segment_story(story_text, min_segment_length=MIN_SEGMENT_LENGTH, max_segments=MAX_SEGMENTS):
-    """
-    Segment a story into logical parts for illustration.
-    Uses paragraph breaks, scene changes, and other indicators to create segments.
-    """
-    # Clean up the text
-    story_text = story_text.strip()
-    
-    # Split by paragraphs first
-    paragraphs = re.split(r'\\n\s*\\n', story_text)
-    
-    # Initialize segments
-    segments = []
-    current_segment = ""
-    
-    for paragraph in paragraphs:
-        # Skip empty paragraphs
-        if not paragraph.strip():
-            continue
-            
-        # If adding this paragraph would make the segment too long, start a new segment
-        if len(current_segment) + len(paragraph) > 1000:  # Limit segment size
-            if current_segment:
-                segments.append(current_segment.strip())
-            current_segment = paragraph
-        else:
-            # Add paragraph to current segment
-            if current_segment:
-                current_segment += "\\n\\n" + paragraph
-            else:
-                current_segment = paragraph
-    
-    # Add the last segment if it exists
-    if current_segment:
-        segments.append(current_segment.strip())
-    
-    # Combine very short segments
-    i = 0
-    while i < len(segments) - 1:
-        if len(segments[i]) < min_segment_length:
-            segments[i] += "\\n\\n" + segments[i+1]
-            segments.pop(i+1)
-        else:
-            i += 1
-    
-    # Limit the number of segments
-    if len(segments) > max_segments:
-        # Combine segments to reduce the total number
-        new_segments = []
-        segment_size = len(segments) / max_segments
-        
-        for i in range(max_segments):
-            start_idx = int(i * segment_size)
-            end_idx = int((i + 1) * segment_size)
-            combined_segment = "\\n\\n".join(segments[start_idx:end_idx])
-            new_segments.append(combined_segment)
-        
-        segments = new_segments
-    
-    return segments
-
-
-def extract_scene_elements(segment):
-    """
-    Extract key scene elements from a story segment using LLM.
-    This helps create more accurate illustration prompts.
-    """
-    from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-    
-    prompt = f"""
-    Analyze the following story segment and extract key visual elements for an illustration:
-    
-    {segment}
-    
-    Please provide:
-    1. Main characters present (with brief visual descriptions)
-    2. Setting/location details
-    3. Key action or emotional moment to illustrate
-    4. Important objects or props
-    5. Time of day and lighting
-    6. Weather or atmospheric conditions (if applicable)
-    
-    Format your response as JSON with these keys: "characters", "setting", "key_moment", "objects", "lighting", "atmosphere"
-    """
-    
-    try:
-        response = llm_text_gen(prompt)
-        
-        # Try to extract JSON from the response
-        try:
-            # Find JSON content between triple backticks if present
-            json_match = re.search(r'```json\s*(.*?)\s*```', response, re.DOTALL)
-            if json_match:
-                json_str = json_match.group(1)
-            else:
-                # Otherwise try to parse the whole response as JSON
-                json_str = response
-                
-            scene_elements = json.loads(json_str)
-            return scene_elements
-        except json.JSONDecodeError:
-            # If JSON parsing fails, extract information using regex
-            characters = re.search(r'"characters":\s*"([^"]*)"', response)
-            setting = re.search(r'"setting":\s*"([^"]*)"', response)
-            
-            return {
-                "characters": characters.group(1) if characters else "",
-                "setting": setting.group(1) if setting else "",
-                "key_moment": "",
-                "objects": "",
-                "lighting": "",
-                "atmosphere": ""
-            }
-    except Exception as e:
-        logger.error(f"Error extracting scene elements: {e}")
-        return {
-            "characters": "",
-            "setting": "",
-            "key_moment": "",
-            "objects": "",
-            "lighting": "",
-            "atmosphere": ""
-        }
-
-
-def generate_illustration_prompt(segment, style, characters=None, setting=None):
-    """
-    Generate a prompt for the illustration based on the segment content.
-    
-    Args:
-        segment: The story segment to illustrate
-        style: The artistic style for the illustration
-        characters: Optional character descriptions
-        setting: Optional setting description
-    
-    Returns:
-        A prompt string for the image generation model
-    """
-    # Create a base prompt
-    base_prompt = f"""
-    Create a detailed illustration for the following story segment in {style} style:
-    
-    {segment[:500]}  # Limit segment length for prompt
-    
-    The illustration should capture the key elements, mood, and action of this scene.
-    """
-    
-    # Add character information if provided
-    if characters:
-        base_prompt += f"\\n\\nThe main characters in this scene are: {characters}"
-    
-    # Add setting information if provided
-    if setting:
-        base_prompt += f"\\n\\nThe setting is: {setting}"
-    
-    # Add style-specific instructions
-    if "watercolor" in style.lower():
-        base_prompt += "\\n\\nUse soft, flowing watercolor techniques with visible brush strokes and color blending."
-    elif "digital art" in style.lower():
-        base_prompt += "\\n\\nCreate a polished digital illustration with clean lines and vibrant colors."
-    elif "pencil sketch" in style.lower():
-        base_prompt += "\\n\\nUse pencil sketch techniques with visible hatching, shading, and line work."
-    
-    # Add final quality instructions
-    base_prompt += """
-    
-    Make the illustration:
-    - Visually engaging and detailed
-    - Appropriate for a storybook
-    - Focused on the main action or emotion of the scene
-    - With good composition and visual storytelling
-    """
-    
-    return base_prompt.strip()
-
-
-def create_illustration(segment, style, aspect_ratio="16:9"):
-    """
-    Create an illustration for a story segment.
-    
-    Args:
-        segment: The story segment to illustrate
-        style: The artistic style for the illustration
-        aspect_ratio: The aspect ratio for the illustration
-    
-    Returns:
-        Path to the generated image
-    """
-    # Import here to avoid circular imports
-    from ...gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image
-    
-    # Extract scene elements to enhance the prompt
-    scene_elements = extract_scene_elements(segment)
-    
-    # Create a detailed prompt for the illustration
-    prompt = generate_illustration_prompt(
-        segment, 
-        style,
-        characters=scene_elements.get("characters", ""),
-        setting=scene_elements.get("setting", "")
-    )
-    
-    # Add key elements to the prompt
-    key_moment = scene_elements.get("key_moment", "")
-    objects = scene_elements.get("objects", "")
-    lighting = scene_elements.get("lighting", "")
-    atmosphere = scene_elements.get("atmosphere", "")
-    
-    if key_moment:
-        prompt += f"\\n\\nFocus on this key moment: {key_moment}"
-    
-    if objects:
-        prompt += f"\\n\\nInclude these important objects: {objects}"
-    
-    if lighting:
-        prompt += f"\\n\\nThe lighting is: {lighting}"
-    
-    if atmosphere:
-        prompt += f"\\n\\nThe atmosphere/weather is: {atmosphere}"
-    
-    # Generate the illustration
-    try:
-        # Parse aspect ratio
-        if aspect_ratio == "16:9":
-            width, height = 16, 9
-        elif aspect_ratio == "4:3":
-            width, height = 4, 3
-        elif aspect_ratio == "1:1":
-            width, height = 1, 1
-        else:
-            width, height = 16, 9  # Default
-        
-        # Generate image using Gemini
-        image_path = generate_gemini_image(
-            prompt=prompt,
-            style=style.lower() if style else None,
-            aspect_ratio=aspect_ratio
-        )
-        
-        return image_path
-    except Exception as e:
-        logger.error(f"Error creating illustration: {e}")
-        return None
-
-
-def create_storybook_pdf(segments, illustrations, title, author, output_path):
-    """
-    Create a PDF storybook with text and illustrations.
-    
-    Args:
-        segments: List of story segments
-        illustrations: List of paths to illustrations
-        title: Book title
-        author: Book author
-        output_path: Path to save the PDF
-    
-    Returns:
-        Path to the created PDF
-    """
-    try:
-        from reportlab.lib.pagesizes import letter, A4
-        from reportlab.lib import colors
-        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage, PageBreak
-        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-        from reportlab.lib.units import inch
-        
-        # Create a PDF document
-        doc = SimpleDocTemplate(output_path, pagesize=A4)
-        story = []
-        
-        # Get styles
-        styles = getSampleStyleSheet()
-        title_style = styles['Title']
-        author_style = styles['Normal']
-        author_style.alignment = 1  # Center alignment
-        normal_style = styles['Normal']
-        
-        # Add title page
-        story.append(Paragraph(title, title_style))
-        story.append(Spacer(1, 0.5*inch))
-        story.append(Paragraph(f"by {author}", author_style))
-        story.append(PageBreak())
-        
-        # Add content pages
-        for i, (segment, illustration_path) in enumerate(zip(segments, illustrations)):
-            if illustration_path and os.path.exists(illustration_path):
-                # Add illustration
-                img = ReportLabImage(illustration_path, width=6*inch, height=4*inch)
-                story.append(img)
-                story.append(Spacer(1, 0.25*inch))
-            
-            # Add text
-            for paragraph in segment.split('\\n\\n'):
-                if paragraph.strip():
-                    story.append(Paragraph(paragraph, normal_style))
-                    story.append(Spacer(1, 0.1*inch))
-            
-            # Add page break between segments
-            if i < len(segments) - 1:
-                story.append(PageBreak())
-        
-        # Build the PDF
-        doc.build(story)
-        return output_path
-    except Exception as e:
-        logger.error(f"Error creating PDF: {e}")
-        return None
-
-
-def create_zip_archive(files, output_path):
-    """
-    Create a ZIP archive containing the provided files.
-    
-    Args:
-        files: Dictionary of {filename: file_path} to include in the archive
-        output_path: Path to save the ZIP file
-    
-    Returns:
-        Path to the created ZIP file
-    """
-    try:
-        with zipfile.ZipFile(output_path, 'w') as zipf:
-            for filename, file_path in files.items():
-                if os.path.exists(file_path):
-                    zipf.write(file_path, arcname=filename)
-        return output_path
-    except Exception as e:
-        logger.error(f"Error creating ZIP archive: {e}")
-        return None
-
-
-def write_story_illustrator():
-    """Main function for the Story Illustrator Streamlit app."""
-    st.title("AI Story Illustrator")
-    st.write("Generate beautiful illustrations for your stories using AI")
-    
-    # Create tabs for different sections
-    tab1, tab2, tab3 = st.tabs(["Story Input", "Illustration Settings", "Generate & Export"])
-    
-    # Initialize session state variables if they don't exist
-    if "story_text" not in st.session_state:
-        st.session_state.story_text = ""
-    if "segments" not in st.session_state:
-        st.session_state.segments = []
-    if "illustrations" not in st.session_state:
-        st.session_state.illustrations = []
-    if "book_title" not in st.session_state:
-        st.session_state.book_title = ""
-    if "book_author" not in st.session_state:
-        st.session_state.book_author = ""
-    if "illustration_style" not in st.session_state:
-        st.session_state.illustration_style = DEFAULT_STYLE
-    if "aspect_ratio" not in st.session_state:
-        st.session_state.aspect_ratio = DEFAULT_ASPECT_RATIO
-    if "temp_files" not in st.session_state:
-        st.session_state.temp_files = []
-    
-    # Tab 1: Story Input
-    with tab1:
-        st.header("Step 1: Input Your Story")
-        
-        # Input method selection
-        input_method = st.radio(
-            "Choose input method:",
-            ["Text Input", "File Upload", "URL"]
-        )
-        
-        if input_method == "Text Input":
-            st.session_state.story_text = st.text_area(
-                "Enter your story text:",
-                value=st.session_state.story_text,
-                height=300,
-                max_chars=MAX_STORY_LENGTH,
-                help="Enter the story text you want to illustrate (max 10,000 characters)"
-            )
-            
-        elif input_method == "File Upload":
-            uploaded_file = st.file_uploader("Upload a text file:", type=["txt", "md"])
-            if uploaded_file is not None:
-                try:
-                    st.session_state.story_text = uploaded_file.getvalue().decode("utf-8")
-                    st.success(f"Successfully loaded file: {uploaded_file.name}")
-                    st.text_area("Preview:", value=st.session_state.story_text[:500] + "...", height=200, disabled=True)
-                except Exception as e:
-                    st.error(f"Error reading file: {e}")
-                    
-        elif input_method == "URL":
-            url = st.text_input("Enter URL containing the story:")
-            if url:
-                if st.button("Extract Text from URL"):
-                    with st.spinner("Extracting text from URL..."):
-                        extracted_text = extract_text_from_url(url)
-                        if extracted_text:
-                            st.session_state.story_text = extracted_text
-                            st.success("Successfully extracted text from URL")
-                            st.text_area("Preview:", value=st.session_state.story_text[:500] + "...", height=200, disabled=True)
-                        else:
-                            st.error("Failed to extract text from URL")
-        
-        # Book metadata
-        st.subheader("Book Metadata")
-        col1, col2 = st.columns(2)
-        with col1:
-            st.session_state.book_title = st.text_input("Book Title:", value=st.session_state.book_title)
-        with col2:
-            st.session_state.book_author = st.text_input("Author:", value=st.session_state.book_author)
-        
-        # Process story into segments
-        if st.session_state.story_text:
-            if st.button("Process Story into Segments"):
-                with st.spinner("Processing story into segments..."):
-                    st.session_state.segments = segment_story(st.session_state.story_text)
-                    st.success(f"Story processed into {len(st.session_state.segments)} segments")
-                    
-                    # Initialize illustrations list with None values
-                    st.session_state.illustrations = [None] * len(st.session_state.segments)
-                    
-                    # Display segments
-                    st.subheader("Story Segments")
-                    for i, segment in enumerate(st.session_state.segments):
-                        with st.expander(f"Segment {i+1}"):
-                            st.write(segment)
-    
-    # Tab 2: Illustration Settings
-    with tab2:
-        st.header("Step 2: Configure Illustration Settings")
-        
-        # Style selection
-        st.subheader("Illustration Style")
-        style_options = [
-            "Digital Art",
-            "Watercolor Painting",
-            "Pencil Sketch",
-            "Oil Painting",
-            "Cartoon",
-            "Anime",
-            "3D Render",
-            "Pixel Art",
-            "Children's Book Illustration",
-            "Comic Book Style",
-            "Fantasy Art",
-            "Realistic"
-        ]
-        
-        st.session_state.illustration_style = st.selectbox(
-            "Choose an illustration style:",
-            style_options,
-            index=style_options.index(st.session_state.illustration_style) if st.session_state.illustration_style in style_options else 0
-        )
-        
-        # Custom style input
-        use_custom_style = st.checkbox("Use custom style")
-        if use_custom_style:
-            custom_style = st.text_input("Describe your custom style:", 
-                                       placeholder="e.g., Impressionist painting with vibrant colors and visible brushstrokes")
-            if custom_style:
-                st.session_state.illustration_style = custom_style
-        
-        # Display style examples
-        st.info("💡 The style you choose will significantly impact the look and feel of your illustrations.")
-        
-        # Aspect ratio selection
-        st.subheader("Image Settings")
-        aspect_ratio_options = {
-            "16:9 (Widescreen)": "16:9",
-            "4:3 (Standard)": "4:3",
-            "1:1 (Square)": "1:1"
-        }
-        
-        selected_ratio = st.selectbox(
-            "Choose aspect ratio:",
-            list(aspect_ratio_options.keys()),
-            index=list(aspect_ratio_options.values()).index(st.session_state.aspect_ratio) if st.session_state.aspect_ratio in aspect_ratio_options.values() else 0
-        )
-        st.session_state.aspect_ratio = aspect_ratio_options[selected_ratio]
-        
-        # Advanced settings
-        with st.expander("Advanced Settings"):
-            st.slider("Number of segments to illustrate:", 1, 
-                     max(len(st.session_state.segments), 1) if st.session_state.segments else 1, 
-                     min(len(st.session_state.segments), MAX_SEGMENTS) if st.session_state.segments else 1,
-                     key="num_segments_to_illustrate")
-            
-            st.checkbox("Generate cover image", value=True, key="generate_cover")
-            
-            st.checkbox("Add text to illustrations", value=False, key="add_text_to_illustrations")
-    
-    # Tab 3: Generate & Export
-    with tab3:
-        st.header("Step 3: Generate Illustrations & Export")
-        
-        if not st.session_state.segments:
-            st.warning("Please process your story into segments in Step 1 before generating illustrations.")
-        else:
-            # Generate illustrations
-            st.subheader("Generate Illustrations")
-            
-            num_segments = min(len(st.session_state.segments), st.session_state.get("num_segments_to_illustrate", len(st.session_state.segments)))
-            
-            if st.button("Generate All Illustrations"):
-                with st.spinner(f"Generating {num_segments} illustrations... This may take a while."):
-                    progress_bar = st.progress(0)
-                    
-                    for i in range(num_segments):
-                        # Update progress
-                        progress_bar.progress((i) / num_segments)
-                        st.write(f"Generating illustration {i+1} of {num_segments}...")
-                        
-                        # Generate illustration
-                        illustration_path = create_illustration(
-                            st.session_state.segments[i],
-                            st.session_state.illustration_style,
-                            st.session_state.aspect_ratio
-                        )
-                        
-                        # Store the illustration path
-                        if illustration_path:
-                            st.session_state.illustrations[i] = illustration_path
-                            st.session_state.temp_files.append(illustration_path)
-                    
-                    # Complete progress
-                    progress_bar.progress(1.0)
-                    st.success(f"Generated {num_segments} illustrations!")
-            
-            # Generate individual illustrations
-            st.subheader("Generate Individual Illustrations")
-            
-            for i in range(num_segments):
-                col1, col2 = st.columns([3, 1])
-                
-                with col1:
-                    with st.expander(f"Segment {i+1}"):
-                        st.write(st.session_state.segments[i][:300] + "..." if len(st.session_state.segments[i]) > 300 else st.session_state.segments[i])
-                
-                with col2:
-                    if st.button(f"Generate #{i+1}", key=f"gen_btn_{i}"):
-                        with st.spinner(f"Generating illustration {i+1}..."):
-                            illustration_path = create_illustration(
-                                st.session_state.segments[i],
-                                st.session_state.illustration_style,
-                                st.session_state.aspect_ratio
-                            )
-                            
-                            if illustration_path:
-                                st.session_state.illustrations[i] = illustration_path
-                                st.session_state.temp_files.append(illustration_path)
-                                st.success(f"Generated illustration {i+1}!")
-            
-            # Display generated illustrations
-            st.subheader("Preview Illustrations")
-            
-            if any(st.session_state.illustrations):
-                for i, illustration_path in enumerate(st.session_state.illustrations[:num_segments]):
-                    if illustration_path and os.path.exists(illustration_path):
-                        with st.expander(f"Illustration {i+1}"):
-                            st.image(illustration_path, caption=f"Illustration for Segment {i+1}", use_column_width=True)
-                            
-                            # Regenerate button
-                            if st.button(f"Regenerate", key=f"regen_btn_{i}"):
-                                with st.spinner(f"Regenerating illustration {i+1}..."):
-                                    new_illustration_path = create_illustration(
-                                        st.session_state.segments[i],
-                                        st.session_state.illustration_style,
-                                        st.session_state.aspect_ratio
-                                    )
-                                    
-                                    if new_illustration_path:
-                                        st.session_state.illustrations[i] = new_illustration_path
-                                        st.session_state.temp_files.append(new_illustration_path)
-                                        st.rerun()
-            else:
-                st.info("No illustrations generated yet. Click 'Generate All Illustrations' or generate individual illustrations.")
-            
-            # Export options
-            st.subheader("Export Options")
-            
-            if any(st.session_state.illustrations):
-                export_format = st.radio(
-                    "Export format:",
-                    ["PDF Storybook", "Individual Images (ZIP)", "Both"]
-                )
-                
-                if st.button("Export"):
-                    with st.spinner("Preparing export..."):
-                        # Create temporary directory for exports
-                        with tempfile.TemporaryDirectory() as temp_dir:
-                            # Filter out None values from illustrations
-                            valid_illustrations = [path for path in st.session_state.illustrations[:num_segments] if path and os.path.exists(path)]
-                            valid_segments = st.session_state.segments[:len(valid_illustrations)]
-                            
-                            # Prepare filenames
-                            safe_title = "".join(c if c.isalnum() else "_" for c in st.session_state.book_title) if st.session_state.book_title else "story"
-                            timestamp = int(time.time())
-                            
-                            # Export as PDF
-                            if export_format in ["PDF Storybook", "Both"]:
-                                pdf_path = os.path.join(temp_dir, f"{safe_title}_{timestamp}.pdf")
-                                
-                                try:
-                                    pdf_result = create_storybook_pdf(
-                                        valid_segments,
-                                        valid_illustrations,
-                                        st.session_state.book_title or "Untitled Story",
-                                        st.session_state.book_author or "Anonymous",
-                                        pdf_path
-                                    )
-                                    
-                                    if pdf_result:
-                                        with open(pdf_path, "rb") as f:
-                                            st.download_button(
-                                                label="Download PDF Storybook",
-                                                data=f,
-                                                file_name=f"{safe_title}.pdf",
-                                                mime="application/pdf"
-                                            )
-                                except Exception as e:
-                                    st.error(f"Error creating PDF: {e}")
-                                    st.info("Please install ReportLab to enable PDF export: pip install reportlab")
-                            
-                            # Export as ZIP of images
-                            if export_format in ["Individual Images (ZIP)", "Both"]:
-                                zip_path = os.path.join(temp_dir, f"{safe_title}_illustrations_{timestamp}.zip")
-                                
-                                # Prepare files for ZIP
-                                files_to_zip = {}
-                                for i, img_path in enumerate(valid_illustrations):
-                                    if img_path and os.path.exists(img_path):
-                                        files_to_zip[f"illustration_{i+1}.png"] = img_path
-                                
-                                zip_result = create_zip_archive(files_to_zip, zip_path)
-                                
-                                if zip_result:
-                                    with open(zip_path, "rb") as f:
-                                        st.download_button(
-                                            label="Download Illustrations ZIP",
-                                            data=f,
-                                            file_name=f"{safe_title}_illustrations.zip",
-                                            mime="application/zip"
-                                        )
-            else:
-                st.info("Generate illustrations before exporting.")
-    
-    # Cleanup temporary files when the session ends
-    def cleanup_temp_files():
-        for file_path in st.session_state.temp_files:
-            try:
-                if file_path and os.path.exists(file_path):
-                    os.remove(file_path)
-            except Exception as e:
-                logger.error(f"Error removing temporary file {file_path}: {e}")
-    
-    # Register the cleanup function to run when the session ends
-    import atexit
-    atexit.register(cleanup_temp_files)
-
-
-if __name__ == "__main__":
-    write_story_illustrator()
--- a/ToBeMigrated/ai_writers/ai_story_illustrator/utils.py
+++ b/ToBeMigrated/ai_writers/ai_story_illustrator/utils.py
@@ -1,450 +0,0 @@
-"""
-Utility functions for the AI Story Illustrator module.
-
-This module provides helper functions for file operations, string manipulation,
-and simple text analysis relevant to story processing.
-"""
-
-import os
-import re
-import tempfile
-import uuid
-import logging
-import shutil
-from pathlib import Path
-from typing import List, Tuple, Optional, Union
-
-# Attempt to import Pillow for image dimensions, but don't fail if not installed
-# unless the specific function is called.
-try:
-    from PIL import Image
-    _PIL_AVAILABLE = True
-except ImportError:
-    _PIL_AVAILABLE = False
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-)
-logger = logging.getLogger('story_illustrator_utils')
-
-# --- Constants ---
-IMAGE_EXTENSIONS = frozenset(['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'])
-TEXT_EXTENSIONS = frozenset(['.txt', '.md', '.text'])
-# Common English words that often start sentences, excluded from simple name detection
-COMMON_START_WORDS = frozenset([
-    'The', 'A', 'An', 'And', 'But', 'Or', 'For', 'Nor', 'So', 'Yet', 'He', 'She',
-    'It', 'They', 'We', 'You', 'I', 'In', 'On', 'At', 'To', 'From', 'With',
-    'About', 'As', 'Is', 'Was', 'Were', 'Be', 'Been', 'Being', 'Have', 'Has',
-    'Had', 'Do', 'Does', 'Did', 'Will', 'Would', 'Shall', 'Should', 'May',
-    'Might', 'Must', 'Can', 'Could'
-])
-
-
-# --- File/Directory Operations ---
-
-def create_temp_directory(prefix: str = "story_illustrator_") -> str:
-    """
-    Creates a temporary directory using tempfile.mkdtemp.
-
-    Args:
-        prefix: A prefix for the temporary directory name.
-
-    Returns:
-        The absolute path to the created temporary directory.
-    """
-    try:
-        temp_dir = tempfile.mkdtemp(prefix=prefix)
-        logger.info(f"Created temporary directory: {temp_dir}")
-        return temp_dir
-    except Exception as e:
-        logger.error(f"Failed to create temporary directory: {e}", exc_info=True)
-        raise  # Re-raise the exception after logging
-
-
-def sanitize_filename(filename: str) -> str:
-    """
-    Sanitizes a filename by removing/replacing invalid characters for common filesystems.
-
-    Args:
-        filename: The original filename string.
-
-    Returns:
-        A sanitized filename string suitable for use in file paths.
-    """
-    if not isinstance(filename, str):
-        logger.warning("sanitize_filename received non-string input, converting.")
-        filename = str(filename)
-
-    # Remove characters invalid for Windows/Unix filenames
-    # Replace them with an underscore.
-    sanitized = re.sub(r'[\\/*?:"<>|\']', "_", filename)
-    # Replace consecutive underscores/spaces with a single underscore
-    sanitized = re.sub(r'[_ ]+', '_', sanitized)
-    # Remove leading/trailing spaces, dots, and underscores
-    sanitized = sanitized.strip("._ ")
-
-    # Ensure the filename is not empty after sanitization
-    if not sanitized:
-        sanitized = "unnamed_file"
-        logger.warning("Filename was empty after sanitization, using default.")
-
-    # Limit filename length (optional, adjust as needed)
-    # max_len = 255 # Example limit
-    # if len(sanitized) > max_len:
-    #     name, ext = os.path.splitext(sanitized)
-    #     sanitized = name[:max_len - len(ext) - 1] + "_" + ext
-    #     logger.warning(f"Filename truncated to maximum length: {sanitized}")
-
-    return sanitized
-
-
-def get_temp_file_path(
-    directory: str, prefix: str = "file_", suffix: str = ".tmp"
-) -> str:
-    """
-    Generates a unique temporary file path within the specified directory.
-
-    Args:
-        directory: The directory where the temporary file should be located.
-        prefix: A prefix for the filename.
-        suffix: A suffix (extension) for the filename.
-
-    Returns:
-        The full path for the unique temporary file.
-    """
-    # Ensure suffix starts with a dot if it's meant to be an extension
-    if suffix and not suffix.startswith("."):
-        suffix = "." + suffix
-
-    unique_id = uuid.uuid4().hex[:12] # Longer hex UUID for better uniqueness
-    filename = f"{prefix}{unique_id}{suffix}"
-    return os.path.join(directory, filename)
-
-
-def ensure_directory_exists(directory: Union[str, Path]) -> str:
-    """
-    Ensures that a directory exists, creating it recursively if necessary.
-
-    Args:
-        directory: The path to the directory (string or Path object).
-
-    Returns:
-        The absolute path to the directory as a string.
-
-    Raises:
-        OSError: If the directory cannot be created (e.g., permission issues).
-    """
-    dir_path = Path(directory).resolve() # Use Pathlib for robust handling
-    try:
-        dir_path.mkdir(parents=True, exist_ok=True)
-        # Log only if it needed creation (or if verbose logging is on)
-        # logger.info(f"Ensured directory exists: {dir_path}")
-        return str(dir_path)
-    except OSError as e:
-        logger.error(f"Failed to create or access directory {dir_path}: {e}", exc_info=True)
-        raise
-
-
-def cleanup_directory(directory: Union[str, Path]) -> None:
-    """
-    Removes a directory and all its contents recursively. Handles errors gracefully.
-
-    Args:
-        directory: The path to the directory to remove (string or Path object).
-    """
-    dir_path = Path(directory)
-    if not dir_path.exists():
-        logger.debug(f"Cleanup skipped: Directory '{directory}' does not exist.")
-        return
-
-    if not dir_path.is_dir():
-         logger.warning(f"Cleanup warning: Path '{directory}' is not a directory.")
-         return
-
-    try:
-        shutil.rmtree(dir_path)
-        logger.info(f"Successfully removed directory: {directory}")
-    except OSError as e:
-        logger.error(f"Error removing directory {directory}: {e}", exc_info=True)
-    except Exception as e:
-        logger.error(
-            f"Unexpected error removing directory {directory}: {e}", exc_info=True
-        )
-
-
-# --- File Type Checks ---
-
-def get_file_extension(file_path: Union[str, Path]) -> str:
-    """
-    Gets the lowercased file extension (including the dot) from a file path.
-
-    Args:
-        file_path: The path to the file (string or Path object).
-
-    Returns:
-        The file extension (e.g., '.txt', '.png') or an empty string if no extension.
-    """
-    return Path(file_path).suffix.lower()
-
-
-def is_image_file(file_path: Union[str, Path]) -> bool:
-    """
-    Checks if a file is likely an image based on its extension.
-
-    Args:
-        file_path: The path to the file (string or Path object).
-
-    Returns:
-        True if the file extension is in IMAGE_EXTENSIONS, False otherwise.
-    """
-    return get_file_extension(file_path) in IMAGE_EXTENSIONS
-
-
-def is_text_file(file_path: Union[str, Path]) -> bool:
-    """
-    Checks if a file is likely a text file based on its extension.
-
-    Args:
-        file_path: The path to the file (string or Path object).
-
-    Returns:
-        True if the file extension is in TEXT_EXTENSIONS, False otherwise.
-    """
-    return get_file_extension(file_path) in TEXT_EXTENSIONS
-
-
-# --- Text Analysis (Simple Heuristics) ---
-
-def extract_story_title_from_text(text: str) -> str:
-    """
-    Attempts to extract a title from story text using simple heuristics.
-
-    Looks for patterns (in order):
-    1. Markdown headers (#, ##, etc.) at the start of a line.
-    2. The first non-empty line if it's short (< 100 chars) and followed by
-       a blank line or is the only line.
-    3. The first non-empty line if it's entirely in uppercase (< 100 chars).
-
-    Args:
-        text: The story text content.
-
-    Returns:
-        An extracted title string, or "Untitled Story" if no pattern matches.
-    """
-    if not isinstance(text, str) or not text.strip():
-        return "Untitled Story"
-
-    # 1. Check for markdown headers ( # Title, ## Title )
-    # Needs to match start of line (^) with optional whitespace before #
-    header_match = re.search(r'^\s*#+\s+(.+)$', text.strip(), re.MULTILINE)
-    if header_match:
-        title = header_match.group(1).strip()
-        if title: return title
-
-    lines = text.strip().split('\n')
-    if not lines:
-        return "Untitled Story"
-
-    first_line = lines[0].strip()
-    if not first_line: # Skip if first line is blank
-        if len(lines) > 1:
-            first_line = lines[1].strip() # Try second line
-        else:
-            return "Untitled Story"
-
-    if not first_line: # Still no title found
-         return "Untitled Story"
-
-    # 2. Check if first line is short and potentially a title
-    is_short = len(first_line) < 100
-    is_followed_by_blank = len(lines) > 1 and not lines[1].strip()
-    is_only_line = len(lines) == 1
-
-    if is_short and (is_followed_by_blank or is_only_line):
-        return first_line
-
-    # 3. Check if first line is all caps (and short)
-    is_all_caps = first_line == first_line.upper() and first_line.isalpha() # Check if it contains letters
-    if is_short and is_all_caps:
-        return first_line
-
-    # Default if no other pattern matched
-    return "Untitled Story"
-
-
-def estimate_reading_time(text: str, words_per_minute: int = 200) -> float:
-    """
-    Estimates the reading time of a text in minutes.
-
-    Args:
-        text: The text content.
-        words_per_minute: The assumed average reading speed.
-
-    Returns:
-        The estimated reading time in minutes. Returns 0.0 for empty text.
-    """
-    if not isinstance(text, str) or not text.strip():
-        return 0.0
-    if words_per_minute <= 0:
-        raise ValueError("words_per_minute must be positive.")
-
-    word_count = len(text.split())
-    minutes = word_count / words_per_minute
-    return minutes
-
-
-def count_sentences(text: str) -> int:
-    """
-    Counts the number of sentences in a text using a very simple heuristic.
-
-    Note: This is a basic implementation counting sentence-ending punctuation
-    (. ! ?). It will be inaccurate with abbreviations (Mr., Mrs., etc.),
-    ellipses, and complex sentence structures.
-
-    Args:
-        text: The text content.
-
-    Returns:
-        An estimated count of sentences. Returns 0 for empty text.
-    """
-    if not isinstance(text, str) or not text.strip():
-        return 0
-
-    # Find sequences of one or more sentence-ending punctuation marks
-    sentence_endings = re.findall(r'[.!?]+', text)
-    count = len(sentence_endings)
-
-    # Handle edge case where text might not end with punctuation but isn't empty
-    if count == 0 and len(text.strip()) > 0:
-        return 1 # Assume at least one sentence if text exists but no terminators found
-    return count
-
-
-def extract_character_names(text: str, min_occurrences: int = 2) -> List[str]:
-    """
-    Attempts to extract potential character names from story text.
-
-    Note: This is a simple heuristic based on finding capitalized words
-    (excluding common sentence starters) that appear multiple times. It has
-    limitations and may produce false positives or miss actual names.
-
-    Args:
-        text: The story text content.
-        min_occurrences: The minimum number of times a capitalized word must
-                         appear to be considered a potential name.
-
-    Returns:
-        A list of potential character name strings.
-    """
-    if not isinstance(text, str) or not text.strip():
-        return []
-    if min_occurrences < 1:
-        min_occurrences = 1 # Ensure at least one occurrence is required
-
-    # Find words starting with an uppercase letter, potentially followed by lowercase
-    # Allows for single-letter names like 'X' but focuses on typical Name structure
-    capitalized_words = re.findall(r'\b[A-Z][a-zA-Z]*\b', text)
-
-    # Count occurrences, excluding common words
-    word_counts: Dict[str, int] = {}
-    for word in capitalized_words:
-        if word not in COMMON_START_WORDS:
-            word_counts[word] = word_counts.get(word, 0) + 1
-
-    # Filter for words that meet the minimum occurrence threshold
-    potential_names = [
-        word for word, count in word_counts.items() if count >= min_occurrences
-    ]
-
-    # Sort for consistency (optional)
-    potential_names.sort()
-
-    return potential_names
-
-
-def extract_setting_details(text: str) -> List[str]:
-    """
-    Attempts to extract potential setting details using simple regex patterns.
-
-    Note: This is a very basic heuristic looking for common prepositional
-    phrases (e.g., "in the forest", "at the castle"). It is highly limited
-    and likely to miss many setting details or extract irrelevant phrases.
-
-    Args:
-        text: The story text content.
-
-    Returns:
-        A list of potential setting phrases found.
-    """
-    if not isinstance(text, str) or not text.strip():
-        return []
-
-    # Patterns looking for prepositions followed by nouns/adjectives
-    # Making patterns slightly more general:
-    # (\b\w+\b) captures single words
-    # (\b\w+\s+\w+\b) captures two-word phrases
-    # (\b[A-Z]\w*\b) captures capitalized words (potential proper nouns)
-    setting_patterns = [
-        r'\b(?:in|on|at|near|beside|inside|outside|under|over|through)\s+(?:the|a|an)\s+((?:[A-Z]\w*|\w+)(?:\s+\w+){0,2})\b', # e.g., in the old house
-        r'\b(?:in|on|at)\s+((?:[A-Z]\w+)(?:\s+[A-Z]\w+)*)\b', # e.g., in New York City
-        r'\b(?:during|before|after)\s+(?:the|a|an)\s+(\w+(?:\s+\w+){0,2})\b', # e.g., during the storm
-    ]
-
-    settings_found = set() # Use a set to avoid duplicates
-    for pattern in setting_patterns:
-        try:
-            matches = re.findall(pattern, text, re.IGNORECASE) # Ignore case
-            for match in matches:
-                 # If match is tuple due to multiple capture groups, join them?
-                 # For these patterns, it should be single strings.
-                 if isinstance(match, str):
-                      phrase = match.strip()
-                      if phrase and len(phrase.split()) <= 5: # Limit phrase length
-                           settings_found.add(phrase)
-        except re.error as e:
-             logger.warning(f"Regex error in extract_setting_details: {e} with pattern: {pattern}")
-
-
-    # Convert set back to list and sort for consistency
-    sorted_settings = sorted(list(settings_found))
-    return sorted_settings
-
-
-# --- Image Operations ---
-
-def get_image_dimensions(image_path: Union[str, Path]) -> Optional[Tuple[int, int]]:
-    """
-    Gets the (width, height) dimensions of an image file using Pillow.
-
-    Args:
-        image_path: The path to the image file (string or Path object).
-
-    Returns:
-        A tuple (width, height) if successful, or None if the file is not
-        a valid image, Pillow is not installed, or an error occurs.
-    """
-    if not _PIL_AVAILABLE:
-        logger.warning("Pillow (PIL) library not installed. Cannot get image dimensions.")
-        return None
-
-    img_path = Path(image_path)
-    if not img_path.is_file():
-        logger.error(f"Image file not found or is not a file: {image_path}")
-        return None
-
-    try:
-        with Image.open(img_path) as img:
-            width, height = img.size
-            logger.debug(f"Dimensions for {image_path}: {width}x{height}")
-            return width, height
-    except FileNotFoundError:
-        logger.error(f"Image file not found at path: {image_path}")
-        return None
-    except UnidentifiedImageError: # Specific Pillow error for invalid images
-         logger.error(f"Could not identify image file (invalid format or corrupted): {image_path}")
-         return None
-    except Exception as e:
-        logger.error(f"Error getting dimensions for image {image_path}: {e}", exc_info=True)
-        return None
--- a/ToBeMigrated/ai_writers/ai_story_video_generator/README.md
+++ b/ToBeMigrated/ai_writers/ai_story_video_generator/README.md
@@ -1,31 +0,0 @@
-# AI Story Video Generator
-
-This module allows users to generate animated story videos using AI. It leverages Google's Gemini model to create stories and generate images for each scene, then combines them into a video.
-
-## Features
-
- Generate complete stories based on user prompts
- Create scene-by-scene storyboards
- Generate images for each scene using Gemini
- Compile images into an animated video
- Add background music and text overlays
- Export videos in MP4 format
-
-## How It Works
-
-1. User provides a story prompt and preferences
-2. AI generates a complete story with multiple scenes
-3. For each scene, an image is generated
-4. Images are compiled into a video with transitions
-5. Optional background music and text overlays are added
-6. The final video is available for download
-
-## Requirements
-
- Google Gemini API key
- FFmpeg for video processing
- Python libraries: moviepy, pillow, requests
-
-## Usage
-
-Access this tool through the Streamlit interface by selecting "AI Story Video Generator" from the main menu.
--- a/ToBeMigrated/ai_writers/ai_story_video_generator/init.py
+++ b/ToBeMigrated/ai_writers/ai_story_video_generator/init.py
@@ -1,4 +0,0 @@
-# AI Story Video Generator module
-from .story_video_generator import write_story_video_generator
-
-__all__ = ["write_story_video_generator"]
--- a/ToBeMigrated/ai_writers/ai_story_video_generator/story_video_generator.py
+++ b/ToBeMigrated/ai_writers/ai_story_video_generator/story_video_generator.py
--- a/ToBeMigrated/ai_writers/ai_story_video_generator/utils.py
+++ b/ToBeMigrated/ai_writers/ai_story_video_generator/utils.py
@@ -1,64 +0,0 @@
-"""
-Utility functions for the AI Story Video Generator.
-"""
-
-import os
-import tempfile
-import uuid
-from pathlib import Path
-from typing import Optional
-
-# Constants
-TEMP_DIR = Path(tempfile.gettempdir()) / "alwrity_story_generator"
-
-def ensure_temp_dir() -> Path:
-    """Ensure the temporary directory exists and return its path."""
-    os.makedirs(TEMP_DIR, exist_ok=True)
-    return TEMP_DIR
-
-def get_temp_filepath(prefix: str, extension: str) -> str:
-    """Generate a temporary file path with the given prefix and extension."""
-    temp_dir = ensure_temp_dir()
-    return str(temp_dir / f"{prefix}_{uuid.uuid4()}.{extension}")
-
-def clean_temp_files(older_than_hours: int = 24) -> int:
-    """
-    Clean temporary files older than the specified number of hours.
-    
-    Args:
-        older_than_hours: Remove files older than this many hours
-        
-    Returns:
-        Number of files removed
-    """
-    import time
-    from datetime import datetime, timedelta
-    
-    temp_dir = ensure_temp_dir()
-    cutoff_time = time.time() - (older_than_hours * 3600)
-    count = 0
-    
-    for file_path in temp_dir.glob("*"):
-        if file_path.is_file() and file_path.stat().st_mtime < cutoff_time:
-            try:
-                file_path.unlink()
-                count += 1
-            except Exception:
-                pass
-    
-    return count
-
-def format_duration(seconds: float) -> str:
-    """Format seconds into a MM:SS string."""
-    minutes = int(seconds // 60)
-    remaining_seconds = int(seconds % 60)
-    return f"{minutes}:{remaining_seconds:02d}"
-
-def sanitize_filename(filename: str) -> str:
-    """Sanitize a string to be used as a filename."""
-    import re
-    # Remove invalid characters
-    sanitized = re.sub(r'[^\w\s-]', '', filename)
-    # Replace spaces with underscores
-    sanitized = sanitized.strip().replace(' ', '_')
-    return sanitized
--- a/ToBeMigrated/ai_writers/ai_story_writer/README.md
+++ b/ToBeMigrated/ai_writers/ai_story_writer/README.md
@@ -1,103 +0,0 @@
-# AI Story Generator App
-
-In the age of AI, creativity and technology are intertwining in ways that are transforming how we tell stories. Imagine having the power to craft a captivating narrative tailored to your exact specifications with just a few clicks. Whether you're an aspiring writer, a seasoned novelist, or just someone who loves a good story, our new AI-powered story writing app is here to make storytelling easier and more engaging than ever before.
-
-## Why an AI Story Writing App?
-
-Storytelling has always been a cherished art form, but not everyone finds it easy to start from scratch. With the AI Story Generator App, you can create detailed and personalized stories by simply providing some key inputs. Our app uses advanced AI to turn your ideas into compelling narratives, helping you overcome writer's block and unleashing your creative potential.
-
-## Features of the AI Story Generator App
-
-### Genre
-Choose from a variety of genres such as Fantasy, Sci-Fi, Mystery, Romance, and Horror to set the tone for your story.
-
-### Story Setting
-Provide a detailed setting for your story, including location and time period. 
-
-For example:
-A bustling futuristic city with towering skyscrapers and flying cars, set in the year 2150. The city is known for its technological advancements but has a dark underbelly of crime and corruption.
-
-
-### Main Characters
-Input the names, descriptions, and roles of your main characters. 
-
-For example:
-Character Names: John, Xishan, Amol
-Character Descriptions: John is a tall, muscular man with a kind heart. Xishan is a clever and resourceful woman. Amol is a mischievous and energetic young boy.
-Character Roles: John - Hero, Xishan - Sidekick, Amol - Supporting Character
-
-
-### Plot Elements
-Outline the key plot elements including the story theme, key events, and main conflict. 
-
-For example:
-Story Theme: Love conquers all, The hero's journey, Good vs. evil
-
-Key Events or Plot Points:
-
-The hero meets the villain
-The hero faces a challenge
-The hero overcomes the conflict
-Main Conflict or Problem:
-The hero must save the world from a powerful enemy, The hero must overcome a personal obstacle to achieve their goal.
-
-
-### Tone and Style
-Choose the writing style, tone, and narrative point of view for your story. 
-
-For example:
-Writing Style: Formal, Casual, Poetic, Humorous
-Story Tone: Dark
-
-### Perspective
-Choose the narrative point of view from which the story is told (e.g., first person, third person limited, third person omniscient).
-
-### Target Audience
-Specify the intended audience age group (Children, Young Adults, Adults) and set a content rating (G, PG, PG-13, R) for appropriateness.
-
-### Ending Preference
-Select the type of ending you prefer for the story (e.g., happy, tragic, cliffhanger, twist).
-
-## How to Use
-
-Choose Genre: Select the genre that best fits your story idea.
-Set Story Setting: Describe the setting and time period where your story unfolds.
-Define Characters: Provide names, descriptions, and roles for your main characters.
-Outline Plot Elements: Detail the story's theme, key events, and main conflict.
-Select Tone and Style: Choose the writing style and tone that align with your story's mood.
-Specify Perspective: Decide on the narrative point of view.
-Target Audience: Specify the age group and content rating.
-Choose Ending: Select the preferred type of story conclusion.
-Generate Story: Click the "Generate Story" button to receive a customized story prompt based on your inputs.
-
-
-### Example Prompt
-
-**Genre:** Fantasy  
-**Setting:** A mystical forest in a medieval realm, where magic thrives and mythical creatures roam freely.  
-**Characters:**  
- Name: Elara  
-  Description: Elara is a young elf with a mischievous glint in her emerald eyes, known for her ability to wield powerful spells.
-  Role: Protagonist  
- Name: Thorne  
-  Description: Thorne is a gruff dwarf with a heart of gold, skilled in forging enchanted weapons.
-  Role: Sidekick  
- Name: Malachai  
-  Description: Malachai is a cunning dragon with shimmering scales of azure, whose allegiance is uncertain.
-  Role: Antagonist  
-
-**Plot Elements:**  
- Theme: The power of friendship and bravery in the face of adversity.
- Key Events: Elara discovers an ancient prophecy that foretells a looming darkness threatening the realm. Thorne crafts a legendary sword to aid in their quest. Malachai challenges Elara's resolve, forcing her to make a difficult choice.
- Conflict: Elara must gather allies and confront the dark sorcerer who seeks to plunge the realm into eternal shadow.
-
-**Writing Style:** Poetic  
-**Tone:** Whimsical  
-**Point of View:** Third Person Limited  
-
-**Audience:** Young Adults, **Content Rating:** PG  
-**Ending:** Happy  
-
-
-
-
--- a/ToBeMigrated/ai_writers/ai_story_writer/ai_story_generator.py
+++ b/ToBeMigrated/ai_writers/ai_story_writer/ai_story_generator.py
@@ -1,238 +0,0 @@
-#####################################################
-#
-# google-gemini-cookbook - Story_Writing_with_Prompt_Chaining
-#
-#####################################################
-
-import os
-from pathlib import Path
-import streamlit as st
-from loguru import logger
-import sys
-
-from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def generate_with_retry(prompt, system_prompt=None):
-    """
-    Generates content using the llm_text_gen function with retry handling for errors.
-
-    Parameters:
-        prompt (str): The prompt to generate content from.
-        system_prompt (str, optional): Custom system prompt to use instead of the default one.
-
-    Returns:
-        str: The generated content.
-    """
-    try:
-        # Use llm_text_gen instead of directly calling the model
-        return llm_text_gen(prompt, system_prompt)
-    except Exception as e:
-        logger.error(f"Error generating content: {e}")
-        return ""
-
-
-def ai_story(persona, story_setting, character_input, 
-                       plot_elements, writing_style, story_tone, narrative_pov,
-                       audience_age_group, content_rating, ending_preference):
-    """
-    Write a story using prompt chaining and iterative generation.
-
-    Parameters:
-        persona (str): The persona statement for the author.
-        story_setting (str): The setting of the story.
-        character_input (str): The characters in the story.
-        plot_elements (str): The plot elements of the story.
-        writing_style (str): The writing style of the story.
-        story_tone (str): The tone of the story.
-        narrative_pov (str): The narrative point of view.
-        audience_age_group (str): The target audience age group.
-        content_rating (str): The content rating of the story.
-        ending_preference (str): The preferred ending of the story.
-    """
-    st.info(f"""
-        You have chosen to create a story set in **{story_setting}**. 
-        The main characters are: **{character_input}**.
-        The plot will revolve around the theme of **{plot_elements}**.
-        The story will be written in a **{writing_style}** style with a **{story_tone}** tone, from a **{narrative_pov}** perspective. 
-        It is intended for a **{audience_age_group}** audience with a **{content_rating}** rating. 
-        You prefer the story to have a **{ending_preference}** ending.
-        """)
-    try:
-        persona = f"""{persona}
-            Write a story with the following details:
-
-		**The stroy Setting is:**
-		{story_setting}
-		
-		**The Characters of the story are:**
-		{character_input}
-		
-		**Plot Elements of the story:**
-		{plot_elements}
-		
-		**Story Writing Style:**
-		{writing_style}
-		
-		**The story Tone is:**
-		{story_tone}
-		
-		**Write story from the Point of View of:**
-		{narrative_pov}
-		
-		**Target Audience of the story:**
-		{audience_age_group}, **Content Rating:** {content_rating}
-		
-		**Story Ending:**
-		{ending_preference}
-		
-		Make sure the story is engaging and tailored to the specified audience and content rating. 
-        Ensure the ending aligns with the preference indicated.
-
-        """
-        # Define persona and writing guidelines
-        guidelines = f'''\
-        Writing Guidelines:
-
-        Delve deeper. Lose yourself in the world you're building. Unleash vivid
-        descriptions to paint the scenes in your reader's mind.
-        Develop your characters — let their motivations, fears, and complexities unfold naturally.
-        Weave in the threads of your outline, but don't feel constrained by it.
-        Allow your story to surprise you as you write. Use rich imagery, sensory details, and
-        evocative language to bring the setting, characters, and events to life.
-        Introduce elements subtly that can blossom into complex subplots, relationships,
-        or worldbuilding details later in the story.
-        Keep things intriguing but not fully resolved.
-        Avoid boxing the story into a corner too early.
-        Plant the seeds of subplots or potential character arc shifts that can be expanded later.
-
-        Remember, your main goal is to write as much as you can. If you get through
-        the story too fast, that is bad. Expand, never summarize.
-        '''
-
-        # Generate prompts
-        premise_prompt = f'''\
-        {persona}
-
-        Write a single sentence premise for a {story_setting} story featuring {character_input}.
-        '''
-
-        outline_prompt = f'''\
-        {persona}
-
-        You have a gripping premise in mind:
-
-        {{premise}}
-
-        Write an outline for the plot of your story.
-        '''
-
-        starting_prompt = f'''\
-        {persona}
-
-        You have a gripping premise in mind:
-
-        {{premise}}
-
-        Your imagination has crafted a rich narrative outline:
-
-        {{outline}}
-
-        First, silently review the outline and the premise. Consider how to start the
-        story.
-
-        Start to write the very beginning of the story. You are not expected to finish
-        the whole story now. Your writing should be detailed enough that you are only
-        scratching the surface of the first bullet of your outline. Try to write AT
-        MINIMUM 4000 WORDS.
-
-        {guidelines}
-        '''
-
-        continuation_prompt = f'''\
-        {persona}
-
-        You have a gripping premise in mind:
-
-        {{premise}}
-
-        Your imagination has crafted a rich narrative outline:
-
-        {{outline}}
-
-        You've begun to immerse yourself in this world, and the words are flowing.
-        Here's what you've written so far:
-
-        {{story_text}}
-
-        =====
-
-        First, silently review the outline and story so far. Identify what the single
-        next part of your outline you should write.
-
-        Your task is to continue where you left off and write the next part of the story.
-        You are not expected to finish the whole story now. Your writing should be
-        detailed enough that you are only scratching the surface of the next part of
-        your outline. Try to write AT MINIMUM 2000 WORDS. However, only once the story
-        is COMPLETELY finished, write IAMDONE. Remember, do NOT write a whole chapter
-        right now.
-
-        {guidelines}
-        '''
-
-        # Generate prompts
-        try:
-            premise = generate_with_retry(premise_prompt)
-            st.info(f"The premise of the story is: {premise}")
-        except Exception as err:
-            st.error(f"Premise Generation Error: {err}")
-            return
-
-        outline = generate_with_retry(outline_prompt.format(premise=premise))
-        with st.expander("Click to Checkout the outline, writing still in progress.."):
-            st.markdown(f"The Outline of the story is: {outline}\n\n")
-        
-        if not outline:
-            st.error("Failed to generate outline. Exiting...")
-            return
-
-        # Generate starting draft
-        try:
-            starting_draft = generate_with_retry(
-                    starting_prompt.format(premise=premise, outline=outline))
-        except Exception as err:
-            st.error(f"Failed to Generate Story draft: {err}")
-            return
-
-        try:
-            draft = starting_draft
-            continuation = generate_with_retry(
-                    continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
-        except Exception as err:
-            st.error(f"Failed to write the initial draft: {err}")
-
-        # Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
-        try:
-            draft += '\n\n' + continuation
-        except Exception as err:
-            st.error(f"Failed as: {err} and {continuation}")
-        
-        with st.status("Story Writing in Progress..", expanded=True) as status:
-            status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
-            while 'IAMDONE' not in continuation:
-                try:
-                    status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
-                    continuation = generate_with_retry(
-                        continuation_prompt.format(premise=premise, outline=outline, story_text=draft))
-                    draft += '\n\n' + continuation
-                except Exception as err:
-                    st.error(f"Failed to continually write the story: {err}")
-                    return
-
-        # Remove 'IAMDONE' and print the final story
-        final = draft.replace('IAMDONE', '').strip()
-        return(final)
-
-    except Exception as e:
-        st.error(f"Main Story writing: An error occurred: {e}")
-        return ""
--- a/ToBeMigrated/ai_writers/ai_story_writer/story_writer.py
+++ b/ToBeMigrated/ai_writers/ai_story_writer/story_writer.py
@@ -1,134 +0,0 @@
-import time
-import os
-import json
-import streamlit as st
-
-from .ai_story_generator import ai_story
-
-
-def story_input_section():
-    st.title("🧕 Alwrity - AI Story Writer")
-    personas = [
-        ("Award-Winning Science Fiction Author", "👽 Award-Winning Science Fiction Author"),
-        ("Historical Fiction Author", "🏺 Historical Fiction Author"),
-        ("Fantasy World Builder", "🧙 Fantasy World Builder"),
-        ("Mystery Novelist", "🕵️ Mystery Novelist"),
-        ("Romantic Poet", "💌 Romantic Poet"),
-        ("Thriller Writer", "🔪 Thriller Writer"),
-        ("Children's Book Author", "📚 Children's Book Author"),
-        ("Satirical Humorist", "😂 Satirical Humorist"),
-        ("Biographical Writer", "📜 Biographical Writer"),
-        ("Dystopian Visionary", "🌆 Dystopian Visionary"),
-        ("Magical Realism Author", "🪄 Magical Realism Author")
-    ]
-
-    selected_persona_name = st.selectbox(
-        "Select Your Story Writing Persona Or Book Genre",
-        options=[persona[0] for persona in personas]
-    )
-
-    persona_descriptions = {
-    "Award-Winning Science Fiction Author": "You are an award-winning science fiction author with a penchant for expansive, intricately woven stories. Your ultimate goal is to write the next award-winning sci-fi novel.",
-    "Historical Fiction Author": "You are a seasoned historical fiction author, meticulously researching past eras to weave captivating narratives. Your goal is to transport readers to different times and places through your vivid storytelling.",
-    "Fantasy World Builder": "You are a world-building enthusiast, crafting intricate realms filled with magic, mythical creatures, and epic quests. Your ambition is to create the next immersive fantasy saga that captivates readers' imaginations.",
-    "Mystery Novelist": "You are a master of suspense and intrigue, intricately plotting out mysteries with unexpected twists and turns. Your aim is to keep readers on the edge of their seats, eagerly turning pages to unravel the truth.",
-    "Romantic Poet": "You are a romantic at heart, composing verses that capture the essence of love, longing, and human connections. Your dream is to write the next timeless love story that leaves readers swooning.",
-    "Thriller Writer": "You are a thrill-seeker, crafting adrenaline-pumping tales of danger, suspense, and high-stakes action. Your mission is to keep readers hooked from start to finish with heart-pounding thrills and unexpected twists.",
-    "Children's Book Author": "You are a storyteller for the young and young at heart, creating whimsical worlds and lovable characters that inspire imagination and wonder. Your goal is to spark joy and curiosity in young readers with enchanting tales.",
-    "Satirical Humorist": "You are a keen observer of society, using humor and wit to satirize the absurdities of everyday life. Your aim is to entertain and provoke thought, delivering biting social commentary through clever and humorous storytelling.",
-    "Biographical Writer": "You are a chronicler of lives, delving into the stories of real people and events to illuminate the human experience. Your passion is to bring history to life through richly detailed biographies that resonate with readers.",
-    "Dystopian Visionary": "You are a visionary writer, exploring dark and dystopian futures that reflect contemporary fears and anxieties. Your vision is to challenge societal norms and provoke reflection on the path humanity is heading.",
-    "Magical Realism Author": "You are a purveyor of magical realism, blending the ordinary with the extraordinary to create enchanting and thought-provoking tales. Your goal is to blur the lines between reality and fantasy, leaving readers enchanted and introspective."
-        }
-
-    # Story Setting
-    st.subheader("🌍 Story Setting")
-    story_setting = st.text_area(
-        label="**Story Setting** (e.g., medieval kingdom in the past, futuristic city in the future, haunted house in the present):",
-        placeholder="""Enter settings for your story, like Location (e.g., medieval kingdom, futuristic city, haunted house),
-        Time period in which your story is set (e.g: Past, Present, Future)
-        Example: 'A bustling futuristic city with towering skyscrapers and flying cars, set in the year 2150. 
-        The city is known for its technological advancements but has a dark underbelly of crime and corruption.'""",
-        help="Describe the main location and time period where the story will unfold in a detailed manner."
-    )
-    
-    # Main Characters
-    st.subheader("👥 Main Characters")
-    character_input = st.text_area(
-        label="**Character Information** (Names, Descriptions, Roles)",
-        placeholder="""Example:
-        Character Names: John, Xishan, Amol
-        Character Descriptions: John is a tall, muscular man with a kind heart. Xishan is a clever and resourceful woman. Amol is a mischievous and energetic young boy.
-        Character Roles: John - Hero, Xishan - Sidekick, Amol - Supporting Character""",
-        help="Enter character information as specified in the placeholder."
-    )
-    
-    # Plot Elements
-    st.subheader("🗺️ Plot Elements")
-    plot_elements = st.text_area(
-        "**Plot Elements** - (Theme, Key Events & Main Conflict)",
-        placeholder="""Example:
-        Story Theme: Love conquers all, The hero's journey, Good vs. evil.
-        Key Events: The hero meets the villain, The hero faces a challenge, The hero overcomes the conflict.
-        Main Conflict: The hero must save the world from a powerful enemy, The hero must overcome a personal obstacle to achieve their goal.""",
-        help="Enter plot elements as specified in the placeholder."
-    )
-    
-    # Tone and Style
-    st.subheader("🎨 Tone and Style")
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        writing_style = st.selectbox(
-            "**Writing Style:**",
-            ["🧐 Formal", "😎 Casual", "🎼 Poetic", "😂 Humorous"],
-            help="Choose the writing style that fits your story."
-        )
-    with col2:
-        story_tone = st.selectbox(
-            "**Story Tone:**",
-            ["🌑 Dark", "☀️ Uplifting", "⏳ Suspenseful", "🎈 Whimsical"],
-            help="Select the overall tone or mood of the story."
-        )
-    with col3:
-        narrative_pov = st.selectbox(
-            "**Narrative Point of View:**",
-            ["👤 First Person", "👥 Third Person Limited", "👁️ Third Person Omniscient"],
-            help="Choose the point of view from which the story is told."
-        )
-    
-    # Target Audience
-    st.subheader("👨‍👩‍👧‍👦 Target Audience")
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        audience_age_group = st.selectbox(
-            "**Audience Age Group:**",
-            ["🧒 Children", "👨‍🎓 Young Adults", "🧑‍🦳 Adults"],
-            help="Choose the intended audience age group."
-        )
-    with col2:
-        content_rating = st.selectbox(
-            "**Content Rating:**",
-            ["🟢 G", "🟡 PG", "🔵 PG-13", "🔴 R"],
-            help="Select a content rating for appropriateness."
-        )
-    with col3:
-        ending_preference = st.selectbox(
-            "Story Conclusion:",
-            ["😊 Happy", "😢 Tragic", "❓ Cliffhanger", "🔀 Twist"],
-            help="Choose the type of ending you prefer for the story."
-        )
-
-    if st.button('AI, Write a Story..'):
-        if character_input.strip():
-            with st.spinner("Generating Story...💥💥"):
-                story_content = ai_story(persona_descriptions[selected_persona_name],
-                        story_setting, character_input, plot_elements, writing_style,
-                        story_tone, narrative_pov, audience_age_group, content_rating,
-                        ending_preference)
-                if story_content:
-                    st.subheader('**🧕 Your Awesome Story:**')
-                    st.markdown(story_content)
-                else:
-                    st.error("💥 **Failed to generate Story. Please try again!**")
-        else:
-            st.error("Describe the story you have in your mind.. !")
--- a/ToBeMigrated/ai_writers/ai_writer_dashboard.py
+++ b/ToBeMigrated/ai_writers/ai_writer_dashboard.py
@@ -1,220 +0,0 @@
-import streamlit as st
-from lib.utils.alwrity_utils import (essay_writer, ai_news_writer, ai_finance_ta_writer)
-
-from lib.ai_writers.ai_story_writer.story_writer import story_input_section
-from lib.ai_writers.ai_product_description_writer import write_ai_prod_desc
-from lib.ai_writers.ai_copywriter.copywriter_dashboard import copywriter_dashboard
-from lib.ai_writers.linkedin_writer import LinkedInAIWriter
-from lib.ai_writers.blog_rewriter_updater.ai_blog_rewriter import write_blog_rewriter
-from lib.ai_writers.ai_blog_faqs_writer.faqs_ui import main as faqs_generator
-from lib.ai_writers.ai_blog_writer.ai_blog_generator import ai_blog_writer_page
-from lib.ai_writers.ai_outline_writer.outline_ui import main as outline_generator
-from lib.alwrity_ui.dashboard_styles import apply_dashboard_style, render_dashboard_header, render_category_header, render_card
-from loguru import logger
-
-# Try to import AI Content Performance Predictor (AI-first approach)
-try:
-    from lib.content_performance_predictor.ai_performance_predictor import render_ai_predictor_ui as render_content_performance_predictor
-    AI_PREDICTOR_AVAILABLE = True
-    logger.info("AI Content Performance Predictor loaded successfully")
-except ImportError:
-    logger.warning("AI Content Performance Predictor not available")
-    render_content_performance_predictor = None
-    AI_PREDICTOR_AVAILABLE = False
-
-# Try to import Bootstrap AI Competitive Suite
-try:
-    from lib.ai_competitive_suite.bootstrap_ai_suite import render_bootstrap_ai_suite
-    BOOTSTRAP_SUITE_AVAILABLE = True
-    logger.info("Bootstrap AI Competitive Suite loaded successfully")
-except ImportError:
-    logger.warning("Bootstrap AI Competitive Suite not available")
-    render_bootstrap_ai_suite = None
-    BOOTSTRAP_SUITE_AVAILABLE = False
-
-def list_ai_writers():
-    """Return a list of available AI writers with their metadata (no UI rendering)."""
-    writers = []
-    
-    # Add Content Performance Predictor if available
-    if render_content_performance_predictor:
-        # AI-first approach description
-        if AI_PREDICTOR_AVAILABLE:
-            description = "🎯 AI-powered content performance prediction with competitive intelligence - perfect for solo entrepreneurs"
-            name = "AI Content Performance Predictor"
-        else:
-            description = "Predict content success before publishing with AI-powered performance analysis"
-            name = "Content Performance Predictor"
-        
-        writers.append({
-            "name": name,
-            "icon": "🎯",
-            "description": description,
-            "category": "⭐ Featured",
-            "function": render_content_performance_predictor,
-            "path": "performance_predictor",
-            "featured": True
-        })
-    
-    # Add Bootstrap AI Competitive Suite if available
-    if render_bootstrap_ai_suite:
-        writers.append({
-            "name": "Bootstrap AI Competitive Suite",
-            "icon": "🚀",
-            "description": "🥷 Complete AI-powered competitive toolkit: content performance prediction + competitive intelligence for solo entrepreneurs",
-            "category": "⭐ Featured",
-            "function": render_bootstrap_ai_suite,
-            "path": "bootstrap_ai_suite",
-            "featured": True
-        })
-    
-    # Add existing writers
-    writers.extend([
-        {
-            "name": "AI Blog Writer",
-            "icon": "📝",
-            "description": "Generate comprehensive blog posts from keywords, URLs, or uploaded content",
-            "category": "Content Creation",
-            "function": ai_blog_writer_page,
-            "path": "ai_blog_writer"
-        },
-        {
-            "name": "AI Blog Rewriter",
-            "icon": "🔄",
-            "description": "Rewrite and update existing blog content with improved quality and SEO optimization",
-            "category": "Content Creation",
-            "function": write_blog_rewriter,
-            "path": "blog_rewriter"
-        },
-        {
-            "name": "Story Writer",
-            "icon": "📚",
-            "description": "Create engaging stories and narratives with AI assistance",
-            "category": "Creative Writing",
-            "function": story_input_section,
-            "path": "story_writer"
-        },
-        {
-            "name": "Essay writer",
-            "icon": "✍️",
-            "description": "Generate well-structured essays on any topic",
-            "category": "Academic",
-            "function": essay_writer,
-            "path": "essay_writer"
-        },
-        {
-            "name": "Write News reports",
-            "icon": "📰",
-            "description": "Create professional news articles and reports",
-            "category": "Journalism",
-            "function": ai_news_writer,
-            "path": "news_writer"
-        },
-        {
-            "name": "Write Financial TA report",
-            "icon": "📊",
-            "description": "Generate technical analysis reports for financial markets",
-            "category": "Finance",
-            "function": ai_finance_ta_writer,
-            "path": "financial_writer"
-        },
-        {
-            "name": "AI Product Description Writer",
-            "icon": "🛍️",
-            "description": "Create compelling product descriptions that drive sales",
-            "category": "E-commerce",
-            "function": write_ai_prod_desc,
-            "path": "product_writer"
-        },
-        {
-            "name": "AI Copywriter",
-            "icon": "✒️",
-            "description": "Generate persuasive copy for marketing and advertising",
-            "category": "Marketing",
-            "function": copywriter_dashboard,
-            "path": "copywriter"
-        },
-        {
-            "name": "LinkedIn AI Writer",
-            "icon": "💼",
-            "description": "Create professional LinkedIn content that engages your network",
-            "category": "Professional",
-            "function": lambda: LinkedInAIWriter().run(),
-            "path": "linkedin_writer"
-        },
-        {
-            "name": "FAQ Generator",
-            "icon": "❓",
-            "description": "Generate comprehensive, well-researched FAQs from any content source with customizable options",
-            "category": "Content Creation",
-            "function": faqs_generator,
-            "path": "faqs_generator"
-        },
-        {
-            "name": "Blog Outline Generator",
-            "icon": "📋",
-            "description": "Create detailed blog outlines with AI-powered content generation and image integration",
-            "category": "Content Creation",
-            "function": outline_generator,
-            "path": "outline_generator"
-        }
-    ])
-    
-    return writers
-
-def get_ai_writers():
-    """Main function to display AI writers dashboard with premium glassmorphic design."""
-    logger.info("Starting AI Writers Dashboard")
-    
-    # Apply common dashboard styling
-    apply_dashboard_style()
-    
-    # Render dashboard header
-    render_dashboard_header(
-        "🤖 AI Content Writers",
-        "Choose from our collection of specialized AI writers, each designed for specific content types and industries. Create engaging, high-quality content with just a few clicks."
-    )
-
-    writers = list_ai_writers()
-    logger.info(f"Found {len(writers)} AI writers")
-
-    # Group writers by category for better organization
-    categories = {}
-    for writer in writers:
-        category = writer["category"]
-        if category not in categories:
-            categories[category] = []
-        categories[category].append(writer)
-
-    # Render writers by category with common cards
-    for category_name, category_writers in categories.items():
-        render_category_header(category_name)
-        
-        # Create columns for this category
-        cols = st.columns(min(len(category_writers), 3))
-        
-        for idx, writer in enumerate(category_writers):
-            with cols[idx % 3]:
-                # Use the common card renderer
-                if render_card(
-                    icon=writer['icon'],
-                    title=writer['name'],
-                    description=writer['description'],
-                    category=writer['category'],
-                    key_suffix=f"{writer['path']}_{category_name}",
-                    help_text=f"Launch {writer['name']} - {writer['description']}"
-                ):
-                    logger.info(f"Selected writer: {writer['name']} with path: {writer['path']}")
-                    st.session_state.selected_writer = writer
-                    st.query_params["writer"] = writer['path']
-                    logger.info(f"Updated query params with writer: {writer['path']}")
-                    st.rerun()
-        
-        # Add spacing between categories
-        st.markdown('<div class="category-spacer"></div>', unsafe_allow_html=True)
-
-    logger.info("Finished rendering AI Writers Dashboard")
-    
-    return writers
-
-# Remove the old ai_writers function since it's now integrated into get_ai_writers
--- a/ToBeMigrated/ai_writers/data_img_slides_analyst.py
+++ b/ToBeMigrated/ai_writers/data_img_slides_analyst.py
--- a/ToBeMigrated/ai_writers/github_blogs/README.md
+++ b/ToBeMigrated/ai_writers/github_blogs/README.md
@@ -1,259 +0,0 @@
-# GitHub Blog Generator
-
-A powerful AI-powered content generation system that automatically creates comprehensive documentation, tutorials, and guides from GitHub repositories. This module transforms GitHub repository data into various types of high-quality technical content.
-
-## Features
-
-### 1. Content Generation Types
-
-The system can generate the following types of content from GitHub repositories:
-
- **Getting Started Guides**
-  - Introduction and Overview
-  - Prerequisites and Setup
-  - Installation Instructions
-  - Basic Usage Examples
-  - Common Use Cases
-  - Best Practices
-  - Next Steps and Resources
-
- **Technical Documentation**
-  - Architecture Overview
-  - Core Components
-  - Technical Specifications
-  - Integration Points
-  - Performance Considerations
-  - Security Features
-  - API Documentation
-  - Configuration Options
-  - Deployment Guidelines
-  - Troubleshooting Guide
-
- **Tutorial Series**
-  - Beginner Tutorials
-    - Basic concepts
-    - Simple examples
-    - Step-by-step instructions
-  - Intermediate Tutorials
-    - Advanced features
-    - Real-world examples
-    - Best practices
-  - Advanced Tutorials
-    - Complex use cases
-    - Performance optimization
-    - Integration patterns
-
- **Comparison Analysis**
-  - Feature Comparison
-  - Performance Analysis
-  - Use Case Suitability
-  - Community and Support
-  - Learning Curve
-  - Integration Capabilities
-  - Future Prospects
-
- **Case Studies**
-  - Problem Statement
-  - Solution Implementation
-  - Technical Challenges
-  - Results and Benefits
-  - Lessons Learned
-  - Future Improvements
-
- **Contribution Guides**
-  - Development Setup
-  - Code Style Guidelines
-  - Testing Requirements
-  - Documentation Standards
-  - Pull Request Process
-  - Review Guidelines
-  - Community Guidelines
-
- **Security Guides**
-  - Security Architecture
-  - Authentication & Authorization
-  - Data Protection
-  - Secure Configuration
-  - Vulnerability Management
-  - Incident Response
-  - Compliance Requirements
-
- **Performance Guides**
-  - Performance Metrics
-  - Optimization Techniques
-  - Benchmarking Guidelines
-  - Resource Management
-  - Scaling Strategies
-  - Monitoring Setup
-  - Troubleshooting
-
-### 2. GitHub Content Scraping
-
-The module includes a sophisticated GitHub content scraper with the following capabilities:
-
- **Rate Limiting**
-  - Configurable API call limits
-  - Automatic request throttling
-  - Concurrent request management
-
- **Caching System**
-  - Configurable cache duration (TTL)
-  - Automatic cache invalidation
-  - Efficient storage of scraped content
-
- **Content Extraction**
-  - Repository metadata
-  - README content
-  - File contents
-  - Repository topics
-  - Contributor information
-  - License information
-
-### 3. Content Enhancement
-
- **Online Research Integration**
-  - Automatic topic research
-  - Related content discovery
-  - Industry trend analysis
-
- **FAQ Generation**
-  - Automatic FAQ creation
-  - Common question identification
-  - Comprehensive answers
-
- **Metadata Generation**
-  - SEO-optimized titles
-  - Meta descriptions
-  - Tags and categories
-  - Content structuring
-
-## Usage Examples
-
-### Basic Usage
-
-```python
-from lib.ai_writers.github_blogs import GitHubBlogGenerator
-
-# Initialize the generator
-generator = GitHubBlogGenerator()
-
-# Generate content for a GitHub repository
-content = await generator.generate_content(
-    github_url="https://github.com/owner/repo",
-    content_types=["getting_started", "technical_docs", "tutorials"]
-)
-
-# Save the generated content
-generator.save_content(content, "my_repository")
-```
-
-### Advanced Usage
-
-```python
-from lib.ai_writers.github_blogs import GitHubBlogGenerator
-
-# Initialize with custom settings
-generator = GitHubBlogGenerator(
-    cache_dir=".custom_cache",
-    ttl_hours=48
-)
-
-# Generate all content types
-content_types = [
-    "getting_started",
-    "technical_docs",
-    "tutorials",
-    "comparison",
-    "case_studies",
-    "contribution",
-    "security",
-    "performance"
-]
-
-# Generate content for multiple repositories
-urls = [
-    "https://github.com/owner/repo1",
-    "https://github.com/owner/repo2"
-]
-
-for url in urls:
-    content = await generator.generate_content(url, content_types)
-    generator.save_content(content, url.split("/")[-1])
-```
-
-## Configuration Options
-
-### GitHubBlogGenerator
-
- `cache_dir` (str): Directory for caching scraped content (default: ".github_cache")
- `ttl_hours` (int): Time-to-live for cached content in hours (default: 24)
-
-### Content Generation
-
- `gpt_provider` (str): Choice of AI provider ("gemini" or "openai")
- `content_types` (List[str]): Types of content to generate
- `github_url` (str): URL of the GitHub repository
-
-## Output Format
-
-All generated content is saved in Markdown format with the following structure:
-
-```markdown
-# [Title]
-
-[Generated content based on content type]
-
-## Metadata
- Title: [SEO-optimized title]
- Description: [Meta description]
- Tags: [Generated tags]
- Categories: [Generated categories]
-```
-
-## Best Practices
-
-1. **Rate Limiting**
-   - Configure appropriate rate limits based on your GitHub API quota
-   - Use caching to minimize API calls
-   - Implement proper error handling for rate limit exceeded scenarios
-
-2. **Content Generation**
-   - Start with basic content types before generating advanced content
-   - Review generated content for accuracy and completeness
-   - Customize prompts for specific repository types
-
-3. **Caching**
-   - Set appropriate TTL based on repository update frequency
-   - Clear cache when repository content changes significantly
-   - Monitor cache size and performance
-
-4. **Error Handling**
-   - Implement proper error handling for API failures
-   - Log errors for debugging
-   - Provide fallback mechanisms for failed content generation
-
-## Dependencies
-
- Python 3.8+
- aiohttp
- beautifulsoup4
- loguru
- pydantic
- requests
- pandas
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Commit your changes
-4. Push to the branch
-5. Create a Pull Request
-
-## License
-
-[Your License Here]
-
-## Support
-
-For support, please [create an issue](https://github.com/your-repo/issues) or contact the maintainers. 
--- a/ToBeMigrated/ai_writers/github_blogs/github_getting_started.py
+++ b/ToBeMigrated/ai_writers/github_blogs/github_getting_started.py
@@ -1,254 +0,0 @@
-"""
-Enhanced GitHub Content Generator
-
-This module provides various content generation capabilities from GitHub repository data,
-including getting started guides, technical documentation, tutorials, and more.
-"""
-
-import sys
-from typing import Dict, List, Optional
-from loguru import logger
-
-from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-logger.remove()
-logger.add(sys.stdout,
-          colorize=True,
-          format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
-
-def generate_technical_documentation(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate comprehensive technical documentation from repository data."""
-    prompt = f"""As an expert technical writer, create detailed technical documentation for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Please create a comprehensive technical documentation that includes:
-1. Architecture Overview
-2. Core Components
-3. Technical Specifications
-4. Integration Points
-5. Performance Considerations
-6. Security Features
-7. API Documentation (if applicable)
-8. Configuration Options
-9. Deployment Guidelines
-10. Troubleshooting Guide
-
-Format the documentation in markdown with appropriate headers, code blocks, and diagrams.
-Include real-world examples and best practices.
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_getting_started_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a beginner-friendly getting started guide."""
-    prompt = f"""As an expert programmer and teacher, create a comprehensive getting started guide for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a step-by-step guide that includes:
-1. Introduction and Overview
-2. Prerequisites and Setup
-3. Installation Instructions
-4. Basic Usage Examples
-5. Common Use Cases
-6. Best Practices
-7. Next Steps and Resources
-
-Make the guide:
- Beginner-friendly with clear explanations
- Include practical examples with code snippets
- Add emojis for better readability
- Include troubleshooting tips
- Provide links to additional resources
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_tutorial_series(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a series of tutorials for different skill levels."""
-    prompt = f"""As an expert educator, create a series of tutorials for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a structured tutorial series that includes:
-1. Beginner Tutorial
-   - Basic concepts
-   - Simple examples
-   - Step-by-step instructions
-
-2. Intermediate Tutorial
-   - Advanced features
-   - Real-world examples
-   - Best practices
-
-3. Advanced Tutorial
-   - Complex use cases
-   - Performance optimization
-   - Integration patterns
-
-Each tutorial should:
- Be self-contained
- Include practical examples
- Have clear learning objectives
- Include exercises and challenges
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_comparison_analysis(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a comparison analysis with similar tools/frameworks."""
-    prompt = f"""As a technical analyst, create a comprehensive comparison analysis for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a detailed comparison that includes:
-1. Feature Comparison
-2. Performance Analysis
-3. Use Case Suitability
-4. Community and Support
-5. Learning Curve
-6. Integration Capabilities
-7. Future Prospects
-
-Include:
- Pros and Cons
- Real-world use cases
- Industry adoption
- Community feedback
- Future roadmap
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_case_studies(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate real-world case studies and success stories."""
-    prompt = f"""As a technical writer, create compelling case studies for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create detailed case studies that include:
-1. Problem Statement
-2. Solution Implementation
-3. Technical Challenges
-4. Results and Benefits
-5. Lessons Learned
-6. Future Improvements
-
-Make the case studies:
- Based on real-world scenarios
- Include technical details
- Show measurable results
- Provide actionable insights
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_contribution_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a comprehensive contribution guide."""
-    prompt = f"""As an open-source maintainer, create a detailed contribution guide for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a contribution guide that includes:
-1. Development Setup
-2. Code Style Guidelines
-3. Testing Requirements
-4. Documentation Standards
-5. Pull Request Process
-6. Review Guidelines
-7. Community Guidelines
-
-Make the guide:
- Clear and concise
- Include examples
- Cover all contribution types
- Provide templates
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_security_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a security best practices guide."""
-    prompt = f"""As a security expert, create a comprehensive security guide for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a security guide that includes:
-1. Security Architecture
-2. Authentication & Authorization
-3. Data Protection
-4. Secure Configuration
-5. Vulnerability Management
-6. Incident Response
-7. Compliance Requirements
-
-Make the guide:
- Practical and actionable
- Include security checklists
- Provide code examples
- Cover common vulnerabilities
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def generate_performance_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
-    """Generate a performance optimization guide."""
-    prompt = f"""As a performance optimization expert, create a detailed performance guide for the following GitHub repository:
-
-Repository Data:
-{repo_data}
-
-Create a performance guide that includes:
-1. Performance Metrics
-2. Optimization Techniques
-3. Benchmarking Guidelines
-4. Resource Management
-5. Scaling Strategies
-6. Monitoring Setup
-7. Troubleshooting
-
-Make the guide:
- Data-driven
- Include benchmarks
- Provide optimization tips
- Cover different scales
-"""
-    return _get_llm_response(prompt, gpt_provider)
-
-def _get_llm_response(prompt: str, gpt_provider: str) -> str:
-    """Get response from the specified LLM provider."""
-    system_prompt = """You are an expert technical writer and GitHub repository analyst with deep expertise in software development, documentation, and technical communication.
-
-  Your role is to create high-quality, accurate, and engaging content based on GitHub repository data. You should:
-
-  1. **Technical Accuracy**
-     - Ensure all technical information is precise and up-to-date
-     - Verify code examples and configurations
-     - Cross-reference documentation and source code
-     - Maintain consistency with repository standards
-
-  2. **Content Structure**
-     - Use clear hierarchical organization
-     - Include appropriate code blocks and examples
-     - Add relevant diagrams and visual aids
-     - Break complex topics into digestible sections
-
-  3. **Writing Style**
-     - Maintain a professional yet approachable tone
-     - Use active voice and clear language
-     - Include practical examples and use cases
-     - Add relevant emojis for better readability
-
-  4. **Best Practices**
-     - Follow industry-standard documentation practices
-     - Include troubleshooting sections
-     - Add performance considerations
-     - Address security implications
-"""
-    try:
-        
-        llm_response = llm_text_gen(prompt, system_prompt=system_prompt)
-    except Exception as err:
-        logger.error(f"Failed to get response from {gpt_provider}: {err}")
-        raise
--- a/ToBeMigrated/ai_writers/github_blogs/main_getting_started_blogs.py
+++ b/ToBeMigrated/ai_writers/github_blogs/main_getting_started_blogs.py
@@ -1,157 +0,0 @@
-"""
-Enhanced GitHub Blog Generator
-
-This module provides comprehensive content generation from GitHub repositories,
-including technical documentation, tutorials, case studies, and more.
-"""
-
-import os
-import sys
-import datetime
-import json
-from typing import Dict, List, Optional
-from pathlib import Path
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-          colorize=True,
-          format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
-
-from .scrape_github_readme import GitHubScraper, GitHubContent
-from .scrape_github_readme import get_gh_details_vision, get_readme_content
-from .scrape_github_readme import research_github_topics, check_if_already_written
-from .github_getting_started import (
-    generate_technical_documentation,
-    generate_getting_started_guide,
-    generate_tutorial_series,
-    generate_comparison_analysis,
-    generate_case_studies,
-    generate_contribution_guide,
-    generate_security_guide,
-    generate_performance_guide
-)
-
-
-class GitHubBlogGenerator:
-    """Generator for various types of GitHub-related content."""
-    
-    def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24):
-        """Initialize the blog generator."""
-        self.cache_dir = Path(cache_dir)
-        self.scraper = GitHubScraper(cache_dir, ttl_hours)
-        self.output_dir = Path("generated_content")
-        self.output_dir.mkdir(exist_ok=True)
-    
-    async def generate_content(self, github_url: str, content_types: List[str] = None) -> Dict[str, str]:
-        """Generate various types of content from a GitHub repository."""
-        if content_types is None:
-            content_types = ["getting_started", "technical_docs", "tutorials"]
-        
-        try:
-            # Scrape GitHub content
-            repo_content = await self.scraper.scrape_github_content(github_url)
-            
-            # Generate different types of content
-            generated_content = {}
-            
-            for content_type in content_types:
-                if content_type == "getting_started":
-                    content = generate_getting_started_guide(repo_content.dict())
-                elif content_type == "technical_docs":
-                    content = generate_technical_documentation(repo_content.dict())
-                elif content_type == "tutorials":
-                    content = generate_tutorial_series(repo_content.dict())
-                elif content_type == "comparison":
-                    content = generate_comparison_analysis(repo_content.dict())
-                elif content_type == "case_studies":
-                    content = generate_case_studies(repo_content.dict())
-                elif content_type == "contribution":
-                    content = generate_contribution_guide(repo_content.dict())
-                elif content_type == "security":
-                    content = generate_security_guide(repo_content.dict())
-                elif content_type == "performance":
-                    content = generate_performance_guide(repo_content.dict())
-                else:
-                    logger.warning(f"Unknown content type: {content_type}")
-                    continue
-                
-                generated_content[content_type] = content
-            
-            # Generate FAQs from online research
-            try:
-                research_report = do_online_research(repo_content.title, "gemini", github_url)
-                faqs = generate_blog_faq(research_report, "gemini")
-                generated_content["faqs"] = faqs
-            except Exception as err:
-                logger.error(f"Failed to generate FAQs: {err}")
-            
-            return generated_content
-            
-        except Exception as err:
-            logger.error(f"Failed to generate content: {err}")
-            raise
-    
-    def save_content(self, content: Dict[str, str], base_filename: str):
-        """Save generated content to files."""
-        try:
-            for content_type, content_text in content.items():
-                # Generate metadata for each content type
-                title, meta_desc, tags, categories = blog_metadata(content_text, "gemini")
-                
-                # Create filename with content type
-                filename = f"{base_filename}_{content_type}.md"
-                
-                # Save content to file
-                save_blog_to_file(
-                    content_text,
-                    title,
-                    meta_desc,
-                    tags,
-                    categories,
-                    None  # No image path for now
-                )
-                
-                logger.info(f"Saved {content_type} content to {filename}")
-                
-        except Exception as err:
-            logger.error(f"Failed to save content: {err}")
-            raise
-
-async def main():
-    """Example usage of the GitHub blog generator."""
-    generator = GitHubBlogGenerator()
-    
-    # Example GitHub URLs
-    urls = [
-        "https://github.com/owner/repo",
-        "https://github.com/owner/another-repo"
-    ]
-    
-    content_types = [
-        "getting_started",
-        "technical_docs",
-        "tutorials",
-        "comparison",
-        "case_studies",
-        "contribution",
-        "security",
-        "performance"
-    ]
-    
-    for url in urls:
-        try:
-            # Generate content
-            content = await generator.generate_content(url, content_types)
-            
-            # Create base filename from URL
-            base_filename = url.split("/")[-1]
-            
-            # Save content
-            generator.save_content(content, base_filename)
-            
-        except Exception as e:
-            logger.error(f"Error processing {url}: {e}")
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/ToBeMigrated/ai_writers/github_blogs/scrape_github_readme.py
+++ b/ToBeMigrated/ai_writers/github_blogs/scrape_github_readme.py
@@ -1,427 +0,0 @@
-"""
-Enhanced GitHub Content Scraper with Rate Limiting and Caching
-
-This module provides functionality to scrape GitHub repositories, READMEs, and code files
-for content marketing purposes. It includes async support, rate limiting, caching,
-and comprehensive metadata collection.
-"""
-
-import os
-import sys
-import json
-import asyncio
-import aiohttp
-from datetime import datetime, timedelta
-from typing import Dict, List, Optional, Union
-from urllib.parse import urljoin, urlparse
-import pandas as pd
-from bs4 import BeautifulSoup
-from loguru import logger
-import requests
-from pydantic import BaseModel, Field
-import time
-import pickle
-from pathlib import Path
-
-# Configure logging
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-          format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
-
-class RateLimiter:
-    """Rate limiter for GitHub API requests."""
-    
-    def __init__(self, calls_per_minute: int = 30):
-        self.calls_per_minute = calls_per_minute
-        self.interval = 60 / calls_per_minute  # seconds between calls
-        self.last_call_time = 0
-        self.lock = asyncio.Lock()
-    
-    async def acquire(self):
-        """Acquire rate limit token."""
-        async with self.lock:
-            current_time = time.time()
-            time_since_last_call = current_time - self.last_call_time
-            
-            if time_since_last_call < self.interval:
-                await asyncio.sleep(self.interval - time_since_last_call)
-            
-            self.last_call_time = time.time()
-
-class Cache:
-    """Cache for GitHub content."""
-    
-    def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24):
-        self.cache_dir = Path(cache_dir)
-        self.ttl = timedelta(hours=ttl_hours)
-        self.cache_dir.mkdir(exist_ok=True)
-    
-    def _get_cache_path(self, key: str) -> Path:
-        """Get cache file path for a key."""
-        return self.cache_dir / f"{hash(key)}.cache"
-    
-    def get(self, key: str) -> Optional[Dict]:
-        """Get cached value for key."""
-        cache_path = self._get_cache_path(key)
-        
-        if not cache_path.exists():
-            return None
-        
-        try:
-            with open(cache_path, 'rb') as f:
-                data = pickle.load(f)
-                if datetime.now() - data['timestamp'] > self.ttl:
-                    cache_path.unlink()
-                    return None
-                return data['value']
-        except Exception as e:
-            logger.warning(f"Cache read error for {key}: {e}")
-            return None
-    
-    def set(self, key: str, value: Dict):
-        """Set cache value for key."""
-        cache_path = self._get_cache_path(key)
-        
-        try:
-            with open(cache_path, 'wb') as f:
-                pickle.dump({
-                    'timestamp': datetime.now(),
-                    'value': value
-                }, f)
-        except Exception as e:
-            logger.warning(f"Cache write error for {key}: {e}")
-
-class GitHubContent(BaseModel):
-    """Model for GitHub content analysis."""
-    title: str = Field("", description="Title of the content")
-    description: str = Field("", description="Description of the content")
-    content: str = Field("", description="Main content")
-    language: str = Field("", description="Programming language")
-    stars: int = Field(0, description="Number of stars")
-    forks: int = Field(0, description="Number of forks")
-    watchers: int = Field(0, description="Number of watchers")
-    last_updated: str = Field("", description="Last update date")
-    topics: List[str] = Field([], description="Repository topics")
-    contributors: List[str] = Field([], description="Contributor usernames")
-    readme_url: str = Field("", description="URL of the README")
-    raw_content_url: str = Field("", description="URL for raw content")
-    license: str = Field("", description="Repository license")
-    dependencies: List[str] = Field([], description="Project dependencies")
-    metadata: Dict = Field({}, description="Additional metadata")
-
-class GitHubScraper:
-    """Service for scraping GitHub content with rate limiting and caching."""
-    
-    def __init__(self, cache_dir: str = ".github_cache", ttl_hours: int = 24, calls_per_minute: int = 30):
-        """Initialize the scraper service."""
-        self.session = None
-        self.headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
-            'Accept': 'application/vnd.github.v3+json'
-        }
-        self.rate_limiter = RateLimiter(calls_per_minute)
-        self.cache = Cache(cache_dir, ttl_hours)
-    
-    async def __aenter__(self):
-        """Create aiohttp session when entering context."""
-        self.session = aiohttp.ClientSession(headers=self.headers)
-        return self
-        
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        """Close aiohttp session when exiting context."""
-        if self.session:
-            await self.session.close()
-    
-    async def fetch_url(self, url: str, use_cache: bool = True) -> str:
-        """Fetch URL content asynchronously with rate limiting and caching."""
-        if use_cache:
-            cached_content = self.cache.get(url)
-            if cached_content:
-                logger.debug(f"Cache hit for {url}")
-                return cached_content
-        
-        await self.rate_limiter.acquire()
-        
-        try:
-            async with self.session.get(url) as response:
-                if response.status == 200:
-                    content = await response.text()
-                    if use_cache:
-                        self.cache.set(url, content)
-                    return content
-                else:
-                    error_msg = f"Failed to fetch URL: Status code {response.status}"
-                    logger.error(error_msg)
-                    raise Exception(error_msg)
-        except Exception as e:
-            logger.error(f"Error fetching URL {url}: {e}")
-            raise
-    
-    def parse_github_url(self, url: str) -> Dict[str, str]:
-        """Parse GitHub URL to extract repository information."""
-        parsed = urlparse(url)
-        path_parts = parsed.path.strip('/').split('/')
-        
-        if len(path_parts) < 2:
-            raise ValueError("Invalid GitHub URL format")
-        
-        return {
-            'owner': path_parts[0],
-            'repo': path_parts[1],
-            'branch': path_parts[3] if len(path_parts) > 3 else 'main',
-            'path': '/'.join(path_parts[4:]) if len(path_parts) > 4 else ''
-        }
-    
-    async def get_repo_metadata(self, owner: str, repo: str) -> Dict:
-        """Get repository metadata from GitHub API with caching."""
-        cache_key = f"metadata_{owner}_{repo}"
-        cached_metadata = self.cache.get(cache_key)
-        if cached_metadata:
-            return cached_metadata
-        
-        await self.rate_limiter.acquire()
-        
-        api_url = f"https://api.github.com/repos/{owner}/{repo}"
-        try:
-            async with self.session.get(api_url) as response:
-                if response.status == 200:
-                    metadata = await response.json()
-                    self.cache.set(cache_key, metadata)
-                    return metadata
-                else:
-                    logger.error(f"Failed to fetch repo metadata: {response.status}")
-                    return {}
-        except Exception as e:
-            logger.error(f"Error fetching repo metadata: {e}")
-            return {}
-    
-    async def get_readme_content(self, owner: str, repo: str, branch: str = 'main') -> Dict:
-        """Get README content from GitHub with caching."""
-        cache_key = f"readme_{owner}_{repo}_{branch}"
-        cached_content = self.cache.get(cache_key)
-        if cached_content:
-            return cached_content
-        
-        try:
-            # Try to get README from API first
-            await self.rate_limiter.acquire()
-            api_url = f"https://api.github.com/repos/{owner}/{repo}/readme"
-            async with self.session.get(api_url) as response:
-                if response.status == 200:
-                    readme_data = await response.json()
-                    content = {
-                        'content': readme_data.get('content', ''),
-                        'encoding': readme_data.get('encoding', 'base64'),
-                        'url': readme_data.get('html_url', '')
-                    }
-                    self.cache.set(cache_key, content)
-                    return content
-            
-            # Fallback to scraping if API fails
-            readme_url = f"https://github.com/{owner}/{repo}/blob/{branch}/README.md"
-            html_content = await self.fetch_url(readme_url, use_cache=True)
-            soup = BeautifulSoup(html_content, 'html.parser')
-            
-            # Find the README content
-            readme_content = soup.find('div', {'class': 'markdown-body'})
-            if readme_content:
-                content = {
-                    'content': readme_content.get_text(),
-                    'encoding': 'text',
-                    'url': readme_url
-                }
-                self.cache.set(cache_key, content)
-                return content
-            
-            return {}
-        except Exception as e:
-            logger.error(f"Error fetching README: {e}")
-            return {}
-    
-    async def get_file_content(self, owner: str, repo: str, path: str, branch: str = 'main') -> Dict:
-        """Get content of a specific file from GitHub with caching."""
-        cache_key = f"file_{owner}_{repo}_{path}_{branch}"
-        cached_content = self.cache.get(cache_key)
-        if cached_content:
-            return cached_content
-        
-        try:
-            # Try to get file content from API first
-            await self.rate_limiter.acquire()
-            api_url = f"https://api.github.com/repos/{owner}/{repo}/contents/{path}?ref={branch}"
-            async with self.session.get(api_url) as response:
-                if response.status == 200:
-                    file_data = await response.json()
-                    content = {
-                        'content': file_data.get('content', ''),
-                        'encoding': file_data.get('encoding', 'base64'),
-                        'url': file_data.get('html_url', '')
-                    }
-                    self.cache.set(cache_key, content)
-                    return content
-            
-            # Fallback to scraping if API fails
-            file_url = f"https://github.com/{owner}/{repo}/blob/{branch}/{path}"
-            html_content = await self.fetch_url(file_url, use_cache=True)
-            soup = BeautifulSoup(html_content, 'html.parser')
-            
-            # Find the file content
-            file_content = soup.find('div', {'class': 'file-content'})
-            if file_content:
-                content = {
-                    'content': file_content.get_text(),
-                    'encoding': 'text',
-                    'url': file_url
-                }
-                self.cache.set(cache_key, content)
-                return content
-            
-            return {}
-        except Exception as e:
-            logger.error(f"Error fetching file content: {e}")
-            return {}
-    
-    async def get_repo_topics(self, owner: str, repo: str) -> List[str]:
-        """Get repository topics with caching."""
-        cache_key = f"topics_{owner}_{repo}"
-        cached_topics = self.cache.get(cache_key)
-        if cached_topics:
-            return cached_topics
-        
-        try:
-            await self.rate_limiter.acquire()
-            api_url = f"https://api.github.com/repos/{owner}/{repo}/topics"
-            async with self.session.get(api_url, headers={'Accept': 'application/vnd.github.mercy-preview+json'}) as response:
-                if response.status == 200:
-                    data = await response.json()
-                    topics = data.get('names', [])
-                    self.cache.set(cache_key, topics)
-                    return topics
-                return []
-        except Exception as e:
-            logger.error(f"Error fetching topics: {e}")
-            return []
-    
-    async def get_contributors(self, owner: str, repo: str) -> List[str]:
-        """Get repository contributors with caching."""
-        cache_key = f"contributors_{owner}_{repo}"
-        cached_contributors = self.cache.get(cache_key)
-        if cached_contributors:
-            return cached_contributors
-        
-        try:
-            await self.rate_limiter.acquire()
-            api_url = f"https://api.github.com/repos/{owner}/{repo}/contributors"
-            async with self.session.get(api_url) as response:
-                if response.status == 200:
-                    contributors = await response.json()
-                    contributor_list = [contributor['login'] for contributor in contributors]
-                    self.cache.set(cache_key, contributor_list)
-                    return contributor_list
-                return []
-        except Exception as e:
-            logger.error(f"Error fetching contributors: {e}")
-            return []
-    
-    async def scrape_github_content(self, url: str) -> GitHubContent:
-        """Main function to scrape GitHub content with caching."""
-        cache_key = f"content_{url}"
-        cached_content = self.cache.get(cache_key)
-        if cached_content:
-            return GitHubContent(**cached_content)
-        
-        try:
-            # Parse the GitHub URL
-            repo_info = self.parse_github_url(url)
-            
-            # Get repository metadata
-            metadata = await self.get_repo_metadata(repo_info['owner'], repo_info['repo'])
-            
-            # Get content based on URL type
-            if not repo_info['path'] or repo_info['path'].lower() == 'readme.md':
-                content_data = await self.get_readme_content(
-                    repo_info['owner'], 
-                    repo_info['repo'], 
-                    repo_info['branch']
-                )
-            else:
-                content_data = await self.get_file_content(
-                    repo_info['owner'], 
-                    repo_info['repo'], 
-                    repo_info['path'], 
-                    repo_info['branch']
-                )
-            
-            # Get additional metadata
-            topics = await self.get_repo_topics(repo_info['owner'], repo_info['repo'])
-            contributors = await self.get_contributors(repo_info['owner'], repo_info['repo'])
-            
-            # Create GitHubContent object
-            content = GitHubContent(
-                title=metadata.get('name', ''),
-                description=metadata.get('description', ''),
-                content=content_data.get('content', ''),
-                language=metadata.get('language', ''),
-                stars=metadata.get('stargazers_count', 0),
-                forks=metadata.get('forks_count', 0),
-                watchers=metadata.get('watchers_count', 0),
-                last_updated=metadata.get('updated_at', ''),
-                topics=topics,
-                contributors=contributors,
-                readme_url=content_data.get('url', ''),
-                raw_content_url=metadata.get('html_url', ''),
-                license=metadata.get('license', {}).get('name', ''),
-                metadata={
-                    'size': metadata.get('size', 0),
-                    'open_issues': metadata.get('open_issues_count', 0),
-                    'default_branch': metadata.get('default_branch', 'main'),
-                    'created_at': metadata.get('created_at', ''),
-                    'pushed_at': metadata.get('pushed_at', '')
-                }
-            )
-            
-            # Cache the complete content
-            self.cache.set(cache_key, content.dict())
-            
-            return content
-            
-        except Exception as e:
-            logger.error(f"Error scraping GitHub content: {e}")
-            raise
-
-async def main():
-    """Example usage of the GitHub scraper with rate limiting and caching."""
-    scraper = GitHubScraper(
-        cache_dir=".github_cache",
-        ttl_hours=24,
-        calls_per_minute=30
-    )
-    
-    async with scraper:
-        # Example URLs
-        urls = [
-            "https://github.com/owner/repo",
-            "https://github.com/owner/repo/blob/main/README.md",
-            "https://github.com/owner/repo/blob/main/src/main.py"
-        ]
-        
-        for url in urls:
-            try:
-                content = await scraper.scrape_github_content(url)
-                print(f"Scraped content from {url}:")
-                print(json.dumps(content.dict(), indent=2))
-            except Exception as e:
-                print(f"Error scraping {url}: {e}")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
-
-
-
-
-
-
-
-
--- a/ToBeMigrated/ai_writers/gpt_blog_sections.py
+++ b/ToBeMigrated/ai_writers/gpt_blog_sections.py
@@ -1,50 +0,0 @@
-import sys
-import os
-import json
-
-from ..gpt_providers.text_generation.openai_text_gen import openai_text_generation
-from ..gpt_providers.text_generation.gemini_pro_text import gemini_text_generation
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-
-# FIXME: Provide num_blogs, num_faqs as inputs.
-def get_blog_sections_from_websearch(search_keyword, search_results):
-    """Combine the given online research and gpt blog content"""
-    gpt_providers = os.environ["GPT_PROVIDER"]
-    prompt = f"""
-        As a SEO expert and content writer, I will provide you with a search keyword and its google search result.
-        Your task is to write a blog title and 5 blog sub titles, from the given google search result.
-        The subtitles should be less than 40 characters and click worthy.
-        Do not explain, describe your response. Respond in json format, always name the key as 'blogSections'.
-
-        Web Research Keyword: "{search_keyword}"
-        Google search Result: "{search_results}"
-        """
-
-    if 'gemini' in gpt_providers:
-        try:
-            response = gemini_text_response(prompt)
-            if '```' in response and '\n' in response:
-                response = response.strip().split('\n')
-                # Remove the first and last lines
-                response = '\n'.join(response[1:-1])
-                response = json.loads(response)
-            return response
-        except Exception as err:
-            logger.error(f"Failed to get response from gemini: {err}")
-            logger.error(f"Gemini Error: {response.prompt_feedback}")
-            raise err
-    elif 'openai' in gpt_providers:
-        try:
-            logger.info("Calling OpenAI LLM.")
-            response = openai_chatgpt(prompt)
-            return response
-        except Exception as err:
-            logger.error(f"Failed to get response from Openai: {err}")
-            raise err
--- a/ToBeMigrated/ai_writers/image_ai_writer.py
+++ b/ToBeMigrated/ai_writers/image_ai_writer.py
@@ -1,109 +0,0 @@
-import sys
-import os
-
-from textwrap import dedent
-import json
-import asyncio
-from pathlib import Path
-from datetime import datetime
-import streamlit as st
-
-from dotenv import load_dotenv
-load_dotenv(Path('../../.env'))
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from ..ai_web_researcher.firecrawl_web_crawler import scrape_url
-from ..blog_metadata.get_blog_metadata import blog_metadata
-from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
-from ..gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-from ..gpt_providers.image_to_text_gen.gemini_image_describe import describe_image, analyze_image_with_prompt
-
-
-def blog_from_image(prompt, uploaded_img):
-    """
-    This function will take a blog Topic to first generate sections for it
-    and then generate content for each section.
-    """
-    # Use to store the blog in a string, to save in a *.md file.
-    blog_markdown_str = None
-    logger.info(f"Researching and Writing Blog on {uploaded_img} and {prompt}")
-    # FIXME: Implement support for Openai.
-    if not os.getenv("GEMINI_API_KEY"):
-        st.error("Only Gemini supported, Open Issue ticket on github for Openai, others.")
-        st.stop()
-
-    with st.status("Started Writing from Image..", expanded=True) as status:
-        st.empty()
-        status.update(label=f"Researching and Writing Blog on given Image")
-        try:
-            blog_markdown_str = write_blog_from_image(prompt, uploaded_img)
-        except Exception as err:
-            st.error(f"Failed to write blog from Image - Error: {err}")
-            logger.error(f"Failed to write blog from image: {err}")
-            st.stop()
-        status.update(label="Successfully wrote blog from image.", expanded=False, state="complete")
-
-        try:
-            status.update(label="🙎 Generating - Title, Meta Description, Tags, Categories for the content.")
-            blog_title, blog_meta_desc, blog_tags, blog_categories = asyncio.run(blog_metadata(blog_markdown_str))
-        except Exception as err:
-            st.error(f"Failed to get blog metadata: {err}")
-
-        try:
-            status.update(label="🙎 Generating Image for the new blog.")
-            generated_image_filepath = generate_image(f"{blog_title} + ' ' + {blog_meta_desc}")
-        except Exception as err:
-            st.warning(f"Failed in Image generation: {err}")
-
-        saved_blog_to_file = save_blog_to_file(blog_markdown_str, blog_title, blog_meta_desc, 
-                            blog_tags, blog_categories, generated_image_filepath)
-        status.update(label=f"Saved the content in this file: {saved_blog_to_file}")
-        logger.info(f"\n\n --------- Finished writing Blog -------------- \n")
-        st.image(generated_image_filepath, caption=blog_title)
-        st.markdown(f"{blog_markdown_str}")
-        status.update(label=f"Finished, Review & Use your Original Content Below: {saved_blog_to_file}", state="complete")
-
-        # Clean up the temporary file after processing (optional)
-        os.remove(uploaded_img)
-
-
-def write_blog_from_image(prompt, uploaded_img):
-    """Combine the given online research and GPT blog content"""
-    try:
-        config_path = Path(os.environ["ALWRITY_CONFIG"])
-        with open(config_path, 'r', encoding='utf-8') as file:
-            config = json.load(file)
-    except Exception as err:
-        logger.error(f"Error: Failed to read values from config: {err}")
-        exit(1)
-
-    blog_characteristics = config['Blog Content Characteristics']
-    
-    if not prompt:
-        prompt = f"""
-            As expert Creative Content writer, analyse the given image carefully.
-            I want you to write a detailed {blog_characteristics['Blog Type']} blog post including 5 FAQs.
-        
-            Below are the guidelines to follow:
-            1). You must respond in {blog_characteristics['Blog Language']} language.
-            2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_characteristics['Blog Tone']} audience.
-            3). Make sure your response content length is of {blog_characteristics['Blog Length']} words.
-        """
-    logger.info("Generating blog and FAQs from image analysis.")
-    
-    try:
-        # Use the gemini_image_describe function to analyze the image with the custom prompt
-        response = analyze_image_with_prompt(uploaded_img, prompt)
-        if not response:
-            logger.error("Failed to get response from image analysis")
-            return "Failed to generate content from image."
-        return response
-    except Exception as err:
-        logger.error(f"Exit: Failed to get response from image analysis: {err}")
-        exit(1)
--- a/ToBeMigrated/ai_writers/long_form_ai_writer.py
+++ b/ToBeMigrated/ai_writers/long_form_ai_writer.py
@@ -1,247 +0,0 @@
-#####################################################
-#
-# Alwrity, AI Long form writer - Writing_with_Prompt_Chaining
-# and generative AI.
-#
-#####################################################
-
-import os
-import re
-import time #iwish
-import sys
-import yaml
-from pathlib import Path
-from dotenv import load_dotenv
-from configparser import ConfigParser
-import streamlit as st
-
-from pprint import pprint
-from textwrap import dedent
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from ..utils.read_main_config_params import read_return_config_section
-from ..ai_web_researcher.gpt_online_researcher import do_metaphor_ai_research
-from ..ai_web_researcher.gpt_online_researcher import do_google_serp_search, do_tavily_ai_search
-from ..blog_metadata.get_blog_metadata import get_blog_metadata_longform
-from ..blog_postprocessing.save_blog_to_file import save_blog_to_file
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def generate_with_retry(prompt, system_prompt=None):
-    """
-    Generates content from the model with retry handling for errors.
-
-    Parameters:
-        prompt (str): The prompt to generate content from.
-        system_prompt (str, optional): Custom system prompt to use instead of the default one.
-
-    Returns:
-        str: The generated content.
-    """
-    try:
-        # FIXME: Need a progress bar here.
-        return llm_text_gen(prompt, system_prompt)
-    except Exception as e:
-        logger.error(f"Error generating content: {e}")
-        st.error(f"Error generating content: {e}")
-        return False
-
-
-def long_form_generator(keywords, search_params=None, blog_params=None):
-    """
-    Generate a long-form blog post based on the given keywords
-    
-    Args:
-        keywords (str): Topic or keywords for the blog post
-        search_params (dict, optional): Search parameters for research
-        blog_params (dict, optional): Blog content characteristics
-    """
-    
-    # Initialize default parameters if not provided
-    if blog_params is None:
-        blog_params = {
-            "blog_length": 3000,  # Default longer for long-form content
-            "blog_tone": "Professional",
-            "blog_demographic": "Professional",
-            "blog_type": "Informational",
-            "blog_language": "English"
-        }
-    else:
-        # Ensure we have a higher word count for long-form content
-        if blog_params.get("blog_length", 0) < 2500:
-            blog_params["blog_length"] = max(3000, blog_params.get("blog_length", 0))
-    
-    # Extract parameters with defaults
-    blog_length = blog_params.get("blog_length", 3000)
-    blog_tone = blog_params.get("blog_tone", "Professional")
-    blog_demographic = blog_params.get("blog_demographic", "Professional")
-    blog_type = blog_params.get("blog_type", "Informational")
-    blog_language = blog_params.get("blog_language", "English")
-    
-    st.subheader(f"Long-form {blog_type} Blog ({blog_length}+ words)")
-    
-    with st.status("Generating comprehensive long-form content...", expanded=True) as status:
-        # Step 1: Generate outline
-        status.update(label="Creating detailed content outline...")
-        
-        # Use a customized prompt based on the blog parameters
-        outline_prompt = f"""
-        As an expert content strategist writing in a {blog_tone} tone for {blog_demographic} audience,
-        create a detailed outline for a comprehensive {blog_type} blog post about "{keywords}" 
-        that will be approximately {blog_length} words in {blog_language}.
-        
-        The outline should include:
-        1. An engaging headline
-        2. 5-7 main sections with descriptive headings
-        3. 2-3 subsections under each main section
-        4. Key points to cover in each section
-        5. Ideas for relevant examples or case studies
-        6. Suggestions for data points or statistics to include
-        
-        Format the outline in markdown with proper headings and bullet points.
-        """
-        
-        try:
-            outline = llm_text_gen(outline_prompt)
-            st.markdown("### Content Outline")
-            st.markdown(outline)
-            status.update(label="Outline created successfully ✓")
-            
-            # Step 2: Research the topic using the search parameters
-            status.update(label="Researching topic details...")
-            research_results = research_topic(keywords, search_params)
-            status.update(label="Research completed ✓")
-            
-            # Step 3: Generate the full content
-            status.update(label=f"Writing {blog_length}+ word {blog_tone} {blog_type} content...")
-            
-            full_content_prompt = f"""
-            You are a professional content writer who specializes in {blog_type} content with a {blog_tone} tone 
-            for {blog_demographic} audiences. Write a comprehensive, in-depth blog post in {blog_language} about:
-            
-            "{keywords}"
-            
-            Use this outline as your structure:
-            {outline}
-            
-            And incorporate these research findings where relevant:
-            {research_results}
-            
-            The blog post should:
-            - Be approximately {blog_length} words
-            - Include an engaging introduction and strong conclusion
-            - Use appropriate subheadings for all sections in the outline
-            - Include examples, data points, and actionable insights
-            - Be formatted in markdown with proper headings, bullet points, and emphasis
-            - Maintain a {blog_tone} tone throughout
-            - Address the needs and interests of a {blog_demographic} audience
-            
-            Do not include phrases like "according to research" or "based on the outline" in your content.
-            """
-            
-            full_content = llm_text_gen(full_content_prompt)
-            status.update(label="Long-form content generated successfully! ✓", state="complete")
-            
-            # Display the full content
-            st.markdown("### Your Complete Long-form Blog Post")
-            st.markdown(full_content)
-            
-            return full_content
-            
-        except Exception as e:
-            status.update(label=f"Error generating long-form content: {str(e)}", state="error")
-            st.error(f"Failed to generate long-form content: {str(e)}")
-            return None
-    
-def research_topic(keywords, search_params=None):
-    """
-    Research a topic using search parameters and return a summary
-    
-    Args:
-        keywords (str): Topic to research
-        search_params (dict, optional): Search parameters
-        
-    Returns:
-        str: Research summary
-    """
-    # Display a placeholder for research results
-    placeholder = st.empty()
-    placeholder.info("Researching topic... Please wait.")
-    
-    try:
-        from .ai_blog_writer.keywords_to_blog_streamlit import do_tavily_ai_search
-        
-        # Use provided search params or defaults
-        if search_params is None:
-            search_params = {
-                "max_results": 10, 
-                "search_depth": "advanced",
-                "time_range": "year"
-            }
-        
-        # Conduct research using Tavily
-        tavily_results = do_tavily_ai_search(
-            keywords,
-            max_results=search_params.get("max_results", 10),
-            search_depth=search_params.get("search_depth", "advanced"),
-            include_domains=search_params.get("include_domains", []),
-            time_range=search_params.get("time_range", "year")
-        )
-        
-        # Extract research data
-        research_data = ""
-        if tavily_results and len(tavily_results) == 3:
-            results, titles, answer = tavily_results
-            
-            if answer and len(answer) > 50:
-                research_data += f"Summary: {answer}\n\n"
-            
-            if results and 'results' in results and len(results['results']) > 0:
-                research_data += "Key Sources:\n"
-                for i, result in enumerate(results['results'][:7], 1):
-                    title = result.get('title', 'Untitled Source')
-                    content_snippet = result.get('content', '')[:300] + "..."
-                    research_data += f"{i}. {title}\n{content_snippet}\n\n"
-        
-        # If research data is empty or too short, provide a generic response
-        if not research_data or len(research_data) < 100:
-            research_data = f"No specific research data found for '{keywords}'. Please provide more specific information in your content."
-        
-        placeholder.success("Research completed successfully!")
-        return research_data
-        
-    except Exception as e:
-        placeholder.error(f"Research failed: {str(e)}")
-        return f"Unable to gather research for '{keywords}'. Please continue with the content based on your knowledge."
-    finally:
-        # Remove the placeholder after a short delay
-        import time
-        time.sleep(1)
-        placeholder.empty()
-
-
-def generate_long_form_content(content_keywords):
-    """
-    Main function to generate long-form content based on the provided keywords.
-    
-    Parameters:
-        content_keywords (str): The main keywords or topic for the long-form content.
-        
-    Returns:
-        str: The generated long-form content.
-    """
-    return long_form_generator(content_keywords)
-
-
-# Example usage
-if __name__ == "__main__":
-    # Example usage of the function
-    content_keywords = "artificial intelligence in healthcare"
-    generated_content = generate_long_form_content(content_keywords)
-    print(f"Generated content: {generated_content[:100]}...")
--- a/ToBeMigrated/ai_writers/scholar_blogs/main_arxiv_to_blog.py
+++ b/ToBeMigrated/ai_writers/scholar_blogs/main_arxiv_to_blog.py
@@ -1,202 +0,0 @@
-import sys
-import os
-import datetime
-
-import tiktoken
-
-from .arxiv_schlorly_research import fetch_arxiv_data, create_dataframe, get_arxiv_main_content
-from .arxiv_schlorly_research import arxiv_bibtex, scrape_images_from_arxiv, download_image
-from .arxiv_schlorly_research import read_written_ids, extract_arxiv_ids_from_line, append_id_to_file
-from .write_research_review_blog import review_research_paper
-from .combine_research_and_blog import blog_with_research
-from .write_blog_scholar_paper import write_blog_from_paper
-from .gpt_providers.gemini_pro_text import gemini_text_response
-from .generate_image_from_prompt import generate_image
-from .convert_content_to_markdown import convert_tomarkdown_format
-from .get_blog_metadata import blog_metadata
-from .get_code_examples import gemini_get_code_samples
-from .save_blog_to_file import save_blog_to_file
-from .take_url_screenshot import screenshot_api
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-
-def blog_arxiv_keyword(query):
-    """ Write blog on given arxiv paper."""
-    arxiv_id = None
-    arxiv_url = None
-    bibtex = None
-    research_review = None
-    column_names = ['Title', 'Date', 'Id', 'Summary', 'PDF URL']
-    papers = fetch_arxiv_data(query)
-    df = create_dataframe(papers, column_names)
-
-    for paper in papers:
-        # Extracting the arxiv_id
-        arxiv_id = paper[2].split('/')[-1]
-        arxiv_url = "https://browse.arxiv.org/html/" + arxiv_id
-        bibtex = arxiv_bibtex(arxiv_id)
-        logger.info(f"Get research paper text from the url: {arxiv_url}")
-        research_content = get_arxiv_main_content(arxiv_url)
-        
-        num_tokens = num_tokens_from_string(research_content, "cl100k_base")
-        logger.info(f"Number of tokens sent: {num_tokens}")
-        # If the number of tokens is below the threshold, process and print the review
-        if 1000 < num_tokens < 30000:
-            logger.info(f"Writing research review on {paper[0]}")
-            research_review = review_research_paper(research_content)
-            research_review = f"\n{research_review}\n\n" + f"```{bibtex}```"
-            #research_review = research_review + "\n\n\n" + f"{df.to_markdown()}"
-            research_review = convert_tomarkdown_format(research_review, "gemini")
-            break
-        else:
-            # Skip to the next iteration if the condition is not met
-            continue
-
-    logger.info(f"Final scholar article: \n\n{research_review}\n")
-    
-    # TBD: Scrape images from research reports and pass to vision to get conclusions out of it.
-    #image_urls = scrape_images_from_arxiv(arxiv_url)
-    #print("Downloading images found on the page:")
-    #for img_url in image_urls:
-    #    download_image(img_url, arxiv_url)
-    try:
-        blog_postprocessing(arxiv_id, research_review)
-    except Exception as err:
-        logger.error(f"Failed in blog post processing: {err}")
-        sys.exit(1)
-
-    logger.info(f"\n\n ################ Finished writing Blog for : #################### \n")
-
-
-def blog_arxiv_url_list(file_path):
-    """ Write blogs on all the arxiv links given in a file. """
-    extracted_ids = []
-    try:
-        with open(file_path, 'r', encoding="utf-8") as file:
-            for line in file:
-                arxiv_id = extract_arxiv_ids_from_line(line)
-                if arxiv_id:
-                    extracted_ids.append(arxiv_id)
-    except FileNotFoundError:
-        logger.error(f"File not found: {file_path}")
-        raise FileNotFoundError
-    except Exception as e:
-        logger.error(f"Error while reading the file: {e}")
-        raise e
-
-    # Read already written IDs
-    written_ids = read_written_ids('papers_already_written_on.txt')
-
-    # Loop through extracted IDs
-    for arxiv_id in extracted_ids:
-        if arxiv_id not in written_ids:
-            # This ID has not been written on yet
-            arxiv_url = "https://browse.arxiv.org/html/" + arxiv_id
-            logger.info(f"Get research paper text from the url: {arxiv_url}")
-            research_content = get_arxiv_main_content(arxiv_url)
-            try:
-                num_tokens = num_tokens_from_string(research_content, "cl100k_base")
-            except Exception as err:
-                logger.error(f"Failed in counting tokens: {err}")
-                sys.exit(1)
-            logger.info(f"Number of tokens sent: {num_tokens}")
-            # If the number of tokens is below the threshold, process and print the review
-            # FIXME: Docs over 30k tokens, need to be chunked and summarized.
-            if 1000 < num_tokens < 30000:
-                try:
-                    logger.info(f"Getting bibtex for arxiv ID: {arxiv_id}")
-                    bibtex = arxiv_bibtex(arxiv_id)
-                except Exception as err:
-                    logger.error(f"Failed to get Bibtex: {err}")
-
-                try:
-                    logger.info(f"Writing a research review..")
-                    research_review = review_research_paper(research_content, "gemini")
-                    logger.info(f"Research Review: \n{research_review}\n\n")
-                except Exception as err:
-                    logger.error(f"Failed to write review on research paper: {arxiv_id}{err}")
-
-                research_blog = write_blog_from_paper(research_content, "gemini")
-                logger.info(f"\n\nResearch Blog: {research_blog}\n\n")
-                research_blog = f"\n{research_review}\n\n" + f"```\n{bibtex}\n```"
-                #research_review = blog_with_research(research_review, research_blog, "gemini")
-                #logger.info(f"\n\n\nBLOG_WITH_RESEARCh: {research_review}\n\n\n")
-                research_review = convert_tomarkdown_format(research_review, "gemini")
-                research_review = f"\n{research_review}\n\n" + f"```{bibtex}```"
-                logger.info(f"Final blog from research paper: \n\n{research_review}\n\n\n")
-
-                try:
-                    blog_postprocessing(arxiv_id, research_review)
-                except Exception as err:
-                    logger.error(f"Failed in blog post processing: {err}")
-                    sys.exit(1)
-
-                logger.info(f"\n\n ################ Finished writing Blog for : #################### \n")
-            else:
-                # Skip to the next iteration if the condition is not met
-                logger.error("FIXME: Docs over 30k tokens, need to be chunked and summarized.")
-                continue
-        else:
-            logger.warning(f"Already written, skip writing on Arxiv paper ID: {arxiv_id}")
-
-
-def blog_postprocessing(arxiv_id, research_review):
-    """ Common function to do blog postprocessing. """
-    try:
-        append_id_to_file(arxiv_id, "papers_already_written_on.txt")
-    except Exception as err:
-        logger.error(f"Failed to write/append ID to papers_already_written_on.txt: {err}")
-        raise err
-
-    try:
-        blog_title, blog_meta_desc, blog_tags, blog_categories = blog_metadata(research_review)
-    except Exception as err:
-        logger.error(f"Failed to get blog metadata: {err}")
-        raise err
-
-    try:
-        arxiv_url_scrnsht = f"https://arxiv.org/abs/{arxiv_id}"
-        generated_image_filepath = take_paper_screenshot(arxiv_url_scrnsht)
-    except Exception as err:
-        logger.error(f"Failed to tsk paper screenshot: {err}")
-        raise err
-
-    try:
-        save_blog_to_file(research_review, blog_title, blog_meta_desc, blog_tags,\
-                blog_categories, generated_image_filepath)
-    except Exception as err:
-        logger.error(f"Failed to save blog to a file: {err}")
-        sys.exit(1)
-
-
-def take_paper_screenshot(arxiv_url):
-    """ Common function to take paper screenshot. """
-    # fixme: Remove the hardcoding, need add another option OR in config ?
-    image_dir = os.path.join(os.getcwd(), "blog_images")
-    generated_image_name = f"generated_image_{datetime.datetime.now():%Y-%m-%d-%H-%M-%S}.png"
-    generated_image_filepath = os.path.join(image_dir, generated_image_name)
-    
-    if arxiv_url:
-        try:
-            generated_image_filepath = screenshot_api(arxiv_url, generated_image_filepath)
-        except Exception as err:
-            logger.error(f"Failed in taking url screenshot: {err}")
-
-    return generated_image_filepath
-
-
-def num_tokens_from_string(string, encoding_name):
-    """Returns the number of tokens in a text string."""
-    try:
-        encoding = tiktoken.get_encoding(encoding_name)
-        num_tokens = len(encoding.encode(string))
-        return num_tokens
-    except Exception as err:
-        logger.error(f"Failed to count tokens: {err}")
-        sys.exit(1)
--- a/ToBeMigrated/ai_writers/scholar_blogs/write_blog_scholar_paper.py
+++ b/ToBeMigrated/ai_writers/scholar_blogs/write_blog_scholar_paper.py
@@ -1,49 +0,0 @@
-import sys
-
-from .gpt_providers.openai_chat_completion import openai_chatgpt
-from .gpt_providers.gemini_pro_text import gemini_text_response
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-
-def write_blog_from_paper(paper_content):
-    """ Write blog from given paper url. """
-    prompt = f"""As an expert in NLP and AI, I will provide you with a content of a research paper. 
-    Your task is to write a highly detailed blog(at least 2000 words), breaking down complex concepts for beginners.
-    Take your time and do not rush to respond.
-    Do not provide explanations, suggestions in your response.
-
-    Include the below section in your blog:
-    Highlights: Include a list of 5 most important and unique claims of the given research paper.
-    Abstract: Start by reading the abstract, which provides a concise summary of the research, including its purpose, methodology, and key findings.
-    Introduction: This section will give you background information and set the context for the research. It often ends with a statement of the research question or hypothesis.
-    Methodology: Include description of how authors conducted the research. This can include data sources, experimental setup, analytical techniques, etc.
-    Results: This section presents the data or findings of the research. Pay attention to figures, tables, and any statistical analysis provided.
-    Discussion/Analysis: In this section, Explain how research paper answers the research questions or how they fit with existing knowledge.
-    Conclusion: This part summarizes the main findings and their implications. It might also suggest areas for further research.
-    References: The cited works can provide additional context or background reading.
-    Remember, Please use MLA format and markdown syntax.
-    Do not provide description, explanations for your response.
-    Take your time in crafting your blog content, do not rush to give the response.
-    Using the blog structure above, please write a detailed and original blog on given research paper: \n'{paper_content}'\n\n"""
-
-    if 'gemini' in gpt_providers:
-        try:
-            response = gemini_text_response(prompt)
-            return response
-        except Exception as err:
-            logger.error(f"Failed to get response from gemini: {err}")
-            raise err
-    elif 'openai' in gpt_providers:
-        try:
-            logger.info("Calling OpenAI LLM.")
-            response = openai_chatgpt(prompt)
-            return response
-        except Exception as err:
-            logger.error(f"failed to get response from Openai: {err}")
-            raise err
--- a/ToBeMigrated/ai_writers/scholar_blogs/write_research_review_blog.py
+++ b/ToBeMigrated/ai_writers/scholar_blogs/write_research_review_blog.py
@@ -1,89 +0,0 @@
-import sys
-
-from .gpt_providers.openai_chat_completion import openai_chatgpt
-from .gpt_providers.gemini_pro_text import gemini_text_response
-from .gpt_providers.mistral_chat_completion import mistral_text_response
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-
-def review_research_paper(research_blog):
-    """ """
-    prompt = f"""As world's top researcher and academician, I will provide you with research paper.
-    Your task is to write a highly detailed review report. 
-    Important, your report should be factual, original and demostrate your expertise.
-
-    Review guidelines:
-    1). Read the Abstract and Introduction Carefully:
-        Begin by thoroughly reading the abstract and introduction of the paper.
-        Try to understand the research question, the objectives, and the background information.
-        Identify the central argument or hypothesis that the study is examining.
-
-    2). Examine the Methodology and Methods:
-        Read closely at the research design, whether it is experimental, observational, qualitative, or a combination of methods.
-        Check the sampling strategy and the size of the sample.
-        Review the methods of data collection and the instruments used for this purpose.
-        Think about any ethical issues and possible biases in the study.
-
-    3). Analyze the Results and Discussion:
-        Review how the results are presented, including any tables, graphs, and statistical analysis.
-        Evaluate the findings' validity and reliability.
-        Analyze whether the results support or contradict the research question and hypothesis.
-        Read the discussion section where the authors interpret their findings and their significance.
-
-    4). Consider the Limitations and Strengths:
-        Spot any limitations or potential weaknesses in the study.
-        Evaluate the strengths and contributions that the research makes.
-        Think about how generalizable the findings are to other populations or situations.
-
-    5). Assess the Writing and Organization:
-        Judge the clarity and structure of the report.
-        Consider the use of language, grammar, and the overall formatting.
-        Assess how well the arguments are logically organized and how coherent the report is.
-
-    6). Evaluate the Literature Review:
-        Examine how comprehensive and relevant the literature review is.
-        Consider how the study adds to or builds upon existing research.
-        Evaluate the timeliness and quality of the sources cited in the research.
-
-    7). Review the Conclusion and Implications:
-        Look at the conclusions drawn from the study and how well they align with the findings.
-        Think about the practical implications and potential applications of the research.
-        Evaluate the suggestions for further research or policy actions.
-
-    8). Overall Assessment:
-        Formulate an overall opinion about the research report's quality and thoroughness.
-        Consider the significance and impact of the findings.
-        Evaluate how the study contributes to its field of research.
-
-    9). Provide Constructive Feedback:
-        Offer constructive criticism and suggestions for improvement, where necessary.
-        Think about possible biases or alternative ways to interpret the findings.
-        Suggest ideas for future research or for replicating the study.
-
-    Do not provide description, explanations for your response.
-    Using the above review guidelines, write a detailed review report on the below research paper.
-    Research Paper: '{research_blog}'
-    """
-
-    if 'gemini' in gpt_providers:
-        try:
-            response = gemini_text_response(prompt)
-            return response
-        except Exception as err:
-            logger.error(f"Failed to get response from gemini: {err}")
-            response = mistral_text_response(prompt)
-            return response
-
-    elif 'openai' in gpt_providers:
-        try:
-            logger.info("Calling OpenAI LLM.")
-            response = openai_chatgpt(prompt)
-            return response
-        except Exception as err:
-            SystemError(f"Failed to get response from Openai: {err}")
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
ajaysi	b894bc0abb	fix: GHSA-426f-p74m-73fv — JWT JWKS issuer confusion auth bypass (CVSS 9.4) Pin issuer and JWKS URL at startup from CLERK_PUBLISHABLE_KEY. Validate token iss claim before any JWKS fetch. Add issuer= to jwt.decode() with verify_iss=True.	2026-06-05 12:07:22 +05:30
ajaysi	70542b32fc	feat: add linkedin and facebook feature flags, clean up dead code - Register 'linkedin' FeatureGroup with routers.linkedin and api.linkedin_image_generation routers - Register 'facebook' FeatureGroup with api.facebook_writer.routers:facebook_router - Add 'linkedin' and 'facebook' profiles to PROFILE_GROUP_MAP - Remove dead imports of linkedin_router, linkedin_image_router, and facebook_router from app.py (router manager handles via CORE_ROUTER_REGISTRY) - Add LINKEDIN and FACEBOOK keys to frontend FEATURE_KEYS - Add route priorities for /linkedin-writer and /facebook-writer - Change route gates from feature='social' to feature='linkedin' and feature='facebook' respectively	2026-06-05 12:07:22 +05:30
ajaysi	9a3d704c5c	feat: add backlinking feature flag following blog_writer pattern - Register 'backlinking' FeatureGroup in feature_registry.py with routers=routers.backlink_outreach:router - Add 'backlinking' profile to PROFILE_GROUP_MAP (core + backlinking) - Add backlink_outreach to OPTIONAL_ROUTER_REGISTRY with features={'all', 'backlinking'} - Remove direct import/include of backlink_outreach from app.py (router manager handles both 'all' and 'backlinking' modes) - Add BACKLINKING key to FEATURE_KEYS and route priority in frontend demoMode.ts - Change frontend route gate from feature='seo' to feature='backlinking' so ALWRITY_ENABLED_FEATURES=backlinking enables the route	2026-06-03 20:19:41 +05:30
ajaysi	8699ffc27d	fix: resolve remaining 5 QA audit findings (#3 , #8 , #10 , #11 , #12 ) #3 — Duplicate prospect handling: add_lead now checks (campaign_id, url) before insert; bulk_add_leads skips existing URLs. #8 — Atomic rate limiting: try_increment_* methods atomically check cap and increment in a single session; router uses these before send. #10 — Reply matching via Message-ID: sender generates Message-ID header, stored on OutreachAttempt; reply monitor parses In-Reply-To/References; poll_replies matches by message_id first, falls back to from_email. #11 — Save-to-campaign uses existing store results instead of re-running expensive deepDiscover. #12 — Lead status Literal type: Pydantic models enforce valid status values; backend validates via LEAD_VALID_STATUSES frozenset; frontend API typed as LeadStatus union.	2026-06-03 20:06:11 +05:30
ajaysi	259194c289	Merge remote-tracking branch 'origin/codex/add-atomic-idempotency-reservation-method' # Conflicts: # backend/routers/backlink_outreach.py # backend/services/backlink_outreach_models.py	2026-06-03 18:52:18 +05:30
ajaysi	2f93ae4891	Merge remote-tracking branch 'origin/codex/add-sender-email-validation-and-logging'	2026-06-03 18:50:53 +05:30
ي	bf22a3d318	Handle backlink outreach idempotency reservations	2026-06-03 18:49:14 +05:30
ajaysi	2a879a6e24	Merge remote-tracking branch 'origin/codex/update-compliance-requirements-for-outreach-send'	2026-06-03 18:49:07 +05:30
ajaysi	7749b4db0e	Merge remote-tracking branch 'origin/codex/refactor-backlink-outreach-services-for-async-support' # Conflicts: # backend/routers/backlink_outreach.py	2026-06-03 18:49:01 +05:30
ي	cbace3b752	Validate backlink outreach sender aliases	2026-06-03 18:48:17 +05:30
ي	98d4ac6dbd	Harden backlink outreach send policy	2026-06-03 18:33:11 +05:30
ي	55b7209554	Refactor backlink discovery HTTP calls	2026-06-03 18:28:40 +05:30
ajaysi	57e46a20f8	Merge remote-tracking branch 'origin/codex/update-backlink-outreach-for-campaign-validation'	2026-06-03 18:22:35 +05:30
ي	ec2f9151b8	Harden backlink lead campaign ownership	2026-06-03 18:19:16 +05:30
ي	40516e5c79	Secure backlink lead status updates	2026-06-03 18:16:10 +05:30
ajaysi	923fa671fe	feat: ContentGuardianAgent, onboarding UX, Team Activity action wiring, docs, agent help modal ContentGuardianAgent consolidation: - Merge 3 duplicate classes into single source in specialized/content_guardian.py - Watchdog audit_committee() with heuristic scoring, coverage gaps, overlaps, alerts - Remove misleading rejection_rate() helper; use acceptance_rate directly - Integrate audit + alerts + trend signals into today_workflow_service.py Team Activity page: - QualityAuditPanel: health ring, per-agent critiques, coverage gaps, overlaps - TrendSignalsPanel: opportunity cards with urgency/impact/coverage bars - AlertBanner: persistent dismiss via POST /alerts/{id}/mark-read - AgentHelpModal: dialog showing all 8 agents with descriptions, tools, schedule - QualityAuditPanel action buttons: Fill gap -> /content-planning, Resolve overlap, View CTA on alerts/issues - TrendSignalsPanel action buttons: Create content from this trend -> /blog-writer with trend context state Onboarding system: - Step 4 validation: no auto-pass via basic_ready; requires persona data or explicit progression - Step 5 validation: logs warning on auto-pass without integration data - OnboardingCompletionService: single DB session, transactional task creation, upsert pattern - Business-without-website: nullable website_url on SIFIndexingTask and MarketTrendsTask - DeepCompetitorAnalysisExecutor: 5-min timeout, 10-competitor cap, asyncio.wait_for - Persona generation: async with 30s timeout, falls back to scheduler - OnboardingProgressService.reset_onboarding(): resets session + pauses all DB tasks - OnboardingControlService.reset_onboarding(): also cancels APScheduler jobs - FinalStep TaskSchedulingPanel: shows scheduled/failed tasks after completion, 8s auto-redirect - onboarding_completed agent activity event logged to feed Documentation: - docs-site/features/onboarding/: overview, steps, scheduler-tasks, technical-reference (4 pages) - docs-site/mkdocs.yml: added Onboarding System nav section - docs-site/features/sif-agents/: overview, agent-directory, committee-system, content-guardian (4 pages) - docs-site/features/team-activity/: overview, quality-audit, trend-signals, alert-system (4 pages) - docs-site/features/todays-workflow/: updated overview, technical-architecture, workflow-guide, api-reference	2026-06-01 12:24:31 +05:30
ajaysi	9b472f1c18	debug: add startup log to suggest-prompts endpoint to diagnose timeout	2026-05-30 11:08:43 +05:30
ajaysi	ce2b8eefba	fix: persist sectionImages to localStorage immediately in onImageGenerated callback, add restore/effect with debug logging	2026-05-30 08:22:04 +05:30
ajaysi	64f1f88cdd	feat: image generation overhaul (model-aware text, dim clamping, \.30 pricing), event-driven dashboard cache invalidation, SEO insights (AI visibility, GSC, keyword gap), YouTube OAuth/publish, blog writer & content planning improvements, scheduler monitoring updates	2026-05-30 07:58:22 +05:30
ajaysi	aaf94049da	feat: validate podcast cost estimation accuracy, document per-token costs, and fix subscription/plan enforcement Issue #543 — Validate Estimated Cost Accuracy (UI vs Backend) Backend: - cost_estimator.py uses pricing catalog (APIProviderPricing) as single source of truth - All 7 cost components: analysis, research (search+LLM), script, TTS, voice clone, avatar, video - initialize_default_pricing() runs on every app startup for auto-sync Frontend cost estimation fixes: - Added missing analysisCost, scriptCost, voiceCloneCost to PodcastEstimate type - toPodcastEstimate() now extracts all 7 backend fields (was dropping 3) - headerCostEst maps analysisCost->Analyze, scriptCost->Write, voiceCloneCost->Produce - EstimateCard shows 5 chips: Analysis, Research, Script, Voice(TTS+clone), Visuals(avatar+video) - Chip sum now equals backend total for all configurations Subscription & plan fixes: - Removed Stripe re-verification from checkSubscription() (downgrade regression fix #539) - Added verifyCheckoutRef pattern for reliable mount-time checkout polling - One-time Stripe sync effect with pending_subscription_change flag for Customer Portal returns - Free plan limits: stability_calls 3->10, audio_calls 5->10 (supports 2 podcasts) - Image enforcement uses actual provider (GPT_PROVIDER), not hardcoded Stability - Billing/pricing pages bypass onboarding check in ProtectedRoute - Gradient buttons + loading spinner on plan chip in UserBadge - Added metadata-based Stripe lookup fallback (Issue #538) Documentation: - TESTING_GUIDE.md: comprehensive testing instructions for non-technical testers - Free plan limits, usage tracking, cost estimation formulas - 10 test cases for UI verification - Troubleshooting guide - Quick-reference cost formulas with all default rates Cleanup: removed legacy ToBeMigrated directory (70+ files, ~22K LOC) GSC Brainstorm: service, hook, modal, and UI components for blog topic brainstorming	2026-05-27 08:46:38 +05:30
ajaysi	96fa469fe8	fix: add metadata-based Stripe customer lookup in verify-checkout for reliable post-subscription plan detection (#538 )	2026-05-26 15:25:05 +05:30
ajaysi	6331671c6a	docs(seo-dashboard): add implementation notes and API endpoint alignment (#537 )	2026-05-25 20:57:48 +05:30
ajaysi	a1a1abb8fd	fix: redact sensitive API key count from log in ai_refresh (#536 )	2026-05-25 20:57:48 +05:30
ajaysi	c47b452943	fix: redact sensitive API key names from log in ai_refresh (#535 )	2026-05-25 20:57:47 +05:30
ي	b805595e3c	docs(seo-dashboard): add recent SEO enhancement coverage from code + docs review	2026-05-25 20:56:27 +05:30
ajaysi	d889e83d6a	fix: harden podcast media path resolution and URL parsing (#530 )	2026-05-25 20:31:08 +05:30
ajaysi	45e9de4a31	fix: replace MD5 with SHA256 in cache key derivation (#528 )	2026-05-25 20:31:07 +05:30
ajaysi	03622fca6e	fix: use canonicalized resolved_video_path in transform serving (#527 )	2026-05-25 20:31:07 +05:30
ajaysi	aba41bc1bf	fix: sanitize target_level in normalize_audio FFmpeg command (#526 )	2026-05-25 20:31:00 +05:30
ي	d0f0c25cf3	Potential fix for code scanning alert no. 87: Clear-text logging of sensitive information Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:41:16 +05:30
ي	0c48e2e0bf	Potential fix for code scanning alert no. 85: Clear-text logging of sensitive information Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:39:49 +05:30
ي	c6c118e7b8	Potential fix for code scanning alert no. 128: Uncontrolled data used in path expression Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:33:13 +05:30
ي	56b2f3afcf	Potential fix for code scanning alert no. 134: Use of a broken or weak cryptographic hashing algorithm on sensitive data Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:29:39 +05:30
ي	8000d21a05	Potential fix for code scanning alert no. 139: Uncontrolled data used in path expression Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:25:28 +05:30
ي	6aca86f087	Potential fix for code scanning alert no. 29: Uncontrolled command line Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2026-05-25 17:24:15 +05:30
				`@@ -1,2 +0,0 @@`
				`1). Replace Firecrawl with scrapy or crawlee : https://crawlee.dev/python/docs/introduction`