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
305 changed files with 19445 additions and 31579 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_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/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/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/ToBeMigrated/ai_writers/youtube_writers/README
+++ b/ToBeMigrated/ai_writers/youtube_writers/README
@@ -1,225 +0,0 @@
 YouTube Description Generator with SEO optimization features. Here's a summary of the improvements I've made:
 1. Added SEO Optimization Features
 Primary and Secondary Keywords:
 Renamed the original keywords field to "Primary Keywords" for clarity
 Added a new field for "Secondary Keywords" in the SEO Optimization tab
 Updated the prompt generation to include both primary and secondary keywords
 Keyword Density Checker:
 Added a new calculate_keyword_density function that:
 Counts occurrences of each keyword in the text
 Calculates the density as a percentage of total words
 Returns a formatted string with density for each keyword
 Character Counter and SEO Score:
 Added a character counter that displays the total length of the description
 Created a comprehensive calculate_seo_score function that evaluates:
 Text length (optimal is between 200-5000 characters)
 Keyword placement in the first 100 characters
 Keyword density (optimal is between 0.5-2.5%)
 Presence of call-to-action phrases
 Inclusion of hashtags
 Presence of links
 Returns a percentage score based on these factors
 Improved User Interface
 Tabbed Interface:
 Organized the interface into three tabs: "Basic Info", "SEO Optimization", and "Advanced Options"
 This makes the interface cleaner and more focused
 Enhanced Input Fields:
 Added more descriptive help text for each field
 Improved field organization and grouping
 Preview Options:
 Added tabs for different views of the generated description:
 "Formatted" - Shows the description with proper formatting
 "Plain Text" - Shows the raw text for copying
 "SEO Analysis" - Shows the SEO metrics and score
 Download Option:
 Added a download button to save the description as a text file
 Improved Prompt Generation
 Dynamic Prompt Building:
 Restructured the prompt generation to be more dynamic
 Only includes sections that are relevant based on user input
 Provides more specific instructions when additional information is available
 Template Support:
 Added support for different description templates
 Includes a custom template option for advanced users
 These enhancements make the YouTube Description Generator much more useful for content creators by providing:
 Better SEO optimization
 More detailed analysis of the generated content
 A more organized and user-friendly interface
 More customization options
 The tool now helps creators not only generate descriptions but also evaluate and optimize them for better performance on YouTube.
 YouTube Title Generator with the following features:
 Character Counter:
 Tracks the length of each generated title
 Indicates if the title is within the optimal length range (50-60 characters)
 Provides visual feedback with success/warning messages
 Clickbait Detector:
 Contains a comprehensive list of clickbait phrases
 Calculates a clickbait score based on the presence of these phrases
 Provides clear visual feedback about clickbait detection
 SEO Score:
 Calculates a score out of 10 based on various SEO elements
 Considers title length, numbers, question marks, colons, and brackets
 Provides visual feedback about the SEO score
 User Interface Improvements:
 Displays each title in an expandable section
 Shows detailed analysis for each title
 Includes a copy button for easy title copying
 Provides visual indicators (✅, ⚠️, ❌) for quick assessment
 Script Structure Templates
 I've expanded the script structure options from just 3 to 14 different formats:
 Problem-Solution: Identifies a problem and presents your solution
 Before-After-Bridge: Shows the problem, solution, and transformation
 Hook-Problem-Solution-Call to Action: Attention-grabbing format with clear problem, solution, and call to action
 Compare and Contrast: Compares different options or approaches
 Step-by-Step Tutorial: Detailed instructions broken down into clear steps
 Case Study: Examines a specific example or scenario in detail
 Interview Format: Structured as an interview with questions and answers
 Review Format: Evaluates a product, service, or topic with pros and cons
 Vlog Format: Personal, conversational style documenting experiences
 Educational Format: Focused on teaching a specific concept or skill
 Entertainment Format: Engaging, fun-focused content with humor or excitement
 Additional Improvements
 Structure Descriptions: Added helpful descriptions for each script structure to help users understand which format best suits their content.
 Advanced Options: Added an expandable section with customizable options:
 Attention-grabbing hooks
 Call-to-action elements
 Viewer engagement prompts
 Suggested timestamps
 Visual cues/transitions with different style options
 Enhanced Script Generation:
 Structure-specific instructions for each template
 Visual cue instructions for better video production
 Improved prompt engineering for more natural, conversational scripts
 Better User Experience:
 Progress bar during generation
 Tabbed preview with formatted and plain text views
 Download button for saving scripts
 Improved error handling
 More Use Cases: Added additional use cases like News Coverage, How-To Guides, Product Demonstrations, Travel Videos, Cooking/Recipe Videos, Gaming Content, and Tech Reviews.
 These enhancements make the YouTube Script Generator much more powerful and flexible, allowing content creators to generate scripts tailored to their specific needs and content types. The structure-specific instructions ensure that each script follows best practices for its format, resulting in more professional and engaging content.
 1. Enhanced Engagement Hooks
 I've added a variety of engagement hook options that users can select to include in their scripts:
 Question Hook: Start with a thought-provoking question
 Story Hook: Begin with a brief, relevant story or anecdote
 Statistic Hook: Open with an interesting statistic or fact
 Controversy Hook: Present a controversial statement to spark interest
 Promise Hook: Make a promise about what viewers will learn
 Scenario Hook: Describe a relatable scenario
 Mystery Hook: Create a sense of mystery or intrigue
 Quote Hook: Start with a relevant quote from an expert
 2. Community Interaction Points
 I've added several options for community interaction that can be included in the script:
 Comment Prompt: Ask viewers to share experiences in comments
 Poll Suggestion: Suggest creating a poll for viewers
 Question for Comments: Pose a specific question for comments
 Challenge: Challenge viewers to try something and report back
 Tag Friends: Encourage tagging friends who might benefit
 Share Request: Ask viewers to share the video
 Community Post: Mention creating a community post
 Live Stream Teaser: Tease an upcoming live stream
 3. Script Export Options
 I've implemented a comprehensive export system with multiple format options:
 Text (.txt): Simple text format
 Markdown (.md): For platforms that support markdown
 HTML (.html): Web-friendly format
 JSON (.json): Structured data format
 Subtitles (SRT): Basic subtitle format for video editing
 Additional export features include:
 Custom filename option
 Copy to clipboard functionality
 Formatted and plain text views of the script
 Download button with the selected format
 UI Improvements
 Added a new "Engagement & Export" tab to organize the new features
 Improved script display with tabs for formatted and plain text views
 Added a subheader for export options
 Included additional export options that can be expanded
 These enhancements make the YouTube Script Generator more powerful and user-friendly, providing creators with more tools to engage their audience and export their content in various formats.
 1. YouTube Thumbnail Generator
 Added a dedicated tab with a "Coming Soon" notice
 Included a comprehensive description of the tool's features:
 Thumbnail concept generation based on video content
 Color scheme suggestions aligned with brand
 Layout recommendations for maximum click-through rate
 Best practices for thumbnail design
 Text placement suggestions for readability
 Added a placeholder image to visually represent the upcoming feature
 2. YouTube Tags Generator
 Created a tab with a "Coming Soon" notice
 Provided a detailed description of the tool's capabilities:
 Relevant tag generation based on video content
 Trending tag suggestions to increase visibility
 Tag combination recommendations
 Tag research tools for finding popular keywords
 Recommendations for tag placement and usage
 Added a placeholder image for visual appeal
 3. YouTube End Screen Generator
 Added a tab with a "Coming Soon" notice
 Included a description of the tool's features:
 End screen template generation based on video type
 Strategic CTA placement recommendations
 Video playlist promotion suggestions
 Best practices for end screen design
 Cross-promotion opportunity recommendations
 Added a placeholder image to represent the upcoming feature
 4. YouTube Playlist Description Generator
 Created a tab with a "Coming Soon" notice
 Provided a description of the tool's capabilities:
 Engaging playlist description generation
 SEO optimization recommendations for playlists
 Playlist organization suggestions
 Best practices for playlist metadata
 Recommendations for playlist thumbnails and titles
 Added a placeholder image for visual appeal
 5. Additional "More Tools" Tab
 Added an extra tab for future tools
 Included a list of potential future features:
 YouTube Analytics Insights
 Channel Trailer Generator
 Video Series Planner
 YouTube Shorts Script Generator
 Community Post Generator
 Added a call for user suggestions for new tools
 Included a placeholder image for visual appeal
 Each tool tab follows a consistent format with:
 A clear title with an emoji for visual identification
 A "Coming Soon" notice using Streamlit's info component
 A detailed description of the tool's features
 A placeholder image to represent the upcoming feature
 This implementation provides users with a clear roadmap of upcoming features while maintaining the existing functionality of the YouTube AI Writer. The "coming soon" state allows you to gauge user interest in these features before fully implementing them.
 TBD:
 Allow alwrity end users to connect their youtube accounts to fetch their youtube data for analytics and then generate YT related content based on their data and needs:
 1). https://developers.google.com/youtube/reporting/v1/code_samples/python
 2). https://github.com/youtube/api-samples/blob/master/python/yt_analytics_report.py
 3). https://developers.google.com/youtube/reporting/guides/authorization/server-side-web-apps#python
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/README_Thumbnail_Generator.md
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/README_Thumbnail_Generator.md
@@ -1,96 +0,0 @@
 # YouTube Thumbnail Generator
 A powerful AI-powered tool for creating engaging, click-worthy thumbnails for your YouTube videos.
 ## Overview
 The YouTube Thumbnail Generator is a specialized module within the AI Writer suite that helps content creators design eye-catching thumbnails optimized for YouTube. Using advanced AI image generation technology, this tool creates custom thumbnails based on your video content, target audience, and style preferences.
 ## Features
 ### 1. AI-Powered Thumbnail Generation
 - **Concept Generation**: Automatically generates multiple thumbnail concept ideas based on your video title, description, and target audience
 - **Visual Design**: Creates high-quality thumbnail images using state-of-the-art AI image generation
 - **Style Customization**: Choose from various style preferences including bold, clean, colorful, dark, professional, playful, retro, and modern
 ### 2. Advanced Customization Options
 - **Aspect Ratio Selection**: Choose from standard YouTube ratios (16:9, 1:1, 4:3, 9:16)
 - **Text Overlay Options**: Add and customize text overlays with different styles
 - **Image Style Selection**: Choose from photorealistic, artistic, cartoon/anime, sketch/drawing, digital art, or 3D render
 - **Focus Selection**: For photorealistic images, specify focus areas like portraits, objects, motion, or wide-angle
 ### 3. Thumbnail Editing
 - **AI-Powered Editing**: Make changes to your generated thumbnails using natural language instructions
 - **Iterative Refinement**: Continue editing until you're satisfied with the result
 - **Preserve Original**: Keep both original and edited versions of your thumbnails
 ### 4. Thumbnail Analysis
 - **AI Analysis**: Get feedback on your thumbnail's effectiveness
 - **Improvement Suggestions**: Receive specific recommendations to enhance your thumbnail's impact
 - **Best Practices**: Learn about visual hierarchy, text readability, emotional impact, and click-worthiness
 ### 5. User-Friendly Interface
 - **Tabbed Interface**: Organize your workflow with intuitive tabs for basic info and style settings
 - **Concept Tabs**: View and select from multiple thumbnail concepts
 - **Real-time Preview**: See your generated thumbnails immediately
 - **Download Options**: Easily download your thumbnails in high resolution
 ## How to Use
 ### Step 1: Enter Basic Information
 - Provide your video title and description
 - Specify your target audience
 - Select your content type (tutorial, vlog, review, etc.)
 ### Step 2: Customize Style Preferences
 - Choose your preferred thumbnail style
 - Select the number of concepts to generate
 - Pick your desired aspect ratio
 - Configure text overlay options
 ### Step 3: Generate Thumbnail Concepts
 - Click "Generate Thumbnail Concepts" to create multiple thumbnail ideas
 - Review each concept in the provided tabs
 - Select the concept you'd like to develop further
 ### Step 4: Generate and Customize Your Thumbnail
 - Click "Generate Image" for your selected concept
 - Use the editing tools to refine your thumbnail
 - Apply changes using natural language instructions
 - Download your final thumbnail when satisfied
 ### Step 5: Analyze Your Thumbnail
 - Use the "Analyze Thumbnail" feature to get AI feedback
 - Review suggestions for improvement
 - Make additional edits based on the analysis
 ## Technical Details
 The Thumbnail Generator uses:
 - **Gemini AI**: For high-quality image generation and editing
 - **Advanced Prompt Engineering**: To ensure consistent and relevant results
 - **Retry Mechanism**: Handles service overloads with exponential backoff
 - **Session State Management**: Preserves your work across page refreshes
 ## Tips for Best Results
 1. **Be Specific**: Provide detailed video descriptions to help the AI understand your content
 2. **Target Your Audience**: Specify your audience demographics and interests
 3. **Choose Appropriate Style**: Select a style that matches your channel's branding
 4. **Use Keywords**: Add relevant keywords to enhance the AI's understanding
 5. **Iterate**: Don't hesitate to generate multiple concepts and make edits
 6. **Analyze**: Use the analysis feature to get objective feedback on your thumbnails
 ## Requirements
 - Internet connection for AI services
 - Modern web browser
 - No additional software installation required
 ## Support
 For technical issues or feature requests, please contact our support team or submit an issue on our GitHub repository.
 ---
 *The YouTube Thumbnail Generator is part of the AI Writer suite, designed to help content creators streamline their workflow and produce high-quality content.* 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/README_endScreen.md
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/README_endScreen.md
@@ -1,108 +0,0 @@
 End Screen Generator feature for YouTube videos.
 ## Step 1: Understanding End Screens
 YouTube end screens are the final elements shown at the end of a video that encourage viewers to take action, such as subscribing, watching another video, or visiting a website. They typically include:
 1. Call-to-action elements (subscribe button, playlists, other videos)
 2. Visual elements (background image, branding)
 3. Text overlays (promotional messages, channel name)
 4. Layout options (different templates for different purposes)
 ## Step 2: Required User Inputs
 Based on the thumbnail generator and YouTube end screen requirements, we'll need these inputs:
 1. **Basic Video Information**:
   - Video title
   - Video description
   - Target audience
   - Content type (tutorial, vlog, review, etc.)
 2. **End Screen Purpose**:
   - Primary goal (drive subscriptions, promote playlist, promote next video, etc.)
   - Secondary goal (if applicable)
 3. **Visual Style Preferences**:
   - Color scheme
   - Style (minimal, bold, branded, etc.)
   - Brand elements to include (logo, channel name, etc.)
 4. **Content Elements**:
   - Number of elements to include (1-4)
   - Types of elements (subscribe button, playlist, video, website)
   - Text for each element
 5. **Advanced Settings**:
   - Background style (solid color, gradient, image, etc.)
   - Animation preferences
   - Custom branding elements
 ## Step 3: Implementation Plan
 Let's create a new module called `end_screen_generator.py` in the same directory as the thumbnail generator. Here's how we'll structure it:
 1. **Functions**:
   - `generate_end_screen_concepts`: Generate end screen design concepts
   - `generate_end_screen_design`: Create visual end screen designs
   - `analyze_end_screen`: Provide feedback on end screen effectiveness
   - `write_yt_end_screen`: Main UI function
 2. **User Interface**:
   - Tabs for different sections (Basic Info, Style & Elements, Preview)
   - Input fields for all required information
   - Preview section to show generated end screens
   - Download options for the end screen designs
 ### End Screen Generator Features
 1. **Comprehensive User Inputs**:
   - Basic video information (title, description, target audience)
   - End screen purpose (subscribe, next video, playlist, website, social media)
   - Visual style preferences (modern, minimalist, bold, playful, elegant)
   - Content elements (text, CTAs, visual elements)
   - Advanced settings (image style, focus, keywords)
 2. **AI-Powered Generation**:
   - Concept generation with detailed descriptions
   - Image generation with style customization
   - Thumbnail analysis for effectiveness
   - Image editing capabilities
 3. **User Interface**:
   - Tabbed interface for multiple end screen concepts
   - Visual preview of generated end screens
   - Download options for all generated images
   - Edit functionality for refining designs
 4. **Integration with Existing Tools**:
   - Reuses the image generation and editing functions from the thumbnail generator
   - Consistent UI/UX with other YouTube tools
   - Proper error handling and logging
 ### How to Use the End Screen Generator
 1. **Access the Tool**:
   - Select "End Screen Generator" from the YouTube tools menu
   - The tool is now active and ready to use
 2. **Generate End Screens**:
   - Enter your video details (title, description, target audience)
   - Select the primary purpose of your end screen
   - Choose your preferred visual style
   - Select content elements to include
   - Optionally customize advanced settings
   - Click "Generate End Screen Concepts"
 3. **Review and Customize**:
   - Browse through the generated concepts in tabs
   - Generate images for concepts you like
   - Edit the generated images with specific instructions
   - Download your final end screen designs
 4. **Analyze Effectiveness**:
   - Get AI-powered analysis of your end screen designs
   - Receive feedback on visual hierarchy, text readability, and more
 The End Screen Generator is now fully integrated into the YouTube AI Writer and ready to use. Would you like me to make any adjustments or enhancements to the implementation?
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/README_yt_shorts_scripts.md
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/README_yt_shorts_scripts.md
@@ -1,273 +0,0 @@
 # YouTube Shorts Script Generator 📱
 Welcome to the ultimate YouTube Shorts Script Generator! This powerful tool helps you create engaging, perfectly-timed scripts optimized for the vertical short-form video format. Whether you're a beginner or an experienced creator, this guide will help you make the most of our script generator.
 ## 🎯 Why Use This Tool?
 - Create attention-grabbing scripts in seconds
 - Optimize for vertical viewing (9:16 aspect ratio)
 - Get perfect timing for 15-60 second videos
 - Include strategic hooks that stop the scroll
 - Generate scripts that work even on mute
 - Receive instant script analysis and optimization tips
 ## 📋 Features Overview
 ### 1. Core Elements Tab
 #### Hook Types
 Choose from 8 proven hook styles:
 - **Question Hook** - Start with an intriguing question
 - **Statistic Hook** - Lead with a surprising fact
 - **Challenge Hook** - Present an engaging challenge
 - **Tutorial Hook** - Jump straight into the how-to
 - **Transformation Hook** - Show before/after concept
 - **Trend Hook** - Leverage current trends
 - **Story Hook** - Begin with a micro-story
 - **Controversy Hook** - Start with a surprising statement
 #### Content Types
 Select from various formats:
 - Tutorial/How-to
 - Life Hack
 - Entertainment
 - Educational
 - Trend
 - Story
 - Challenge
 - Review
 #### Tone Options
 Match your brand voice:
 - Energetic
 - Professional
 - Casual
 - Humorous
 - Dramatic
 - Inspirational
 ### 2. Style & Format Tab
 #### Duration Control
 - Adjustable from 15 to 60 seconds
 - Optimal timing suggestions
 - Pattern interrupt reminders
 #### Format Options
 - Captions for accessibility
 - Text overlay positioning
 - Sound effect suggestions
 - Vertical framing notes
 #### Language Support
 Multiple languages including:
 - English
 - Spanish
 - French
 - German
 - Italian
 - Portuguese
 - Russian
 - Japanese
 - Korean
 - Chinese
 ### 3. Preview & Export Tab
 #### Script Analysis
 Get instant feedback on:
 - Estimated duration
 - Pattern interrupt count
 - Text overlay optimization
 - Overall engagement score
 - Script optimization metrics
 #### Export Options
 Download your script in various formats:
 - Text format
 - Markdown
 - Shot List
 - Storyboard
 ## 🎬 How to Create the Perfect Shorts Script
 ### Step 1: Plan Your Content
 1. **Choose Your Topic**
   - Keep it focused and specific
   - Think about what's trending
   - Consider your target audience
 2. **Select Your Hook**
   - Match the hook to your content type
   - Consider what would make YOU stop scrolling
   - Think about the first 2 seconds
 ### Step 2: Generate Your Script
 1. Fill in the Core Elements:
   - Main topic/concept
   - Target audience
   - Hook type
   - Content type
   - Tone/style
 2. Customize Style & Format:
   - Set your desired duration
   - Choose language
   - Select formatting options
   - Enable/disable features as needed
 ### Step 3: Optimize Your Script
 Use the Analysis tab to:
 - Check estimated duration
 - Review pattern interrupts
 - Verify text overlay count
 - Aim for an optimization score above 80%
 ## 📈 Best Practices for Shorts Scripts
 ### Timing & Structure
 - **First 2 seconds**: Hook viewer attention
 - **3-50 seconds**: Main content with pattern interrupts
 - **Last 10 seconds**: Clear call-to-action
 - Add pattern interrupts every 3-5 seconds
 ### Text & Visuals
 - Center text in middle 50% of vertical frame
 - Keep text concise and readable
 - Use contrasting colors for text
 - Include visual transitions
 - Consider viewing without sound
 ### Engagement Tips
 - Start with your strongest point
 - Use pattern interrupts to maintain interest
 - End with a clear call-to-action
 - Include viewer prompts when relevant
 ## 🎯 Script Structure Template
 ```
 1. HOOK (0-2 seconds)
   - Visual: [What viewers see]
   - Text: [On-screen text]
   - Audio: [Voice/sound]
   - Framing: [Camera angle/composition]
 2. MAIN CONTENT (3-50 seconds)
   - Key Points
   - Pattern Interrupts
   - Visual Elements
   - Text Overlays
 3. CALL TO ACTION (last 10 seconds)
   - Clear instruction
   - Engagement prompt
   - Next steps
 ```
 ## 🚀 Pro Tips
 1. **Hook Optimization**
   - Test different hook types
   - Keep hooks under 2 seconds
   - Make them visually striking
 2. **Content Pacing**
   - Use quick cuts
   - Keep segments short
   - Maintain visual interest
 3. **Text Overlay Best Practices**
   - Use readable fonts
   - Keep text brief
   - Position strategically
 4. **Sound Strategy**
   - Design for silent viewing
   - Add captions when needed
   - Use sound effects strategically
 ## 🔍 Script Analysis Guide
 Understanding your script analysis:
 - **Duration Score**
  - Green: Perfect length
  - Orange: Slightly long/short
  - Red: Needs significant timing adjustment
 - **Pattern Interrupts**
  - Aim for 1 every 5 seconds
  - Include visual transitions
  - Mix up shot types
 - **Text Overlay Score**
  - Minimum 3 overlays recommended
  - Space them throughout video
  - Keep them readable
 - **Overall Optimization**
  - 90-100%: Excellent
  - 80-89%: Good
  - Below 80%: Needs improvement
 ## 🎨 Export Options Explained
 1. **Text Format**
   - Clean, simple script
   - Easy to copy/paste
   - Basic formatting
 2. **Markdown**
   - Formatted sections
   - Easy to read
   - Good for documentation
 3. **Shot List**
   - Detailed scene breakdown
   - Technical instructions
   - Timing markers
 4. **Storyboard**
   - Scene-by-scene format
   - Visual instructions
   - Technical notes
 ## 🆘 Troubleshooting
 Common issues and solutions:
 1. **Script Too Long**
   - Reduce main points
   - Shorten sentences
   - Speed up pacing
 2. **Low Optimization Score**
   - Add more pattern interrupts
   - Include more text overlays
   - Strengthen hook
   - Add clear CTA
 3. **Weak Hook**
   - Try different hook types
   - Make it more surprising
   - Focus on visual impact
 Remember: The best Shorts scripts are concise, engaging, and optimized for vertical viewing. Use this tool to create scripts that grab attention and keep viewers watching!
 ## 🔄 Regular Updates
 We regularly update our tool with:
 - New hook types
 - Trending formats
 - Additional languages
 - Enhanced analysis features
 - New export options
 Stay tuned for more features and improvements!
 ---
 Happy Creating! 🎥 ✨
 For more YouTube content creation tools, check out our other AI-powered generators in the YouTube AI Writer suite. 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/init.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/init.py
@@ -1,5 +0,0 @@
 """
 YouTube AI Writer Modules
 This package contains modular components for the YouTube AI Writer functionality.
 """ 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/channel_trailer_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/channel_trailer_generator.py
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/community_post_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/community_post_generator.py
@@ -1,591 +0,0 @@
 """
 YouTube Community Post Generator Module
 This module provides sophisticated functionality for generating engaging community posts
 with AI-powered content suggestions, engagement analysis, and timing optimization.
 """
 import streamlit as st
 import time
 import logging
 import random
 from datetime import datetime, timedelta
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 import re
 from textblob import TextBlob
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_community_post_generator')
 def generate_community_post(post_type, main_topic, target_audience, tone_style, 
                          content_purpose, channel_niche, include_emoji=True,
                          include_hashtags=True, include_poll=False, 
                          include_image_prompt=False, include_timing_suggestion=True,
                          max_length=None, language="English"):
    """Generate an AI-optimized community post with engagement features."""
    # Create a custom system prompt for community post generation
    system_prompt = f"""You are a YouTube Community Post expert specializing in creating highly engaging, 
    conversion-optimized posts that drive channel growth and viewer interaction.
    Focus on creating posts that encourage meaningful engagement while maintaining the channel's voice.
    Write the entire post in {language}.
    Consider timing, audience psychology, and platform-specific best practices."""
    # Build post type-specific instructions
    post_instructions = {
        "Question": "Create an thought-provoking question that sparks discussion",
        "Poll": "Design a compelling poll with strategic options that drive engagement",
        "Behind the Scenes": "Share an authentic, exclusive glimpse into the content creation process",
        "Sneak Peek": "Tease upcoming content in an exciting way",
        "Channel Update": "Share channel news in an engaging format",
        "Milestone Celebration": "Celebrate achievements while engaging the community",
        "Content Preview": "Preview upcoming video content engagingly",
        "Fan Spotlight": "Highlight community members/comments",
        "Quick Tip": "Share a valuable tip related to your niche",
        "Discussion Starter": "Begin a meaningful community discussion"
    }
    # Build engagement hooks based on content purpose
    engagement_hooks = {
        "Build Hype": [
            "Create anticipation for upcoming content",
            "Use countdown elements",
            "Include exclusive previews"
        ],
        "Drive Discussion": [
            "Ask open-ended questions",
            "Present contrasting viewpoints",
            "Share controversial opinions"
        ],
        "Gather Feedback": [
            "Ask specific questions",
            "Create focused polls",
            "Request detailed responses"
        ],
        "Share Updates": [
            "Create excitement around news",
            "Include behind-the-scenes elements",
            "Add personal touches"
        ],
        "Boost Engagement": [
            "Include call-to-actions",
            "Create interactive elements",
            "Use engagement triggers"
        ]
    }
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Create a YouTube Community Post about **{main_topic}** with these specifications:
    **Core Elements:**
    - Post Type: {post_type} - {post_instructions.get(post_type, "Create an engaging post")}
    - Target Audience: {target_audience}
    - Tone/Style: {tone_style}
    - Content Purpose: {content_purpose}
    - Channel Niche: {channel_niche}
    - Language: {language}
    {"- Maximum Length: " + str(max_length) + " characters" if max_length else ""}
    **Required Elements:**
    {"- Include strategic emoji placement" if include_emoji else ""}
    {"- Include relevant hashtags" if include_hashtags else ""}
    {"- Include poll options" if include_poll else ""}
    {"- Include image prompt suggestions" if include_image_prompt else ""}
    {"- Include optimal posting time suggestion" if include_timing_suggestion else ""}
    **Engagement Hooks:**
    {" ".join(engagement_hooks.get(content_purpose, ["Create engaging content"]))}
    **Format the post with:**
    1. Main Content
    2. Engagement Elements
    3. Call-to-Action
    4. Additional Components (hashtags, etc.)
    **Remember:**
    - Keep the tone consistent with channel voice
    - Use psychology triggers for engagement
    - Include clear call-to-actions
    - Make it easy to respond to
    - Create shareable content
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def analyze_post_engagement(post_content):
    """Analyze a community post for engagement potential using advanced AI metrics."""
    analysis = {
        'engagement_score': 0,
        'emotional_triggers': 0,
        'call_to_action_strength': 0,
        'readability_score': 0,
        'hashtag_optimization': 0,
        'timing_recommendation': None,
        'sentiment_analysis': {},
        'virality_potential': 0,
        'audience_resonance': 0,
        'content_uniqueness': 0,
        'psychological_triggers': [],
        'improvement_suggestions': [],
        'engagement_patterns': {},
        'content_structure': {},
        'seo_optimization': 0
    }
    # Sentiment Analysis using TextBlob
    blob = TextBlob(post_content)
    analysis['sentiment_analysis'] = {
        'polarity': round((blob.sentiment.polarity + 1) * 50, 2),  # Convert to 0-100 scale
        'subjectivity': round(blob.sentiment.subjectivity * 100, 2),
        'tone': 'Positive' if blob.sentiment.polarity > 0 else 'Negative' if blob.sentiment.polarity < 0 else 'Neutral'
    }
    # Analyze emotional triggers with expanded vocabulary
    emotional_categories = {
        'excitement': ['excited', 'amazing', 'incredible', 'awesome', 'mind-blowing'],
        'curiosity': ['guess what', 'secret', 'revealed', 'discover', 'mystery'],
        'urgency': ['limited', 'hurry', 'soon', 'don\'t miss', 'last chance'],
        'social_proof': ['everyone', 'community', 'fans', 'you all', 'together'],
        'exclusivity': ['exclusive', 'special', 'limited', 'only', 'selected']
    }
    trigger_counts = {category: 0 for category in emotional_categories}
    for category, words in emotional_categories.items():
        trigger_counts[category] = sum(post_content.lower().count(word) for word in words)
    analysis['emotional_triggers'] = min(sum(trigger_counts.values()) * 15, 100)
    analysis['psychological_triggers'] = [cat for cat, count in trigger_counts.items() if count > 0]
    # Analyze call-to-action strength with pattern recognition
    cta_patterns = {
        'question_cta': r'\?',
        'direct_command': r'(?i)(comment|share|like|subscribe|follow)',
        'engagement_request': r'(?i)(let (me|us) know|tell (me|us)|what do you think)',
        'time_sensitive': r'(?i)(today|now|limited time|hurry)',
        'value_proposition': r'(?i)(learn|discover|find out|get|access)'
    }
    cta_strength = 0
    for pattern_type, pattern in cta_patterns.items():
        matches = len(re.findall(pattern, post_content))
        cta_strength += matches * 20
    analysis['call_to_action_strength'] = min(cta_strength, 100)
    # Content Structure Analysis
    analysis['content_structure'] = {
        'length_score': min(len(post_content.split()) / 5, 100),  # Optimal length analysis
        'paragraph_breaks': min(post_content.count('\n\n') * 20, 100),  # Readability through structure
        'emoji_balance': min(len(re.findall(r'[\U0001F300-\U0001F9FF]', post_content)) * 10, 100),  # Emoji usage score
        'formatting_score': min((post_content.count('*') + post_content.count('_')) * 5, 100)  # Text formatting score
    }
    # Virality Potential Analysis
    virality_factors = {
        'emotional_impact': analysis['emotional_triggers'],
        'shareability': analysis['content_structure']['length_score'],
        'uniqueness': random.randint(60, 100),  # Simulated uniqueness score
        'timeliness': 80 if any(word in post_content.lower() for word in ['new', 'breaking', 'update', 'just']) else 50
    }
    analysis['virality_potential'] = sum(virality_factors.values()) / len(virality_factors)
    # Audience Resonance Analysis
    resonance_factors = {
        'relevance': analysis['sentiment_analysis']['subjectivity'],
        'engagement_hooks': analysis['call_to_action_strength'],
        'emotional_connection': analysis['emotional_triggers']
    }
    analysis['audience_resonance'] = sum(resonance_factors.values()) / len(resonance_factors)
    # SEO Optimization
    seo_factors = {
        'hashtag_quality': analyze_hashtag_quality(post_content),
        'keyword_density': analyze_keyword_density(post_content),
        'url_presence': 100 if 'http' in post_content else 0,
        'mention_optimization': analyze_mentions(post_content)
    }
    analysis['seo_optimization'] = sum(seo_factors.values()) / len(seo_factors)
    # Engagement Pattern Analysis
    analysis['engagement_patterns'] = analyze_engagement_patterns(post_content)
    # Calculate overall engagement score with weighted components
    analysis['engagement_score'] = calculate_weighted_score({
        'emotional_triggers': (analysis['emotional_triggers'], 0.2),
        'call_to_action_strength': (analysis['call_to_action_strength'], 0.2),
        'virality_potential': (analysis['virality_potential'], 0.15),
        'audience_resonance': (analysis['audience_resonance'], 0.15),
        'seo_optimization': (analysis['seo_optimization'], 0.1),
        'sentiment_balance': (analysis['sentiment_analysis']['polarity'], 0.1),
        'content_structure': (sum(analysis['content_structure'].values()) / len(analysis['content_structure']), 0.1)
    })
    # Generate AI-powered improvement suggestions
    analysis['improvement_suggestions'] = generate_ai_suggestions(analysis)
    # Timing optimization
    analysis['timing_recommendation'] = get_optimal_posting_time(analysis)
    return analysis
 def analyze_hashtag_quality(content):
    """Analyze the quality and relevance of hashtags."""
    hashtags = re.findall(r'#\w+', content)
    if not hashtags:
        return 0
    score = 0
    score += min(len(hashtags), 5) * 20  # Optimal number of hashtags (1-5)
    score += sum(10 for tag in hashtags if 4 <= len(tag) <= 20)  # Length optimization
    score += 20 if len(set(hashtags)) == len(hashtags) else 0  # No duplicates
    return min(score, 100)
 def analyze_keyword_density(content):
    """Analyze keyword density and distribution."""
    words = content.lower().split()
    if not words:
        return 0
    word_freq = {}
    for word in words:
        if len(word) > 3:  # Ignore short words
            word_freq[word] = word_freq.get(word, 0) + 1
    if not word_freq:
        return 0
    # Calculate density score
    max_density = max(word_freq.values()) / len(words)
    return 100 if 0.01 <= max_density <= 0.04 else 50  # Optimal density between 1-4%
 def analyze_mentions(content):
    """Analyze the use of @mentions and their placement."""
    mentions = re.findall(r'@\w+', content)
    if not mentions:
        return 0
    score = 0
    score += min(len(mentions), 3) * 25  # Optimal number of mentions (1-3)
    score += 25 if mentions[0] in content.split()[:len(content.split())//2] else 0  # Early mention bonus
    return min(score, 100)
 def analyze_engagement_patterns(content):
    """Analyze patterns that typically drive engagement."""
    patterns = {
        'question_hooks': len(re.findall(r'\?', content)),
        'emotional_words': len(re.findall(r'\b(love|hate|amazing|awesome|incredible|excited)\b', content.lower())),
        'community_references': len(re.findall(r'\b(we|our|community|together|everyone)\b', content.lower())),
        'action_words': len(re.findall(r'\b(get|do|make|try|click|watch|share)\b', content.lower())),
        'urgency_triggers': len(re.findall(r'\b(now|today|limited|soon|hurry)\b', content.lower()))
    }
    return {k: min(v * 20, 100) for k, v in patterns.items()}
 def calculate_weighted_score(components):
    """Calculate weighted score from multiple components."""
    return sum(score * weight for (score, weight) in components.values())
 def generate_ai_suggestions(analysis):
    """Generate AI-powered improvement suggestions based on analysis."""
    suggestions = []
    if analysis['emotional_triggers'] < 70:
        suggestions.append({
            'category': 'Emotional Impact',
            'suggestion': 'Add more emotional triggers to increase engagement',
            'examples': ['amazing', 'incredible', 'exciting']
        })
    if analysis['call_to_action_strength'] < 70:
        suggestions.append({
            'category': 'Call-to-Action',
            'suggestion': 'Strengthen your call-to-action',
            'examples': ['Comment below', 'Share your thoughts', 'Let me know']
        })
    if analysis['virality_potential'] < 70:
        suggestions.append({
            'category': 'Virality',
            'suggestion': 'Increase viral potential by adding trending elements',
            'examples': ['Current trends', 'Popular hashtags', 'Timely topics']
        })
    if analysis['seo_optimization'] < 70:
        suggestions.append({
            'category': 'SEO',
            'suggestion': 'Optimize for better discovery',
            'examples': ['Strategic hashtags', 'Relevant keywords', 'Proper mentions']
        })
    return suggestions
 def get_optimal_posting_time(analysis):
    """Determine optimal posting time based on content analysis."""
    current_hour = datetime.now().hour
    # Factor in content type and engagement patterns
    if analysis['sentiment_analysis']['tone'] == 'Positive' and analysis['virality_potential'] > 70:
        prime_times = {
            'Morning Rush': (8, 10),
            'Lunch Break': (12, 14),
            'Evening Prime': (18, 21)
        }
    else:
        prime_times = {
            'Mid-Morning': (10, 12),
            'Afternoon': (14, 16),
            'Late Evening': (20, 22)
        }
    # Find next available prime time
    for time_slot, (start, end) in prime_times.items():
        if start <= current_hour <= end:
            return f"Post now ({time_slot})"
        elif current_hour < start:
            return f"Schedule for {time_slot} ({start}:00 - {end}:00)"
    return "Schedule for tomorrow morning (8:00 - 10:00)"
 def write_yt_community_post():
    """Create a user interface for YouTube Community Post Generator."""
    st.write("Generate engaging community posts that drive interaction and channel growth.")
    # Initialize session state
    if "generated_post" not in st.session_state:
        st.session_state.generated_post = None
    if "post_history" not in st.session_state:
        st.session_state.post_history = []
    # Create tabs for different sections
    tab1, tab2, tab3 = st.tabs(["Post Creation", "Engagement Strategy", "Preview & Analytics"])
    with tab1:
        # Core elements
        main_topic = st.text_area("Main Topic/Message", 
                                placeholder="e.g., New video announcement, Channel update, Question for viewers")
        col1, col2 = st.columns(2)
        with col1:
            post_type = st.selectbox("Post Type", [
                "Question",
                "Poll",
                "Behind the Scenes",
                "Sneak Peek",
                "Channel Update",
                "Milestone Celebration",
                "Content Preview",
                "Fan Spotlight",
                "Quick Tip",
                "Discussion Starter"
            ])
            target_audience = st.text_input("Target Audience", 
                                          placeholder="e.g., Tech enthusiasts, Gamers, DIY lovers")
        with col2:
            content_purpose = st.selectbox("Content Purpose", [
                "Build Hype",
                "Drive Discussion",
                "Gather Feedback",
                "Share Updates",
                "Boost Engagement"
            ])
            tone_style = st.selectbox("Tone/Style", [
                "Casual",
                "Professional",
                "Excited",
                "Mysterious",
                "Humorous",
                "Informative"
            ])
        channel_niche = st.text_input("Channel Niche", 
                                    placeholder="e.g., Tech Reviews, Gaming, Education")
    with tab2:
        # Engagement options
        st.subheader("Engagement Elements")
        col1, col2 = st.columns(2)
        with col1:
            include_emoji = st.checkbox("Include Emojis", value=True)
            include_hashtags = st.checkbox("Include Hashtags", value=True)
            max_length = st.number_input("Maximum Length (characters)", 
                                       min_value=100, max_value=2000, value=500)
        with col2:
            include_poll = st.checkbox("Include Poll", value=False)
            include_image_prompt = st.checkbox("Include Image Suggestions", value=True)
            include_timing_suggestion = st.checkbox("Include Timing Suggestion", value=True)
        # Advanced options
        st.subheader("Advanced Options")
        language = st.selectbox("Language", [
            "English",
            "Spanish",
            "French",
            "German",
            "Italian",
            "Portuguese",
            "Russian",
            "Japanese",
            "Korean",
            "Chinese"
        ])
    with tab3:
        if st.session_state.generated_post:
            # Display the generated post
            st.subheader("Generated Community Post")
            # Create tabs for different views
            post_tab1, post_tab2, post_tab3 = st.tabs(["Preview", "Analytics", "History"])
            with post_tab1:
                st.markdown(st.session_state.generated_post)
                # Quick actions
                col1, col2 = st.columns(2)
                with col1:
                    if st.button("Copy to Clipboard"):
                        st.code(st.session_state.generated_post)
                        st.success("Post copied to clipboard!")
                with col2:
                    if st.button("Save to History"):
                        st.session_state.post_history.append({
                            'post': st.session_state.generated_post,
                            'timestamp': datetime.now(),
                            'type': post_type
                        })
                        st.success("Post saved to history!")
            with post_tab2:
                # Analyze the post
                analysis = analyze_post_engagement(st.session_state.generated_post)
                # Create expandable sections for different analysis categories
                with st.expander("📊 Overall Performance Metrics", expanded=True):
                    cols = st.columns(3)
                    with cols[0]:
                        score = analysis['engagement_score']
                        color = "red" if score < 60 else "orange" if score < 80 else "green"
                        st.markdown(f"### Overall Score: <span style='color: {color}'>{score:.1f}%</span>", 
                                  unsafe_allow_html=True)
                        # Sentiment Analysis
                        st.markdown("#### Sentiment Analysis")
                        st.metric("Polarity", f"{analysis['sentiment_analysis']['polarity']}%")
                        st.metric("Subjectivity", f"{analysis['sentiment_analysis']['subjectivity']}%")
                        st.info(f"Tone: {analysis['sentiment_analysis']['tone']}")
                    with cols[1]:
                        st.markdown("#### Engagement Metrics")
                        st.metric("Emotional Impact", f"{analysis['emotional_triggers']}%")
                        st.metric("CTA Strength", f"{analysis['call_to_action_strength']}%")
                        st.metric("Virality Potential", f"{analysis['virality_potential']:.1f}%")
                    with cols[2]:
                        st.markdown("#### Content Quality")
                        st.metric("Audience Resonance", f"{analysis['audience_resonance']:.1f}%")
                        st.metric("SEO Score", f"{analysis['seo_optimization']:.1f}%")
                        if analysis['timing_recommendation']:
                            st.success(f"📅 {analysis['timing_recommendation']}")
                with st.expander("🎯 Psychological Triggers & Patterns"):
                    col1, col2 = st.columns(2)
                    with col1:
                        st.markdown("#### Active Psychological Triggers")
                        if analysis['psychological_triggers']:
                            for trigger in analysis['psychological_triggers']:
                                st.markdown(f"✓ {trigger.title()}")
                        else:
                            st.info("No strong psychological triggers detected")
                    with col2:
                        st.markdown("#### Engagement Patterns")
                        patterns = analysis['engagement_patterns']
                        for pattern, score in patterns.items():
                            st.metric(pattern.replace('_', ' ').title(), f"{score}%")
                with st.expander("📝 Content Structure Analysis"):
                    col1, col2 = st.columns(2)
                    with col1:
                        structure = analysis['content_structure']
                        st.markdown("#### Structure Metrics")
                        for metric, score in structure.items():
                            st.metric(
                                metric.replace('_', ' ').title(),
                                f"{score:.1f}%"
                            )
                    with col2:
                        st.markdown("#### SEO Analysis")
                        st.metric("Hashtag Quality", f"{analyze_hashtag_quality(st.session_state.generated_post)}%")
                        st.metric("Keyword Density", f"{analyze_keyword_density(st.session_state.generated_post)}%")
                        st.metric("Mention Optimization", f"{analyze_mentions(st.session_state.generated_post)}%")
                # Show improvement suggestions
                if analysis['improvement_suggestions']:
                    with st.expander("💡 AI-Powered Suggestions", expanded=True):
                        for suggestion in analysis['improvement_suggestions']:
                            with st.container():
                                st.markdown(f"#### {suggestion['category']}")
                                st.info(suggestion['suggestion'])
                                if suggestion['examples']:
                                    st.markdown("**Examples:**")
                                    for example in suggestion['examples']:
                                        st.markdown(f"- {example}")
                # Add a refresh button for analysis
                if st.button("🔄 Refresh Analysis"):
                    st.rerun()
            with post_tab3:
                if st.session_state.post_history:
                    st.subheader("Previous Posts")
                    for i, post in enumerate(reversed(st.session_state.post_history)):
                        with st.expander(f"Post {len(st.session_state.post_history)-i}: "
                                       f"{post['type']} - "
                                       f"{post['timestamp'].strftime('%Y-%m-%d %H:%M')}"):
                            st.write(post['post'])
                else:
                    st.info("No post history yet. Save posts to see them here!")
    # Generate button
    if st.button("Generate Community Post"):
        if not main_topic:
            st.error("Please enter a main topic/message.")
            return
        with st.spinner("Generating community post..."):
            post = generate_community_post(
                post_type, main_topic, target_audience, tone_style,
                content_purpose, channel_niche, include_emoji,
                include_hashtags, include_poll, include_image_prompt,
                include_timing_suggestion, max_length, language
            )
            if post:
                st.session_state.generated_post = post
                st.success("✨ Post generated successfully! Check the 'Preview & Analytics' tab to view, analyze, and save your post.")
                st.rerun()
            else:
                st.error("Failed to generate post. Please try again.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/description_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/description_generator.py
@@ -1,404 +0,0 @@
 """
 YouTube Description Generator Module
 This module provides functionality for generating YouTube video descriptions.
 """
 import streamlit as st
 import time
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 def calculate_keyword_density(text, keywords):
    """Calculate the density of keywords in the text."""
    if not text or not keywords:
        return 0
    text = text.lower()
    keywords = [k.lower() for k in keywords]
    total_words = len(text.split())
    keyword_count = sum(text.count(k) for k in keywords)
    return (keyword_count / total_words) * 100 if total_words > 0 else 0
 def calculate_seo_score(text, keywords):
    """Calculate the SEO score of the description."""
    score = 0
    # Text length (optimal: 250-300 words)
    word_count = len(text.split())
    if 250 <= word_count <= 300:
        score += 3
    elif 200 <= word_count <= 350:
        score += 2
    elif 150 <= word_count <= 400:
        score += 1
    # Keyword presence
    text_lower = text.lower()
    keywords_lower = [k.lower() for k in keywords]
    keyword_count = sum(text_lower.count(k) for k in keywords_lower)
    if keyword_count >= 3:
        score += 3
    elif keyword_count >= 2:
        score += 2
    elif keyword_count >= 1:
        score += 1
    # Call to action phrases
    cta_phrases = ["subscribe", "like", "comment", "share", "follow", "check out", "visit", "learn more"]
    cta_count = sum(text_lower.count(phrase) for phrase in cta_phrases)
    if cta_count >= 2:
        score += 2
    elif cta_count >= 1:
        score += 1
    # Hashtags
    hashtag_count = text.count("#")
    if 3 <= hashtag_count <= 5:
        score += 2
    elif 1 <= hashtag_count <= 8:
        score += 1
    # Links
    link_count = text.count("http")
    if 1 <= link_count <= 3:
        score += 2
    elif link_count > 3:
        score += 1
    return min(score, 10)  # Cap at 10
 def generate_youtube_description(target_audience, main_points, tone_style, use_case, primary_keywords, 
                               secondary_keywords, language, seo_goals, include_timestamps=False, 
                               include_hashtags=False, include_social_handles=False):
    """Generate a YouTube description based on the provided parameters."""
    # Create a custom system prompt for YouTube description generation
    system_prompt = """You are a YouTube description expert specializing in creating engaging, SEO-optimized video descriptions.
    Your task is to generate YouTube video descriptions based on the provided information.
    Focus ONLY on creating descriptions that are optimized for YouTube, with proper formatting, keywords, and calls to action.
    Return ONLY the description text, without any additional commentary or explanations."""
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate a YouTube description for a video about **{main_points}** based on the following information:
    **Target Audience:** {target_audience}
    **Tone and Style:** {tone_style}
    **Use Case:** {use_case}
    **Language:** {language}
    **Primary Keywords:** {primary_keywords}
    **Secondary Keywords:** {secondary_keywords}
    **SEO Goals:** {seo_goals}
    **Additional Elements:**
    {"- Include timestamps for key sections." if include_timestamps else ""}
    {"- Include relevant hashtags." if include_hashtags else ""}
    {"- Include social media handles." if include_social_handles else ""}
    **Specific Instructions:**
    * Keep the description informative and engaging.
    * Use a conversational tone that matches the target audience.
    * Include relevant keywords naturally.
    * Add a call to action.
    * Keep the length between 250-300 words for optimal SEO.
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def write_yt_description():
    """Create a user interface for YouTube Description Generator."""
    st.write("Generate SEO-optimized YouTube video descriptions that drive engagement.")
    # Initialize session state for generated description if it doesn't exist
    if "generated_description" not in st.session_state:
        st.session_state.generated_description = None
    # Create tabs for different sections
    tab1, tab2, tab3 = st.tabs(["Basic Info", "SEO Optimization", "Advanced Options"])
    with tab1:
        # Basic information inputs
        main_points = st.text_area("Main Points/Keywords (comma-separated)", 
                                 placeholder="e.g., cooking tips, healthy recipes, quick meals")
        # Create columns for the other inputs
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            tone_style = st.selectbox("Tone/Style", 
                                    ["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
        with col2:
            target_audience = st.text_input("Target Audience", 
                                          placeholder="e.g., beginners, professionals, parents")
        with col3:
            use_case = st.selectbox("Use Case", 
                                  ["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
        with col4:
            language = st.selectbox("Language", ["English", "Spanish", "French", "German", "Italian", "Portuguese"])
    with tab2:
        # SEO optimization inputs
        primary_keywords = st.text_input("Primary Keywords (comma-separated)", 
                                       placeholder="e.g., cooking, recipes, healthy food")
        secondary_keywords = st.text_input("Secondary Keywords (comma-separated)", 
                                         placeholder="e.g., quick meals, budget cooking")
        seo_goals = st.multiselect("SEO Goals", 
                                 ["Increase Views", "Drive Engagement", "Build Subscribers", "Promote Products/Services"])
    with tab3:
        # Advanced options
        st.subheader("Additional Elements")
        include_timestamps = st.checkbox("Include Timestamps", value=True)
        include_hashtags = st.checkbox("Include Hashtags", value=True)
        include_social_handles = st.checkbox("Include Social Media Handles", value=True)
    if st.button("Generate Description"):
        if not main_points:
            st.error("Please enter main points/keywords.")
            return
        with st.spinner("Generating description..."):
            description = generate_youtube_description(
                target_audience, main_points, tone_style, use_case, primary_keywords, 
                secondary_keywords, language, seo_goals, include_timestamps, 
                include_hashtags, include_social_handles
            )
            if description:
                # Store the description in session state
                st.session_state.generated_description = description
                # Store other parameters in session state for regeneration
                st.session_state.description_params = {
                    "target_audience": target_audience,
                    "main_points": main_points,
                    "tone_style": tone_style,
                    "use_case": use_case,
                    "primary_keywords": primary_keywords,
                    "secondary_keywords": secondary_keywords,
                    "language": language,
                    "seo_goals": seo_goals,
                    "include_timestamps": include_timestamps,
                    "include_hashtags": include_hashtags,
                    "include_social_handles": include_social_handles
                }
                st.subheader("Generated Description")
                # Display description with analysis
                st.text_area("Description", description, height=200)
                # Calculate and display metrics
                all_keywords = primary_keywords.split(",") + secondary_keywords.split(",")
                keyword_density = calculate_keyword_density(description, all_keywords)
                seo_score = calculate_seo_score(description, all_keywords)
                col1, col2 = st.columns(2)
                with col1:
                    st.metric("Keyword Density", f"{keyword_density:.1f}%")
                with col2:
                    st.metric("SEO Score", f"{seo_score}/10")
                # Create columns for the buttons
                btn_col1, btn_col2 = st.columns(2)
                with btn_col1:
                    # Download button
                    st.download_button(
                        label="Download Description",
                        data=description,
                        file_name="youtube_description.txt",
                        mime="text/plain"
                    )
                with btn_col2:
                    # Regenerate button
                    if st.button("Regenerate"):
                        st.session_state.show_regenerate_popover = True
                # Regenerate popover
                if st.session_state.get("show_regenerate_popover", False):
                    with st.form("regenerate_form"):
                        st.subheader("Regenerate Description")
                        st.write("Specify changes you'd like to make to the description:")
                        changes = st.text_area("Changes to make", 
                                             placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
                        submitted = st.form_submit_button("Regenerate with Changes")
                        if submitted and changes:
                            with st.spinner("Regenerating description..."):
                                # Get the stored parameters
                                params = st.session_state.description_params
                                # Add the changes to the prompt
                                params["changes"] = changes
                                # Generate a new description with the changes
                                new_description = generate_youtube_description_with_changes(
                                    params["target_audience"], 
                                    params["main_points"], 
                                    params["tone_style"], 
                                    params["use_case"], 
                                    params["primary_keywords"], 
                                    params["secondary_keywords"], 
                                    params["language"], 
                                    params["seo_goals"], 
                                    params["include_timestamps"], 
                                    params["include_hashtags"], 
                                    params["include_social_handles"],
                                    changes
                                )
                                if new_description:
                                    # Update the stored description
                                    st.session_state.generated_description = new_description
                                    st.session_state.show_regenerate_popover = False
                                    st.rerun()
                                else:
                                    st.error("Failed to regenerate description. Please try again.")
            else:
                st.error("Failed to generate description. Please try again.")
    # Display previously generated description if it exists in session state
    elif st.session_state.generated_description:
        description = st.session_state.generated_description
        params = st.session_state.description_params
        st.subheader("Generated Description")
        # Display description with analysis
        st.text_area("Description", description, height=200)
        # Calculate and display metrics
        all_keywords = params["primary_keywords"].split(",") + params["secondary_keywords"].split(",")
        keyword_density = calculate_keyword_density(description, all_keywords)
        seo_score = calculate_seo_score(description, all_keywords)
        col1, col2 = st.columns(2)
        with col1:
            st.metric("Keyword Density", f"{keyword_density:.1f}%")
        with col2:
            st.metric("SEO Score", f"{seo_score}/10")
        # Create columns for the buttons
        btn_col1, btn_col2 = st.columns(2)
        with btn_col1:
            # Download button
            st.download_button(
                label="Download Description",
                data=description,
                file_name="youtube_description.txt",
                mime="text/plain"
            )
        with btn_col2:
            # Regenerate button
            if st.button("Regenerate"):
                st.session_state.show_regenerate_popover = True
        # Regenerate popover
        if st.session_state.get("show_regenerate_popover", False):
            with st.form("regenerate_form"):
                st.subheader("Regenerate Description")
                st.write("Specify changes you'd like to make to the description:")
                changes = st.text_area("Changes to make", 
                                     placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
                submitted = st.form_submit_button("Regenerate with Changes")
                if submitted and changes:
                    with st.spinner("Regenerating description..."):
                        # Add the changes to the prompt
                        params["changes"] = changes
                        # Generate a new description with the changes
                        new_description = generate_youtube_description_with_changes(
                            params["target_audience"], 
                            params["main_points"], 
                            params["tone_style"], 
                            params["use_case"], 
                            params["primary_keywords"], 
                            params["secondary_keywords"], 
                            params["language"], 
                            params["seo_goals"], 
                            params["include_timestamps"], 
                            params["include_hashtags"], 
                            params["include_social_handles"],
                            changes
                        )
                        if new_description:
                            # Update the stored description
                            st.session_state.generated_description = new_description
                            st.session_state.show_regenerate_popover = False
                            st.rerun()
                        else:
                            st.error("Failed to regenerate description. Please try again.")
 def generate_youtube_description_with_changes(target_audience, main_points, tone_style, use_case, primary_keywords, 
                                            secondary_keywords, language, seo_goals, include_timestamps=False, 
                                            include_hashtags=False, include_social_handles=False, changes=""):
    """Generate a YouTube description based on the provided parameters and requested changes."""
    # Create a custom system prompt for YouTube description generation
    system_prompt = """You are a YouTube description expert specializing in creating engaging, SEO-optimized video descriptions.
    Your task is to generate YouTube video descriptions based on the provided information.
    Focus ONLY on creating descriptions that are optimized for YouTube, with proper formatting, keywords, and calls to action.
    Return ONLY the description text, without any additional commentary or explanations."""
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate a YouTube description for a video about **{main_points}** based on the following information:
    **Target Audience:** {target_audience}
    **Tone and Style:** {tone_style}
    **Use Case:** {use_case}
    **Language:** {language}
    **Primary Keywords:** {primary_keywords}
    **Secondary Keywords:** {secondary_keywords}
    **SEO Goals:** {seo_goals}
    **Additional Elements:**
    {"- Include timestamps for key sections." if include_timestamps else ""}
    {"- Include relevant hashtags." if include_hashtags else ""}
    {"- Include social media handles." if include_social_handles else ""}
    **Requested Changes:**
    {changes}
    **Specific Instructions:**
    * Keep the description informative and engaging.
    * Use a conversational tone that matches the target audience.
    * Include relevant keywords naturally.
    * Add a call to action.
    * Keep the length between 250-300 words for optimal SEO.
    * Incorporate the requested changes into the description.
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/end_screen_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/end_screen_generator.py
@@ -1,740 +0,0 @@
 """
 YouTube End Screen Generator Module
 This module provides functionality for generating YouTube video end screens.
 """
 import streamlit as st
 import time
 import logging
 import traceback
 from PIL import Image
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 from lib.gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image, edit_image
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_end_screen_generator')
 def generate_end_screen_concepts(video_title, video_description, target_audience, content_type, 
                               primary_goal, secondary_goal=None, num_concepts=3):
    """Generate end screen concept ideas based on video content."""
    logger.info(f"Generating end screen concepts for: '{video_title}'")
    logger.info(f"Parameters: target_audience={target_audience}, content_type={content_type}, "
                f"primary_goal={primary_goal}, secondary_goal={secondary_goal}, num_concepts={num_concepts}")
    # Create a system prompt for end screen concept generation
    system_prompt = """You are a YouTube end screen expert specializing in creating engaging, action-driving end screen concepts.
    Your task is to generate end screen concept ideas based on the provided video information.
    Focus ONLY on creating end screens that are optimized for YouTube, with proper visual hierarchy, element placement, and call-to-action triggers.
    Return ONLY the concept descriptions, without any additional commentary or explanations.
    Each concept should include:
    1. A main visual element or background
    2. Element placement and content (subscribe button, playlist, video, website)
    3. Color scheme suggestions
    4. Text content for each element
    5. Brief explanation of why this concept would be effective for the specified goals
    IMPORTANT: Format each concept with a clear numbered heading like "1. [Concept Name]" to ensure proper parsing."""
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate {num_concepts} end screen concept ideas for a YouTube video with the following information:
    **Video Title:** {video_title}
    **Video Description:** {video_description}
    **Target Audience:** {target_audience}
    **Content Type:** {content_type}
    **Primary Goal:** {primary_goal}
    **Secondary Goal:** {secondary_goal if secondary_goal else "None specified"}
    **Specific Instructions:**
    * Each concept should be clearly separated and numbered with a heading like "1. [Concept Name]".
    * Focus on creating end screens that drive the specified goals.
    * Consider the target audience's interests and preferences.
    * Include specific details about visual elements, element placement, and color schemes.
    * Explain why each concept would be effective for this specific video and goals.
    * Include text suggestions for each element (subscribe button, playlist, video, website).
    """
    try:
        logger.info("Sending request to LLM for end screen concepts")
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        logger.info(f"Received response from LLM: {len(response)} characters")
        return response
    except Exception as err:
        logger.error(f"Error generating end screen concepts: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to generate end screen concepts: {err}")
        return None
 def generate_end_screen_design(concept_description, style_preference, element_count=2, 
                             element_types=None, element_texts=None, aspect_ratio="16:9", 
                             keywords=None, style=None, focus=None):
    """Generate an end screen image based on the concept description."""
    logger.info(f"Generating end screen design for concept: '{concept_description[:50]}...'")
    logger.info(f"Parameters: style_preference={style_preference}, element_count={element_count}, "
                f"element_types={element_types}, element_texts={element_texts}, aspect_ratio={aspect_ratio}")
    # Extract key elements from the concept description
    # This helps focus the prompt on the most important aspects
    concept_lines = concept_description.split('\n')
    main_visual = ""
    element_placement = ""
    color_scheme = ""
    text_content = ""
    for line in concept_lines:
        if "visual" in line.lower() or "background" in line.lower():
            main_visual = line
        elif "placement" in line.lower() or "layout" in line.lower():
            element_placement = line
        elif "color" in line.lower() or "scheme" in line.lower():
            color_scheme = line
        elif "text" in line.lower() or "content" in line.lower():
            text_content = line
    # Create a more focused prompt for the image generation
    image_prompt = f"""
    Create a YouTube end screen image with the following specifications:
    MAIN VISUAL: {main_visual if main_visual else "Not specified"}
    ELEMENT PLACEMENT: {element_placement if element_placement else "Not specified"}
    COLOR SCHEME: {color_scheme if color_scheme else "Not specified"}
    TEXT CONTENT: {text_content if text_content else "Not specified"}
    STYLE: {style_preference}
    ASPECT RATIO: {aspect_ratio}
    NUMBER OF ELEMENTS: {element_count}
    ELEMENT TYPES: {', '.join(element_types) if element_types else 'Not specified'}
    ELEMENT TEXTS: {', '.join(element_texts) if element_texts else 'Not specified'}
    IMPORTANT REQUIREMENTS:
    1. This must be a VISUAL IMAGE of a YouTube end screen, not just a text description
    2. The image should be high contrast and visually striking
    3. All text should be large and readable
    4. Elements should be properly placed for optimal viewer engagement
    5. The design should follow the specified color scheme
    6. The image should be optimized for the specified aspect ratio
    PLEASE GENERATE AN ACTUAL IMAGE, NOT JUST A TEXT DESCRIPTION.
    """
    try:
        logger.info("Sending request to Gemini for end screen image")
        # Generate the image using Gemini with enhanced prompt
        img_path = generate_gemini_image(
            image_prompt, 
            keywords=keywords,
            style=style,
            focus=focus,
            enhance_prompt=True
        )
        logger.info(f"Received image from Gemini: {img_path}")
        return img_path
    except Exception as err:
        logger.error(f"Error generating end screen image: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to generate end screen image: {err}")
        return None
 def edit_end_screen_image(img_path, edit_instructions):
    """Edit an end screen image based on user instructions."""
    logger.info(f"Editing end screen image: '{img_path}'")
    logger.info(f"Edit instructions: '{edit_instructions}'")
    try:
        logger.info("Sending request to Gemini for image editing")
        # Edit the image using Gemini
        edited_img_path = edit_image(img_path, f"Edit this image according to these instructions: {edit_instructions}. IMPORTANT: Please generate an actual edited image, not just a text description. I need a visual representation of the edited end screen.")
        logger.info(f"Image editing completed. Edited image path: {edited_img_path}")
        # Return the path to the edited image
        return edited_img_path
    except Exception as err:
        logger.error(f"Error editing end screen image: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to edit end screen image: {err}")
        return None
 def analyze_end_screen(end_screen_path):
    """Analyze an end screen for effectiveness."""
    logger.info(f"Analyzing end screen: '{end_screen_path}'")
    # This would typically involve image analysis, but for now we'll use AI to provide feedback
    system_prompt = """You are a YouTube end screen expert specializing in analyzing and providing feedback on end screen designs.
    Your task is to analyze the end screen and provide constructive feedback on its effectiveness.
    Focus on aspects like visual hierarchy, element placement, call-to-action clarity, and overall effectiveness."""
    # For now, we'll just return a placeholder analysis
    # In a real implementation, we would analyze the actual image
    logger.info("Generating end screen analysis")
    return """
    **End Screen Analysis:**
    - **Visual Hierarchy:** The main elements are well-positioned and stand out against the background.
    - **Element Placement:** The call-to-action elements are strategically placed for optimal viewer engagement.
    - **Call-to-Action Clarity:** The text and visual cues clearly communicate the desired actions.
    - **Overall Effectiveness:** The design is likely to drive the specified goals due to its visual appeal and clear value proposition.
    **Suggestions for Improvement:**
    - Consider adding a subtle animation hint to draw attention to the most important element.
    - The text could be slightly larger for better readability on mobile devices.
    - Adding a small icon or logo could help with brand recognition.
    """
 def parse_concepts(concepts_text):
    """Parse the concepts text into a list of individual concepts."""
    logger.info("Parsing concepts text into individual concepts")
    # Split the concepts text by main concept headers
    concepts = []
    current_concept = ""
    # Look for patterns like numbered headings (e.g., "1.", "2.", "3.") or "Concept 1:", "Concept 2:", etc.
    concept_patterns = ["1.", "2.", "3.", "4.", "5.", "Concept 1:", "Concept 2:", "Concept 3:", "Concept 4:", "Concept 5:"]
    for line in concepts_text.split('\n'):
        # Check if line starts with a concept pattern
        is_new_concept = False
        for pattern in concept_patterns:
            if line.strip().startswith(pattern):
                # If we have a previous concept, add it to the list
                if current_concept:
                    concepts.append(current_concept.strip())
                # Start a new concept
                current_concept = line
                is_new_concept = True
                break
        if not is_new_concept:
            # Add the line to the current concept
            current_concept += "\n" + line
    # Add the last concept
    if current_concept:
        concepts.append(current_concept.strip())
    logger.info(f"Parsed {len(concepts)} concepts from the response")
    return concepts
 def write_yt_end_screen():
    """Create a user interface for YouTube End Screen Generator."""
    logger.info("Initializing YouTube End Screen Generator UI")
    st.title("YouTube End Screen Generator")
    st.write("Create engaging, action-driving end screens for your YouTube videos.")
    # Initialize session state for generated end screens if it doesn't exist
    if "generated_end_screens" not in st.session_state:
        st.session_state.generated_end_screens = []
    if "end_screen_concepts" not in st.session_state:
        st.session_state.end_screen_concepts = None
    if "current_end_screen_path" not in st.session_state:
        st.session_state.current_end_screen_path = None
    if "concept_list" not in st.session_state:
        st.session_state.concept_list = []
    if "editing_end_screen" not in st.session_state:
        st.session_state.editing_end_screen = False
    if "edit_instructions" not in st.session_state:
        st.session_state.edit_instructions = ""
    if "edited_end_screen_path" not in st.session_state:
        st.session_state.edited_end_screen_path = None
    if "show_edit_form" not in st.session_state:
        st.session_state.show_edit_form = False
    # Create tabs for different sections
    tab1, tab2 = st.tabs(["Basic Info", "Style & Elements"])
    with tab1:
        # Basic information inputs
        video_title = st.text_input("Video Title", 
                                  placeholder="e.g., 10 Tips for Better Photography")
        video_description = st.text_area("Video Description", 
                                       placeholder="Brief description of your video content")
        target_audience = st.text_input("Target Audience", 
                                      placeholder="e.g., photography enthusiasts, beginners")
        # Content type selection
        content_type = st.selectbox("Content Type", [
            "Tutorial/How-to",
            "Vlog",
            "Review",
            "Educational",
            "Entertainment",
            "News/Update",
            "Product Showcase",
            "Challenge",
            "Reaction",
            "Comparison"
        ])
        # End screen goals
        st.subheader("End Screen Goals")
        primary_goal = st.selectbox("Primary Goal", [
            "Drive Subscriptions",
            "Promote Playlist",
            "Promote Next Video",
            "Promote Website",
            "Promote Social Media",
            "Promote Product/Service",
            "Encourage Comments",
            "Mixed Goals"
        ])
        secondary_goal = st.selectbox("Secondary Goal (Optional)", [
            "None",
            "Drive Subscriptions",
            "Promote Playlist",
            "Promote Next Video",
            "Promote Website",
            "Promote Social Media",
            "Promote Product/Service",
            "Encourage Comments"
        ])
        if secondary_goal == "None":
            secondary_goal = None
    with tab2:
        # Style preferences
        st.subheader("Style Preferences")
        # Create columns for style options
        col1, col2 = st.columns(2)
        with col1:
            style_preference = st.selectbox("End Screen Style", [
                "Bold and Dramatic",
                "Clean and Minimal",
                "Colorful and Vibrant",
                "Dark and Moody",
                "Professional and Corporate",
                "Playful and Fun",
                "Retro/Vintage",
                "Modern and Sleek"
            ])
            num_concepts = st.slider("Number of Concepts", 1, 5, 3)
        with col2:
            aspect_ratio = st.selectbox("Aspect Ratio", [
                "16:9 (Standard)",
                "1:1 (Square)",
                "4:3 (Classic)",
                "9:16 (Vertical)"
            ])
            include_branding = st.checkbox("Include Branding Elements", value=True)
            if include_branding:
                branding_elements = st.multiselect("Branding Elements", [
                    "Channel Logo",
                    "Channel Name",
                    "Channel Tagline",
                    "Brand Colors",
                    "Watermark"
                ])
        # Element configuration
        st.subheader("End Screen Elements")
        # Number of elements
        element_count = st.slider("Number of Elements", 1, 4, 2)
        # Element types
        element_types = []
        element_texts = []
        for i in range(element_count):
            st.write(f"Element {i+1}")
            col1, col2 = st.columns(2)
            with col1:
                element_type = st.selectbox(
                    f"Type",
                    ["Subscribe Button", "Playlist", "Video", "Website", "Social Media"],
                    key=f"element_type_{i}"
                )
                element_types.append(element_type)
            with col2:
                element_text = st.text_input(
                    f"Text",
                    placeholder=f"Text for {element_type}",
                    key=f"element_text_{i}"
                )
                element_texts.append(element_text)
        # Advanced AI Prompt Settings
        st.subheader("Advanced AI Prompt Settings")
        # Create columns for advanced settings
        col3, col4 = st.columns(2)
        with col3:
            # Image style selection
            image_style = st.selectbox("Image Style", [
                "Auto (AI will choose best style)",
                "Photorealistic",
                "Artistic",
                "Cartoon/Anime",
                "Sketch/Drawing",
                "Digital Art",
                "3D Render"
            ])
            # Extract style for the generate_gemini_image function
            style = None
            if image_style == "Photorealistic":
                style = "photorealistic"
            elif image_style == "Artistic":
                style = "artistic"
            elif image_style == "Cartoon/Anime":
                style = "cartoon"
            elif image_style == "Sketch/Drawing":
                style = "sketch"
            elif image_style == "Digital Art":
                style = "digital_art"
            elif image_style == "3D Render":
                style = "3d_render"
        with col4:
            # Focus selection for photorealistic images
            focus = None
            if style == "photorealistic":
                focus = st.selectbox("Image Focus", [
                    "Auto (AI will choose best focus)",
                    "Portraits",
                    "Objects",
                    "Motion",
                    "Wide-angle"
                ])
                # Extract focus for the generate_gemini_image function
                if focus == "Portraits":
                    focus = "portraits"
                elif focus == "Objects":
                    focus = "objects"
                elif focus == "Motion":
                    focus = "motion"
                elif focus == "Wide-angle":
                    focus = "wide-angle"
                elif focus == "Auto (AI will choose best focus)":
                    focus = None
        # Keywords for enhanced prompt generation
        st.subheader("Keywords for Enhanced Prompt")
        st.write("Add keywords to enhance the AI prompt generation. These will help create more detailed and accurate end screens.")
        # Create a text area for keywords
        keywords_input = st.text_area(
            "Keywords (comma-separated)", 
            placeholder="e.g., vibrant, energetic, bold, eye-catching, professional"
        )
        # Process keywords
        keywords = None
        if keywords_input:
            keywords = [k.strip() for k in keywords_input.split(",") if k.strip()]
            logger.info(f"User provided keywords: {keywords}")
    # Generate button - placed outside of tabs for better visibility
    st.markdown("---")
    st.subheader("Generate End Screen Concepts")
    st.write("Click the button below to generate end screen concepts based on your inputs.")
    if st.button("Generate End Screen Concepts", type="primary"):
        if not video_title:
            st.error("Please enter a video title.")
            return
        with st.spinner("Generating end screen concepts..."):
            logger.info("User clicked Generate End Screen Concepts button")
            concepts = generate_end_screen_concepts(
                video_title, 
                video_description, 
                target_audience, 
                content_type, 
                primary_goal,
                secondary_goal,
                num_concepts
            )
            if concepts:
                # Store the concepts in session state
                st.session_state.end_screen_concepts = concepts
                # Parse the concepts and store in session state
                st.session_state.concept_list = parse_concepts(concepts)
                logger.info("Stored end screen concepts in session state")
                # Display the concepts in tabs
                st.subheader("End Screen Concepts")
                # Create tabs for each concept
                concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
                for i, tab in enumerate(concept_tabs):
                    with tab:
                        st.markdown(st.session_state.concept_list[i])
                        # Add a button to generate image for this concept
                        if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_{i}"):
                            with st.spinner(f"Generating end screen image for concept {i+1}..."):
                                logger.info(f"User selected concept {i+1} for image generation")
                                # Get the selected concept
                                selected_concept = st.session_state.concept_list[i]
                                # Generate the end screen image with enhanced prompt
                                img_path = generate_end_screen_design(
                                    selected_concept,
                                    style_preference,
                                    element_count,
                                    element_types,
                                    element_texts,
                                    aspect_ratio.split()[0],  # Extract just the ratio part
                                    keywords=keywords,
                                    style=style,
                                    focus=focus
                                )
                                if img_path:
                                    # Store the current end screen path in session state
                                    st.session_state.current_end_screen_path = img_path
                                    logger.info(f"Stored current end screen path in session state: {img_path}")
                                    # Display the generated image
                                    st.subheader("Generated End Screen")
                                    st.image(img_path, use_container_width=True)
                                    # Add download button
                                    with open(img_path, "rb") as file:
                                        st.download_button(
                                            label="Download End Screen",
                                            data=file,
                                            file_name=f"youtube_end_screen_{int(time.time())}.png",
                                            mime="image/png"
                                        )
                                    # Add image editing section
                                    st.subheader("Edit End Screen")
                                    st.write("Make changes to your end screen by providing instructions below:")
                                    # Create a text area for edit instructions
                                    edit_instructions = st.text_area(
                                        "Edit Instructions", 
                                        placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
                                        key=f"edit_instructions_{i}"
                                    )
                                    # Store edit instructions in session state
                                    st.session_state.edit_instructions = edit_instructions
                                    # Add a button to apply edits
                                    if st.button("Apply Edits", key=f"apply_edits_{i}"):
                                        if not edit_instructions:
                                            st.warning("Please provide edit instructions.")
                                        else:
                                            # Set editing flag
                                            st.session_state.editing_end_screen = True
                                            st.session_state.show_edit_form = True
                                            # Rerun to update the UI
                                            st.rerun()
                                    # Add analysis button
                                    if st.button("Analyze End Screen", key=f"analyze_{i}"):
                                        logger.info("User clicked Analyze End Screen button")
                                        analysis = analyze_end_screen(img_path)
                                        st.subheader("End Screen Analysis")
                                        st.markdown(analysis)
            else:
                st.error("Failed to generate end screen concepts. Please try again.")
    # Display previously generated concepts if they exist in session state
    elif st.session_state.end_screen_concepts and st.session_state.concept_list:
        logger.info("Displaying previously generated concepts from session state")
        st.subheader("End Screen Concepts")
        # Create tabs for each concept
        concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
        for i, tab in enumerate(concept_tabs):
            with tab:
                st.markdown(st.session_state.concept_list[i])
                # Add a button to generate image for this concept
                if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_existing_{i}"):
                    with st.spinner(f"Generating end screen image for concept {i+1}..."):
                        logger.info(f"User selected concept {i+1} for image generation")
                        # Get the selected concept
                        selected_concept = st.session_state.concept_list[i]
                        # Generate the end screen image with enhanced prompt
                        img_path = generate_end_screen_design(
                            selected_concept,
                            style_preference,
                            element_count,
                            element_types,
                            element_texts,
                            aspect_ratio.split()[0],  # Extract just the ratio part
                            keywords=keywords,
                            style=style,
                            focus=focus
                        )
                        if img_path:
                            # Store the current end screen path in session state
                            st.session_state.current_end_screen_path = img_path
                            logger.info(f"Stored current end screen path in session state: {img_path}")
                            # Display the generated image
                            st.subheader("Generated End Screen")
                            st.image(img_path, use_container_width=True)
                            # Add download button
                            with open(img_path, "rb") as file:
                                st.download_button(
                                    label="Download End Screen",
                                    data=file,
                                    file_name=f"youtube_end_screen_{int(time.time())}.png",
                                    mime="image/png"
                                )
                            # Add image editing section
                            st.subheader("Edit End Screen")
                            st.write("Make changes to your end screen by providing instructions below:")
                            # Create a text area for edit instructions
                            edit_instructions = st.text_area(
                                "Edit Instructions", 
                                placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
                                key=f"edit_instructions_existing_{i}"
                            )
                            # Store edit instructions in session state
                            st.session_state.edit_instructions = edit_instructions
                            # Add a button to apply edits
                            if st.button("Apply Edits", key=f"apply_edits_existing_{i}"):
                                if not edit_instructions:
                                    st.warning("Please provide edit instructions.")
                                else:
                                    # Set editing flag
                                    st.session_state.editing_end_screen = True
                                    st.session_state.show_edit_form = True
                                    # Rerun to update the UI
                                    st.rerun()
                            # Add analysis button
                            if st.button("Analyze End Screen", key=f"analyze_existing_{i}"):
                                logger.info("User clicked Analyze End Screen button")
                                analysis = analyze_end_screen(img_path)
                                st.subheader("End Screen Analysis")
                                st.markdown(analysis)
    # Display current end screen if it exists in session state
    elif st.session_state.current_end_screen_path:
        logger.info(f"Displaying current end screen from session state: {st.session_state.current_end_screen_path}")
        st.subheader("Current End Screen")
        st.image(st.session_state.current_end_screen_path, use_container_width=True)
        # Add download button
        with open(st.session_state.current_end_screen_path, "rb") as file:
            st.download_button(
                label="Download End Screen",
                data=file,
                file_name=f"youtube_end_screen_{int(time.time())}.png",
                mime="image/png"
            )
        # Add image editing section
        st.subheader("Edit End Screen")
        st.write("Make changes to your end screen by providing instructions below:")
        # Create a text area for edit instructions
        edit_instructions = st.text_area(
            "Edit Instructions", 
            placeholder="e.g., Make the background darker, Add a new element, Change the text color to white",
            key="edit_instructions_current",
            value=st.session_state.edit_instructions if st.session_state.edit_instructions else ""
        )
        # Store edit instructions in session state
        st.session_state.edit_instructions = edit_instructions
        # Add a button to apply edits
        if st.button("Apply Edits", key="apply_edits_current"):
            if not edit_instructions:
                st.warning("Please provide edit instructions.")
            else:
                # Set editing flag
                st.session_state.editing_end_screen = True
                st.session_state.show_edit_form = True
                # Rerun to update the UI
                st.rerun()
        # Add analysis button
        if st.button("Analyze End Screen", key="analyze_current"):
            logger.info("User clicked Analyze End Screen button")
            analysis = analyze_end_screen(st.session_state.current_end_screen_path)
            st.subheader("End Screen Analysis")
            st.markdown(analysis)
    # Handle the editing process
    if st.session_state.editing_end_screen and st.session_state.show_edit_form:
        st.subheader("Editing End Screen")
        # Show a spinner while editing
        with st.spinner("Editing end screen..."):
            logger.info(f"User provided edit instructions: '{st.session_state.edit_instructions}'")
            # Edit the end screen image
            edited_img_path = edit_end_screen_image(st.session_state.current_end_screen_path, st.session_state.edit_instructions)
            if edited_img_path:
                # Update the current end screen path in session state
                st.session_state.edited_end_screen_path = edited_img_path
                logger.info(f"Updated current end screen path in session state: {edited_img_path}")
                # Reset editing flags
                st.session_state.editing_end_screen = False
                st.session_state.show_edit_form = False
                # Display the edited image
                st.subheader("Edited End Screen")
                st.image(edited_img_path, use_container_width=True)
                # Add download button for the edited image
                with open(edited_img_path, "rb") as file:
                    st.download_button(
                        label="Download Edited End Screen",
                        data=file,
                        file_name=f"youtube_end_screen_edited_{int(time.time())}.png",
                        mime="image/png"
                    )
                # Update the current end screen path to the edited one
                st.session_state.current_end_screen_path = edited_img_path
                # Add a button to continue editing
                if st.button("Continue Editing"):
                    st.session_state.show_edit_form = True
                    st.rerun()
            else:
                # Reset editing flags
                st.session_state.editing_end_screen = False
                st.session_state.show_edit_form = False
                st.error("Failed to edit the end screen. Please try again with different instructions.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/script_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/script_generator.py
@@ -1,556 +0,0 @@
 """
 YouTube Script Generator Module
 This module provides functionality for generating YouTube video scripts.
 """
 import streamlit as st
 import time
 import json
 import os
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 def generate_youtube_script(target_audience, main_points, tone_style, use_case, script_structure, 
                          include_hook=False, include_cta=False, include_engagement=False, 
                          include_timestamps=False, include_visual_cues=False, engagement_hooks=None,
                          community_interactions=None, language="English"):
    """Generate a YouTube script based on the provided parameters."""
    # Create a custom system prompt for YouTube script generation
    system_prompt = f"""You are a YouTube script expert specializing in creating engaging, well-structured video scripts in {language}.
    Your task is to generate YouTube video scripts based on the provided information.
    Focus ONLY on creating scripts that are optimized for YouTube, with proper structure, engagement hooks, and calls to action.
    Return ONLY the script text, without any additional commentary or explanations.
    Format the script with clear sections, speaker notes, and visual cues where appropriate.
    Write the entire script in {language}."""
    # Build structure-specific instructions
    structure_instructions = {
        "Problem-Solution": "Structure the script to first present a problem, then provide a solution.",
        "Before-After-Bridge": "Structure the script to show the before state, the transformation process, and the after state.",
        "Hook-Problem-Solution-Call to Action": "Start with a hook, present the problem, provide the solution, and end with a call to action.",
        "Compare and Contrast": "Structure the script to compare and contrast different options or approaches.",
        "Step-by-Step Tutorial": "Break down the content into clear, sequential steps.",
        "Case Study": "Present a real-world example or case study to illustrate the main points.",
        "Interview Format": "Structure the script as an interview with questions and answers.",
        "Review Format": "Structure the script as a review with pros, cons, and a final verdict.",
        "Vlog Format": "Structure the script as a personal video blog with a conversational tone.",
        "Educational Format": "Structure the script to teach a concept with examples and explanations.",
        "Entertainment Format": "Structure the script to entertain while delivering the main message."
    }
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate a YouTube script in {language} for a video about **{main_points}** based on the following information:
    **Target Audience:** {target_audience}
    **Tone and Style:** {tone_style}
    **Use Case:** {use_case}
    **Script Structure:** {script_structure}
    **Language:** {language}
    **Structure Instructions:**
    {structure_instructions.get(script_structure, "Follow a logical flow to present the content.")}
    **Additional Elements:**
    {"- Include a hook at the beginning to grab attention." if include_hook else ""}
    {"- End with a strong call to action." if include_cta else ""}
    {"- Include prompts for viewer engagement (e.g., questions, polls)." if include_engagement else ""}
    {"- Include suggested timestamps for key sections." if include_timestamps else ""}
    {"- Include visual cues and transitions." if include_visual_cues else ""}
    """
    # Add engagement hooks if provided
    if engagement_hooks:
        prompt += "\n**Engagement Hooks:**\n"
        for hook in engagement_hooks:
            prompt += f"- {hook}\n"
    # Add community interaction points if provided
    if community_interactions:
        prompt += "\n**Community Interaction Points:**\n"
        for interaction in community_interactions:
            prompt += f"- {interaction}\n"
    prompt += """
    **Specific Instructions:**
    * Keep the language clear and engaging.
    * Use a conversational tone that matches the target audience.
    * Include relevant examples and explanations.
    * Ensure the script flows naturally and maintains viewer interest.
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def generate_youtube_script_with_changes(target_audience, main_points, tone_style, use_case, script_structure, 
                                       include_hook=False, include_cta=False, include_engagement=False, 
                                       include_timestamps=False, include_visual_cues=False, engagement_hooks=None,
                                       community_interactions=None, changes="", language="English"):
    """Generate a YouTube script based on the provided parameters and requested changes."""
    # Create a custom system prompt for YouTube script generation
    system_prompt = f"""You are a YouTube script expert specializing in creating engaging, well-structured video scripts in {language}.
    Your task is to generate YouTube video scripts based on the provided information.
    Focus ONLY on creating scripts that are optimized for YouTube, with proper structure, engagement hooks, and calls to action.
    Return ONLY the script text, without any additional commentary or explanations.
    Format the script with clear sections, speaker notes, and visual cues where appropriate.
    Write the entire script in {language}."""
    # Build structure-specific instructions
    structure_instructions = {
        "Problem-Solution": "Structure the script to first present a problem, then provide a solution.",
        "Before-After-Bridge": "Structure the script to show the before state, the transformation process, and the after state.",
        "Hook-Problem-Solution-Call to Action": "Start with a hook, present the problem, provide the solution, and end with a call to action.",
        "Compare and Contrast": "Structure the script to compare and contrast different options or approaches.",
        "Step-by-Step Tutorial": "Break down the content into clear, sequential steps.",
        "Case Study": "Present a real-world example or case study to illustrate the main points.",
        "Interview Format": "Structure the script as an interview with questions and answers.",
        "Review Format": "Structure the script as a review with pros, cons, and a final verdict.",
        "Vlog Format": "Structure the script as a personal video blog with a conversational tone.",
        "Educational Format": "Structure the script to teach a concept with examples and explanations.",
        "Entertainment Format": "Structure the script to entertain while delivering the main message."
    }
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate a YouTube script in {language} for a video about **{main_points}** based on the following information:
    **Target Audience:** {target_audience}
    **Tone and Style:** {tone_style}
    **Use Case:** {use_case}
    **Script Structure:** {script_structure}
    **Language:** {language}
    **Structure Instructions:**
    {structure_instructions.get(script_structure, "Follow a logical flow to present the content.")}
    **Additional Elements:**
    {"- Include a hook at the beginning to grab attention." if include_hook else ""}
    {"- End with a strong call to action." if include_cta else ""}
    {"- Include prompts for viewer engagement (e.g., questions, polls)." if include_engagement else ""}
    {"- Include suggested timestamps for key sections." if include_timestamps else ""}
    {"- Include visual cues and transitions." if include_visual_cues else ""}
    """
    # Add engagement hooks if provided
    if engagement_hooks:
        prompt += "\n**Engagement Hooks:**\n"
        for hook in engagement_hooks:
            prompt += f"- {hook}\n"
    # Add community interaction points if provided
    if community_interactions:
        prompt += "\n**Community Interaction Points:**\n"
        for interaction in community_interactions:
            prompt += f"- {interaction}\n"
    # Add requested changes
    prompt += f"""
    **Requested Changes:**
    {changes}
    **Specific Instructions:**
    * Keep the language clear and engaging.
    * Use a conversational tone that matches the target audience.
    * Include relevant examples and explanations.
    * Ensure the script flows naturally and maintains viewer interest.
    * Incorporate the requested changes into the script.
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def export_script(script, format_type, filename=None):
    """Export the script in various formats."""
    if not filename:
        filename = "youtube_script"
    if format_type == "Text":
        return script, f"{filename}.txt", "text/plain"
    elif format_type == "Markdown":
        return script, f"{filename}.md", "text/markdown"
    elif format_type == "HTML":
        html_content = f"<html><body><pre>{script}</pre></body></html>"
        return html_content, f"{filename}.html", "text/html"
    elif format_type == "JSON":
        json_content = json.dumps({"script": script}, indent=2)
        return json_content, f"{filename}.json", "application/json"
    elif format_type == "Subtitles (SRT)":
        # Convert script to basic SRT format
        lines = script.split('\n')
        srt_content = ""
        for i, line in enumerate(lines):
            if line.strip():
                start_time = f"00:00:{i*5:02d},000"
                end_time = f"00:00:{(i+1)*5:02d},000"
                srt_content += f"{i+1}\n{start_time} --> {end_time}\n{line}\n\n"
        return srt_content, f"{filename}.srt", "text/plain"
    else:
        return script, f"{filename}.txt", "text/plain"
 def write_yt_script():
    """Create a user interface for YouTube Script Generator."""
    st.write("Generate professional YouTube video scripts with optimized structures for engagement.")
    # Initialize session state for generated script if it doesn't exist
    if "generated_script" not in st.session_state:
        st.session_state.generated_script = None
    # Create tabs for different sections
    tab1, tab2, tab3 = st.tabs(["Basic Info", "Advanced Options", "Engagement & Export"])
    with tab1:
        # Basic information inputs
        main_points = st.text_area("Main Points/Keywords (comma-separated)", 
                                 placeholder="e.g., cooking tips, healthy recipes, quick meals")
        target_audience = st.text_input("Target Audience", 
                                      placeholder="e.g., beginners, professionals, parents")
        # Create columns for tone, use case, structure, and language
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            tone_style = st.selectbox("Tone/Style", 
                                    ["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
        with col2:
            use_case = st.selectbox("Use Case", 
                                  ["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
        with col3:
            script_structure = st.selectbox("Script Structure", [
                "Problem-Solution",
                "Before-After-Bridge",
                "Hook-Problem-Solution-Call to Action",
                "Compare and Contrast",
                "Step-by-Step Tutorial",
                "Case Study",
                "Interview Format",
                "Review Format",
                "Vlog Format",
                "Educational Format",
                "Entertainment Format"
            ])
        with col4:
            language = st.selectbox("Language", [
                "English",
                "Spanish",
                "French",
                "German",
                "Italian",
                "Portuguese",
                "Russian",
                "Japanese",
                "Korean",
                "Chinese",
                "Hindi",
                "Arabic"
            ])
    with tab2:
        # Advanced options
        st.subheader("Additional Elements")
        include_hook = st.checkbox("Include Hook", value=True)
        include_cta = st.checkbox("Include Call to Action", value=True)
        include_engagement = st.checkbox("Include Viewer Engagement Prompts", value=True)
        include_timestamps = st.checkbox("Include Suggested Timestamps", value=True)
        include_visual_cues = st.checkbox("Include Visual Cues/Transitions", value=True)
    with tab3:
        # Engagement hooks
        st.subheader("Engagement Hooks")
        st.write("Select engagement hooks to include in your script:")
        engagement_hooks = []
        if st.checkbox("Question Hook", value=False):
            engagement_hooks.append("Start with a thought-provoking question to engage viewers immediately")
        if st.checkbox("Story Hook", value=False):
            engagement_hooks.append("Begin with a brief, relevant story or anecdote")
        if st.checkbox("Statistic Hook", value=False):
            engagement_hooks.append("Open with an interesting statistic or fact")
        if st.checkbox("Controversy Hook", value=False):
            engagement_hooks.append("Present a controversial statement or opinion to spark interest")
        if st.checkbox("Promise Hook", value=False):
            engagement_hooks.append("Make a promise about what viewers will learn or gain")
        if st.checkbox("Scenario Hook", value=False):
            engagement_hooks.append("Describe a scenario or situation viewers can relate to")
        if st.checkbox("Mystery Hook", value=False):
            engagement_hooks.append("Create a sense of mystery or intrigue")
        if st.checkbox("Quote Hook", value=False):
            engagement_hooks.append("Start with a relevant quote from an expert or notable figure")
        # Community interaction points
        st.subheader("Community Interaction Points")
        st.write("Select community interaction points to include in your script:")
        community_interactions = []
        if st.checkbox("Comment Prompt", value=False):
            community_interactions.append("Ask viewers to share their experiences or opinions in the comments")
        if st.checkbox("Poll Suggestion", value=False):
            community_interactions.append("Suggest creating a poll for viewers to vote on")
        if st.checkbox("Question for Comments", value=False):
            community_interactions.append("Pose a specific question for viewers to answer in the comments")
        if st.checkbox("Challenge", value=False):
            community_interactions.append("Challenge viewers to try something and report back")
        if st.checkbox("Tag Friends", value=False):
            community_interactions.append("Encourage viewers to tag friends who might benefit from the content")
        if st.checkbox("Share Request", value=False):
            community_interactions.append("Ask viewers to share the video with others who might find it helpful")
        if st.checkbox("Community Post", value=False):
            community_interactions.append("Mention creating a community post to continue the discussion")
        if st.checkbox("Live Stream Teaser", value=False):
            community_interactions.append("Tease an upcoming live stream on the same topic")
        # Export options
        st.subheader("Export Options")
        export_format = st.selectbox("Export Format", [
            "Text",
            "Markdown",
            "HTML",
            "JSON",
            "Subtitles (SRT)"
        ])
        custom_filename = st.text_input("Custom Filename (optional)", 
                                      placeholder="Leave blank for default filename")
    if st.button("Generate Script"):
        if not main_points:
            st.error("Please enter main points/keywords.")
            return
        with st.spinner("Generating script..."):
            script = generate_youtube_script(
                target_audience, main_points, tone_style, use_case, script_structure,
                include_hook, include_cta, include_engagement, include_timestamps, include_visual_cues,
                engagement_hooks if engagement_hooks else None,
                community_interactions if community_interactions else None,
                language
            )
            if script:
                # Store the script in session state
                st.session_state.generated_script = script
                # Store other parameters in session state for regeneration
                st.session_state.script_params = {
                    "target_audience": target_audience,
                    "main_points": main_points,
                    "tone_style": tone_style,
                    "use_case": use_case,
                    "script_structure": script_structure,
                    "include_hook": include_hook,
                    "include_cta": include_cta,
                    "include_engagement": include_engagement,
                    "include_timestamps": include_timestamps,
                    "include_visual_cues": include_visual_cues,
                    "engagement_hooks": engagement_hooks if engagement_hooks else None,
                    "community_interactions": community_interactions if community_interactions else None,
                    "language": language
                }
                st.subheader("Generated Script")
                # Display script with tabs for different views
                script_tab1, script_tab2 = st.tabs(["Formatted View", "Plain Text"])
                with script_tab1:
                    st.markdown(script)
                with script_tab2:
                    st.code(script)
                # Export options
                st.subheader("Export Script")
                # Get export data
                export_data, export_filename, mime_type = export_script(
                    script, 
                    export_format, 
                    custom_filename if custom_filename else None
                )
                # Create columns for the buttons
                btn_col1, btn_col2 = st.columns(2)
                with btn_col1:
                    # Download button
                    st.download_button(
                        label=f"Download as {export_format}",
                        data=export_data,
                        file_name=export_filename,
                        mime=mime_type
                    )
                with btn_col2:
                    # Regenerate button
                    if st.button("Regenerate"):
                        st.session_state.show_regenerate_popover = True
                # Regenerate popover
                if st.session_state.get("show_regenerate_popover", False):
                    with st.form("regenerate_form"):
                        st.subheader("Regenerate Script")
                        st.write("Specify changes you'd like to make to the script:")
                        changes = st.text_area("Changes to make", 
                                             placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
                        submitted = st.form_submit_button("Regenerate with Changes")
                        if submitted and changes:
                            with st.spinner("Regenerating script..."):
                                # Get the stored parameters
                                params = st.session_state.script_params
                                # Generate a new script with the changes
                                new_script = generate_youtube_script_with_changes(
                                    params["target_audience"], 
                                    params["main_points"], 
                                    params["tone_style"], 
                                    params["use_case"], 
                                    params["script_structure"],
                                    params["include_hook"], 
                                    params["include_cta"], 
                                    params["include_engagement"], 
                                    params["include_timestamps"], 
                                    params["include_visual_cues"],
                                    params["engagement_hooks"],
                                    params["community_interactions"],
                                    changes,
                                    params["language"]
                                )
                                if new_script:
                                    # Update the stored script
                                    st.session_state.generated_script = new_script
                                    st.session_state.show_regenerate_popover = False
                                    st.rerun()
                                else:
                                    st.error("Failed to regenerate script. Please try again.")
                # Additional export options
                if st.checkbox("Show additional export options"):
                    col1, col2 = st.columns(2)
                    with col1:
                        if st.button("Copy to Clipboard"):
                            st.code(script)
                            st.success("Script copied to clipboard!")
                    with col2:
                        if st.button("Save to Local File"):
                            # This is a placeholder - actual file saving would require additional backend functionality
                            st.info("This feature would save the file locally on your device.")
            else:
                st.error("Failed to generate script. Please try again.")
    # Display previously generated script if it exists in session state
    elif st.session_state.generated_script:
        script = st.session_state.generated_script
        params = st.session_state.script_params
        st.subheader("Generated Script")
        # Display script with tabs for different views
        script_tab1, script_tab2 = st.tabs(["Formatted View", "Plain Text"])
        with script_tab1:
            st.markdown(script)
        with script_tab2:
            st.code(script)
        # Export options
        st.subheader("Export Script")
        # Get export data
        export_data, export_filename, mime_type = export_script(
            script, 
            export_format, 
            custom_filename if custom_filename else None
        )
        # Create columns for the buttons
        btn_col1, btn_col2 = st.columns(2)
        with btn_col1:
            # Download button
            st.download_button(
                label=f"Download as {export_format}",
                data=export_data,
                file_name=export_filename,
                mime=mime_type
            )
        with btn_col2:
            # Regenerate button
            if st.button("Regenerate"):
                st.session_state.show_regenerate_popover = True
        # Regenerate popover
        if st.session_state.get("show_regenerate_popover", False):
            with st.form("regenerate_form"):
                st.subheader("Regenerate Script")
                st.write("Specify changes you'd like to make to the script:")
                changes = st.text_area("Changes to make", 
                                     placeholder="e.g., Make it more casual, add more call-to-actions, focus on product benefits")
                submitted = st.form_submit_button("Regenerate with Changes")
                if submitted and changes:
                    with st.spinner("Regenerating script..."):
                        # Generate a new script with the changes
                        new_script = generate_youtube_script_with_changes(
                            params["target_audience"], 
                            params["main_points"], 
                            params["tone_style"], 
                            params["use_case"], 
                            params["script_structure"],
                            params["include_hook"], 
                            params["include_cta"], 
                            params["include_engagement"], 
                            params["include_timestamps"], 
                            params["include_visual_cues"],
                            params["engagement_hooks"],
                            params["community_interactions"],
                            changes,
                            params["language"]
                        )
                        if new_script:
                            # Update the stored script
                            st.session_state.generated_script = new_script
                            st.session_state.show_regenerate_popover = False
                            st.rerun()
                        else:
                            st.error("Failed to regenerate script. Please try again.")
        # Additional export options
        if st.checkbox("Show additional export options"):
            col1, col2 = st.columns(2)
            with col1:
                if st.button("Copy to Clipboard"):
                    st.code(script)
                    st.success("Script copied to clipboard!")
            with col2:
                if st.button("Save to Local File"):
                    # This is a placeholder - actual file saving would require additional backend functionality
                    st.info("This feature would save the file locally on your device.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/shorts_script_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/shorts_script_generator.py
@@ -1,314 +0,0 @@
 """
 YouTube Shorts Script Generator Module
 This module provides functionality for generating optimized scripts for YouTube Shorts.
 """
 import streamlit as st
 import time
 import logging
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_shorts_generator')
 def generate_shorts_script(hook_type, main_topic, target_audience, tone_style, 
                         content_type, duration_seconds=60, include_captions=True,
                         include_text_overlay=True, include_sound_effects=False,
                         vertical_framing_notes=True, language="English"):
    """Generate a YouTube Shorts script optimized for vertical format and short duration."""
    # Create a custom system prompt for Shorts script generation
    system_prompt = f"""You are a YouTube Shorts expert specializing in creating viral, engaging scripts for vertical short-form videos.
    Your task is to generate scripts that are perfectly timed for {duration_seconds} seconds or less.
    Focus on hooks that grab attention in the first 1-2 seconds.
    Format the script with clear sections for visuals, audio, and text overlays.
    Write the entire script in {language}.
    Remember that Shorts are viewed vertically (9:16 aspect ratio) and need to work without sound."""
    # Build hook-specific instructions
    hook_instructions = {
        "Question": "Start with an intriguing question that stops the scroll",
        "Statistic": "Begin with a surprising statistic or fact",
        "Challenge": "Present a challenge or dare to the viewer",
        "Tutorial": "Jump straight into a quick how-to or life hack",
        "Transformation": "Show a before/after or transformation hook",
        "Trend": "Leverage a current trend or sound",
        "Story": "Start with a captivating micro-story",
        "Controversy": "Present a controversial or surprising statement"
    }
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Create a YouTube Shorts script about **{main_topic}** with these specifications:
    **Core Elements:**
    - Hook Type: {hook_type} - {hook_instructions.get(hook_type, "Create an attention-grabbing opening")}
    - Target Audience: {target_audience}
    - Tone/Style: {tone_style}
    - Content Type: {content_type}
    - Duration: {duration_seconds} seconds
    - Language: {language}
    **Required Elements:**
    {"- Include caption suggestions for accessibility" if include_captions else ""}
    {"- Include text overlay positions and timing" if include_text_overlay else ""}
    {"- Include sound effect suggestions" if include_sound_effects else ""}
    {"- Include vertical framing notes for optimal composition" if vertical_framing_notes else ""}
    **Format the script in this structure:**
    1. HOOK (0-2 seconds)
    2. MAIN CONTENT (3-50 seconds)
    3. CALL TO ACTION (last 10 seconds)
    **For each section, specify:**
    - Visual Instructions (what to show)
    - Text Overlays (what text appears and where)
    - Audio/Voiceover
    - Timing (in seconds)
    - Camera Angles/Framing Notes
    **Remember:**
    - Scripts must work without sound (many viewers watch on mute)
    - Text should be centered in the middle 50% of the vertical frame
    - Keep text concise and readable
    - Include pattern interrupts every 3-5 seconds
    - End with a clear call-to-action
    """
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response
    except Exception as err:
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def analyze_shorts_script(script):
    """Analyze a Shorts script for optimal engagement metrics."""
    analysis = {
        'duration_estimate': 0,
        'hook_strength': 0,
        'pattern_interrupts': 0,
        'text_overlay_count': 0,
        'readability_score': 0,
        'optimization_score': 0
    }
    # Basic analysis (can be enhanced with more sophisticated metrics)
    lines = script.split('\n')
    word_count = len(script.split())
    # Estimate duration (rough approximation)
    analysis['duration_estimate'] = word_count * 0.4  # Average speaking speed
    # Count pattern interrupts
    analysis['pattern_interrupts'] = script.lower().count('cut to') + script.lower().count('transition')
    # Count text overlays
    analysis['text_overlay_count'] = script.lower().count('text:') + script.lower().count('overlay:')
    # Calculate optimization score
    score = 100
    # Penalize if estimated duration is too long
    if analysis['duration_estimate'] > 60:
        score -= (analysis['duration_estimate'] - 60) * 2
    # Check for hook presence
    if not any(hook in script.lower() for hook in ['hook:', 'opening:', '0-2 seconds:']):
        score -= 20
    # Check for pattern interrupts (ideal is 1 every 5 seconds)
    ideal_interrupts = analysis['duration_estimate'] / 5
    if analysis['pattern_interrupts'] < ideal_interrupts:
        score -= 10
    # Check for text overlay usage
    if analysis['text_overlay_count'] < 3:
        score -= 10
    # Check for call-to-action
    if not any(cta in script.lower() for cta in ['call to action', 'cta:', 'subscribe', 'follow']):
        score -= 15
    analysis['optimization_score'] = max(0, score)
    return analysis
 def generate_shorts_narration(shorts_script, language="English"):
    system_prompt = f"""You are an expert at converting YouTube Shorts scripts into natural, engaging narration.\nYour task is to read the provided Shorts script and output only the narration lines, as they would be spoken in the video.\nOmit all visual instructions, timing, text overlays, and technical cues. Write the narration in {language}."""
    prompt = f"""Shorts Script:\n{shorts_script}\n\nInstructions:\nExtract and rewrite only the narration lines, as they would be spoken in the video. Do not include any section headers, cues, or formatting. Output only the narration text."""
    try:
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        return response.strip()
    except Exception as err:
        st.error(f"Error: Failed to get narration from LLM: {err}")
        return ""
 def write_yt_shorts():
    """Create a user interface for YouTube Shorts Script Generator."""
    st.write("Generate optimized scripts for YouTube Shorts that grab attention and drive engagement.")
    # Initialize session state for generated script and active tab if they don't exist
    if "generated_shorts_script" not in st.session_state:
        st.session_state.generated_shorts_script = None
    if "active_tab" not in st.session_state:
        st.session_state.active_tab = "Core Elements"
    # Create tabs for different sections
    tab1, tab2, tab3 = st.tabs(["Core Elements", "Style & Format", "Preview & Export"])
    # Set the active tab based on session state
    if st.session_state.active_tab == "Core Elements":
        tab1.active = True
    elif st.session_state.active_tab == "Style & Format":
        tab2.active = True
    elif st.session_state.active_tab == "Preview & Export":
        tab3.active = True
    with tab1:
        # Core elements
        main_topic = st.text_area("Main Topic/Concept", 
                                placeholder="e.g., Quick cooking hack, Life-changing productivity tip")
        col1, col2 = st.columns(2)
        with col1:
            hook_type = st.selectbox("Hook Type", [
                "Question",
                "Statistic",
                "Challenge",
                "Tutorial",
                "Transformation",
                "Trend",
                "Story",
                "Controversy"
            ])
            target_audience = st.text_input("Target Audience", 
                                          placeholder="e.g., Gen Z, busy professionals")
        with col2:
            content_type = st.selectbox("Content Type", [
                "Tutorial/How-to",
                "Life Hack",
                "Entertainment",
                "Educational",
                "Trend",
                "Story",
                "Challenge",
                "Review"
            ])
            tone_style = st.selectbox("Tone/Style", [
                "Energetic",
                "Professional",
                "Casual",
                "Humorous",
                "Dramatic",
                "Inspirational"
            ])
    with tab2:
        # Style and format options
        col1, col2 = st.columns(2)
        with col1:
            duration_seconds = st.slider("Duration (seconds)", 15, 60, 60)
            language = st.selectbox("Language", [
                "English",
                "Spanish",
                "French",
                "German",
                "Italian",
                "Portuguese",
                "Russian",
                "Japanese",
                "Korean",
                "Chinese"
            ])
        with col2:
            include_captions = st.checkbox("Include Captions", value=True)
            include_text_overlay = st.checkbox("Include Text Overlay Positions", value=True)
            include_sound_effects = st.checkbox("Include Sound Effects", value=False)
            vertical_framing_notes = st.checkbox("Include Vertical Framing Notes", value=True)
    with tab3:
        if st.session_state.generated_shorts_script:
            # Display the generated script
            st.subheader("Generated Shorts Script")
            # Create tabs for different views
            script_tab1, script_tab2, script_tab3 = st.tabs(["Formatted", "Analysis", "Export"])
            with script_tab1:
                st.markdown(st.session_state.generated_shorts_script)
            with script_tab2:
                # Analyze the script
                analysis = analyze_shorts_script(st.session_state.generated_shorts_script)
                # Display analysis results
                col1, col2 = st.columns(2)
                with col1:
                    st.metric("Estimated Duration", f"{analysis['duration_estimate']:.1f}s")
                    st.metric("Pattern Interrupts", analysis['pattern_interrupts'])
                    st.metric("Text Overlays", analysis['text_overlay_count'])
                with col2:
                    # Display optimization score with color
                    score = analysis['optimization_score']
                    color = "red" if score < 60 else "orange" if score < 80 else "green"
                    st.markdown(f"### Optimization Score: <span style='color: {color}'>{score}%</span>", 
                              unsafe_allow_html=True)
            with script_tab3:
                # Export options
                export_format = st.selectbox("Export Format", [
                    "Text",
                    "Markdown",
                    "Shot List",
                    "Storyboard"
                ])
                if st.button("Export Script"):
                    # Implement export functionality based on selected format
                    st.success(f"Script exported in {export_format} format!")
                    st.download_button(
                        "Download Script",
                        st.session_state.generated_shorts_script,
                        file_name=f"shorts_script.{export_format.lower()}",
                        mime="text/plain"
                    )
    # Generate button
    if st.button("Generate Shorts Script"):
        if not main_topic:
            st.error("Please enter a main topic/concept.")
            return
        with st.spinner("Generating Shorts script..."):
            script = generate_shorts_script(
                hook_type, main_topic, target_audience, tone_style, content_type,
                duration_seconds, include_captions, include_text_overlay,
                include_sound_effects, vertical_framing_notes, language
            )
            if script:
                st.session_state.generated_shorts_script = script
                # Set active tab to Preview & Export
                st.session_state.active_tab = "Preview & Export"
                st.success("✨ Script generated successfully! Check the 'Preview & Export' tab to view, analyze, and download your script.")
                st.rerun()
            else:
                st.error("Failed to generate script. Please try again.")
    # Add a message about preview and export if script exists but we're not on the Preview tab
    if st.session_state.generated_shorts_script and st.session_state.active_tab != "Preview & Export":
        st.info("💡 Your generated script is ready! Go to the 'Preview & Export' tab to view, analyze, and download it.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/shorts_video_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/shorts_video_generator.py
@@ -1,972 +0,0 @@
 """
 YouTube Shorts Video Generator
 This module provides functionality to generate YouTube Shorts videos using AI.
 It adapts the story video generator for the vertical format and shorter duration of Shorts.
 """
 import os
 import re
 import time
 import json
 import uuid
 import tempfile
 import logging
 import traceback
 from pathlib import Path
 from typing import List, Dict, Any, Tuple, Optional, Union, Callable
 from functools import wraps
 from datetime import datetime
 import random
 import functools
 import streamlit as st
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont
 import requests
 # Try importing moviepy with proper error handling
 try:
    from moviepy.editor import (
        ImageSequenceClip,
        TextClip,
        CompositeVideoClip,
        AudioFileClip,
        AudioClip,
        CompositeAudioClip,
    )
    MOVIEPY_AVAILABLE = True
 except ImportError as e:
    st.error(
        "MoviePy is not properly installed. Please install it using:\n"
        "pip install moviepy imageio imageio-ffmpeg"
    )
    MOVIEPY_AVAILABLE = False
 # Try importing gTTS with proper error handling
 try:
    from gtts import gTTS
    GTTS_AVAILABLE = True
 except ImportError:
    st.error(
        "gTTS is not installed. Please install it using:\n"
        "pip install gTTS"
    )
    GTTS_AVAILABLE = False
 # Import LLM text generation and image generation
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 from lib.gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
 from .shorts_script_generator import generate_shorts_script, generate_shorts_narration
 from lib.ai_writers.ai_story_video_generator.story_video_generator import StoryVideoGenerator
 # Configure logging
 log_dir = Path("logs")
 log_dir.mkdir(exist_ok=True)
 log_file = log_dir / f"shorts_generator_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
 logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler()
    ]
 )
 logger = logging.getLogger(__name__)
 # Constants
 DEFAULT_FPS = 30  # Higher FPS for smoother Shorts
 DEFAULT_DURATION = 2  # seconds per scene (shorter for Shorts)
 DEFAULT_TRANSITION_DURATION = 0.5  # seconds for transition
 DEFAULT_FONT_SIZE = 32  # Larger font for vertical format
 DEFAULT_FONT_COLOR = "white"
 DEFAULT_MUSIC_URL = "https://freepd.com/music/Upbeat%20Uplifting%20Corporate.mp3"  # Example free music URL
 DEFAULT_IMAGE_WIDTH = 1080  # Standard Shorts width
 DEFAULT_IMAGE_HEIGHT = 1920  # Standard Shorts height (9:16 aspect ratio)
 TEXT_AREA_HEIGHT_RATIO = 1/4  # Smaller text area for vertical format
 TEXT_PADDING = 30
 TEXT_OVERLAY_ALPHA = 180  # More opaque overlay for better readability
 # Shorts-specific constants
 MAX_SHORTS_DURATION = 60  # Maximum duration for YouTube Shorts
 MIN_SHORTS_DURATION = 15  # Minimum duration for YouTube Shorts
 DEFAULT_SHORTS_DURATION = 30  # Default duration for Shorts
 MAX_SCENES = 15  # Maximum number of scenes to generate
 MIN_SCENES = 5   # Minimum number of scenes
 WORDS_PER_SECOND = 2.5  # Average speaking rate for narration
 # Video resolutions for Shorts (vertical format)
 VIDEO_RESOLUTIONS = {
    "1080p": (1080, 1920),  # Standard Shorts resolution
    "720p": (720, 1280),    # Lower resolution option
 }
 # Transition styles optimized for Shorts
 TRANSITION_STYLES = {
    "None": None,
    "Fade": "fade",
    "Slide Up": "slide_up",
    "Slide Down": "slide_down",
    "Zoom": "zoom",
    "Wipe": "wipe"
 }
 # Content styles for Shorts
 CONTENT_STYLES = {
    "Tutorial": {
        "style": "tutorial",
        "description": "Step-by-step instructional content"
    },
    "Story": {
        "style": "story",
        "description": "Narrative-driven content"
    },
    "Tips": {
        "style": "tips",
        "description": "Quick tips and tricks"
    },
    "Review": {
        "style": "review",
        "description": "Product or service reviews"
    },
    "Behind the Scenes": {
        "style": "behind_scenes",
        "description": "Behind-the-scenes content"
    }
 }
 # Narration languages
 NARRATION_LANGUAGES = {
    "English (US)": "en-us",
    "English (UK)": "en-gb",
    "Spanish": "es",
    "French": "fr",
    "German": "de",
    "Italian": "it",
    "Japanese": "ja",
    "Korean": "ko",
    "Chinese": "zh-cn",
    "Hindi": "hi"
 }
 # Retry configuration
 MAX_RETRIES = 3
 INITIAL_RETRY_DELAY = 1  # Initial delay in seconds
 MAX_RETRY_DELAY = 30     # Maximum delay in seconds
 RETRYABLE_ERRORS = (
    ConnectionError,
    TimeoutError,
    requests.exceptions.RequestException,
    OSError,  # For file system operations
    IOError,  # For file system operations
 )
 def retry_on_error(max_retries: int = MAX_RETRIES, initial_delay: int = INITIAL_RETRY_DELAY, max_delay: int = MAX_RETRY_DELAY):
    """
    Decorator for retrying functions on specific errors with exponential backoff.
    # ... existing code ...
 """
 def extract_narration_from_shorts_script(script: str) -> str:
    """
    Extract and optimize narration from the script for Shorts format.
    Ensures narration is concise, valuable, and properly timed.
    """
    scenes = re.split(r'\n\n+', script)
    narration_lines = []
    total_words = 0
    max_words = 75  # Target for 30-second video (2.5 words per second)
    # Extract all potential narration lines first
    potential_lines = []
    for scene in scenes:
        match = re.search(r'Audio/Voiceover:\s*(.*)', scene)
        if match:
            narration = match.group(1).strip()
            narration = re.split(r'\n[A-Z][^:]+:', narration)[0].strip()
            if narration:
                potential_lines.append(narration)
    # Process lines to create engaging narration
    if potential_lines:
        # Start with a hook
        first_line = potential_lines[0]
        if not any(word in first_line.lower() for word in ['discover', 'learn', 'find out', 'see how', 'watch']):
            first_line = f"Discover how to {first_line.lower()}"
        narration_lines.append(first_line)
        total_words += len(first_line.split())
        # Process middle lines
        for line in potential_lines[1:-1]:
            # Add value-focused phrases
            if not any(word in line.lower() for word in ['because', 'why', 'how', 'what', 'when', 'where']):
                line = f"Here's why: {line}"
            # Check word count
            words = line.split()
            if total_words + len(words) <= max_words:
                narration_lines.append(line)
                total_words += len(words)
            else:
                break
        # Add a strong closing
        if len(potential_lines) > 1:
            last_line = potential_lines[-1]
            if not any(phrase in last_line.lower() for phrase in ['try it', 'get started', 'follow for more']):
                last_line = f"Ready to try it? {last_line}"
            if total_words + len(last_line.split()) <= max_words:
                narration_lines.append(last_line)
    # If we have too few words, add a call to action
    if total_words < 50 and narration_lines:
        cta = "Follow for more tips like this!"
        if total_words + len(cta.split()) <= max_words:
            narration_lines.append(cta)
    # Join with proper pacing and emphasis
    final_narration = ' '.join(narration_lines)
    # Add emphasis to key points
    final_narration = re.sub(r'([.!?])\s+', r'\1\n\n', final_narration)  # Add pauses
    return final_narration
 def generate_shorts_narration(script: str, language: str = "en-us", target_duration: int = 30) -> str:
    """
    Generate a clean, natural-sounding narration script for YouTube Shorts.
    Focuses only on what the listener needs to hear, without technical details.
    """
    # Calculate target word count based on duration and user-defined speaking rate
    words_per_second = getattr(st.session_state, 'svgen_words_per_second', WORDS_PER_SECOND)
    narration_padding = getattr(st.session_state, 'svgen_narration_padding', 0.5)
    target_words = int((target_duration - narration_padding) * words_per_second)
    # Extract key information from the script
    scenes = re.split(r'\n\n+', script)
    audio_lines = []
    for scene in scenes:
        # Extract only the audio/voiceover content
        audio_match = re.search(r'Audio/Voiceover:\s*(.*?)(?=\n|$)', scene)
        if audio_match:
            audio_lines.append(audio_match.group(1).strip())
    # Create a specialized prompt for clean narration generation
    narration_prompt = f"""
    Create a natural, conversational narration script for a YouTube Shorts video.
    Focus ONLY on what the listener needs to hear - no technical details, scene descriptions, or timing markers.
    Content Context:
    {script}
    Requirements:
    1. Length: {target_duration} seconds (approximately {target_words} words)
    2. Style: Natural, conversational, and engaging
    3. Structure:
       - Start with a hook
       - Present key points
       - End with a call to action
    4. Tone: {st.session_state.svgen_content_style.lower()}
    Important Guidelines:
    - Write ONLY the spoken words - no descriptions, timing, or technical details
    - Use natural language that sounds good when spoken
    - Keep sentences short and clear
    - Add natural pauses with ellipsis (...)
    - No scene numbers, timing markers, or technical instructions
    - No sound effect descriptions or music cues
    - No formatting markers or special characters
    - Target word count: {target_words} words (±10%)
    - Speaking rate: {words_per_second} words per second
    Example of good narration:
    "Writer's block got you down? Meet your new secret weapon: an AI content writer! This tool helps you write ten times faster. No more blank page terror! Blog posts, social media, even killer emails - all generated in seconds. Ready to unleash your content creation superpowers? Try it free today!"
    Format the narration as a single, flowing script with natural pauses.
    """
    try:
        # Generate narration using LLM
        narration = llm_text_gen(narration_prompt)
        if narration:
            # Clean up the narration
            narration = re.sub(r'\s+', ' ', narration)  # Remove extra spaces
            narration = re.sub(r'[^\w\s.,!?…-]', '', narration)  # Keep only essential punctuation
            narration = re.sub(r'([.!?])\s+', r'\1\n\n', narration)  # Add natural pauses
            narration = re.sub(r'\*\*.*?\*\*', '', narration)  # Remove any markdown
            narration = re.sub(r'\(.*?\)', '', narration)  # Remove any parenthetical notes
            narration = re.sub(r'\n\s*\n', '\n\n', narration)  # Clean up extra line breaks
            # Verify word count
            word_count = len(narration.split())
            if word_count < target_words * 0.9 or word_count > target_words * 1.1:
                print(f'[WARNING] Generated narration word count ({word_count}) is outside target range ({target_words}±10%)')
            return narration.strip()
    except Exception as e:
        print(f'[ERROR] Failed to generate narration: {e}')
        return None
 def write_yt_shorts_video():
    """
    Main function to generate a YouTube Shorts video.
    This function provides a Streamlit interface for users to generate Shorts videos.
    """
    st.markdown("""
        <style>
        .stepper {
            display: flex;
            justify-content: space-between;
            margin-bottom: 2rem;
        }
        .step {
            flex: 1;
            text-align: center;
            padding: 0.5rem 0;
            border-bottom: 4px solid #e0e0e0;
            color: #888;
            font-weight: 600;
            font-size: 1.1rem;
        }
        .step.active {
            color: #2563eb;
            border-bottom: 4px solid #2563eb;
            background: #f0f6ff;
            border-radius: 8px 8px 0 0;
        }
        .card {
            background: #f8fafc;
            border-radius: 12px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.04);
            padding: 2rem 2rem 1.5rem 2rem;
            margin-bottom: 2rem;
        }
        .section-title {
            font-size: 1.3rem;
            font-weight: 700;
            margin-bottom: 1rem;
            color: #222;
            display: flex;
            align-items: center;
        }
        .section-title svg {
            margin-right: 0.5rem;
        }
        .primary-btn {
            background: #2563eb;
            color: #fff;
            border-radius: 8px;
            font-size: 1.1rem;
            font-weight: 600;
            padding: 0.75rem 2.5rem;
            border: none;
            margin-top: 1.5rem;
            margin-bottom: 0.5rem;
            box-shadow: 0 2px 8px rgba(37,99,235,0.08);
        }
        </style>
    """, unsafe_allow_html=True)
    # Stepper logic
    if 'shorts_stage' not in st.session_state:
        st.session_state.shorts_stage = 1
    if 'generated_script' not in st.session_state:
        st.session_state.generated_script = None
    if 'script_approved' not in st.session_state:
        st.session_state.script_approved = False
    # Stepper UI
    st.markdown(f'''
    <div class="stepper">
        <div class="step {'active' if st.session_state.shorts_stage == 1 else ''}">1. Input Details</div>
        <div class="step {'active' if st.session_state.shorts_stage == 2 else ''}">2. Script Review</div>
        <div class="step {'active' if st.session_state.shorts_stage == 3 else ''}">3. Video Generation</div>
    </div>
    ''', unsafe_allow_html=True)
    # --- Stage 1: Input Details ---
    if st.session_state.shorts_stage == 1:
        print('[DEBUG] Stage 1: Input Details loaded')
        st.markdown('---')
        st.markdown('### 1️⃣ Input Video Details')
        st.info("Fill in all details below, then click **Generate Script** to continue.")
        with st.container():
            st.markdown('<div class="card">', unsafe_allow_html=True)
            st.markdown('<div class="section-title">📝 Video Content</div>', unsafe_allow_html=True)
            video_topic = st.text_input(
                "What's your video about?",
                placeholder="Enter the main topic or theme of your Shorts video",
                help="Be specific about what you want to create"
            )
            style_col, duration_col = st.columns(2)
            with style_col:
                content_style = st.selectbox(
                    "Content Style",
                    list(CONTENT_STYLES.keys()),
                    help="Select the style that best fits your content"
                )
            with duration_col:
                video_duration = st.slider(
                    "Duration (seconds)",
                    MIN_SHORTS_DURATION,
                    MAX_SHORTS_DURATION,
                    DEFAULT_SHORTS_DURATION,
                    help=f"Shorts must be between {MIN_SHORTS_DURATION} and {MAX_SHORTS_DURATION} seconds"
                )
                # Calculate and display scene count based on duration
                scene_duration = DEFAULT_DURATION  # seconds per scene
                max_possible_scenes = min(MAX_SCENES, int(video_duration / scene_duration))
                min_possible_scenes = max(MIN_SCENES, int(video_duration / (scene_duration * 2)))
                scene_count = st.slider(
                    "Number of Scenes",
                    min_possible_scenes,
                    max_possible_scenes,
                    min(max_possible_scenes, 10),  # Default to 10 or max possible
                    help=f"Based on {scene_duration}s per scene, you can have {min_possible_scenes}-{max_possible_scenes} scenes"
                )
            st.markdown('</div>', unsafe_allow_html=True)
        with st.container():
            settings_col = st.columns(1)[0]
            with settings_col:
                with st.expander("⚙️ Video Settings", expanded=True):
                    res_col, trans_col = st.columns(2)
                    with res_col:
                        resolution = st.selectbox(
                            "Resolution",
                            list(VIDEO_RESOLUTIONS.keys()),
                            help="Higher resolution = better quality but longer processing time"
                        )
                    with trans_col:
                        transition_style = st.selectbox(
                            "Transition Style",
                            list(TRANSITION_STYLES.keys()),
                            help="How scenes transition between each other"
                        )
                    # Add timing controls
                    st.markdown("---")
                    st.markdown("#### ⏱️ Timing Settings")
                    # Scene timing controls
                    timing_col1, timing_col2 = st.columns(2)
                    with timing_col1:
                        scene_duration = st.slider(
                            "Seconds per Scene",
                            min_value=1.0,
                            max_value=5.0,
                            value=DEFAULT_DURATION,
                            step=0.5,
                            help="How long each scene should be displayed"
                        )
                        st.session_state.svgen_scene_duration = scene_duration
                    with timing_col2:
                        transition_duration = st.slider(
                            "Transition Duration (seconds)",
                            min_value=0.1,
                            max_value=1.0,
                            value=DEFAULT_TRANSITION_DURATION,
                            step=0.1,
                            help="Duration of transitions between scenes"
                        )
                        st.session_state.svgen_transition_duration = transition_duration
                    # Narration timing controls
                    narr_timing_col1, narr_timing_col2 = st.columns(2)
                    with narr_timing_col1:
                        words_per_second = st.slider(
                            "Speaking Rate (words/second)",
                            min_value=1.5,
                            max_value=3.5,
                            value=WORDS_PER_SECOND,
                            step=0.1,
                            help="Adjust narration speed (default: 2.5 words/second)"
                        )
                        st.session_state.svgen_words_per_second = words_per_second
                    with narr_timing_col2:
                        narration_padding = st.slider(
                            "Narration Padding (seconds)",
                            min_value=0.0,
                            max_value=2.0,
                            value=0.5,
                            step=0.1,
                            help="Extra time to add to narration duration"
                        )
                        st.session_state.svgen_narration_padding = narration_padding
                    # Calculate and display timing information
                    total_scene_time = scene_duration * scene_count
                    total_transition_time = transition_duration * (scene_count - 1)
                    total_video_time = total_scene_time + total_transition_time
                    st.info(f"""
                    **Timing Summary:**
                    - Total Scene Time: {total_scene_time:.1f}s
                    - Total Transition Time: {total_transition_time:.1f}s
                    - Estimated Video Duration: {total_video_time:.1f}s
                    - Target Narration Length: {int(total_video_time * words_per_second)} words
                    """)
                with st.expander("🎙️ Narration Settings", expanded=True):
                    narr_col1, narr_col2 = st.columns(2)
                    with narr_col1:
                        narration_language = st.selectbox(
                            "Language",
                            list(NARRATION_LANGUAGES.keys()),
                            help="Select the language for narration"
                        )
                    with narr_col2:
                        include_music = st.checkbox(
                            "Include Background Music",
                            value=True,
                            help="Add background music to enhance the video"
                        )
        st.markdown('---')
        can_generate_script = bool(video_topic and content_style and video_duration and resolution and narration_language)
        if st.button("📝 Generate Script", key="generate_script_btn", help="Generate a script for your Shorts video", use_container_width=True, disabled=not can_generate_script):
            print(f'[DEBUG] Generate Script button clicked. Topic: {video_topic}, Style: {content_style}, Duration: {video_duration}, Resolution: {resolution}, Language: {narration_language}')
            try:
                with st.spinner("Generating script..."):
                    script = generate_shorts_script(
                        hook_type="Question",
                        main_topic=video_topic,
                        target_audience="general",
                        tone_style=content_style,
                        content_type=CONTENT_STYLES[content_style]["style"],
                        duration_seconds=video_duration,
                        include_captions=True,
                        include_text_overlay=True,
                        include_sound_effects=True,
                        vertical_framing_notes=True,
                        language=narration_language
                    )
                    print(f'[DEBUG] Script generated: {bool(script)}')
                    if script:
                        st.session_state.generated_script = script
                        st.session_state.script_approved = False
                        st.session_state.shorts_stage = 2
                        st.session_state.svgen_resolution = resolution
                        st.session_state.svgen_transition_style = transition_style
                        st.session_state.svgen_narration_language = narration_language
                        st.session_state.svgen_include_music = include_music
                        st.session_state.svgen_content_style = content_style
                        st.session_state.svgen_video_duration = video_duration
                        st.session_state.svgen_video_topic = video_topic
                        print('[DEBUG] Script saved to session state and moving to Stage 2')
                        st.success("Script generated! Review and edit below.")
                    else:
                        print('[ERROR] Script generation failed')
                        st.error("Failed to generate script. Please try again.")
            except Exception as e:
                print(f'[ERROR] Exception during script generation: {e}')
                st.error(f"An error occurred while generating the script: {str(e)}")
                logger.error(f"Error in script generation: {str(e)}")
                logger.error(traceback.format_exc())
        if not can_generate_script:
            st.warning("Please fill in all required fields above to enable script generation.")
        st.markdown('---')
        st.info("Next: Review and edit your script.")
    # --- Stage 2: Script Review & Edit ---
    if st.session_state.shorts_stage == 2:
        print('[DEBUG] Stage 2: Script Review & Edit loaded')
        st.markdown('---')
        st.markdown('### 2️⃣ Script Review & Edit')
        st.info("Review your generated script. Use the Edit tab to make changes. Approve to continue.")
        st.markdown('<div class="card">', unsafe_allow_html=True)
        st.markdown('<div class="section-title">📄 Script Preview & Edit</div>', unsafe_allow_html=True)
        preview_tab, edit_tab = st.tabs(["Preview", "Edit"])
        with preview_tab:
            st.markdown(st.session_state.generated_script)
            if not st.session_state.script_approved:
                if st.button("✅ Approve Script", key="approve_script_btn", use_container_width=True):
                    st.session_state.script_approved = True
                    print('[DEBUG] Script approved by user')
                    st.success("Script approved! You can now generate your video.")
        with edit_tab:
            edited_script = st.text_area(
                "Edit Script",
                value=st.session_state.generated_script,
                height=400,
                help="Make any necessary changes to the script. The format should be maintained."
            )
            if edited_script != st.session_state.generated_script:
                print('[DEBUG] Script edited by user')
                st.session_state.generated_script = edited_script
                st.session_state.script_approved = False
                st.info("Script updated. Please review and approve the changes.")
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown('---')
        st.button("⬅️ Back to Details", key="back_to_details_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 1}))
        if st.session_state.script_approved:
            st.success("Script approved! You can now generate your video.")
            st.button("🎬 Proceed to Video Generation", key="proceed_to_video_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 3}))
        else:
            st.warning("Please approve your script before proceeding.")
        st.markdown('---')
        st.info("Next: Review and edit narration, then generate your video.")
    # --- Stage 3: Video Generation ---
    if st.session_state.shorts_stage == 3:
        print('[DEBUG] Stage 3: Narration & Video Generation loaded')
        st.markdown('---')
        st.markdown('### 3️⃣ Narration & Video Generation')
        st.info("Edit or generate narration, preview audio, then click **Generate Video**.")
        st.markdown('<div class="card">', unsafe_allow_html=True)
        st.markdown('<div class="section-title">🗣️ Narration for Review & Edit</div>', unsafe_allow_html=True)
        narr_col1, narr_col2 = st.columns([4, 1])
        with narr_col1:
            if 'editable_narration' not in st.session_state:
                st.session_state.editable_narration = generate_shorts_narration(
                    st.session_state.generated_script,
                    language=st.session_state.svgen_narration_language,
                    target_duration=st.session_state.svgen_video_duration
                )
                print('[DEBUG] Initial narration generated')
            edited_narration = st.text_area(
                "Edit narration to be used for TTS:",
                value=st.session_state.editable_narration,
                height=120,
                key="editable_narration_area",
                help="Edit the narration to sound natural when spoken. No technical details needed."
            )
            st.session_state.editable_narration = edited_narration
            # Calculate and display timing information
            narration_word_count = len(edited_narration.split())
            words_per_second = 2.5  # Standard speaking rate
            estimated_duration = narration_word_count / words_per_second
            narration_stats = (
                f"Words: {narration_word_count} | "
                f"Est. duration: {estimated_duration:.1f}s | "
                f"Target: {st.session_state.svgen_video_duration}s"
            )
            st.caption(narration_stats)
            # Display timing warnings
            if estimated_duration < 20:
                st.warning("⚠️ Narration is too short for a 30-second video. Consider generating a new narration.")
            elif estimated_duration > 35:
                st.warning("⚠️ Narration is too long for a 30-second video. Consider generating a new narration.")
            # Narration Tips in an expander
            with st.expander("💡 Narration Tips", expanded=False):
                st.markdown("""
                ### Tips for Natural Narration
                - Write only what should be spoken
                - Keep it conversational and clear
                - Use natural pauses (...)
                - Focus on the message, not the technical details
                - End with a clear call to action
                """)
            tts_col1, tts_col2 = st.columns(2)
            with tts_col1:
                tts_gender = st.selectbox("Voice Gender (affects some TTS engines)", ["Default", "Female", "Male"], key="tts_gender_select")
            with tts_col2:
                tts_speed = st.selectbox("Speech Speed", ["Normal", "Slow"], key="tts_speed_select")
            if st.button("🔊 Preview Narration Audio", key="preview_tts_btn"):
                print('[DEBUG] TTS preview button clicked')
                try:
                    tts_kwargs = {"lang": NARRATION_LANGUAGES[st.session_state.svgen_narration_language]}
                    tts_kwargs["slow"] = tts_speed == "Slow"
                    tts = gTTS(text=edited_narration, **tts_kwargs)
                    preview_audio_path = os.path.join(tempfile.gettempdir(), f"tts_preview_{os.getpid()}.mp3")
                    tts.save(preview_audio_path)
                    with open(preview_audio_path, "rb") as audio_file:
                        audio_bytes = audio_file.read()
                    st.audio(audio_bytes, format="audio/mp3")
                    print('[DEBUG] TTS preview audio generated and played')
                except Exception as tts_err:
                    print(f'[ERROR] Failed to generate TTS preview: {tts_err}')
                    st.error(f"Failed to generate TTS preview: {tts_err}")
            if narration_word_count < 10:
                st.warning("Narration is very short. Consider adding more detail.")
            elif narration_word_count > 120:
                st.warning("Narration is quite long. Consider shortening for Shorts.")
        with narr_col2:
            if st.button("🔄 Generate New Narration", key="generate_narration_btn"):
                with st.spinner("Generating engaging narration..."):
                    new_narration = generate_shorts_narration(
                        st.session_state.generated_script,
                        language=st.session_state.svgen_narration_language,
                        target_duration=st.session_state.svgen_video_duration
                    )
                    if new_narration:
                        st.session_state.editable_narration = new_narration
                        print('[DEBUG] New narration generated')
                        st.success("New narration generated successfully!")
                        st.rerun()
                    else:
                        st.error("Failed to generate narration. Please try again.")
            if st.button("🤖 Generate AI Narration", key="ai_narration_btn"):
                with st.spinner("Generating AI-optimized narration..."):
                    ai_narr = generate_shorts_narration(
                        st.session_state.generated_script,
                        language=st.session_state.svgen_narration_language,
                        target_duration=st.session_state.svgen_video_duration
                    )
                    if ai_narr:
                        st.session_state.editable_narration = ai_narr
                        print('[DEBUG] AI-generated narration updated')
                        st.success("AI-generated narration updated.")
                        st.rerun()
                    else:
                        st.error("Failed to generate AI narration. Please try again.")
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown('---')
        st.markdown('### 3️⃣ Video Generation')
        st.info("Click **Generate Video** to start the final process. This may take a few minutes.")
        st.markdown('<div class="card">', unsafe_allow_html=True)
        st.markdown('<div class="section-title"> Video Generation</div>', unsafe_allow_html=True)
        # Video Information in an expander
        with st.expander("📋 Video Information", expanded=True):
            st.markdown("""
            ### Video Details
            | Setting | Value |
            |---------|--------|
            | Video Topic | {} |
            | Content Style | {} |
            | Duration | {} seconds |
            | Resolution | {} |
            | Narration Language | {} |
            | Background Music | {} |
            """.format(
                st.session_state.svgen_video_topic,
                st.session_state.svgen_content_style,
                st.session_state.svgen_video_duration,
                st.session_state.svgen_resolution,
                st.session_state.svgen_narration_language,
                "Yes" if st.session_state.svgen_include_music else "No"
            ))
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown('<div style="text-align:center">', unsafe_allow_html=True)
        st.button("⬅️ Back to Script Review", key="back_to_script_btn", use_container_width=True, on_click=lambda: st.session_state.update({'shorts_stage': 2}))
        if st.button("🚀 Generate Video", key="generate_video_btn", use_container_width=True):
            print('[DEBUG] Generate Video button clicked')
            try:
                with st.spinner("Generating your Shorts video..."):
                    st.info("Step 1/3: Generating images...")
                    image_paths = []
                    temp_dir = Path(tempfile.mkdtemp())
                    # Filter out empty scenes and limit to MAX_SCENES
                    scenes = [s.strip() for s in st.session_state.generated_script.split("\n\n") if s.strip()][:MAX_SCENES]
                    resolution = st.session_state.svgen_resolution
                    narration_language = st.session_state.svgen_narration_language
                    scene_count = 0
                    num_scenes_total = len(scenes)
                    progress_bar = st.progress(0.0)
                    status_text = st.empty()
                    # Initialize or load image cache
                    if 'generated_image_paths' not in st.session_state:
                        st.session_state.generated_image_paths = {}
                    generated_image_paths = st.session_state.generated_image_paths
                    # Clear any invalid cache entries
                    generated_image_paths = {k: v for k, v in generated_image_paths.items() 
                                          if os.path.exists(v) and k < num_scenes_total}
                    st.session_state.generated_image_paths = generated_image_paths
                    preview_container = st.container()
                    preview_thumbnails = []
                    def retry_on_error(max_retries=3, initial_delay=1, max_delay=10):
                        def decorator(func):
                            @functools.wraps(func)
                            def wrapper(*args, **kwargs):
                                delay = initial_delay
                                for attempt in range(max_retries):
                                    try:
                                        return func(*args, **kwargs)
                                    except Exception as e:
                                        if attempt == max_retries - 1:
                                            raise
                                        print(f'[WARN] Retry {attempt+1}/{max_retries} for image generation: {e}')
                                        time.sleep(delay)
                                        delay = min(delay * 2, max_delay)
                                return None
                            return wrapper
                        return decorator
                    @retry_on_error(max_retries=3, initial_delay=2, max_delay=10)
                    def safe_generate_image(prompt):
                        return generate_image(prompt)
                    for i, scene in enumerate(scenes):
                        print(f'[DEBUG] Processing scene {i+1}/{num_scenes_total}')
                        status_text.text(f"Generating image for scene {i+1}/{num_scenes_total}...")
                        # Check cache first
                        if i in generated_image_paths:
                            image_paths.append(generated_image_paths[i])
                            preview_thumbnails.append((generated_image_paths[i], i+1))
                            print(f'[DEBUG] Using cached image for scene {i+1}')
                            scene_count += 1
                            progress_bar.progress(scene_count / num_scenes_total)
                            continue
                        # Extract details for a more specific prompt
                        visual_desc = scene.split("Visual Instructions:")[1].split("\n")[0] if "Visual Instructions:" in scene else scene
                        narration_match = re.search(r'Audio/Voiceover:\s*(.*)', scene)
                        narration_line = narration_match.group(1).strip() if narration_match else ""
                        # Enhanced prompt with more specific details and style guidance
                        prompt = (
                            f"Create a vertical (9:16) image for YouTube Shorts video.\n"
                            f"Scene {i+1} of {num_scenes_total}:\n"
                            f"Visual Description: {visual_desc}\n"
                            f"Context: {narration_line}\n"
                            f"Style Requirements:\n"
                            f"- High contrast and vibrant colors for better mobile viewing\n"
                            f"- Clear focal point in the center for vertical format\n"
                            f"- Professional quality, cinematic lighting\n"
                            f"- Text-safe areas on top and bottom\n"
                            f"- Visually distinct from other scenes\n"
                            f"- Modern, engaging composition\n"
                            f"- Suitable for {st.session_state.svgen_content_style} style content\n"
                            f"Technical Requirements:\n"
                            f"- Vertical 9:16 aspect ratio\n"
                            f"- High resolution, sharp details\n"
                            f"- No text or watermarks\n"
                            f"- No blurry or low-quality elements"
                        )
                        try:
                            image_path = safe_generate_image(prompt)
                            if image_path:
                                img = Image.open(image_path)
                                target_size = VIDEO_RESOLUTIONS[resolution]
                                img = img.resize(target_size, Image.LANCZOS)
                                resized_path = temp_dir / f"scene_{i}.png"
                                img.save(resized_path)
                                image_paths.append(str(resized_path))
                                generated_image_paths[i] = str(resized_path)
                                st.session_state.generated_image_paths = generated_image_paths
                                preview_thumbnails.append((str(resized_path), i+1))
                                print(f'[DEBUG] Generated and cached new image for scene {i+1}')
                            else:
                                print(f'[ERROR] Image generation failed for scene {i+1}')
                                st.warning(f"Image generation failed for scene {i+1}. Skipping.")
                        except Exception as img_err:
                            print(f'[ERROR] Exception during image generation for scene {i+1}: {img_err}')
                            st.warning(f"Error generating image for scene {i+1}: {img_err}")
                        scene_count += 1
                        progress_bar.progress(scene_count / num_scenes_total)
                        # Update preview after each image
                        with preview_container:
                            preview_container.empty()  # Clear previous preview
                            if preview_thumbnails:
                                # Create a grid layout with 5 columns
                                cols = st.columns(5)
                                # Display thumbnails in a grid
                                for idx, (img_path, sc_num) in enumerate(preview_thumbnails):
                                    with cols[idx % 5]:
                                        # Create a smaller thumbnail
                                        img = Image.open(img_path)
                                        # Calculate aspect ratio to maintain 9:16
                                        target_width = 100  # Smaller width
                                        target_height = int(target_width * (16/9))
                                        img = img.resize((target_width, target_height), Image.LANCZOS)
                                        # Display with a compact caption
                                        st.image(
                                            img,
                                            caption=f"Scene {sc_num}",
                                            use_column_width=True,
                                            key=f"preview_{sc_num}"  # Add unique key for each image
                                        )
                                        # Add a small progress indicator
                                        if idx == len(preview_thumbnails) - 1:
                                            st.caption(f"Generating scene {scene_count + 1}...")
                                # Add a clear divider between preview and next steps
                                st.markdown("---")
                    status_text.text("Image generation complete!")
                    print(f'[DEBUG] Image generation complete. Total images: {len(image_paths)}')
                    if not image_paths:
                        print('[ERROR] No images generated')
                        st.error("Failed to generate images. Please try again.")
                        return
                    st.info("Step 2/3: Generating narration...")
                    narration_path = temp_dir / "narration.mp3"
                    narration_text = st.session_state.editable_narration
                    try:
                        tts = gTTS(text=narration_text, lang=NARRATION_LANGUAGES[narration_language])
                        tts.save(str(narration_path))
                        print('[DEBUG] Narration audio generated and saved')
                        # Verify the audio file was created and is valid
                        if not os.path.exists(str(narration_path)):
                            raise Exception("Narration audio file was not created")
                        # Test the audio file by loading it
                        test_audio = AudioFileClip(str(narration_path))
                        if test_audio.duration <= 0:
                            raise Exception("Generated audio file is invalid or empty")
                        test_audio.close()
                    except Exception as tts_err:
                        print(f'[ERROR] Failed to generate narration: {tts_err}')
                        st.error(f"Failed to generate narration: {tts_err}")
                        return
                    st.info("Step 3/3: Creating video...")
                    video_generator = StoryVideoGenerator()
                    try:
                        # Verify audio file exists before video creation
                        if not os.path.exists(str(narration_path)):
                            raise Exception("Narration audio file not found")
                        video_path = video_generator.create_video(
                            image_paths=image_paths,
                            audio_path=str(narration_path),
                            fps=DEFAULT_FPS,
                            duration_per_image=getattr(st.session_state, 'svgen_scene_duration', DEFAULT_DURATION)
                        )
                        if video_path and os.path.exists(video_path):
                            print(f'[DEBUG] Video generated at {video_path}')
                            st.success("✨ Video generated successfully! Preview below and download your video.")
                            st.video(video_path)
                            safe_topic = re.sub(r'[^\w\-]+', '_', st.session_state.svgen_video_topic)
                            download_filename = f"{safe_topic}_shorts_video.mp4"
                            with open(video_path, "rb") as f:
                                video_bytes = f.read()
                            st.download_button(
                                label="⬇️ Download Video",
                                data=video_bytes,
                                file_name=download_filename,
                                mime="video/mp4"
                            )
                        else:
                            print('[ERROR] Video file not found after generation')
                            st.error("Failed to create video. Please try again.")
                    except Exception as vid_err:
                        print(f'[ERROR] Exception during video creation: {vid_err}')
                        st.error(f"An error occurred while creating the video: {vid_err}")
                        logger.error(f"Error in video generation: {vid_err}")
                        logger.error(traceback.format_exc())
            except Exception as e:
                print(f'[ERROR] Exception during full video generation: {e}')
                st.error(f"An error occurred while generating the video: {str(e)}")
                logger.error(f"Error in video generation: {str(e)}")
                logger.error(traceback.format_exc())
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown('---')
        st.info("All done! You can download your video above or go back to make changes.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/tags_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/tags_generator.py
@@ -1,406 +0,0 @@
 """
 YouTube Tags Generator Module
 This module provides functionality for generating and optimizing YouTube video tags.
 """
 import streamlit as st
 import time
 import logging
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 from pytrends.request import TrendReq
 import pandas as pd
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_tags_generator')
 def get_pytrends_data(keyword):
    """Get trending data using PyTrends with simplified, reliable approach."""
    logger.info(f"Getting PyTrends data for: '{keyword}'")
    # Initialize empty results
    results = {
        'topics': [],
        'queries': [],
        'trending': []
    }
    try:
        # Initialize PyTrends with minimal configuration
        pytrends = TrendReq(hl='en-US', tz=360)
        time.sleep(1)  # Basic rate limiting
        # 1. Get suggestions (most reliable method)
        try:
            suggestions = pytrends.suggestions(keyword)
            if suggestions:
                results['trending'] = [sugg['title'] for sugg in suggestions if sugg['title']][:3]
        except Exception as e:
            logger.warning(f"Error getting suggestions: {str(e)}")
        # 2. Get trending searches as backup
        if not results['trending']:
            try:
                trending = pytrends.trending_searches(pn='united_states')
                if not trending.empty:
                    results['trending'] = trending.head(3).values.tolist()
            except Exception as e:
                logger.warning(f"Error getting trending searches: {str(e)}")
        # 3. Use keyword variations as fallback
        if not any(results.values()):
            results['trending'] = [keyword]
            results['queries'] = [keyword.lower(), keyword.title()]
            results['topics'] = [keyword.capitalize()]
        return results
    except Exception as e:
        logger.error(f"Error in PyTrends: {str(e)}")
        # Return basic keyword variations as fallback
        return {
            'topics': [keyword.capitalize()],
            'queries': [keyword.lower()],
            'trending': [keyword]
        }
 def get_comprehensive_trends(title, description):
    """Get trending data from title and description keywords."""
    logger.info(f"Getting comprehensive trends for title: '{title}'")
    # Extract main keywords (only words longer than 3 chars)
    words = [w for w in title.split() if len(w) > 3]
    if description:
        desc_words = [w for w in description.split() if len(w) > 3]
        words.extend(desc_words)
    # Remove duplicates and limit to 2 keywords to prevent rate limiting
    keywords = list(dict.fromkeys(words))[:2]
    # Get trending data for main keywords
    all_trends = {
        'topics': [],
        'queries': [],
        'trending': []
    }
    for keyword in keywords:
        try:
            trends = get_pytrends_data(keyword)
            for key in all_trends:
                if trends[key]:
                    all_trends[key].extend(trends[key])
            time.sleep(1)  # Rate limiting between keywords
        except Exception as e:
            logger.warning(f"Error getting trends for keyword '{keyword}': {str(e)}")
            continue
    # Remove duplicates while preserving order
    for key in all_trends:
        seen = set()
        all_trends[key] = [x for x in all_trends[key] if x and not (x.lower() in seen or seen.add(x.lower()))][:5]
    return all_trends
 def generate_tags_from_title_description(title, description, num_tags=10):
    """Generate relevant tags from video title, description, and trending data."""
    logger.info(f"Generating tags for title: '{title}'")
    # Get comprehensive trending data
    trends = get_comprehensive_trends(title, description)
    # Create a comprehensive context for GPT
    trend_context = f"""
    Related Topics: {', '.join(trends['topics'][:10])}
    Related Queries: {', '.join(trends['queries'][:10])}
    Trending Suggestions: {', '.join(trends['trending'][:10])}
    """
    system_prompt = """You are a YouTube SEO expert specializing in tag optimization.
    Generate relevant, searchable tags based on the video title, description, and trending data provided.
    Focus on a mix of specific and broad tags that will help with video discovery.
    Consider the trending topics and queries provided to maximize searchability.
    Return only the tags, separated by commas."""
    user_prompt = f"""Generate {num_tags} relevant YouTube tags for a video with:
    Title: {title}
    Description: {description}
    Consider this trending data:
    {trend_context}
    Include a mix of:
    - Exact match phrases from title and description
    - Related trending topics and queries
    - Broader category tags
    - Specific niche tags
    - Popular search variations
    Format: Return only the tags, separated by commas."""
    try:
        tags = llm_text_gen(user_prompt, system_prompt=system_prompt)
        generated_tags = [tag.strip() for tag in tags.split(',')]
        # Add some trending tags directly
        trending_tags = (
            trends['topics'][:3] +  # Top 3 related topics
            trends['queries'][:3] +  # Top 3 related queries
            trends['trending'][:3]   # Top 3 trending suggestions
        )
        # Combine and remove duplicates
        all_tags = generated_tags + trending_tags
        seen = set()
        final_tags = [tag for tag in all_tags if not (tag.lower() in seen or seen.add(tag.lower()))]
        return final_tags
    except Exception as e:
        logger.error(f"Error generating tags: {str(e)}")
        return []
 def analyze_tags(tags):
    """Analyze tags for optimization opportunities."""
    analysis = {
        'total_tags': len(tags),
        'total_characters': sum(len(tag) for tag in tags),
        'avg_tag_length': sum(len(tag) for tag in tags) / len(tags) if tags else 0,
        'duplicate_tags': len(tags) - len(set(tags)),
        'tags_too_long': [tag for tag in tags if len(tag) > 30],
        'single_word_tags': [tag for tag in tags if len(tag.split()) == 1],
        'optimization_score': 0
    }
    # Calculate optimization score (0-100)
    score = 100
    if analysis['total_tags'] < 5:
        score -= 30
    if analysis['total_characters'] > 500:
        score -= 20
    if analysis['duplicate_tags'] > 0:
        score -= 10 * analysis['duplicate_tags']
    if len(analysis['tags_too_long']) > 0:
        score -= 5 * len(analysis['tags_too_long'])
    if len(analysis['single_word_tags']) > len(tags) * 0.5:
        score -= 15
    analysis['optimization_score'] = max(0, score)
    return analysis
 def display_tags(tags):
    """Display tags in a visually appealing format."""
    if not tags:
        return
    # Create a container for all tags
    st.markdown("""
        <style>
            .tag-container {
                display: flex;
                flex-wrap: wrap;
                gap: 8px;
                margin-bottom: 16px;
                padding: 12px;
                background-color: #f8f9fa;
                border-radius: 8px;
            }
            .tag {
                display: inline-flex;
                align-items: center;
                background-color: #f0f2f6;
                border-radius: 16px;
                padding: 6px 12px;
                font-size: 13px;
                color: #2c3e50;
                border: 1px solid #e6e9ef;
                white-space: nowrap;
                transition: all 0.2s ease;
            }
            .tag:hover {
                background-color: #e6e9ef;
                border-color: #d1d5db;
                transform: translateY(-1px);
            }
        </style>
        <div class="tag-container">
    """, unsafe_allow_html=True)
    # Display tags
    for tag in tags:
        st.markdown(f'<div class="tag">{tag}</div>', unsafe_allow_html=True)
    st.markdown('</div>', unsafe_allow_html=True)
    # Display tag count and character count
    tags_text = ", ".join(tags)
    char_count = len(tags_text)
    col1, col2 = st.columns(2)
    with col1:
        st.caption(f"Total tags: {len(tags)}")
    with col2:
        st.caption(f"Characters: {char_count}/500")
 def write_yt_tags():
    """Create a user interface for YouTube Tags Generator."""
    logger.info("Initializing YouTube Tags Generator UI")
    st.write("Generate optimized tags for your videos with trending tag suggestions to improve discoverability.")
    # Initialize session state
    if "generated_tags" not in st.session_state:
        st.session_state.generated_tags = None
    if "tag_analysis" not in st.session_state:
        st.session_state.tag_analysis = None
    # Create tabs for different sections
    tab1, tab2, tab3 = st.tabs(["Quick Generate", "Advanced Options", "Analysis"])
    with tab1:
        # Basic information inputs
        title = st.text_input("Video Title", 
                            placeholder="Enter your video title")
        description = st.text_area("Video Description", 
                                 placeholder="Enter your video description")
        col1, col2 = st.columns(2)
        with col1:
            num_tags = st.number_input("Number of Tags", 
                                     min_value=5, 
                                     max_value=30, 
                                     value=15)
        with col2:
            include_trending = st.checkbox("Include Trending Suggestions", value=True)
        if st.button("Generate Tags"):
            if not title:
                st.error("Please enter a video title.")
                return
            with st.spinner("Generating tags..."):
                # Generate tags using the comprehensive method
                tags = generate_tags_from_title_description(title, description, num_tags)
                if tags:
                    # Analyze tags
                    st.session_state.tag_analysis = analyze_tags(tags)
                    st.session_state.generated_tags = tags
                    # Display tags in the new format
                    st.subheader("Generated Tags")
                    display_tags(tags)
                    # Add copy button for all tags
                    tags_text = ", ".join(tags)
                    st.text_area("Tags (copy to use)", value=tags_text, height=100)
                    # Display character count
                    char_count = len(tags_text)
                    st.info(f"Total characters: {char_count}/500 ({500 - char_count} remaining)")
                    # Quick analysis summary
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Number of Tags", len(tags))
                    with col2:
                        st.metric("Optimization Score", f"{st.session_state.tag_analysis['optimization_score']}%")
                    with col3:
                        st.metric("Avg Tag Length", f"{st.session_state.tag_analysis['avg_tag_length']:.1f}")
                    # Display trending data summary if enabled
                    if include_trending:
                        st.subheader("Trending Data Used")
                        trends = get_comprehensive_trends(title, description)
                        # Create columns for different trend types
                        tcol1, tcol2, tcol3 = st.columns(3)
                        with tcol1:
                            st.markdown("##### Related Topics")
                            if trends['topics']:
                                for topic in trends['topics'][:5]:
                                    st.markdown(f"• {topic}")
                            else:
                                st.markdown("*No related topics found*")
                        with tcol2:
                            st.markdown("##### Related Queries")
                            if trends['queries']:
                                for query in trends['queries'][:5]:
                                    st.markdown(f"• {query}")
                            else:
                                st.markdown("*No related queries found*")
                        with tcol3:
                            st.markdown("##### Trending Suggestions")
                            if trends['trending']:
                                for trend in trends['trending'][:5]:
                                    st.markdown(f"• {trend}")
                            else:
                                st.markdown("*No trending suggestions found*")
                else:
                    st.error("Failed to generate tags. Please try again.")
    with tab2:
        st.info("Advanced tag generation options coming soon!")
        st.markdown("""
        Future features will include:
        - Competitor tag analysis
        - Tag performance tracking
        - Category-specific tag suggestions
        - Multi-language tag generation
        - Tag sets management
        """)
    with tab3:
        if st.session_state.tag_analysis:
            st.subheader("Tag Analysis")
            # Create metrics
            col1, col2 = st.columns(2)
            with col1:
                st.metric("Total Tags", st.session_state.tag_analysis['total_tags'])
                st.metric("Total Characters", st.session_state.tag_analysis['total_characters'])
                st.metric("Average Tag Length", f"{st.session_state.tag_analysis['avg_tag_length']:.1f}")
            with col2:
                st.metric("Duplicate Tags", st.session_state.tag_analysis['duplicate_tags'])
                st.metric("Single Word Tags", len(st.session_state.tag_analysis['single_word_tags']))
                st.metric("Tags Too Long", len(st.session_state.tag_analysis['tags_too_long']))
            # Optimization score with color
            score = st.session_state.tag_analysis['optimization_score']
            score_color = 'red' if score < 50 else 'orange' if score < 80 else 'green'
            st.markdown(f"""
                <div style='background-color: {score_color}; padding: 10px; border-radius: 5px; margin: 10px 0;'>
                    <h3 style='color: white; margin: 0;'>Optimization Score: {score}%</h3>
                </div>
            """, unsafe_allow_html=True)
            # Optimization suggestions
            st.subheader("Optimization Suggestions")
            suggestions = []
            if st.session_state.tag_analysis['total_tags'] < 5:
                suggestions.append("❌ Add more tags (aim for at least 15)")
            if st.session_state.tag_analysis['total_characters'] > 500:
                suggestions.append("❌ Total character count exceeds limit (max 500)")
            if st.session_state.tag_analysis['duplicate_tags'] > 0:
                suggestions.append("❌ Remove duplicate tags")
            if len(st.session_state.tag_analysis['tags_too_long']) > 0:
                suggestions.append("❌ Some tags are too long (max 30 characters)")
            if len(st.session_state.tag_analysis['single_word_tags']) > st.session_state.tag_analysis['total_tags'] * 0.5:
                suggestions.append("❌ Too many single-word tags (use more specific phrases)")
            if not suggestions:
                st.success("✅ Your tags are well-optimized!")
            else:
                for suggestion in suggestions:
                    st.warning(suggestion)
        else:
            st.info("Generate tags first to see analysis") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/thumbnail_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/thumbnail_generator.py
@@ -1,622 +0,0 @@
 """
 YouTube Thumbnail Generator Module
 This module provides functionality for generating YouTube video thumbnails.
 """
 import streamlit as st
 import time
 import logging
 import os
 import traceback
 from PIL import Image
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 from lib.gpt_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image, edit_image
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_thumbnail_generator')
 def generate_thumbnail_concepts(video_title, video_description, target_audience, content_type, style_preference, num_concepts=3):
    """Generate thumbnail concept ideas based on video content."""
    logger.info(f"Generating thumbnail concepts for: '{video_title}'")
    logger.info(f"Parameters: target_audience={target_audience}, content_type={content_type}, style_preference={style_preference}, num_concepts={num_concepts}")
    # Create a system prompt for thumbnail concept generation
    system_prompt = """You are a YouTube thumbnail expert specializing in creating engaging, click-worthy thumbnail concepts.
    Your task is to generate thumbnail concept ideas based on the provided video information.
    Focus ONLY on creating concepts that are optimized for YouTube, with proper visual hierarchy, text placement, and emotional triggers.
    Return ONLY the concept descriptions, without any additional commentary or explanations.
    Each concept should include:
    1. A main visual element or scene
    2. Text placement and content
    3. Color scheme suggestions
    4. Emotional trigger or hook
    5. Brief explanation of why this concept would be effective"""
    # Build the prompt
    prompt = f"""
    **Instructions:**
    Please generate {num_concepts} thumbnail concept ideas for a YouTube video with the following information:
    **Video Title:** {video_title}
    **Video Description:** {video_description}
    **Target Audience:** {target_audience}
    **Content Type:** {content_type}
    **Style Preference:** {style_preference}
    **Specific Instructions:**
    * Each concept should be clearly separated and numbered.
    * Focus on creating thumbnails that stand out in search results and recommendations.
    * Consider the target audience's interests and preferences.
    * Include specific details about visual elements, text placement, and color schemes.
    * Explain why each concept would be effective for this specific video.
    """
    try:
        logger.info("Sending request to LLM for thumbnail concepts")
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        logger.info(f"Received response from LLM: {len(response)} characters")
        return response
    except Exception as err:
        logger.error(f"Error generating thumbnail concepts: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to generate thumbnail concepts: {err}")
        return None
 def generate_thumbnail_design(concept_description, style_preference, aspect_ratio="16:9", keywords=None, style=None, focus=None):
    """Generate a thumbnail image based on the concept description."""
    logger.info(f"Generating thumbnail design for concept: '{concept_description[:50]}...'")
    logger.info(f"Parameters: style_preference={style_preference}, aspect_ratio={aspect_ratio}, keywords={keywords}, style={style}, focus={focus}")
    # Create a prompt for the image generation
    image_prompt = f"""
    Create a YouTube thumbnail image with the following specifications:
    Concept: {concept_description}
    Style: {style_preference}
    Aspect Ratio: {aspect_ratio}
    The image should be:
    - High contrast and visually striking
    - Suitable for a YouTube thumbnail
    - Include the specified visual elements and text
    - Follow the color scheme described
    - Optimized for small display sizes
    Make sure the text is large and readable, and the main subject is centered and prominent.
    """
    try:
        logger.info("Sending request to Gemini for thumbnail image")
        # Generate the image using Gemini with enhanced prompt
        img_path = generate_gemini_image(
            image_prompt, 
            keywords=keywords,
            style=style,
            focus=focus,
            enhance_prompt=True
        )
        logger.info(f"Received image from Gemini: {img_path}")
        return img_path
    except Exception as err:
        logger.error(f"Error generating thumbnail image: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to generate thumbnail image: {err}")
        return None
 def edit_thumbnail_image(img_path, edit_instructions):
    """Edit a thumbnail image based on user instructions."""
    logger.info(f"Editing thumbnail image: '{img_path}'")
    logger.info(f"Edit instructions: '{edit_instructions}'")
    try:
        logger.info("Sending request to Gemini for image editing")
        # Edit the image using Gemini
        edited_img_path = edit_image(img_path, edit_instructions)
        logger.info(f"Image editing completed. Edited image path: {edited_img_path}")
        # Return the path to the edited image
        return edited_img_path
    except Exception as err:
        logger.error(f"Error editing thumbnail image: {err}")
        logger.error(traceback.format_exc())
        st.error(f"Error: Failed to edit thumbnail image: {err}")
        return None
 def analyze_thumbnail(thumbnail_path):
    """Analyze a thumbnail for effectiveness."""
    logger.info(f"Analyzing thumbnail: '{thumbnail_path}'")
    # This would typically involve image analysis, but for now we'll use AI to provide feedback
    system_prompt = """You are a YouTube thumbnail expert specializing in analyzing and providing feedback on thumbnail designs.
    Your task is to analyze the thumbnail and provide constructive feedback on its effectiveness.
    Focus on aspects like visual hierarchy, text readability, emotional impact, and click-worthiness."""
    # For now, we'll just return a placeholder analysis
    # In a real implementation, we would analyze the actual image
    logger.info("Generating thumbnail analysis")
    return """
    **Thumbnail Analysis:**
    - **Visual Hierarchy:** The main subject is well-positioned and stands out against the background.
    - **Text Readability:** The text is clear and readable, with good contrast against the background.
    - **Emotional Impact:** The thumbnail creates curiosity and emotional connection with the target audience.
    - **Click-worthiness:** The design is likely to attract clicks due to its visual appeal and clear value proposition.
    **Suggestions for Improvement:**
    - Consider adding a subtle border to make the thumbnail stand out more in search results.
    - The text could be slightly larger for better readability on mobile devices.
    - Adding a small icon or logo could help with brand recognition.
    """
 def parse_concepts(concepts_text):
    """Parse the concepts text into a list of individual concepts."""
    logger.info("Parsing concepts text into individual concepts")
    concept_list = []
    current_concept = ""
    for line in concepts_text.split('\n'):
        if line.strip().startswith(('1.', '2.', '3.', '4.', '5.')):
            if current_concept:
                concept_list.append(current_concept.strip())
            current_concept = line
        else:
            current_concept += "\n" + line
    if current_concept:
        concept_list.append(current_concept.strip())
    logger.info(f"Parsed {len(concept_list)} concepts from the response")
    return concept_list
 def write_yt_thumbnail():
    """Create a user interface for YouTube Thumbnail Generator."""
    logger.info("Initializing YouTube Thumbnail Generator UI")
    st.title("YouTube Thumbnail Generator")
    st.write("Create engaging, click-worthy thumbnails for your YouTube videos.")
    # Initialize session state for generated thumbnails if it doesn't exist
    if "generated_thumbnails" not in st.session_state:
        st.session_state.generated_thumbnails = []
    if "thumbnail_concepts" not in st.session_state:
        st.session_state.thumbnail_concepts = None
    if "current_thumbnail_path" not in st.session_state:
        st.session_state.current_thumbnail_path = None
    if "concept_list" not in st.session_state:
        st.session_state.concept_list = []
    if "editing_thumbnail" not in st.session_state:
        st.session_state.editing_thumbnail = False
    if "edit_instructions" not in st.session_state:
        st.session_state.edit_instructions = ""
    if "edited_thumbnail_path" not in st.session_state:
        st.session_state.edited_thumbnail_path = None
    if "show_edit_form" not in st.session_state:
        st.session_state.show_edit_form = False
    # Create tabs for different sections
    tab1, tab2 = st.tabs(["Basic Info", "Style & Generation"])
    with tab1:
        # Basic information inputs
        video_title = st.text_input("Video Title", 
                                  placeholder="e.g., 10 Tips for Better Photography")
        video_description = st.text_area("Video Description", 
                                       placeholder="Brief description of your video content")
        target_audience = st.text_input("Target Audience", 
                                      placeholder="e.g., photography enthusiasts, beginners")
        # Content type selection
        content_type = st.selectbox("Content Type", [
            "Tutorial/How-to",
            "Vlog",
            "Review",
            "Educational",
            "Entertainment",
            "News/Update",
            "Product Showcase",
            "Challenge",
            "Reaction",
            "Comparison"
        ])
    with tab2:
        # Style preferences
        st.subheader("Style Preferences")
        # Create columns for style options
        col1, col2 = st.columns(2)
        with col1:
            style_preference = st.selectbox("Thumbnail Style", [
                "Bold and Dramatic",
                "Clean and Minimal",
                "Colorful and Vibrant",
                "Dark and Moody",
                "Professional and Corporate",
                "Playful and Fun",
                "Retro/Vintage",
                "Modern and Sleek"
            ])
            num_concepts = st.slider("Number of Concepts", 1, 5, 3)
        with col2:
            aspect_ratio = st.selectbox("Aspect Ratio", [
                "16:9 (Standard)",
                "1:1 (Square)",
                "4:3 (Classic)",
                "9:16 (Vertical)"
            ])
            include_text = st.checkbox("Include Text Overlay", value=True)
            if include_text:
                text_style = st.selectbox("Text Style", [
                    "Bold and Impactful",
                    "Clean and Readable",
                    "Stylized and Thematic",
                    "Minimal and Subtle"
                ])
        # Advanced AI Prompt Settings
        st.subheader("Advanced AI Prompt Settings")
        # Create columns for advanced settings
        col3, col4 = st.columns(2)
        with col3:
            # Image style selection
            image_style = st.selectbox("Image Style", [
                "Auto (AI will choose best style)",
                "Photorealistic",
                "Artistic",
                "Cartoon/Anime",
                "Sketch/Drawing",
                "Digital Art",
                "3D Render"
            ])
            # Extract style for the generate_gemini_image function
            style = None
            if image_style == "Photorealistic":
                style = "photorealistic"
            elif image_style == "Artistic":
                style = "artistic"
            elif image_style == "Cartoon/Anime":
                style = "cartoon"
            elif image_style == "Sketch/Drawing":
                style = "sketch"
            elif image_style == "Digital Art":
                style = "digital_art"
            elif image_style == "3D Render":
                style = "3d_render"
        with col4:
            # Focus selection for photorealistic images
            focus = None
            if style == "photorealistic":
                focus = st.selectbox("Image Focus", [
                    "Auto (AI will choose best focus)",
                    "Portraits",
                    "Objects",
                    "Motion",
                    "Wide-angle"
                ])
                # Extract focus for the generate_gemini_image function
                if focus == "Portraits":
                    focus = "portraits"
                elif focus == "Objects":
                    focus = "objects"
                elif focus == "Motion":
                    focus = "motion"
                elif focus == "Wide-angle":
                    focus = "wide-angle"
                elif focus == "Auto (AI will choose best focus)":
                    focus = None
        # Keywords for enhanced prompt generation
        st.subheader("Keywords for Enhanced Prompt")
        st.write("Add keywords to enhance the AI prompt generation. These will help create more detailed and accurate thumbnails.")
        # Create a text area for keywords
        keywords_input = st.text_area(
            "Keywords (comma-separated)", 
            placeholder="e.g., vibrant, energetic, bold, eye-catching, professional"
        )
        # Process keywords
        keywords = None
        if keywords_input:
            keywords = [k.strip() for k in keywords_input.split(",") if k.strip()]
            logger.info(f"User provided keywords: {keywords}")
    # Generate button
    if st.button("Generate Thumbnail Concepts"):
        if not video_title:
            st.error("Please enter a video title.")
            return
        with st.spinner("Generating thumbnail concepts..."):
            logger.info("User clicked Generate Thumbnail Concepts button")
            concepts = generate_thumbnail_concepts(
                video_title, 
                video_description, 
                target_audience, 
                content_type, 
                style_preference,
                num_concepts
            )
            if concepts:
                # Store the concepts in session state
                st.session_state.thumbnail_concepts = concepts
                # Parse the concepts and store in session state
                st.session_state.concept_list = parse_concepts(concepts)
                logger.info("Stored thumbnail concepts in session state")
                # Display the concepts in tabs
                st.subheader("Thumbnail Concepts")
                # Create tabs for each concept
                concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
                for i, tab in enumerate(concept_tabs):
                    with tab:
                        st.markdown(st.session_state.concept_list[i])
                        # Add a button to generate image for this concept
                        if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_{i}"):
                            with st.spinner(f"Generating thumbnail image for concept {i+1}..."):
                                logger.info(f"User selected concept {i+1} for image generation")
                                # Get the selected concept
                                selected_concept = st.session_state.concept_list[i]
                                # Generate the thumbnail image with enhanced prompt
                                img_path = generate_thumbnail_design(
                                    selected_concept,
                                    style_preference,
                                    aspect_ratio.split()[0],  # Extract just the ratio part
                                    keywords=keywords,
                                    style=style,
                                    focus=focus
                                )
                                if img_path:
                                    # Store the current thumbnail path in session state
                                    st.session_state.current_thumbnail_path = img_path
                                    logger.info(f"Stored current thumbnail path in session state: {img_path}")
                                    # Display the generated image
                                    st.subheader("Generated Thumbnail")
                                    st.image(img_path, use_container_width=True)
                                    # Add download button
                                    with open(img_path, "rb") as file:
                                        st.download_button(
                                            label="Download Thumbnail",
                                            data=file,
                                            file_name=f"youtube_thumbnail_{int(time.time())}.png",
                                            mime="image/png"
                                        )
                                    # Add image editing section
                                    st.subheader("Edit Thumbnail")
                                    st.write("Make changes to your thumbnail by providing instructions below:")
                                    # Create a text area for edit instructions
                                    edit_instructions = st.text_area(
                                        "Edit Instructions", 
                                        placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
                                        key=f"edit_instructions_{i}"
                                    )
                                    # Store edit instructions in session state
                                    st.session_state.edit_instructions = edit_instructions
                                    # Add a button to apply edits
                                    if st.button("Apply Edits", key=f"apply_edits_{i}"):
                                        if not edit_instructions:
                                            st.warning("Please provide edit instructions.")
                                        else:
                                            # Set editing flag
                                            st.session_state.editing_thumbnail = True
                                            st.session_state.show_edit_form = True
                                            # Rerun to update the UI
                                            st.rerun()
                                    # Add analysis button
                                    if st.button("Analyze Thumbnail", key=f"analyze_{i}"):
                                        logger.info("User clicked Analyze Thumbnail button")
                                        analysis = analyze_thumbnail(img_path)
                                        st.subheader("Thumbnail Analysis")
                                        st.markdown(analysis)
            else:
                st.error("Failed to generate thumbnail concepts. Please try again.")
    # Display previously generated concepts if they exist in session state
    elif st.session_state.thumbnail_concepts and st.session_state.concept_list:
        logger.info("Displaying previously generated concepts from session state")
        st.subheader("Thumbnail Concepts")
        # Create tabs for each concept
        concept_tabs = st.tabs([f"Concept {i+1}" for i in range(len(st.session_state.concept_list))])
        for i, tab in enumerate(concept_tabs):
            with tab:
                st.markdown(st.session_state.concept_list[i])
                # Add a button to generate image for this concept
                if st.button(f"Generate Image for Concept {i+1}", key=f"gen_img_existing_{i}"):
                    with st.spinner(f"Generating thumbnail image for concept {i+1}..."):
                        logger.info(f"User selected concept {i+1} for image generation")
                        # Get the selected concept
                        selected_concept = st.session_state.concept_list[i]
                        # Generate the thumbnail image with enhanced prompt
                        img_path = generate_thumbnail_design(
                            selected_concept,
                            style_preference,
                            aspect_ratio.split()[0],  # Extract just the ratio part
                            keywords=keywords,
                            style=style,
                            focus=focus
                        )
                        if img_path:
                            # Store the current thumbnail path in session state
                            st.session_state.current_thumbnail_path = img_path
                            logger.info(f"Stored current thumbnail path in session state: {img_path}")
                            # Display the generated image
                            st.subheader("Generated Thumbnail")
                            st.image(img_path, use_container_width=True)
                            # Add download button
                            with open(img_path, "rb") as file:
                                st.download_button(
                                    label="Download Thumbnail",
                                    data=file,
                                    file_name=f"youtube_thumbnail_{int(time.time())}.png",
                                    mime="image/png"
                                )
                            # Add image editing section
                            st.subheader("Edit Thumbnail")
                            st.write("Make changes to your thumbnail by providing instructions below:")
                            # Create a text area for edit instructions
                            edit_instructions = st.text_area(
                                "Edit Instructions", 
                                placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
                                key=f"edit_instructions_existing_{i}"
                            )
                            # Store edit instructions in session state
                            st.session_state.edit_instructions = edit_instructions
                            # Add a button to apply edits
                            if st.button("Apply Edits", key=f"apply_edits_existing_{i}"):
                                if not edit_instructions:
                                    st.warning("Please provide edit instructions.")
                                else:
                                    # Set editing flag
                                    st.session_state.editing_thumbnail = True
                                    st.session_state.show_edit_form = True
                                    # Rerun to update the UI
                                    st.rerun()
                            # Add analysis button
                            if st.button("Analyze Thumbnail", key=f"analyze_existing_{i}"):
                                logger.info("User clicked Analyze Thumbnail button")
                                analysis = analyze_thumbnail(img_path)
                                st.subheader("Thumbnail Analysis")
                                st.markdown(analysis)
    # Display current thumbnail if it exists in session state
    elif st.session_state.current_thumbnail_path:
        logger.info(f"Displaying current thumbnail from session state: {st.session_state.current_thumbnail_path}")
        st.subheader("Current Thumbnail")
        st.image(st.session_state.current_thumbnail_path, use_container_width=True)
        # Add download button
        with open(st.session_state.current_thumbnail_path, "rb") as file:
            st.download_button(
                label="Download Thumbnail",
                data=file,
                file_name=f"youtube_thumbnail_{int(time.time())}.png",
                mime="image/png"
            )
        # Add image editing section
        st.subheader("Edit Thumbnail")
        st.write("Make changes to your thumbnail by providing instructions below:")
        # Create a text area for edit instructions
        edit_instructions = st.text_area(
            "Edit Instructions", 
            placeholder="e.g., Make the background darker, Add a red border, Change the text color to white",
            key="edit_instructions_current",
            value=st.session_state.edit_instructions if st.session_state.edit_instructions else ""
        )
        # Store edit instructions in session state
        st.session_state.edit_instructions = edit_instructions
        # Add a button to apply edits
        if st.button("Apply Edits", key="apply_edits_current"):
            if not edit_instructions:
                st.warning("Please provide edit instructions.")
            else:
                # Set editing flag
                st.session_state.editing_thumbnail = True
                st.session_state.show_edit_form = True
                # Rerun to update the UI
                st.rerun()
        # Add analysis button
        if st.button("Analyze Thumbnail", key="analyze_current"):
            logger.info("User clicked Analyze Thumbnail button")
            analysis = analyze_thumbnail(st.session_state.current_thumbnail_path)
            st.subheader("Thumbnail Analysis")
            st.markdown(analysis)
    # Handle the editing process
    if st.session_state.editing_thumbnail and st.session_state.show_edit_form:
        st.subheader("Editing Thumbnail")
        # Show a spinner while editing
        with st.spinner("Editing thumbnail..."):
            logger.info(f"User provided edit instructions: '{st.session_state.edit_instructions}'")
            # Edit the thumbnail image
            edited_img_path = edit_thumbnail_image(st.session_state.current_thumbnail_path, st.session_state.edit_instructions)
            if edited_img_path:
                # Update the current thumbnail path in session state
                st.session_state.edited_thumbnail_path = edited_img_path
                logger.info(f"Updated current thumbnail path in session state: {edited_img_path}")
                # Reset editing flags
                st.session_state.editing_thumbnail = False
                st.session_state.show_edit_form = False
                # Display the edited image
                st.subheader("Edited Thumbnail")
                st.image(edited_img_path, use_container_width=True)
                # Add download button for the edited image
                with open(edited_img_path, "rb") as file:
                    st.download_button(
                        label="Download Edited Thumbnail",
                        data=file,
                        file_name=f"youtube_thumbnail_edited_{int(time.time())}.png",
                        mime="image/png"
                    )
                # Update the current thumbnail path to the edited one
                st.session_state.current_thumbnail_path = edited_img_path
                # Add a button to continue editing
                if st.button("Continue Editing"):
                    st.session_state.show_edit_form = True
                    st.rerun()
            else:
                # Reset editing flags
                st.session_state.editing_thumbnail = False
                st.session_state.show_edit_form = False
                st.error("Failed to edit the thumbnail. Please try again with different instructions.") 
--- a/ToBeMigrated/ai_writers/youtube_writers/modules/title_generator.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/modules/title_generator.py
@@ -1,452 +0,0 @@
 """
 YouTube Title Generator Module
 This module provides functionality for generating YouTube video titles.
 """
 import streamlit as st
 import time
 import logging
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger('youtube_title_generator')
 def analyze_title(title):
    """Analyze a YouTube title for SEO and clickbait."""
    logger.info(f"Analyzing title: '{title}'")
    # Character count
    char_count = len(title)
    optimal_length = 50 <= char_count <= 60
    logger.info(f"Character count: {char_count}, Optimal length: {optimal_length}")
    # Clickbait detection. TBD: Use AI to detect clickbait.
    clickbait_phrases = [
        "shocking", "you won't believe", "gone wrong", "gone sexual", 
        "free v-bucks", "free robux", "100%", "gone viral", "viral",
        "you need to see this", "wait till the end", "at 3am", "3am",
        "don't watch this", "watch till the end", "gone too far",
        "insane", "unbelievable", "mind-blowing", "life-changing",
        "secret", "hidden", "revealed", "exposed", "leaked",
        "never before seen", "first time ever", "world's first",
        "no one knows", "experts hate this", "doctors hate this",
        "this will change your life", "this will blow your mind",
        "you've been doing it wrong", "the truth about", "the real reason",
        "what they don't want you to know", "what they're hiding",
        "what they don't tell you", "what you need to know",
        "what you should know", "what you must know", "what you must see",
        "what you must watch", "what you must do", "what you must have",
        "what you must buy", "what you must try", "what you must avoid",
        "what you must stop doing", "what you must start doing",
        "what you must change", "what you must learn", "what you must understand",
        "what you must realize", "what you must accept", "what you must believe",
        "what you must know about", "what you must see about", "what you must watch about",
        "what you must do about", "what you must have about", "what you must buy about",
        "what you must try about", "what you must avoid about", "what you must stop doing about",
        "what you must start doing about", "what you must change about", "what you must learn about",
        "what you must understand about", "what you must realize about", "what you must accept about",
        "what you must believe about", "what you must know about", "what you must see about",
        "what you must watch about", "what you must do about", "what you must have about",
        "what you must buy about", "what you must try about", "what you must avoid about",
        "what you must stop doing about", "what you must start doing about", "what you must change about",
        "what you must learn about", "what you must understand about", "what you must realize about",
        "what you must accept about", "what you must believe about"
    ]
    clickbait_score = 0
    detected_phrases = []
    for phrase in clickbait_phrases:
        if phrase.lower() in title.lower():
            clickbait_score += 1
            detected_phrases.append(phrase)
    is_clickbait = clickbait_score > 0
    logger.info(f"Clickbait detection: score={clickbait_score}, is_clickbait={is_clickbait}")
    if detected_phrases:
        logger.info(f"Detected clickbait phrases: {', '.join(detected_phrases)}")
    # SEO elements
    has_number = any(char.isdigit() for char in title)
    has_question = "?" in title
    has_colon = ":" in title
    has_brackets = "[" in title or "]" in title or "(" in title or ")" in title
    logger.info(f"SEO elements: has_number={has_number}, has_question={has_question}, has_colon={has_colon}, has_brackets={has_brackets}")
    # Calculate SEO score
    seo_score = 0
    if optimal_length:
        seo_score += 3
    if has_number:
        seo_score += 1
    if has_question:
        seo_score += 1
    if has_colon:
        seo_score += 1
    if has_brackets:
        seo_score += 1
    if not is_clickbait:
        seo_score += 2
    logger.info(f"Final SEO score: {seo_score}/10")
    return {
        "char_count": char_count,
        "optimal_length": optimal_length,
        "is_clickbait": is_clickbait,
        "clickbait_score": clickbait_score,
        "seo_score": seo_score,
        "has_number": has_number,
        "has_question": has_question,
        "has_colon": has_colon,
        "has_brackets": has_brackets
    }
 def generate_youtube_title(target_audience, main_points, tone_style, use_case, num_titles=5, progress_bar=None):
    """ Generate youtube title generator """
    logger.info(f"Starting title generation with parameters: target_audience='{target_audience}', main_points='{main_points}', tone_style='{tone_style}', use_case='{use_case}', num_titles={num_titles}")
    # Create a custom system prompt that doesn't include blog-specific instructions
    system_prompt = """You are a YouTube title expert specializing in creating engaging, clickable video titles.
    Your task is to generate YouTube video titles based on the provided information.
    Focus ONLY on creating titles that are optimized for YouTube.
    Return ONLY the titles, one per line, without any numbering or additional text."""
    prompt = f"""
    **Instructions:**
    Please generate {num_titles} YouTube title options for a video about **{main_points}** based on the following information:
    **Target Audience:** {target_audience}
    **Tone and Style:** {tone_style}
    **Use Case:** {use_case}
    **Specific Instructions:**
    * Make the titles catchy and attention-grabbing.
    * Use relevant keywords to improve SEO.
    * Tailor the language and tone to the target audience.
    * Ensure the title reflects the content and use case of the video.
    * Return ONLY the titles, one per line, without any numbering or additional text.
    """
    logger.info("Generated prompt for title generation")
    logger.debug(f"Prompt: {prompt}")
    logger.debug(f"System prompt: {system_prompt}")
    try:
        # Update progress bar if provided
        if progress_bar:
            progress_bar.progress(30)
            progress_bar.text("Analyzing your content and target audience...")
            logger.info("Progress bar updated: 30% - Analyzing content and target audience")
        # Simulate some processing time to show progress
        time.sleep(1)
        if progress_bar:
            progress_bar.progress(60)
            progress_bar.text("Generating creative title options...")
            logger.info("Progress bar updated: 60% - Generating creative title options")
        # Get the response from the language model with custom system prompt
        logger.info("Calling LLM for title generation with custom system prompt")
        start_time = time.time()
        response = llm_text_gen(prompt, system_prompt=system_prompt)
        end_time = time.time()
        logger.info(f"LLM response received in {end_time - start_time:.2f} seconds")
        logger.debug(f"Raw LLM response: {response}")
        if progress_bar:
            progress_bar.progress(90)
            progress_bar.text("Processing and formatting titles...")
            logger.info("Progress bar updated: 90% - Processing and formatting titles")
        # Split the response into individual titles
        titles = [title.strip() for title in response.split('\n') if title.strip()]
        logger.info(f"Generated {len(titles)} titles")
        for i, title in enumerate(titles, 1):
            logger.info(f"Title {i}: '{title}'")
        if progress_bar:
            progress_bar.progress(100)
            progress_bar.text("Titles generated successfully!")
            logger.info("Progress bar updated: 100% - Titles generated successfully")
        return titles
    except Exception as err:
        logger.error(f"Error generating titles: {err}", exc_info=True)
        if progress_bar:
            progress_bar.progress(100)
            progress_bar.text("Error generating titles. Please try again.")
            logger.info("Progress bar updated: 100% - Error generating titles")
        st.error(f"Error: Failed to get response from LLM: {err}")
        return None
 def write_yt_title():
    """Create a user interface for YouTube Title Generator."""
    logger.info("Initializing YouTube Title Generator UI")
    st.write("Generate engaging YouTube video titles that drive clicks and views.")
    # Initialize session state for generated titles if it doesn't exist
    if "generated_titles" not in st.session_state:
        st.session_state.generated_titles = None
    # Main points input (full width)
    main_points = st.text_area("Main Points/Keywords (comma-separated)", 
                              placeholder="e.g., cooking tips, healthy recipes, quick meals")
    # Create columns for the other inputs
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        tone_style = st.selectbox("Tone/Style", 
                                ["Professional", "Casual", "Humorous", "Educational", "Entertaining", "Inspirational"])
    with col2:
        target_audience = st.text_input("Target Audience", 
                                      placeholder="e.g., beginners, professionals, parents")
    with col3:
        use_case = st.selectbox("Use Case", 
                              ["How-to/Tutorial", "Vlog", "Review", "Educational", "Entertainment", "News"])
    with col4:
        num_titles = st.number_input("Number of Titles", 
                                   min_value=1, 
                                   max_value=20, 
                                   value=5, 
                                   step=1)
    if st.button("Generate Titles"):
        logger.info("Generate Titles button clicked")
        logger.info(f"User inputs: main_points='{main_points}', tone_style='{tone_style}', target_audience='{target_audience}', use_case='{use_case}', num_titles={num_titles}")
        if not main_points:
            logger.warning("No main points provided")
            st.error("Please enter main points/keywords.")
            return
        # Create a progress bar
        progress_bar = st.progress(0)
        progress_bar.text("Initializing title generation...")
        logger.info("Created progress bar for title generation")
        # Generate titles with progress updates
        logger.info("Calling generate_youtube_title function")
        titles = generate_youtube_title(main_points, tone_style, target_audience, use_case, num_titles, progress_bar)
        # Clear the progress bar after a short delay
        time.sleep(1)
        progress_bar.empty()
        logger.info("Cleared progress bar")
        if titles:
            logger.info(f"Successfully generated {len(titles)} titles")
            # Store titles in session state for persistence
            st.session_state.generated_titles = titles
            # Display titles section
            st.markdown("""
            <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
                <h2 style='color: #FF0000; text-align: center;'>Generated YouTube Titles</h2>
                <p style='text-align: center;'>Click on a title to see detailed analysis and copy options</p>
            </div>
            """, unsafe_allow_html=True)
            # Display titles with analysis
            for i, title in enumerate(titles, 1):
                logger.info(f"Analyzing title {i}: '{title}'")
                # Create a more visually appealing expander
                with st.expander(f"Title {i}: {title}", expanded=False):
                    # Add a divider for better visual separation
                    st.markdown("---")
                    # Title display with better formatting
                    st.markdown(f"""
                    <div style='background-color: #f8f9fa; padding: 15px; border-radius: 5px; border-left: 5px solid #FF0000;'>
                        <h3 style='margin: 0;'>{title}</h3>
                    </div>
                    """, unsafe_allow_html=True)
                    # Analysis section
                    st.markdown("### Analysis")
                    analysis = analyze_title(title)
                    # Create columns for analysis metrics
                    col1, col2 = st.columns(2)
                    with col1:
                        # Character count
                        st.markdown("#### Character Count")
                        st.write(f"**{analysis['char_count']}** characters")
                        if analysis['optimal_length']:
                            st.success("✅ Optimal length (50-60 characters)")
                        else:
                            st.warning("⚠️ Not optimal length (should be 50-60 characters)")
                        # Clickbait detection
                        st.markdown("#### Clickbait Detection")
                        if analysis['is_clickbait']:
                            st.error(f"⚠️ Possible clickbait detected (score: {analysis['clickbait_score']})")
                        else:
                            st.success("✅ No clickbait detected")
                    with col2:
                        # SEO score
                        st.markdown("#### SEO Score")
                        score_color = "#28a745" if analysis['seo_score'] >= 7 else "#ffc107" if analysis['seo_score'] >= 5 else "#dc3545"
                        st.markdown(f"<h2 style='color: {score_color};'>{analysis['seo_score']}/10</h2>", unsafe_allow_html=True)
                        if analysis['seo_score'] >= 7:
                            st.success("✅ Good SEO score")
                        elif analysis['seo_score'] >= 5:
                            st.warning("⚠️ Moderate SEO score")
                        else:
                            st.error("❌ Low SEO score")
                        # SEO elements
                        st.markdown("#### SEO Elements")
                        elements = []
                        if analysis['has_number']:
                            elements.append("✅ Contains numbers")
                        if analysis['has_question']:
                            elements.append("✅ Contains question mark")
                        if analysis['has_colon']:
                            elements.append("✅ Contains colon")
                        if analysis['has_brackets']:
                            elements.append("✅ Contains brackets/parentheses")
                        for element in elements:
                            st.write(element)
                    # Copy functionality using session state
                    st.markdown("### Copy Title")
                    st.code(title, language="text")
                    # Use a different approach for copy functionality
                    copy_key = f"copy_{i}"
                    if st.button(f"Copy Title {i}", key=copy_key):
                        # Use JavaScript to copy to clipboard
                        escaped_title = title.replace('"', '\\"')
                        st.markdown(
                            f"""
                            <script>
                                navigator.clipboard.writeText("{escaped_title}");
                            </script>
                            """,
                            unsafe_allow_html=True
                        )
                        st.success(f"✅ Title {i} copied to clipboard!")
        else:
            logger.error("Failed to generate titles")
            st.error("Failed to generate titles. Please try again.")
    # Display previously generated titles if they exist in session state
    elif st.session_state.generated_titles:
        titles = st.session_state.generated_titles
        # Display titles section
        st.markdown("""
        <div style='background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
            <h2 style='color: #FF0000; text-align: center;'>Generated YouTube Titles</h2>
            <p style='text-align: center;'>Click on a title to see detailed analysis and copy options</p>
        </div>
        """, unsafe_allow_html=True)
        # Display titles with analysis
        for i, title in enumerate(titles, 1):
            logger.info(f"Analyzing title {i}: '{title}'")
            # Create a more visually appealing expander
            with st.expander(f"Title {i}: {title}", expanded=False):
                # Add a divider for better visual separation
                st.markdown("---")
                # Title display with better formatting
                st.markdown(f"""
                <div style='background-color: #f8f9fa; padding: 15px; border-radius: 5px; border-left: 5px solid #FF0000;'>
                    <h3 style='margin: 0;'>{title}</h3>
                </div>
                """, unsafe_allow_html=True)
                # Analysis section
                st.markdown("### Analysis")
                analysis = analyze_title(title)
                # Create columns for analysis metrics
                col1, col2 = st.columns(2)
                with col1:
                    # Character count
                    st.markdown("#### Character Count")
                    st.write(f"**{analysis['char_count']}** characters")
                    if analysis['optimal_length']:
                        st.success("✅ Optimal length (50-60 characters)")
                    else:
                        st.warning("⚠️ Not optimal length (should be 50-60 characters)")
                    # Clickbait detection
                    st.markdown("#### Clickbait Detection")
                    if analysis['is_clickbait']:
                        st.error(f"⚠️ Possible clickbait detected (score: {analysis['clickbait_score']})")
                    else:
                        st.success("✅ No clickbait detected")
                with col2:
                    # SEO score
                    st.markdown("#### SEO Score")
                    score_color = "#28a745" if analysis['seo_score'] >= 7 else "#ffc107" if analysis['seo_score'] >= 5 else "#dc3545"
                    st.markdown(f"<h2 style='color: {score_color};'>{analysis['seo_score']}/10</h2>", unsafe_allow_html=True)
                    if analysis['seo_score'] >= 7:
                        st.success("✅ Good SEO score")
                    elif analysis['seo_score'] >= 5:
                        st.warning("⚠️ Moderate SEO score")
                    else:
                        st.error("❌ Low SEO score")
                    # SEO elements
                    st.markdown("#### SEO Elements")
                    elements = []
                    if analysis['has_number']:
                        elements.append("✅ Contains numbers")
                    if analysis['has_question']:
                        elements.append("✅ Contains question mark")
                    if analysis['has_colon']:
                        elements.append("✅ Contains colon")
                    if analysis['has_brackets']:
                        elements.append("✅ Contains brackets/parentheses")
                    for element in elements:
                        st.write(element)
                # Copy functionality using session state
                st.markdown("### Copy Title")
                st.code(title, language="text")
                # Use a different approach for copy functionality
                copy_key = f"copy_{i}"
                if st.button(f"Copy Title {i}", key=copy_key):
                    # Use JavaScript to copy to clipboard
                    escaped_title = title.replace('"', '\\"')
                    st.markdown(
                        f"""
                        <script>
                            navigator.clipboard.writeText("{escaped_title}");
                        </script>
                        """,
                        unsafe_allow_html=True
                    )
                    st.success(f"✅ Title {i} copied to clipboard!") 
--- a/ToBeMigrated/ai_writers/youtube_writers/youtube_ai_writer.py
+++ b/ToBeMigrated/ai_writers/youtube_writers/youtube_ai_writer.py
@@ -1,237 +0,0 @@
 """
 YouTube AI Writer
 This module provides a comprehensive suite of tools for generating YouTube content.
 """
 import streamlit as st
 import importlib
 import sys
 import os
 from pathlib import Path
 from .modules.title_generator import write_yt_title
 from .modules.description_generator import write_yt_description
 from .modules.script_generator import write_yt_script
 from .modules.thumbnail_generator import write_yt_thumbnail
 from .modules.end_screen_generator import write_yt_end_screen
 from .modules.tags_generator import write_yt_tags
 from .modules.shorts_script_generator import write_yt_shorts
 from .modules.community_post_generator import write_yt_community_post
 from .modules.shorts_video_generator import write_yt_shorts_video
 from .modules.channel_trailer_generator import write_yt_channel_trailer
 def youtube_main_menu():
    """Main function for the YouTube AI Writer."""
    # Initialize session state for selected tool if it doesn't exist
    if "selected_tool" not in st.session_state:
        st.session_state.selected_tool = None
    # Define the YouTube tools with their details
    youtube_tools = [
        # Content Creation Tools
        {
            "name": "YT Title Generator",
            "icon": "📝",
            "description": "Create engaging YouTube video titles that drive clicks and views.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_title,
            "status": "active"
        },
        {
            "name": "YT Description Generator",
            "icon": "📄",
            "description": "Generate SEO-optimized descriptions for your YouTube videos.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_description,
            "status": "active"
        },
        {
            "name": "YT Script Generator",
            "icon": "🎬",
            "description": "Create professional YouTube scripts with optimized structures for engagement.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_script,
            "status": "active"
        },
        {
            "name": "YT Shorts Script Generator",
            "icon": "📱",
            "description": "Create engaging scripts optimized for YouTube Shorts format with vertical framing and hooks.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_shorts,
            "status": "active"
        },
        {
            "name": "YT Shorts Video Generator",
            "icon": "🎥",
            "description": "Generate complete YouTube Shorts videos with AI-generated images, narration, and music.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_shorts_video,
            "status": "active"
        },
        {
            "name": "Channel Trailer Generator",
            "icon": "🎥",
            "description": "Create compelling channel trailers that convert visitors into subscribers.",
            "color": "#FF0000",  # YouTube red
            "category": "Content Creation",
            "function": write_yt_channel_trailer,
            "status": "active"
        },
        # Optimization Tools
        {
            "name": "Thumbnail Generator",
            "icon": "🎨",
            "description": "Create engaging thumbnail ideas and descriptions with color scheme suggestions based on your brand.",
            "color": "#FF0000",  # YouTube red
            "category": "Optimization",
            "function": write_yt_thumbnail,
            "status": "active"
        },
        {
            "name": "YouTube Tags Generator",
            "icon": "🏷️",
            "description": "Generate optimized tags for your videos with trending tag suggestions to improve discoverability.",
            "color": "#FF0000",  # YouTube red
            "category": "Optimization",
            "function": write_yt_tags,
            "status": "active"
        },
        # Engagement Tools
        {
            "name": "End Screen Generator",
            "icon": "🎬",
            "description": "Create effective end screen content and CTAs with template suggestions based on video type.",
            "color": "#FF0000",  # YouTube red
            "category": "Engagement",
            "function": write_yt_end_screen,
            "status": "active"
        },
        {
            "name": "Community Post Generator",
            "icon": "💬",
            "description": "Generate engaging community posts with AI-powered content suggestions and timing optimization.",
            "color": "#FF0000",  # YouTube red
            "category": "Engagement",
            "function": write_yt_community_post,
            "status": "active"
        },
        {
            "name": "Playlist Description Generator",
            "icon": "📚",
            "description": "Generate SEO-optimized descriptions for your playlists with organization suggestions.",
            "color": "#CC0000",  # Darker red for coming soon
            "category": "Engagement",
            "function": None,
            "status": "coming_soon"
        },
        # Future Tools
        {
            "name": "Analytics Insights",
            "icon": "📊",
            "description": "Get AI-powered insights and recommendations based on your channel analytics.",
            "color": "#990000",  # Even darker red for future
            "category": "Future Tools",
            "function": None,
            "status": "future"
        },
        {
            "name": "Video Series Planner",
            "icon": "📅",
            "description": "Plan and organize your video series with content calendars and topic ideas.",
            "color": "#990000",  # Even darker red for future
            "category": "Future Tools",
            "function": None,
            "status": "future"
        }
    ]
    # Create a container for the dashboard
    dashboard_container = st.container()
    # Create a container for the tool input section
    tool_container = st.container()
    # If a tool is selected, show its input section
    if st.session_state.selected_tool is not None:
        with tool_container:
            # Display the selected tool's input section
            st.markdown("---")
            st.markdown(f"# {st.session_state.selected_tool['icon']} {st.session_state.selected_tool['name']}")
            # Add a back button
            if st.button("← Back to Dashboard", key="back_to_dashboard"):
                # Clear the selected tool from session state
                st.session_state.selected_tool = None
                st.rerun()
            # Call the function for the selected tool
            if st.session_state.selected_tool["function"]:
                # Directly call the function instead of using it as a reference
                st.session_state.selected_tool["function"]()
            else:
                # Display coming soon or future tool information
                st.info(f"**{st.session_state.selected_tool['status'].replace('_', ' ').title()}!**")
                st.write(st.session_state.selected_tool["description"])
                st.image(f"https://via.placeholder.com/600x300?text={st.session_state.selected_tool['name']}+Coming+Soon", use_column_width=True)
    else:
        with dashboard_container:
            # Display the dashboard
            # Header
            st.markdown("""
            <div style='background-color: #f0f2f6; padding: 10px; border-radius: 5px; margin-bottom: 10px;'>
                <h1 style='color: #FF0000; text-align: center;'>🎥 YouTube AI Writer</h1>
                <p style='text-align: center;'>Generate professional YouTube content with ALwrity's AI-powered tools</p>
            </div>
            """, unsafe_allow_html=True)
            # Group tools by category
            categories = {}
            for tool in youtube_tools:
                category = tool["category"]
                if category not in categories:
                    categories[category] = []
                categories[category].append(tool)
            # Display tools by category
            for category, tools in categories.items():
                st.markdown(f"## {category}")
                # Create a 3-column layout for the tool cards
                cols = st.columns(3)
                # Display the tool cards
                for i, tool in enumerate(tools):
                    # Determine which column to use
                    col = cols[i % 3]
                    with col:
                        # Create a card for each tool
                        status_badge = ""
                        if tool["status"] == "coming_soon":
                            status_badge = "<span style='background-color: #FFA500; color: white; padding: 2px 8px; border-radius: 10px; font-size: 0.8em;'>Coming Soon</span>"
                        elif tool["status"] == "future":
                            status_badge = "<span style='background-color: #808080; color: white; padding: 2px 8px; border-radius: 10px; font-size: 0.8em;'>Future</span>"
                        st.markdown(f"""
                        <div style='background-color: {tool["color"]}; padding: 20px; border-radius: 10px; margin-bottom: 20px; color: white;'>
                            <h2 style='color: white;'>{tool["icon"]} {tool["name"]} {status_badge}</h2>
                            <p>{tool["description"]}</p>
                        </div>
                        """, unsafe_allow_html=True)
                        # Add a button to access the tool
                        if st.button(f"Use {tool['name']}", key=f"btn_{tool['name']}"):
                            # Store the selected tool in session state
                            st.session_state.selected_tool = tool
                            st.rerun()
--- a/backend/alwrity_utils/feature_registry.py
+++ b/backend/alwrity_utils/feature_registry.py
@@ -58,6 +58,21 @@ FEATURE_GROUPS: Dict[str, FeatureGroup] = {
            "api.blog_writer.seo_analysis:router",
        ),
    ),
    "backlinking": FeatureGroup(
        features=("backlinking",),
        routers=("routers.backlink_outreach:router",),
    ),
    "linkedin": FeatureGroup(
        features=("linkedin",),
        routers=(
            "routers.linkedin:router",
            "api.linkedin_image_generation:router",
        ),
    ),
    "facebook": FeatureGroup(
        features=("facebook",),
        routers=("api.facebook_writer.routers:facebook_router",),
    ),
 }
@@ -67,5 +82,8 @@ PROFILE_GROUP_MAP: Dict[str, Tuple[str, ...]] = {
    "podcast": ("core", "podcast"),
    "youtube": ("core", "youtube"),
    "blog_writer": ("core", "blog_writer"),
    "backlinking": ("core", "backlinking"),
    "linkedin": ("core", "linkedin"),
    "facebook": ("core", "facebook"),
    "planning": ("core", "content_planning"),
 }
--- a/backend/alwrity_utils/router_manager.py
+++ b/backend/alwrity_utils/router_manager.py
@@ -19,6 +19,7 @@ CORE_ROUTER_REGISTRY = [
    {"name": "step4_assets", "module": "api.onboarding_utils.step4_asset_routes", "attr": "router", "features": {"all", "core", "podcast"}},
    {"name": "step4_persona", "module": "api.onboarding_utils.step4_persona_routes_optimized", "attr": "router", "features": {"all", "core"}},
    {"name": "gsc_auth", "module": "routers.gsc_auth", "attr": "router", "features": {"all", "core", "seo", "blog_writer"}},
    {"name": "ai_visibility", "module": "routers.ai_visibility", "attr": "router", "features": {"all", "core", "seo", "blog_writer"}},
    {"name": "wordpress", "module": "routers.wordpress", "attr": "router", "features": {"all", "core", "blog_writer"}},
    {"name": "wordpress_oauth", "module": "routers.wordpress_oauth", "attr": "router", "features": {"all", "core", "blog_writer"}},
    {"name": "bing_oauth", "module": "routers.bing_oauth", "attr": "router", "features": {"all", "core"}},
@@ -53,7 +54,7 @@ OPTIONAL_ROUTER_REGISTRY = [
    {"name": "stability", "module": "routers.stability", "attr": "router", "features": {"all", "image_studio"}},
    {"name": "stability_advanced", "module": "routers.stability_advanced", "attr": "router", "features": {"all", "image_studio"}},
    {"name": "stability_admin", "module": "routers.stability_admin", "attr": "router", "features": {"all", "image_studio"}},
-    {"name": "images", "module": "api.images", "attr": "router", "features": {"all", "image_studio"}},
+    {"name": "images", "module": "api.images", "attr": "router", "features": {"all", "image_studio", "blog_writer"}},
    {"name": "image_studio", "module": "routers.image_studio", "attr": "router", "features": {"all", "image_studio"}},
    {"name": "product_marketing", "module": "routers.product_marketing", "attr": "router", "features": {"all", "product_marketing"}},
    {"name": "campaign_creator", "module": "routers.campaign_creator", "attr": "router", "features": {"all"}},
@@ -66,6 +67,7 @@ OPTIONAL_ROUTER_REGISTRY = [
    {"name": "oauth_token_monitoring", "module": "api.oauth_token_monitoring_routes", "attr": "router", "features": {"all", "core"}},
    {"name": "agents", "module": "api.agents_api", "attr": "router", "features": {"all"}},
    {"name": "today_workflow", "module": "api.today_workflow", "attr": "router", "features": {"all"}},
    {"name": "backlink_outreach", "module": "routers.backlink_outreach", "attr": "router", "features": {"all", "backlinking"}},
 ]
 OPTIONAL_MODULE_MATRIX = {
--- a/backend/api/blog_writer/router.py
+++ b/backend/api/blog_writer/router.py
@@ -66,6 +66,7 @@ class RecommendationItem(BaseModel):
 class SEOApplyRecommendationsRequest(BaseModel):
    title: str = Field(..., description="Current blog title")
    introduction: str | None = Field(default=None, description="Current blog introduction text")
    sections: List[Dict[str, Any]] = Field(..., description="Array of sections with id, heading, content")
    outline: List[Dict[str, Any]] = Field(default_factory=list, description="Outline structure for context")
    research: Dict[str, Any] = Field(default_factory=dict, description="Research data used for the blog")
@@ -122,7 +123,7 @@ async def section_originality_tools(
            raise HTTPException(status_code=401, detail="User ID not found in authentication token")
        from services.intelligence.sif_integration import SIFIntegrationService
-        from services.intelligence.sif_agents import ContentGuardianAgent
+        from services.intelligence.agents.specialized import ContentGuardianAgent
        sif_service = SIFIntegrationService(user_id)
        intelligence = sif_service.intelligence_service
--- a/backend/api/content_planning/api/content_strategy/endpoints/ai_generation_endpoints.py
+++ b/backend/api/content_planning/api/content_strategy/endpoints/ai_generation_endpoints.py
@@ -20,6 +20,9 @@ from ....services.enhanced_strategy_db_service import EnhancedStrategyDBService
 # Import educational content manager
 from .content_strategy.educational_content import EducationalContentManager
 # Import authentication
 from middleware.auth_middleware import get_current_user
 # Import utilities
 from ....utils.error_handlers import ContentPlanningErrorHandler
 from ....utils.response_builders import ResponseBuilder
@@ -40,13 +43,14 @@ _latest_strategies = {}
@router.post("/generate-comprehensive-strategy")
 async def generate_comprehensive_strategy(
-    user_id: int,
+    current_user: Dict[str, Any] = Depends(get_current_user),
    strategy_name: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Generate a comprehensive AI-powered content strategy."""
    try:
        user_id = current_user.get('id')
        logger.info(f"🚀 Generating comprehensive AI strategy for user: {user_id}")
        # Get user context and onboarding data
@@ -103,7 +107,7 @@ async def generate_comprehensive_strategy(
@router.post("/generate-strategy-component")
 async def generate_strategy_component(
-    user_id: int,
+    current_user: Dict[str, Any] = Depends(get_current_user),
    component_type: str,
    base_strategy: Optional[Dict[str, Any]] = None,
    context: Optional[Dict[str, Any]] = None,
@@ -111,6 +115,7 @@ async def generate_strategy_component(
 ) -> Dict[str, Any]:
    """Generate a specific strategy component using AI."""
    try:
        user_id = current_user.get('id')
        logger.info(f"🚀 Generating strategy component '{component_type}' for user: {user_id}")
        # Validate component type
@@ -187,11 +192,12 @@ async def generate_strategy_component(
@router.get("/strategy-generation-status")
 async def get_strategy_generation_status(
-    user_id: int,
+    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get the status of strategy generation for a user."""
    try:
        user_id = current_user.get('id')
        logger.info(f"Getting strategy generation status for user: {user_id}")
        # Get user's strategies
@@ -247,6 +253,7 @@ async def get_strategy_generation_status(
 async def optimize_existing_strategy(
    strategy_id: int,
    optimization_type: str = "comprehensive",
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Optimize an existing strategy using AI."""
@@ -309,12 +316,13 @@ async def optimize_existing_strategy(
@router.post("/generate-comprehensive-strategy-polling")
 async def generate_comprehensive_strategy_polling(
    request: Dict[str, Any],
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Generate a comprehensive AI-powered content strategy using polling approach."""
    try:
        # Extract parameters from request body
-        user_id = request.get("user_id", 1)
+        user_id = current_user.get('id')
        strategy_name = request.get("strategy_name")
        config = request.get("config", {})
@@ -611,6 +619,7 @@ async def generate_comprehensive_strategy_polling(
@router.get("/strategy-generation-status/{task_id}")
 async def get_strategy_generation_status_by_task(
    task_id: str,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get the status of strategy generation for a specific task."""
@@ -647,11 +656,12 @@ async def get_strategy_generation_status_by_task(
@router.get("/latest-strategy")
 async def get_latest_generated_strategy(
-    user_id: int = Query(1, description="User ID"),
+    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get the latest generated strategy from the polling system or database."""
    try:
        user_id = current_user.get('id')
        logger.info(f"🔍 Getting latest generated strategy for user: {user_id}")
        # First, try to get from database (most reliable)
--- a/backend/api/content_planning/api/content_strategy/endpoints/analytics_endpoints.py
+++ b/backend/api/content_planning/api/content_strategy/endpoints/analytics_endpoints.py
@@ -19,6 +19,9 @@ from ....services.enhanced_strategy_db_service import EnhancedStrategyDBService
 # Import models
 from models.enhanced_strategy_models import EnhancedContentStrategy, EnhancedAIAnalysisResult
 # Import authentication
 from middleware.auth_middleware import get_current_user
 # Import utilities
 from ....utils.error_handlers import ContentPlanningErrorHandler
 from ....utils.response_builders import ResponseBuilder
@@ -37,6 +40,7 @@ def get_db():
@router.get("/{strategy_id}/analytics")
 async def get_enhanced_strategy_analytics(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get comprehensive analytics for an enhanced strategy."""
@@ -72,6 +76,7 @@ async def get_enhanced_strategy_analytics(
 async def get_enhanced_strategy_ai_analysis(
    strategy_id: int,
    limit: int = Query(10, description="Number of AI analysis results to return"),
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get AI analysis history for an enhanced strategy."""
@@ -108,6 +113,7 @@ async def get_enhanced_strategy_ai_analysis(
@router.get("/{strategy_id}/completion")
 async def get_enhanced_strategy_completion_stats(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get completion statistics for an enhanced strategy."""
@@ -147,6 +153,7 @@ async def get_enhanced_strategy_completion_stats(
@router.get("/{strategy_id}/onboarding-integration")
 async def get_enhanced_strategy_onboarding_integration(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Get onboarding data integration for an enhanced strategy."""
@@ -177,6 +184,7 @@ async def get_enhanced_strategy_onboarding_integration(
@router.post("/{strategy_id}/ai-recommendations")
 async def generate_enhanced_ai_recommendations(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Generate AI recommendations for an enhanced strategy."""
@@ -216,6 +224,7 @@ async def generate_enhanced_ai_recommendations(
 async def regenerate_enhanced_strategy_ai_analysis(
    strategy_id: int,
    analysis_type: str,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Regenerate AI analysis for an enhanced strategy."""
--- a/backend/api/content_planning/api/content_strategy/endpoints/autofill_endpoints.py
+++ b/backend/api/content_planning/api/content_strategy/endpoints/autofill_endpoints.py
@@ -21,6 +21,9 @@ from ....services.enhanced_strategy_service import EnhancedStrategyService
 from ....services.enhanced_strategy_db_service import EnhancedStrategyDBService
 from ....services.content_strategy.autofill.ai_refresh import AutoFillRefreshService
 # Import authentication
 from middleware.auth_middleware import get_current_user
 # Import utilities
 from ....utils.error_handlers import ContentPlanningErrorHandler
 from ....utils.response_builders import ResponseBuilder
@@ -49,12 +52,13 @@ async def stream_data(data_generator):
 async def accept_autofill_inputs(
    strategy_id: int,
    payload: Dict[str, Any],
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Persist end-user accepted auto-fill inputs and associate with the strategy."""
    try:
        logger.info(f"🚀 Accepting autofill inputs for strategy: {strategy_id}")
-        user_id = str(payload.get('user_id') or "")
+        user_id = str(current_user.get('id'))
        accepted_fields = payload.get('accepted_fields') or {}
        # Optional transparency bundles
        sources = payload.get('sources') or {}
@@ -99,7 +103,7 @@ async def accept_autofill_inputs(
@router.get("/autofill/refresh/stream")
 async def stream_autofill_refresh(
-    user_id: Optional[int] = Query(None, description="User ID to build auto-fill for"),
+    current_user: Dict[str, Any] = Depends(get_current_user),
    use_ai: bool = Query(True, description="Use AI augmentation during refresh"),
    ai_only: bool = Query(False, description="AI-first refresh: return AI overrides when available"),
    db: Session = Depends(get_db)
@@ -107,7 +111,7 @@ async def stream_autofill_refresh(
    """SSE endpoint to stream steps while generating a fresh auto-fill payload (no DB writes)."""
    async def refresh_generator():
        try:
-            actual_user_id = user_id or 1
+            actual_user_id = current_user.get('id', 1)
            start_time = datetime.utcnow()
            logger.info(f"🚀 Starting auto-fill refresh stream for user: {actual_user_id}")
            yield {"type": "status", "phase": "init", "message": "Starting…", "progress": 5}
@@ -203,14 +207,14 @@ async def stream_autofill_refresh(
@router.post("/autofill/refresh")
 async def refresh_autofill(
-    user_id: Optional[int] = Query(None, description="User ID to build auto-fill for"),
+    current_user: Dict[str, Any] = Depends(get_current_user),
    use_ai: bool = Query(True, description="Use AI augmentation during refresh"),
    ai_only: bool = Query(False, description="AI-first refresh: return AI overrides when available"),
    db: Session = Depends(get_db)
 ) -> Dict[str, Any]:
    """Non-stream endpoint to return a fresh auto-fill payload (no DB writes)."""
    try:
-        actual_user_id = user_id or 1
+        actual_user_id = current_user.get('id', 1)
        started = datetime.utcnow()
        refresh_service = AutoFillRefreshService(db)
        payload = await refresh_service.build_fresh_payload_with_transparency(actual_user_id, use_ai=use_ai, ai_only=ai_only)
--- a/backend/api/content_planning/api/content_strategy/endpoints/streaming_endpoints.py
+++ b/backend/api/content_planning/api/content_strategy/endpoints/streaming_endpoints.py
@@ -4,7 +4,7 @@ Handles streaming endpoints for enhanced content strategies.
 """
 from typing import Dict, Any, Optional
-from fastapi import APIRouter, Depends, HTTPException, Query
+from fastapi import APIRouter, Depends, Query
 from fastapi.responses import StreamingResponse
 from starlette.requests import Request
 from sqlalchemy.orm import Session
@@ -12,8 +12,6 @@ from loguru import logger
 import json
 import asyncio
 from datetime import datetime
 from collections import defaultdict
 import time
 # Import database
 from services.database import get_db_session
@@ -25,31 +23,13 @@ from middleware.auth_middleware import get_current_user, get_current_user_with_q
 from ....services.enhanced_strategy_service import EnhancedStrategyService
 from ....services.enhanced_strategy_db_service import EnhancedStrategyDBService
-# Import utilities
+# Use bounded shared cache instead of process-local unbounded dict
-from ....utils.error_handlers import ContentPlanningErrorHandler
+from ...services.content_strategy.performance.caching import CachingService
 from ....utils.response_builders import ResponseBuilder
 from ....utils.constants import ERROR_MESSAGES, SUCCESS_MESSAGES
 router = APIRouter(tags=["Strategy Streaming"])
-# Cache for streaming endpoints (5 minutes cache)
+# Shared bounded cache for streaming endpoints
-streaming_cache = defaultdict(dict)
+streaming_cache_service = CachingService()
 CACHE_DURATION = 300  # 5 minutes
 def get_cached_data(cache_key: str) -> Optional[Dict[str, Any]]:
    """Get cached data if it exists and is not expired."""
    if cache_key in streaming_cache:
        cached_data = streaming_cache[cache_key]
        if time.time() - cached_data.get("timestamp", 0) < CACHE_DURATION:
            return cached_data.get("data")
    return None
 def set_cached_data(cache_key: str, data: Dict[str, Any]):
    """Set cached data with timestamp."""
    streaming_cache[cache_key] = {
        "data": data,
        "timestamp": time.time()
    }
 # Helper function to get database session
 def get_db():
@@ -123,11 +103,7 @@ async def stream_enhanced_strategies(
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
+            "Connection": "keep-alive"
            "Access-Control-Allow-Origin": "*",
            "Access-Control-Allow-Headers": "*",
            "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
            "Access-Control-Allow-Credentials": "true"
        }
    )
@@ -150,9 +126,9 @@ async def stream_strategic_intelligence(
            logger.info(f"🚀 Starting strategic intelligence stream for authenticated user: {authenticated_user_id}")
-            # Check cache first
+            # Check bounded shared cache first
            cache_key = f"strategic_intelligence_{authenticated_user_id}"
-            cached_data = get_cached_data(cache_key)
+            cached_data = await streaming_cache_service.get_cached_data("streaming_intelligence", cache_key)
            if cached_data:
                logger.info(f"✅ Returning cached strategic intelligence data for user: {authenticated_user_id}")
                yield {"type": "result", "status": "success", "data": cached_data, "progress": 100}
@@ -167,7 +143,6 @@ async def stream_strategic_intelligence(
            # Send progress update
            yield {"type": "progress", "message": "Retrieving strategies...", "progress": 20}
            # Use authenticated user_id to ensure users can only see their own strategies
            strategies_data = await enhanced_service.get_enhanced_strategies(authenticated_user_id, None, db)
            # Send progress update
@@ -194,54 +169,29 @@ async def stream_strategic_intelligence(
            # Send progress update
            yield {"type": "progress", "message": "Processing intelligence data...", "progress": 60}
            # Build strategic intelligence from actual strategy data — no hardcoded fallback defaults
            strategic_intelligence = {
                "market_positioning": {
-                    "current_position": strategy.get("competitive_position", "Challenger"),
+                    "current_position": strategy.get("competitive_position") or None,
-                    "target_position": "Market Leader",
+                    "differentiation_factors": strategy.get("differentiation_factors") or None
                    "differentiation_factors": [
                        "AI-powered content optimization",
                        "Data-driven strategy development",
                        "Personalized user experience"
                    ]
                },
                "competitive_analysis": {
-                    "top_competitors": strategy.get("top_competitors", [])[:3] or [
+                    "top_competitors": (strategy.get("top_competitors") or [None])[:3],
-                        "Competitor A", "Competitor B", "Competitor C"
+                    "competitive_advantages": strategy.get("competitive_advantages") or None,
-                    ],
+                    "market_gaps": strategy.get("market_gaps") or None
                    "competitive_advantages": [
                        "Advanced AI capabilities",
                        "Comprehensive data integration",
                        "User-centric design"
                    ],
                    "market_gaps": strategy.get("market_gaps", []) or [
                        "AI-driven content personalization",
                        "Real-time performance optimization",
                        "Predictive analytics"
                    ]
                },
-                "ai_insights": ai_recommendations.get("strategic_insights", []) or [
+                "ai_insights": ai_recommendations.get("strategic_insights") if ai_recommendations else None,
-                    "Focus on pillar content strategy",
+                "opportunities": strategy.get("opportunities") or None
-                    "Implement topic clustering",
+            }
-                    "Optimize for voice search"
+            
-                ],
+            # Filter out null-only sections for cleaner responses
-                "opportunities": [
+            strategic_intelligence = {
-                    {
+                k: v for k, v in strategic_intelligence.items()
-                        "area": "Content Personalization",
+                if v is not None and v != [None]
                        "potential_impact": "High",
                        "implementation_timeline": "3-6 months",
                        "estimated_roi": "25-40%"
                    },
                    {
                        "area": "AI-Powered Optimization",
                        "potential_impact": "Medium",
                        "implementation_timeline": "6-12 months",
                        "estimated_roi": "15-30%"
                    }
                ]
            }
            # Cache the strategic intelligence data
-            set_cached_data(cache_key, strategic_intelligence)
+            await streaming_cache_service.set_cached_data("streaming_intelligence", cache_key, strategic_intelligence)
            # Send progress update
            yield {"type": "progress", "message": "Finalizing strategic intelligence...", "progress": 80}
@@ -260,11 +210,7 @@ async def stream_strategic_intelligence(
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
+            "Connection": "keep-alive"
            "Access-Control-Allow-Origin": "*",
            "Access-Control-Allow-Headers": "*",
            "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
            "Access-Control-Allow-Credentials": "true"
        }
    )
@@ -287,9 +233,9 @@ async def stream_keyword_research(
            logger.info(f"🚀 Starting keyword research stream for authenticated user: {authenticated_user_id}")
-            # Check cache first
+            # Check bounded shared cache first
            cache_key = f"keyword_research_{authenticated_user_id}"
-            cached_data = get_cached_data(cache_key)
+            cached_data = await streaming_cache_service.get_cached_data("streaming_intelligence", cache_key)
            if cached_data:
                logger.info(f"✅ Returning cached keyword research data for user: {authenticated_user_id}")
                yield {"type": "result", "status": "success", "data": cached_data, "progress": 100}
@@ -333,33 +279,24 @@ async def stream_keyword_research(
            # Send progress update
            yield {"type": "progress", "message": "Processing keyword data...", "progress": 60}
            # Build keyword data from actual analysis — no hardcoded fallback defaults
            keyword_data = {
                "trend_analysis": {
-                    "high_volume_keywords": analysis_results.get("opportunities", [])[:3] or [
+                    "high_volume_keywords": (analysis_results.get("opportunities") or [None])[:3],
-                        {"keyword": "AI marketing automation", "volume": "10K-100K", "difficulty": "Medium"},
+                    "trending_keywords": analysis_results.get("trending_keywords") or None
                        {"keyword": "content strategy 2024", "volume": "1K-10K", "difficulty": "Low"},
                        {"keyword": "digital marketing trends", "volume": "10K-100K", "difficulty": "High"}
                    ],
                    "trending_keywords": [
                        {"keyword": "AI content generation", "growth": "+45%", "opportunity": "High"},
                        {"keyword": "voice search optimization", "growth": "+32%", "opportunity": "Medium"},
                        {"keyword": "video marketing strategy", "growth": "+28%", "opportunity": "High"}
                    ]
                },
-                "intent_analysis": {
+                "intent_analysis": analysis_results.get("intent_analysis") or None,
-                    "informational": ["how to", "what is", "guide to"],
+                "opportunities": analysis_results.get("opportunities") or None
-                    "navigational": ["company name", "brand name", "website"],
+            }
-                    "transactional": ["buy", "purchase", "download", "sign up"]
+            
-                },
+            # Filter out null-only sections
-                "opportunities": analysis_results.get("opportunities", []) or [
+            keyword_data = {
-                    {"keyword": "AI content tools", "search_volume": "5K-10K", "competition": "Low", "cpc": "$2.50"},
+                k: v for k, v in keyword_data.items()
-                    {"keyword": "content marketing ROI", "search_volume": "1K-5K", "competition": "Medium", "cpc": "$4.20"},
+                if v is not None and v != [None]
                    {"keyword": "social media strategy", "search_volume": "10K-50K", "competition": "High", "cpc": "$3.80"}
                ]
            }
            # Cache the keyword data
-            set_cached_data(cache_key, keyword_data)
+            await streaming_cache_service.set_cached_data("streaming_intelligence", cache_key, keyword_data)
            # Send progress update
            yield {"type": "progress", "message": "Finalizing keyword research...", "progress": 80}
@@ -378,10 +315,71 @@ async def stream_keyword_research(
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
+            "Connection": "keep-alive"
            "Access-Control-Allow-Origin": "*",
            "Access-Control-Allow-Headers": "*",
            "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
            "Access-Control-Allow-Credentials": "true"
        }
-    ) 
+    )
@router.get("/stream/ai-generation-status")
 async def stream_ai_generation_status(
    request: Request,
    strategy_id: int = Query(..., description="Strategy ID"),
    current_user: Dict[str, Any] = Depends(get_current_user_with_query_token),
    db: Session = Depends(get_db)
 ):
    """Stream AI generation status for a strategy with real-time updates."""
    async def status_generator():
        try:
            clerk_user_id = str(current_user.get('id', ''))
            if not clerk_user_id:
                yield {"type": "error", "detail": "Invalid user ID", "progress": 0}
                return
            authenticated_user_id = clerk_user_id
            logger.info(f"🚀 Starting AI generation status stream for user: {authenticated_user_id}, strategy: {strategy_id}")
            yield {"type": "progress", "detail": "Fetching AI generation status...", "progress": 10}
            db_service = EnhancedStrategyDBService(db)
            enhanced_service = EnhancedStrategyService(db_service)
            strategy = await enhanced_service.get_enhanced_strategy(strategy_id, authenticated_user_id, db)
            if not strategy or strategy.get("status") == "not_found":
                yield {"type": "error", "detail": "Strategy not found", "progress": 0}
                return
            yield {"type": "progress", "detail": "Checking AI analysis status...", "progress": 30}
            ai_recommendations = strategy.get("ai_recommendations")
            if ai_recommendations:
                if isinstance(ai_recommendations, str):
                    try:
                        ai_recommendations = json.loads(ai_recommendations)
                    except (json.JSONDecodeError, TypeError):
                        ai_recommendations = {}
            ai_status = "completed" if ai_recommendations else "pending"
            if ai_status == "completed":
                yield {"type": "progress", "detail": "AI analysis completed", "progress": 80}
                yield {"type": "result", "status": "completed", "detail": "AI generation completed", "progress": 100}
            else:
                yield {"type": "progress", "detail": "AI analysis is pending", "progress": 50}
                yield {"type": "result", "status": "pending", "detail": "AI generation is in progress", "progress": 50}
            logger.info(f"✅ AI generation status stream completed for user: {authenticated_user_id}")
        except Exception as e:
            logger.error(f"❌ Error in AI generation status stream: {str(e)}")
            yield {"type": "error", "detail": str(e), "progress": 0}
    return StreamingResponse(
        stream_data(status_generator()),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive"
        }
    )
--- a/backend/api/content_planning/api/routes/ai_analytics.py
+++ b/backend/api/content_planning/api/routes/ai_analytics.py
@@ -65,12 +65,16 @@ async def analyze_content_evolution(
        )
@router.post("/performance-trends", response_model=AIAnalyticsResponse)
-async def analyze_performance_trends(request: PerformanceTrendsRequest):
+async def analyze_performance_trends(
    request: PerformanceTrendsRequest,
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Analyze performance trends for content strategy.
    """
    try:
-        logger.info(f"Starting performance trends analysis for strategy {request.strategy_id}")
+        user_id = current_user.get("user_id")
        logger.info(f"Starting performance trends analysis for strategy {request.strategy_id} (user {user_id})")
        result = await ai_analytics_service.analyze_performance_trends(
            strategy_id=request.strategy_id,
@@ -87,12 +91,16 @@ async def analyze_performance_trends(request: PerformanceTrendsRequest):
        )
@router.post("/predict-performance", response_model=AIAnalyticsResponse)
-async def predict_content_performance(request: ContentPerformancePredictionRequest):
+async def predict_content_performance(
    request: ContentPerformancePredictionRequest,
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Predict content performance using AI models.
    """
    try:
-        logger.info(f"Starting content performance prediction for strategy {request.strategy_id}")
+        user_id = current_user.get("user_id")
        logger.info(f"Starting content performance prediction for strategy {request.strategy_id} (user {user_id})")
        result = await ai_analytics_service.predict_content_performance(
            strategy_id=request.strategy_id,
@@ -137,12 +145,13 @@ async def generate_strategic_intelligence(
@router.get("/", response_model=Dict[str, Any])
 async def get_ai_analytics(
    user_id: Optional[int] = Query(None, description="User ID"),
    strategy_id: Optional[int] = Query(None, description="Strategy ID"),
-    force_refresh: bool = Query(False, description="Force refresh AI analysis")
+    force_refresh: bool = Query(False, description="Force refresh AI analysis"),
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """Get AI analytics with real personalized insights - Database first approach."""
    try:
        user_id = current_user.get("user_id") or current_user.get("id")
        logger.info(f"🚀 Starting AI analytics for user: {user_id}, strategy: {strategy_id}, force_refresh: {force_refresh}")
        result = await ai_analytics_service.get_ai_analytics(user_id, strategy_id, force_refresh)
@@ -153,11 +162,14 @@ async def get_ai_analytics(
        raise HTTPException(status_code=500, detail=f"Error generating AI analytics: {str(e)}")
@router.get("/health")
-async def ai_analytics_health_check():
+async def ai_analytics_health_check(
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Health check for AI analytics services.
    """
    try:
        logger.debug(f"AI analytics health check by user: {current_user.get('id')}")
        # Check AI analytics service
        service_status = {}
@@ -197,14 +209,16 @@ async def ai_analytics_health_check():
 async def get_user_ai_analysis_results(
    user_id: int,
    analysis_type: Optional[str] = Query(None, description="Filter by analysis type"),
-    limit: int = Query(10, description="Number of results to return")
+    limit: int = Query(10, description="Number of results to return"),
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
-    """Get AI analysis results for a specific user."""
+    """Get AI analysis results for the authenticated user."""
    try:
-        logger.info(f"Fetching AI analysis results for user {user_id}")
+        authenticated_user_id = current_user.get("user_id") or current_user.get("id")
        logger.info(f"Fetching AI analysis results for authenticated user {authenticated_user_id}")
        result = await ai_analytics_service.get_user_ai_analysis_results(
-            user_id=user_id,
+            user_id=authenticated_user_id,
            analysis_type=analysis_type,
            limit=limit
        )
@@ -219,14 +233,16 @@ async def get_user_ai_analysis_results(
 async def refresh_ai_analysis(
    user_id: int,
    analysis_type: str = Query(..., description="Type of analysis to refresh"),
-    strategy_id: Optional[int] = Query(None, description="Strategy ID")
+    strategy_id: Optional[int] = Query(None, description="Strategy ID"),
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
-    """Force refresh of AI analysis for a user."""
+    """Force refresh of AI analysis for the authenticated user."""
    try:
-        logger.info(f"Force refreshing AI analysis for user {user_id}, type: {analysis_type}")
+        authenticated_user_id = current_user.get("user_id") or current_user.get("id")
        logger.info(f"Force refreshing AI analysis for authenticated user {authenticated_user_id}, type: {analysis_type}")
        result = await ai_analytics_service.refresh_ai_analysis(
-            user_id=user_id,
+            user_id=authenticated_user_id,
            analysis_type=analysis_type,
            strategy_id=strategy_id
        )
@@ -240,14 +256,16 @@ async def refresh_ai_analysis(
@router.delete("/cache/{user_id}")
 async def clear_ai_analysis_cache(
    user_id: int,
-    analysis_type: Optional[str] = Query(None, description="Specific analysis type to clear")
+    analysis_type: Optional[str] = Query(None, description="Specific analysis type to clear"),
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
-    """Clear AI analysis cache for a user."""
+    """Clear AI analysis cache for the authenticated user."""
    try:
-        logger.info(f"Clearing AI analysis cache for user {user_id}")
+        authenticated_user_id = current_user.get("user_id") or current_user.get("id")
        logger.info(f"Clearing AI analysis cache for authenticated user {authenticated_user_id}")
        result = await ai_analytics_service.clear_ai_analysis_cache(
-            user_id=user_id,
+            user_id=authenticated_user_id,
            analysis_type=analysis_type
        )
@@ -259,13 +277,15 @@ async def clear_ai_analysis_cache(
@router.get("/statistics")
 async def get_ai_analysis_statistics(
    current_user: Dict[str, Any] = Depends(get_current_user),
    user_id: Optional[int] = Query(None, description="User ID for user-specific stats")
 ):
    """Get AI analysis statistics."""
    try:
-        logger.info(f"📊 Getting AI analysis statistics for user: {user_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"📊 Getting AI analysis statistics for authenticated user: {clerk_user_id}")
-        result = await ai_analytics_service.get_ai_analysis_statistics(user_id)
+        result = await ai_analytics_service.get_ai_analysis_statistics(user_id or clerk_user_id)
        return result
    except Exception as e:
--- a/backend/api/content_planning/api/routes/calendar_events.py
+++ b/backend/api/content_planning/api/routes/calendar_events.py
@@ -9,6 +9,9 @@ from typing import Dict, Any, List, Optional
 from datetime import datetime
 from loguru import logger
 # Import authentication
 from middleware.auth_middleware import get_current_user
 # Import database service
 from services.database import get_db_session, get_db
 from services.content_planning_db import ContentPlanningDBService
@@ -34,13 +37,16 @@ router = APIRouter(prefix="/calendar-events", tags=["calendar-events"])
@router.post("/", response_model=CalendarEventResponse)
 async def create_calendar_event(
    event: CalendarEventCreate,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Create a new calendar event."""
    try:
-        logger.info(f"Creating calendar event: {event.title}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Creating calendar event: {event.title} for user: {clerk_user_id}")
        event_data = event.dict()
        event_data['user_id'] = clerk_user_id
        created_event = await calendar_service.create_calendar_event(event_data, db)
        return CalendarEventResponse(**created_event)
@@ -54,11 +60,13 @@ async def create_calendar_event(
@router.get("/", response_model=List[CalendarEventResponse])
 async def get_calendar_events(
    strategy_id: Optional[int] = Query(None, description="Filter by strategy ID"),
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get calendar events, optionally filtered by strategy."""
    try:
-        logger.info("Fetching calendar events")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching calendar events for user: {clerk_user_id}")
        events = await calendar_service.get_calendar_events(strategy_id, db)
        return [CalendarEventResponse(**event) for event in events]
@@ -70,11 +78,13 @@ async def get_calendar_events(
@router.get("/{event_id}", response_model=CalendarEventResponse)
 async def get_calendar_event(
    event_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get a specific calendar event by ID."""
    try:
-        logger.info(f"Fetching calendar event: {event_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching calendar event: {event_id} for user: {clerk_user_id}")
        event = await calendar_service.get_calendar_event_by_id(event_id, db)
        return CalendarEventResponse(**event)
@@ -89,11 +99,13 @@ async def get_calendar_event(
 async def update_calendar_event(
    event_id: int,
    update_data: Dict[str, Any],
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Update a calendar event."""
    try:
-        logger.info(f"Updating calendar event: {event_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Updating calendar event: {event_id} for user: {clerk_user_id}")
        updated_event = await calendar_service.update_calendar_event(event_id, update_data, db)
        return CalendarEventResponse(**updated_event)
@@ -107,11 +119,13 @@ async def update_calendar_event(
@router.delete("/{event_id}")
 async def delete_calendar_event(
    event_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Delete a calendar event."""
    try:
-        logger.info(f"Deleting calendar event: {event_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Deleting calendar event: {event_id} for user: {clerk_user_id}")
        deleted = await calendar_service.delete_calendar_event(event_id, db)
@@ -129,11 +143,13 @@ async def delete_calendar_event(
@router.post("/schedule", response_model=Dict[str, Any])
 async def schedule_calendar_event(
    event: CalendarEventCreate,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Schedule a calendar event with conflict checking."""
    try:
-        logger.info(f"Scheduling calendar event: {event.title}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Scheduling calendar event: {event.title} for user: {clerk_user_id}")
        event_data = event.dict()
        result = await calendar_service.schedule_event(event_data, db)
@@ -147,11 +163,13 @@ async def schedule_calendar_event(
 async def get_strategy_events(
    strategy_id: int,
    status: Optional[str] = Query(None, description="Filter by event status"),
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get calendar events for a specific strategy."""
    try:
-        logger.info(f"Fetching events for strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching events for strategy: {strategy_id} for user: {clerk_user_id}")
        if status:
            events = await calendar_service.get_events_by_status(strategy_id, status, db)
--- a/backend/api/content_planning/api/routes/calendar_generation.py
+++ b/backend/api/content_planning/api/routes/calendar_generation.py
@@ -114,25 +114,23 @@ async def generate_comprehensive_calendar(
        )
@router.post("/optimize-content", response_model=ContentOptimizationResponse)
-async def optimize_content_for_platform(request: ContentOptimizationRequest, db: Session = Depends(get_db)):
+async def optimize_content_for_platform(
    request: ContentOptimizationRequest,
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
-    Optimize content for specific platforms using database insights.
+    Optimize content for specific platforms using database insights with user isolation.
    This endpoint optimizes content based on:
    - Historical performance data for the platform
    - Audience preferences from onboarding data
    - Gap analysis insights for content improvement
    - Competitor analysis for differentiation
    - Active strategy data for optimal alignment
    """
    try:
-        logger.info(f"🔧 Starting content optimization for user {request.user_id}")
+        clerk_user_id = str(current_user.get('id'))
        logger.info(f"🔧 Starting content optimization for authenticated user {clerk_user_id}")
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
        result = await calendar_service.optimize_content_for_platform(
-            user_id=request.user_id,
+            user_id=clerk_user_id,
            title=request.title,
            description=request.description,
            content_type=request.content_type,
@@ -152,24 +150,23 @@ async def optimize_content_for_platform(request: ContentOptimizationRequest, db:
        )
@router.post("/performance-predictions", response_model=PerformancePredictionResponse)
-async def predict_content_performance(request: PerformancePredictionRequest, db: Session = Depends(get_db)):
+async def predict_content_performance(
    request: PerformancePredictionRequest,
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
-    Predict content performance using database insights.
+    Predict content performance using database insights with user isolation.
    This endpoint predicts performance based on:
    - Historical performance data
    - Audience demographics and preferences
    - Content type and platform patterns
    - Gap analysis opportunities
    """
    try:
-        logger.info(f"📊 Starting performance prediction for user {request.user_id}")
+        clerk_user_id = str(current_user.get('id'))
        logger.info(f"📊 Starting performance prediction for authenticated user {clerk_user_id}")
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
        result = await calendar_service.predict_content_performance(
-            user_id=request.user_id,
+            user_id=clerk_user_id,
            content_type=request.content_type,
            platform=request.platform,
            content_data=request.content_data,
@@ -186,24 +183,23 @@ async def predict_content_performance(request: PerformancePredictionRequest, db:
        )
@router.post("/repurpose-content", response_model=ContentRepurposingResponse)
-async def repurpose_content_across_platforms(request: ContentRepurposingRequest, db: Session = Depends(get_db)):
+async def repurpose_content_across_platforms(
    request: ContentRepurposingRequest,
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
-    Repurpose content across different platforms using database insights.
+    Repurpose content across different platforms using database insights with user isolation.
    This endpoint suggests content repurposing based on:
    - Existing content and strategy data
    - Gap analysis opportunities
    - Platform-specific requirements
    - Audience preferences
    """
    try:
-        logger.info(f"🔄 Starting content repurposing for user {request.user_id}")
+        clerk_user_id = str(current_user.get('id'))
        logger.info(f"🔄 Starting content repurposing for authenticated user {clerk_user_id}")
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
        result = await calendar_service.repurpose_content_across_platforms(
-            user_id=request.user_id,
+            user_id=clerk_user_id,
            original_content=request.original_content,
            target_platforms=request.target_platforms,
            strategy_id=request.strategy_id
@@ -312,12 +308,16 @@ async def get_comprehensive_user_data(
        )
@router.get("/health")
-async def calendar_generation_health_check(db: Session = Depends(get_db)):
+async def calendar_generation_health_check(
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
    Health check for calendar generation services.
    """
    try:
-        logger.info("🏥 Performing calendar generation health check")
+        clerk_user_id = str(current_user.get('id'))
        logger.info(f"🏥 Performing calendar generation health check for user {clerk_user_id}")
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
@@ -337,12 +337,17 @@ async def calendar_generation_health_check(db: Session = Depends(get_db)):
        }
@router.get("/progress/{session_id}")
-async def get_calendar_generation_progress(session_id: str, db: Session = Depends(get_db)):
+async def get_calendar_generation_progress(
    session_id: str,
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
    Get real-time progress of calendar generation for a specific session.
    This endpoint is polled by the frontend modal to show progress updates.
    """
    try:
        clerk_user_id = str(current_user.get('id'))
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
@@ -433,11 +438,16 @@ async def start_calendar_generation(
        raise HTTPException(status_code=500, detail="Failed to start calendar generation")
@router.delete("/cancel/{session_id}")
-async def cancel_calendar_generation(session_id: str, db: Session = Depends(get_db)):
+async def cancel_calendar_generation(
    session_id: str,
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
    Cancel an ongoing calendar generation session.
    """
    try:
        clerk_user_id = str(current_user.get('id'))
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
@@ -463,9 +473,13 @@ async def cancel_calendar_generation(session_id: str, db: Session = Depends(get_
 # Cache Management Endpoints
@router.get("/cache/stats")
-async def get_cache_stats(db: Session = Depends(get_db)) -> Dict[str, Any]:
+async def get_cache_stats(
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ) -> Dict[str, Any]:
    """Get comprehensive user data cache statistics."""
    try:
        clerk_user_id = str(current_user.get('id'))
        from services.comprehensive_user_data_cache_service import ComprehensiveUserDataCacheService
        cache_service = ComprehensiveUserDataCacheService(db)
        stats = cache_service.get_cache_stats()
@@ -478,19 +492,21 @@ async def get_cache_stats(db: Session = Depends(get_db)) -> Dict[str, Any]:
 async def invalidate_user_cache(
    user_id: str,
    strategy_id: Optional[int] = Query(None, description="Strategy ID to invalidate (optional)"),
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ) -> Dict[str, Any]:
-    """Invalidate cache for a specific user/strategy."""
+    """Invalidate cache for the authenticated user."""
    try:
        clerk_user_id = str(current_user.get('id'))
        from services.comprehensive_user_data_cache_service import ComprehensiveUserDataCacheService
        cache_service = ComprehensiveUserDataCacheService(db)
-        success = cache_service.invalidate_cache(user_id, strategy_id)
+        success = cache_service.invalidate_cache(clerk_user_id, strategy_id)
        if success:
            return {
                "status": "success",
-                "message": f"Cache invalidated for user {user_id}" + (f" and strategy {strategy_id}" if strategy_id else ""),
+                "message": f"Cache invalidated for user {clerk_user_id}" + (f" and strategy {strategy_id}" if strategy_id else ""),
-                "user_id": user_id,
+                "user_id": clerk_user_id,
                "strategy_id": strategy_id
            }
        else:
@@ -501,9 +517,13 @@ async def invalidate_user_cache(
        raise HTTPException(status_code=500, detail="Failed to invalidate cache")
@router.post("/cache/cleanup")
-async def cleanup_expired_cache(db: Session = Depends(get_db)) -> Dict[str, Any]:
+async def cleanup_expired_cache(
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ) -> Dict[str, Any]:
    """Clean up expired cache entries."""
    try:
        clerk_user_id = str(current_user.get('id'))
        from services.comprehensive_user_data_cache_service import ComprehensiveUserDataCacheService
        cache_service = ComprehensiveUserDataCacheService(db)
        deleted_count = cache_service.cleanup_expired_cache()
@@ -519,16 +539,22 @@ async def cleanup_expired_cache(db: Session = Depends(get_db)) -> Dict[str, Any]
        raise HTTPException(status_code=500, detail="Failed to clean up cache")
@router.get("/sessions")
-async def list_active_sessions(db: Session = Depends(get_db)):
+async def list_active_sessions(
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
-    List all active calendar generation sessions.
+    List active calendar generation sessions for the authenticated user.
    """
    try:
        clerk_user_id = str(current_user.get('id'))
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
        sessions = []
        for session_id, session_data in calendar_service.orchestrator_sessions.items():
            if str(session_data.get("user_id", "")) != clerk_user_id:
                continue
            sessions.append({
                "session_id": session_id,
                "user_id": session_data.get("user_id"),
@@ -548,11 +574,15 @@ async def list_active_sessions(db: Session = Depends(get_db)):
        raise HTTPException(status_code=500, detail="Failed to list sessions")
@router.delete("/sessions/cleanup")
-async def cleanup_old_sessions(db: Session = Depends(get_db)):
+async def cleanup_old_sessions(
    db: Session = Depends(get_db),
    current_user: dict = Depends(get_current_user)
 ):
    """
-    Clean up old sessions.
+    Clean up old sessions for the authenticated user.
    """
    try:
        clerk_user_id = str(current_user.get('id'))
        # Initialize service with database session for active strategy access
        calendar_service = CalendarGenerationService(db)
--- a/backend/api/content_planning/api/routes/gap_analysis.py
+++ b/backend/api/content_planning/api/routes/gap_analysis.py
@@ -38,13 +38,16 @@ router = APIRouter(prefix="/gap-analysis", tags=["gap-analysis"])
@router.post("/", response_model=ContentGapAnalysisResponse)
 async def create_content_gap_analysis(
    analysis: ContentGapAnalysisCreate,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Create a new content gap analysis."""
    try:
-        logger.info(f"Creating content gap analysis for: {analysis.website_url}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Creating content gap analysis for: {analysis.website_url} by user: {clerk_user_id}")
        analysis_data = analysis.dict()
        analysis_data['user_id'] = clerk_user_id
        created_analysis = await gap_analysis_service.create_gap_analysis(analysis_data, db)
        return ContentGapAnalysisResponse(**created_analysis)
@@ -76,11 +79,13 @@ async def get_content_gap_analyses(
@router.get("/{analysis_id}", response_model=ContentGapAnalysisResponse)
 async def get_content_gap_analysis(
    analysis_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get a specific content gap analysis by ID."""
    try:
-        logger.info(f"Fetching content gap analysis: {analysis_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching content gap analysis: {analysis_id} for user: {clerk_user_id}")
        analysis = await gap_analysis_service.get_gap_analysis_by_id(analysis_id, db)
        return ContentGapAnalysisResponse(**analysis)
@@ -117,15 +122,17 @@ async def analyze_content_gaps(
@router.get("/user/{user_id}/analyses")
 async def get_user_gap_analyses(
    user_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
-    """Get all gap analyses for a specific user."""
+    """Get all gap analyses for the authenticated user."""
    try:
-        logger.info(f"Fetching gap analyses for user: {user_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching gap analyses for authenticated user: {clerk_user_id}")
-        analyses = await gap_analysis_service.get_user_gap_analyses(user_id, db)
+        analyses = await gap_analysis_service.get_user_gap_analyses(clerk_user_id, db)
        return {
-            "user_id": user_id,
+            "user_id": clerk_user_id,
            "analyses": analyses,
            "total_count": len(analyses)
        }
@@ -138,11 +145,13 @@ async def get_user_gap_analyses(
 async def update_content_gap_analysis(
    analysis_id: int,
    update_data: Dict[str, Any],
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Update a content gap analysis."""
    try:
-        logger.info(f"Updating content gap analysis: {analysis_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Updating content gap analysis: {analysis_id} for user: {clerk_user_id}")
        updated_analysis = await gap_analysis_service.update_gap_analysis(analysis_id, update_data, db)
        return ContentGapAnalysisResponse(**updated_analysis)
@@ -156,11 +165,13 @@ async def update_content_gap_analysis(
@router.delete("/{analysis_id}")
 async def delete_content_gap_analysis(
    analysis_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Delete a content gap analysis."""
    try:
-        logger.info(f"Deleting content gap analysis: {analysis_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Deleting content gap analysis: {analysis_id} for user: {clerk_user_id}")
        deleted = await gap_analysis_service.delete_gap_analysis(analysis_id, db)
--- a/backend/api/content_planning/api/routes/health_monitoring.py
+++ b/backend/api/content_planning/api/routes/health_monitoring.py
@@ -9,6 +9,9 @@ from typing import Dict, Any, List, Optional
 from datetime import datetime
 from loguru import logger
 # Import authentication
 from middleware.auth_middleware import get_current_user
 # Import database service
 from services.database import get_db_session, get_db
 from services.content_planning_db import ContentPlanningDBService
@@ -28,7 +31,9 @@ ai_analysis_db_service = AIAnalysisDBService()
 router = APIRouter(prefix="/health", tags=["health-monitoring"])
@router.get("/backend", response_model=Dict[str, Any])
-async def check_backend_health():
+async def check_backend_health(
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Check core backend health (independent of AI services)
    """
@@ -77,7 +82,9 @@ async def check_backend_health():
        }
@router.get("/ai", response_model=Dict[str, Any])
-async def check_ai_services_health():
+async def check_ai_services_health(
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Check AI services health separately
    """
@@ -136,7 +143,10 @@ async def check_ai_services_health():
        }
@router.get("/database", response_model=Dict[str, Any])
-async def database_health_check(db: Session = Depends(get_db)):
+async def database_health_check(
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """
    Health check for database operations.
    """
@@ -157,7 +167,10 @@ async def database_health_check(db: Session = Depends(get_db)):
        )
@router.get("/debug/strategies/{user_id}")
-async def debug_content_strategies(user_id: int):
+async def debug_content_strategies(
    user_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Debug endpoint to print content strategy data directly.
    """
@@ -203,7 +216,9 @@ async def debug_content_strategies(user_id: int):
        )
@router.get("/comprehensive", response_model=Dict[str, Any])
-async def comprehensive_health_check():
+async def comprehensive_health_check(
    current_user: Dict[str, Any] = Depends(get_current_user)
 ):
    """
    Comprehensive health check for all content planning services.
    """
--- a/backend/api/content_planning/api/routes/monitoring.py
+++ b/backend/api/content_planning/api/routes/monitoring.py
@@ -93,7 +93,10 @@ async def get_lightweight_statistics(current_user: Dict[str, Any] = Depends(get_
        }
@router.get("/cache-stats")
-async def get_cache_statistics(db = None) -> Dict[str, Any]:
+async def get_cache_statistics(
    current_user: Dict[str, Any] = Depends(get_current_user),
    db = None
 ) -> Dict[str, Any]:
    """Get comprehensive user data cache statistics."""
    try:
        if not db:
--- a/backend/api/content_planning/api/routes/strategies.py
+++ b/backend/api/content_planning/api/routes/strategies.py
@@ -35,15 +35,18 @@ router = APIRouter(prefix="/strategies", tags=["strategies"])
@router.post("/", response_model=ContentStrategyResponse)
 async def create_content_strategy(
    strategy: ContentStrategyCreate,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Create a new content strategy."""
    try:
-        logger.info(f"Creating content strategy: {strategy.name}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Creating content strategy: {strategy.name} for user: {clerk_user_id}")
        db_service = EnhancedStrategyDBService(db)
        strategy_service = EnhancedStrategyService(db_service)
        strategy_data = strategy.dict()
        strategy_data['user_id'] = clerk_user_id
        created_strategy = await strategy_service.create_enhanced_strategy(strategy_data, db)
        return ContentStrategyResponse(**created_strategy)
@@ -105,11 +108,13 @@ async def get_content_strategies(
@router.get("/{strategy_id}", response_model=ContentStrategyResponse)
 async def get_content_strategy(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get a specific content strategy by ID."""
    try:
-        logger.info(f"Fetching content strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching content strategy: {strategy_id} for user: {clerk_user_id}")
        db_service = EnhancedStrategyDBService(db)
        strategy_service = EnhancedStrategyService(db_service)
@@ -127,11 +132,13 @@ async def get_content_strategy(
 async def update_content_strategy(
    strategy_id: int,
    update_data: Dict[str, Any],
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Update a content strategy."""
    try:
-        logger.info(f"Updating content strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Updating content strategy: {strategy_id} for user: {clerk_user_id}")
        db_service = EnhancedStrategyDBService(db)
        updated_strategy = await db_service.update_enhanced_strategy(strategy_id, update_data)
@@ -150,11 +157,13 @@ async def update_content_strategy(
@router.delete("/{strategy_id}")
 async def delete_content_strategy(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Delete a content strategy."""
    try:
-        logger.info(f"Deleting content strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Deleting content strategy: {strategy_id} for user: {clerk_user_id}")
        db_service = EnhancedStrategyDBService(db)
        deleted = await db_service.delete_enhanced_strategy(strategy_id)
@@ -173,11 +182,13 @@ async def delete_content_strategy(
@router.get("/{strategy_id}/analytics")
 async def get_strategy_analytics(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get analytics for a specific strategy."""
    try:
-        logger.info(f"Fetching analytics for strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching analytics for strategy: {strategy_id} for user: {clerk_user_id}")
        db_service = EnhancedStrategyDBService(db)
        analytics = await db_service.get_enhanced_strategies_with_analytics(strategy_id)
@@ -194,11 +205,13 @@ async def get_strategy_analytics(
@router.get("/{strategy_id}/summary")
 async def get_strategy_summary(
    strategy_id: int,
    current_user: Dict[str, Any] = Depends(get_current_user),
    db: Session = Depends(get_db)
 ):
    """Get a comprehensive summary of a strategy with analytics."""
    try:
-        logger.info(f"Fetching summary for strategy: {strategy_id}")
+        clerk_user_id = str(current_user.get('id', ''))
        logger.info(f"Fetching summary for strategy: {strategy_id} for user: {clerk_user_id}")
        # Get strategy with analytics for comprehensive summary
        db_service = EnhancedStrategyDBService(db)
--- a/backend/api/content_planning/services/content_strategy/ai_analysis/quality_validation.py
+++ b/backend/api/content_planning/services/content_strategy/ai_analysis/quality_validation.py
@@ -1,19 +1,20 @@
 """
 Quality Validation Service
 AI response quality assessment and strategic analysis.
 All methods derive results from actual input data — no hardcoded defaults.
 """
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional
 logger = logging.getLogger(__name__)
 class QualityValidationService:
    """Service for quality validation and strategic analysis."""
-    
+
    def __init__(self):
        pass
-    
+
    def validate_against_schema(self, data: Dict[str, Any], schema: Dict[str, Any]) -> None:
        """Validate data against a minimal JSON-like schema definition.
        Raises ValueError on failure.
@@ -54,7 +55,10 @@ class QualityValidationService:
        _check(data, schema)
    def calculate_strategic_scores(self, ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
-        """Calculate strategic performance scores from AI recommendations."""
+        """Calculate strategic performance scores from AI recommendations.
        Scores are derived per analysis type from actual metrics, then aggregated
        with dimension-specific weightings — no blanket multipliers.
        """
        scores = {
            'overall_score': 0.0,
            'content_quality_score': 0.0,
@@ -62,87 +66,214 @@ class QualityValidationService:
            'conversion_score': 0.0,
            'innovation_score': 0.0
        }
-        
+
-        # Calculate scores based on AI recommendations
+        analysis_count = 0
-        total_confidence = 0
+        weighted_total = 0.0
-        total_score = 0
+        weight_sum = 0.0
-        
+
-        for analysis_type, recommendations in ai_recommendations.items():
+        # Dimension-specific weights
-            if isinstance(recommendations, dict) and 'metrics' in recommendations:
+        dimension_weights = {
-                metrics = recommendations['metrics']
+            'comprehensive_strategy': {'quality': 0.35, 'engagement': 0.20, 'conversion': 0.25, 'innovation': 0.20},
-                score = metrics.get('score', 50)
+            'audience_intelligence': {'quality': 0.25, 'engagement': 0.40, 'conversion': 0.20, 'innovation': 0.15},
-                confidence = metrics.get('confidence', 0.5)
+            'competitive_intelligence': {'quality': 0.30, 'engagement': 0.15, 'conversion': 0.25, 'innovation': 0.30},
-                
+            'performance_optimization': {'quality': 0.20, 'engagement': 0.15, 'conversion': 0.45, 'innovation': 0.20},
-                total_score += score * confidence
+            'content_calendar_optimization': {'quality': 0.30, 'engagement': 0.25, 'conversion': 0.20, 'innovation': 0.25},
                total_confidence += confidence
        if total_confidence > 0:
            scores['overall_score'] = total_score / total_confidence
        # Set other scores based on overall score
        scores['content_quality_score'] = scores['overall_score'] * 1.1
        scores['engagement_score'] = scores['overall_score'] * 0.9
        scores['conversion_score'] = scores['overall_score'] * 0.95
        scores['innovation_score'] = scores['overall_score'] * 1.05
        return scores
    def extract_market_positioning(self, ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """Extract market positioning from AI recommendations."""
        return {
            'industry_position': 'emerging',
            'competitive_advantage': 'AI-powered content',
            'market_share': '2.5%',
            'positioning_score': 4
        }
-    
+
        for analysis_type, recommendations in ai_recommendations.items():
            if not isinstance(recommendations, dict):
                continue
            metrics = recommendations.get('metrics')
            if not isinstance(metrics, dict):
                continue
            score = metrics.get('score', 50)
            confidence = metrics.get('confidence', 0.5)
            weight = confidence
            weighted_total += score * weight
            weight_sum += weight
            analysis_count += 1
            weights = dimension_weights.get(analysis_type, {'quality': 0.25, 'engagement': 0.25, 'conversion': 0.25, 'innovation': 0.25})
            scores['content_quality_score'] += (score * weights['quality'] * weight)
            scores['engagement_score'] += (score * weights['engagement'] * weight)
            scores['conversion_score'] += (score * weights['conversion'] * weight)
            scores['innovation_score'] += (score * weights['innovation'] * weight)
        if weight_sum > 0:
            scores['overall_score'] = round(weighted_total / weight_sum, 2)
            scores['content_quality_score'] = round(scores['content_quality_score'] / weight_sum, 2)
            scores['engagement_score'] = round(scores['engagement_score'] / weight_sum, 2)
            scores['conversion_score'] = round(scores['conversion_score'] / weight_sum, 2)
            scores['innovation_score'] = round(scores['innovation_score'] / weight_sum, 2)
        return scores
    def extract_market_positioning(self, ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """Extract market positioning from AI recommendations.
        Scans all analysis types for positioning, competitive_advantage, and market_share signals.
        Returns empty dict if no data is available instead of synthetic defaults.
        """
        positioning = {}
        best_confidence = 0.0
        for analysis_type, recommendations in ai_recommendations.items():
            if not isinstance(recommendations, dict):
                continue
            metrics = recommendations.get('metrics', {})
            confidence = metrics.get('confidence', 0.0)
            if confidence <= best_confidence:
                continue
            recs = recommendations.get('recommendations', [])
            if isinstance(recs, list):
                for r in recs:
                    if not isinstance(r, dict):
                        continue
                    pos = r.get('market_position') or r.get('positioning')
                    adv = r.get('competitive_advantage')
                    share = r.get('market_share')
                    score = r.get('positioning_score') or metrics.get('positioning_score')
                    if any([pos, adv, share, score]):
                        best_confidence = confidence
                        if pos:
                            positioning['industry_position'] = pos
                        if adv:
                            positioning['competitive_advantage'] = adv
                        if share:
                            positioning['market_share'] = str(share)
                        if score is not None:
                            positioning['positioning_score'] = score
        # Check top-level keys as fallback
        if not positioning:
            for key in ('industry_position', 'competitive_advantage', 'market_share', 'positioning_score'):
                val = ai_recommendations.get(key)
                if val is not None:
                    positioning[key] = val
        return positioning
    def extract_competitive_advantages(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Extract competitive advantages from AI recommendations."""
+        """Extract competitive advantages from AI recommendations.
-        return [
+        Scans competitive_intelligence and other analysis types for advantage signals.
-            {
+        Returns empty list if no data is available.
-                'advantage': 'AI-powered content creation',
+        """
-                'impact': 'High',
+        advantages = []
-                'implementation': 'In Progress'
+
-            },
+        for analysis_type, recommendations in ai_recommendations.items():
-            {
+            if not isinstance(recommendations, dict):
-                'advantage': 'Data-driven strategy',
+                continue
-                'impact': 'Medium',
+            recs = recommendations.get('recommendations', [])
-                'implementation': 'Complete'
+            if not isinstance(recs, list):
-            }
+                continue
-        ]
+            for r in recs:
-    
+                if not isinstance(r, dict):
                    continue
                adv = r.get('advantage') or r.get('competitive_advantage')
                if adv:
                    advantages.append({
                        'advantage': adv,
                        'impact': r.get('impact', 'Medium'),
                        'implementation': r.get('implementation', 'Planned')
                    })
        # Deduplicate by advantage text
        seen = set()
        unique = []
        for a in advantages:
            key = a['advantage'].strip().lower()
            if key not in seen:
                seen.add(key)
                unique.append(a)
        return unique
    def extract_strategic_risks(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Extract strategic risks from AI recommendations."""
+        """Extract strategic risks from AI recommendations.
-        return [
+        Scans all analysis types for risk signals.
-            {
+        Returns empty list if no data is available.
-                'risk': 'Content saturation in market',
+        """
-                'probability': 'Medium',
+        risks = []
-                'impact': 'High'
+
-            },
+        for analysis_type, recommendations in ai_recommendations.items():
-            {
+            if not isinstance(recommendations, dict):
-                'risk': 'Algorithm changes affecting reach',
+                continue
-                'probability': 'High',
+            recs = recommendations.get('recommendations', [])
-                'impact': 'Medium'
+            if not isinstance(recs, list):
-            }
+                continue
-        ]
+            for r in recs:
-    
+                if not isinstance(r, dict):
                    continue
                risk_text = r.get('risk') or r.get('strategic_risk') or r.get('threat')
                if risk_text:
                    risks.append({
                        'risk': risk_text,
                        'probability': r.get('probability', 'Medium'),
                        'impact': r.get('impact', 'Medium')
                    })
            risks_list = recommendations.get('risks') or recommendations.get('strategic_risks')
            if isinstance(risks_list, list):
                for r in risks_list:
                    if isinstance(r, dict) and r.get('risk'):
                        risks.append(r)
        seen = set()
        unique = []
        for r in risks:
            key = r['risk'].strip().lower()
            if key not in seen:
                seen.add(key)
                unique.append(r)
        return unique
    def extract_opportunity_analysis(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Extract opportunity analysis from AI recommendations."""
+        """Extract opportunity analysis from AI recommendations.
-        return [
+        Scans all analysis types for opportunity signals.
-            {
+        Returns empty list if no data is available.
-                'opportunity': 'Video content expansion',
+        """
-                'potential_impact': 'High',
+        opportunities = []
-                'implementation_ease': 'Medium'
+
-            },
+        for analysis_type, recommendations in ai_recommendations.items():
-            {
+            if not isinstance(recommendations, dict):
-                'opportunity': 'Social media engagement',
+                continue
-                'potential_impact': 'Medium',
+            recs = recommendations.get('recommendations', [])
-                'implementation_ease': 'High'
+            if not isinstance(recs, list):
-            }
+                continue
-        ]
+            for r in recs:
-    
+                if not isinstance(r, dict):
                    continue
                opp = r.get('opportunity') or r.get('growth_opportunity')
                if opp:
                    opportunities.append({
                        'opportunity': opp,
                        'potential_impact': r.get('potential_impact', 'Medium'),
                        'implementation_ease': r.get('implementation_ease', 'Medium')
                    })
            opps_list = recommendations.get('opportunities') or recommendations.get('growth_opportunities')
            if isinstance(opps_list, list):
                for o in opps_list:
                    if isinstance(o, dict) and o.get('opportunity'):
                        opportunities.append(o)
        seen = set()
        unique = []
        for o in opportunities:
            key = o['opportunity'].strip().lower()
            if key not in seen:
                seen.add(key)
                unique.append(o)
        return unique
    def validate_ai_response_quality(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
-        """Validate the quality of AI response."""
+        """Validate the quality of AI response using multi-dimensional analysis.
        Scores are derived from actual content, not placeholders.
        """
        quality_metrics = {
            'completeness': 0.0,
            'relevance': 0.0,
@@ -150,30 +281,76 @@ class QualityValidationService:
            'confidence': 0.0,
            'overall_quality': 0.0
        }
-        
+
-        # Calculate completeness
+        # Completeness: weighted by field importance
-        required_fields = ['recommendations', 'insights', 'metrics']
+        field_weights = {
-        present_fields = sum(1 for field in required_fields if field in ai_response)
+            'recommendations': 0.35,
-        quality_metrics['completeness'] = present_fields / len(required_fields)
+            'insights': 0.30,
-        
+            'metrics': 0.20,
-        # Calculate relevance (placeholder logic)
+            'analysis_type': 0.15
-        quality_metrics['relevance'] = 0.8 if ai_response.get('analysis_type') else 0.5
+        }
-        
+        weighted_present = 0.0
-        # Calculate actionability (placeholder logic)
+        total_weight = 0.0
        for field, weight in field_weights.items():
            total_weight += weight
            val = ai_response.get(field)
            if field == 'recommendations':
                if isinstance(val, list) and len(val) > 0:
                    weighted_present += weight
            elif field == 'insights':
                if isinstance(val, list) and len(val) > 0:
                    weighted_present += weight
            elif field == 'metrics':
                if isinstance(val, dict) and len(val) > 0:
                    weighted_present += weight
            else:
                if val is not None:
                    weighted_present += weight
        quality_metrics['completeness'] = round(weighted_present / total_weight, 2) if total_weight > 0 else 0.0
        # Relevance: evaluate recommendations content quality
        recommendations = ai_response.get('recommendations', [])
-        quality_metrics['actionability'] = min(1.0, len(recommendations) / 5.0)
+        if isinstance(recommendations, list) and len(recommendations) > 0:
-        
+            scored = 0
-        # Calculate confidence
+            total_recs = len(recommendations)
            for r in recommendations:
                if isinstance(r, dict):
                    has_action = bool(r.get('action') or r.get('recommendation') or r.get('step'))
                    has_reason = bool(r.get('reason') or r.get('rationale') or r.get('impact'))
                    if has_action and has_reason:
                        scored += 1
            quality_metrics['relevance'] = round(scored / total_recs, 2) if total_recs > 0 else 0.5
        else:
            quality_metrics['relevance'] = 0.0
        # Actionability: recommendation detail score
        if isinstance(recommendations, list) and len(recommendations) > 0:
            actionable = 0
            for r in recommendations:
                if isinstance(r, dict):
                    has_timeline = bool(r.get('timeline') or r.get('effort'))
                    has_impact = bool(r.get('impact') or r.get('expected_outcome'))
                    if has_timeline or has_impact:
                        actionable += 1
            quality_metrics['actionability'] = round(min(1.0, actionable / max(len(recommendations), 1)), 2)
        else:
            quality_metrics['actionability'] = 0.0
        # Confidence from metrics
        metrics = ai_response.get('metrics', {})
-        quality_metrics['confidence'] = metrics.get('confidence', 0.5)
+        quality_metrics['confidence'] = round(metrics.get('confidence', 0.0), 2) if isinstance(metrics, dict) else 0.0
-        
+
-        # Calculate overall quality
+        # Overall weighted quality
-        quality_metrics['overall_quality'] = sum(quality_metrics.values()) / len(quality_metrics)
+        weights = {'completeness': 0.25, 'relevance': 0.30, 'actionability': 0.25, 'confidence': 0.20}
-        
+        overall = sum(quality_metrics[k] * weights[k] for k in weights)
        quality_metrics['overall_quality'] = round(overall, 2)
        return quality_metrics
-    
+
    def assess_strategy_quality(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Assess the overall quality of a content strategy."""
+        """Assess the overall quality of a content strategy.
        Uses field-level analysis with content-aware scoring — not simple presence checks.
        """
        quality_assessment = {
            'data_completeness': 0.0,
            'strategic_clarity': 0.0,
@@ -181,25 +358,59 @@ class QualityValidationService:
            'competitive_positioning': 0.0,
            'overall_quality': 0.0
        }
-        
+
-        # Assess data completeness
+        # Data completeness with weighted field groups
-        required_fields = [
+        field_groups = {
-            'business_objectives', 'target_metrics', 'content_budget',
+            'objectives': {'fields': ['business_objectives', 'target_metrics'], 'weight': 0.25},
-            'team_size', 'implementation_timeline'
+            'resources': {'fields': ['content_budget', 'team_size', 'implementation_timeline'], 'weight': 0.25},
-        ]
+            'audience': {'fields': ['content_preferences', 'consumption_patterns', 'audience_pain_points'], 'weight': 0.25},
-        present_fields = sum(1 for field in required_fields if strategy_data.get(field))
+            'competition': {'fields': ['top_competitors', 'market_gaps', 'competitive_position'], 'weight': 0.25}
-        quality_assessment['data_completeness'] = present_fields / len(required_fields)
+        }
-        
+        total_weight = 0.0
-        # Assess strategic clarity (placeholder logic)
+        weighted_score = 0.0
-        quality_assessment['strategic_clarity'] = 0.7 if strategy_data.get('business_objectives') else 0.3
+        for group_name, group in field_groups.items():
-        
+            group_present = sum(1 for f in group['fields'] if strategy_data.get(f) not in (None, '', []))
-        # Assess implementation readiness (placeholder logic)
+            group_score = group_present / len(group['fields']) if group['fields'] else 0
-        quality_assessment['implementation_readiness'] = 0.6 if strategy_data.get('team_size') else 0.2
+            weighted_score += group_score * group['weight']
-        
+            total_weight += group['weight']
-        # Assess competitive positioning (placeholder logic)
+        quality_assessment['data_completeness'] = round(weighted_score / total_weight, 2) if total_weight > 0 else 0.0
-        quality_assessment['competitive_positioning'] = 0.5 if strategy_data.get('competitive_position') else 0.2
+
-        
+        # Strategic clarity: evaluate quality of business objectives
-        # Calculate overall quality
+        objectives = strategy_data.get('business_objectives')
-        quality_assessment['overall_quality'] = sum(quality_assessment.values()) / len(quality_assessment)
+        if isinstance(objectives, str) and len(objectives) > 20:
-        
+            quality_assessment['strategic_clarity'] = 0.9
        elif isinstance(objectives, str) and len(objectives) > 0:
            quality_assessment['strategic_clarity'] = 0.6
        elif isinstance(objectives, list) and len(objectives) > 0:
            quality_assessment['strategic_clarity'] = 0.8
        else:
            quality_assessment['strategic_clarity'] = 0.0
        # Implementation readiness: budget + team + timeline
        readiness_signals = 0
        if strategy_data.get('content_budget') not in (None, '', 0):
            readiness_signals += 1
        if strategy_data.get('team_size') not in (None, '', 0):
            readiness_signals += 1
        if strategy_data.get('implementation_timeline') not in (None, '', []):
            readiness_signals += 1
        quality_assessment['implementation_readiness'] = round(readiness_signals / 3.0, 2)
        # Competitive positioning: evaluate depth of competitive data
        comp_signals = 0
        if strategy_data.get('top_competitors') not in (None, '', []):
            comp_signals += 1
        if strategy_data.get('market_gaps') not in (None, '', []):
            comp_signals += 1
        if strategy_data.get('competitive_position') not in (None, ''):
            comp_signals += 1
        if strategy_data.get('industry_trends') not in (None, '', []):
            comp_signals += 1
        quality_assessment['competitive_positioning'] = round(comp_signals / 4.0, 2)
        # Overall quality
        quality_assessment['overall_quality'] = round(
            sum(quality_assessment.values()) / len(quality_assessment), 2
        )
        return quality_assessment 
--- a/backend/api/content_planning/services/content_strategy/core/strategy_service.py
+++ b/backend/api/content_planning/services/content_strategy/core/strategy_service.py
@@ -510,7 +510,7 @@ class EnhancedStrategyService:
    async def get_system_health(self, db: Session) -> Dict[str, Any]:
        """Get system health status."""
        try:
-            return await self.health_monitoring_service.get_system_health(db)
+            return await self.health_monitoring_service.check_system_health(db)
        except Exception as e:
            logger.error(f"Error getting system health: {str(e)}")
            raise
@@ -583,7 +583,7 @@ class EnhancedStrategyService:
    async def optimize_strategy_operation(self, operation_name: str, operation_func, *args, **kwargs) -> Dict[str, Any]:
        """Optimize strategy operation with performance monitoring."""
        try:
-            return await self.performance_optimization_service.optimize_operation(
+            return await self.performance_optimization_service.optimize_response_time(
                operation_name, operation_func, *args, **kwargs
            )
        except Exception as e:
--- a/backend/api/content_planning/services/content_strategy/onboarding/field_transformation.py
+++ b/backend/api/content_planning/services/content_strategy/onboarding/field_transformation.py
@@ -176,11 +176,7 @@ class FieldTransformationService:
                        # Default transformation - use first available source data
                        field_value = self._default_transformation(source_data, field_name)
-                    # If no value found, provide default based on field type
+                    if field_value is not None and field_value != "":
                    if field_value is None or field_value == "":
                        field_value = self._get_default_value_for_field(field_name)
                    if field_value is not None:
                        transformed_fields[field_name] = {
                            'value': field_value,
                            'source': sources[0] if sources else 'default',
@@ -943,44 +939,6 @@ class FieldTransformationService:
            logger.error(f"Error extracting A/B testing capabilities: {str(e)}")
            return False
    def _get_default_value_for_field(self, field_name: str) -> Any:
        """Get default value for a field when no data is available."""
        # Provide sensible defaults for required fields
        default_values = {
            'business_objectives': 'Lead Generation, Brand Awareness',
            'target_metrics': 'Traffic Growth: 30%, Engagement Rate: 5%, Conversion Rate: 2%',
            'content_budget': 1000,
            'team_size': 1,
            'implementation_timeline': '3 months',
            'market_share': 'Small but growing',
            'competitive_position': 'Niche',
            'performance_metrics': 'Current Traffic: 1000, Current Engagement: 3%',
            'content_preferences': 'Blog posts, Social media content',
            'consumption_patterns': 'Mobile: 60%, Desktop: 40%',
            'audience_pain_points': 'Time constraints, Content quality',
            'buying_journey': 'Awareness: 40%, Consideration: 35%, Decision: 25%',
            'seasonal_trends': 'Q4 peak, Summer slowdown',
            'engagement_metrics': 'Likes: 100, Shares: 20, Comments: 15',
            'top_competitors': 'Competitor A, Competitor B',
            'competitor_content_strategies': 'Blog-focused, Video-heavy',
            'market_gaps': 'Underserved niche, Content gap',
            'industry_trends': 'AI integration, Video content',
            'emerging_trends': 'Voice search, Interactive content',
            'preferred_formats': ['Blog Posts', 'Videos', 'Infographics'],
            'content_mix': 'Educational: 40%, Entertaining: 30%, Promotional: 30%',
            'content_frequency': 'Weekly',
            'optimal_timing': 'Best Days: Tuesday, Thursday, Best Time: 10 AM',
            'quality_metrics': 'Readability: 8, Engagement: 7, SEO Score: 6',
            'editorial_guidelines': 'Professional tone, Clear structure',
            'brand_voice': 'Professional yet approachable',
            'traffic_sources': 'Organic: 60%, Social: 25%, Direct: 15%',
            'conversion_rates': 'Overall: 2%, Blog: 3%, Landing Pages: 5%',
            'content_roi_targets': 'Target ROI: 300%, Break Even: 6 months',
            'ab_testing_capabilities': False
        }
        return default_values.get(field_name, None)
    def _default_transformation(self, source_data: Dict[str, Any], field_name: str) -> Any:
        """Default transformation when no specific method is available."""
        try:
--- a/backend/api/content_planning/services/content_strategy/performance/caching.py
+++ b/backend/api/content_planning/services/content_strategy/performance/caching.py
@@ -44,6 +44,11 @@ class CachingService:
                'ttl': 900,  # 15 minutes
                'max_size': 1000,
                'priority': 'low'
            },
            'streaming_intelligence': {
                'ttl': 300,  # 5 minutes
                'max_size': 500,
                'priority': 'medium'
            }
        }
--- a/backend/api/content_planning/services/content_strategy/utils/init.py
+++ b/backend/api/content_planning/services/content_strategy/utils/init.py
@@ -9,7 +9,6 @@ from .data_processors import (
    transform_onboarding_data_to_fields,
    get_data_sources,
    get_detailed_input_data_points,
    get_fallback_onboarding_data,
    get_website_analysis_data,
    get_research_preferences_data,
    get_api_keys_data
@@ -36,7 +35,6 @@ __all__ = [
    'transform_onboarding_data_to_fields',
    'get_data_sources',
    'get_detailed_input_data_points',
    'get_fallback_onboarding_data',
    'get_website_analysis_data',
    'get_research_preferences_data',
    'get_api_keys_data',
--- a/backend/api/content_planning/services/content_strategy/utils/data_processors.py
+++ b/backend/api/content_planning/services/content_strategy/utils/data_processors.py
@@ -179,17 +179,13 @@ class DataProcessorService:
        }
        fields['seasonal_trends'] = {
-            'value': ['Q1: Planning', 'Q2: Execution', 'Q3: Optimization', 'Q4: Review'],
+            'value': research_data.get('seasonal_trends', []),
            'source': 'research_preferences',
            'confidence': research_data.get('confidence_level', 0.7)
        }
        fields['engagement_metrics'] = {
-            'value': {
+            'value': website_data.get('performance_metrics', {}),
                'avg_session_duration': website_data.get('performance_metrics', {}).get('avg_session_duration', 180),
                'bounce_rate': website_data.get('performance_metrics', {}).get('bounce_rate', 45.5),
                'pages_per_session': 2.5
            },
            'source': 'website_analysis',
            'confidence': website_data.get('confidence_level', 0.8)
        }
@@ -411,15 +407,6 @@ class DataProcessorService:
            }
        }
    def get_fallback_onboarding_data(self) -> Dict[str, Any]:
        """
        Get fallback onboarding data for compatibility.
        Returns:
            Dictionary with fallback data (raises error as fallbacks are disabled)
        """
        raise RuntimeError("Fallback onboarding data is disabled. Real data required.")
    async def get_website_analysis_data(self, user_id: int) -> Dict[str, Any]:
        """
        Get website analysis data from onboarding.
@@ -534,12 +521,6 @@ def get_detailed_input_data_points(processed_data: Dict[str, Any]) -> Dict[str,
    return processor.get_detailed_input_data_points(processed_data)
 def get_fallback_onboarding_data() -> Dict[str, Any]:
    """Get fallback onboarding data for compatibility."""
    processor = DataProcessorService()
    return processor.get_fallback_onboarding_data()
 async def get_website_analysis_data(user_id: int) -> Dict[str, Any]:
    """Get website analysis data from onboarding."""
    processor = DataProcessorService()
--- a/backend/api/content_planning/services/content_strategy/utils/strategy_utils.py
+++ b/backend/api/content_planning/services/content_strategy/utils/strategy_utils.py
@@ -14,6 +14,7 @@ logger = logging.getLogger(__name__)
 def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
    """
    Calculate strategic performance scores from AI recommendations.
    Dimension-specific weights — no blanket multipliers.
    Args:
        ai_recommendations: Dictionary containing AI analysis results
@@ -28,35 +29,48 @@ def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str,
        'conversion_score': 0.0,
        'innovation_score': 0.0
    }
-    
+
-    # Calculate scores based on AI recommendations
+    weight_sum = 0.0
-    total_confidence = 0
+
-    total_score = 0
+    dimension_weights = {
-    
+        'comprehensive_strategy': {'quality': 0.35, 'engagement': 0.20, 'conversion': 0.25, 'innovation': 0.20},
        'audience_intelligence': {'quality': 0.25, 'engagement': 0.40, 'conversion': 0.20, 'innovation': 0.15},
        'competitive_intelligence': {'quality': 0.30, 'engagement': 0.15, 'conversion': 0.25, 'innovation': 0.30},
        'performance_optimization': {'quality': 0.20, 'engagement': 0.15, 'conversion': 0.45, 'innovation': 0.20},
        'content_calendar_optimization': {'quality': 0.30, 'engagement': 0.25, 'conversion': 0.20, 'innovation': 0.25},
    }
    for analysis_type, recommendations in ai_recommendations.items():
-        if isinstance(recommendations, dict) and 'metrics' in recommendations:
+        if not isinstance(recommendations, dict):
-            metrics = recommendations['metrics']
+            continue
-            score = metrics.get('score', 50)
+        metrics = recommendations.get('metrics')
-            confidence = metrics.get('confidence', 0.5)
+        if not isinstance(metrics, dict):
-            
+            continue
-            total_score += score * confidence
+
-            total_confidence += confidence
+        score = metrics.get('score', 50)
-    
+        confidence = metrics.get('confidence', 0.5)
-    if total_confidence > 0:
+        weight = confidence
-        scores['overall_score'] = total_score / total_confidence
+
-    
+        scores['overall_score'] += score * weight
-    # Set other scores based on overall score
+        weight_sum += weight
-    scores['content_quality_score'] = scores['overall_score'] * 1.1
+
-    scores['engagement_score'] = scores['overall_score'] * 0.9
+        weights = dimension_weights.get(analysis_type, {'quality': 0.25, 'engagement': 0.25, 'conversion': 0.25, 'innovation': 0.25})
-    scores['conversion_score'] = scores['overall_score'] * 0.95
+        scores['content_quality_score'] += score * weights['quality'] * weight
-    scores['innovation_score'] = scores['overall_score'] * 1.05
+        scores['engagement_score'] += score * weights['engagement'] * weight
-    
+        scores['conversion_score'] += score * weights['conversion'] * weight
        scores['innovation_score'] += score * weights['innovation'] * weight
    if weight_sum > 0:
        for k in scores:
            scores[k] = round(scores[k] / weight_sum, 2)
    return scores
 def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extract market positioning insights from AI recommendations.
    Scans all analysis types for positioning signals. Returns empty dict if none found.
    Args:
        ai_recommendations: Dictionary containing AI analysis results
@@ -64,17 +78,50 @@ def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str,
    Returns:
        Dictionary with market positioning data
    """
-    return {
+    positioning = {}
-        'industry_position': 'emerging',
+    best_confidence = 0.0
-        'competitive_advantage': 'AI-powered content',
+
-        'market_share': '2.5%',
+    for analysis_type, recommendations in ai_recommendations.items():
-        'positioning_score': 4
+        if not isinstance(recommendations, dict):
-    }
+            continue
        metrics = recommendations.get('metrics', {})
        confidence = metrics.get('confidence', 0.0)
        if confidence <= best_confidence:
            continue
        recs = recommendations.get('recommendations', [])
        if isinstance(recs, list):
            for r in recs:
                if not isinstance(r, dict):
                    continue
                pos = r.get('market_position') or r.get('positioning')
                adv = r.get('competitive_advantage')
                share = r.get('market_share')
                score = r.get('positioning_score') or metrics.get('positioning_score')
                if any([pos, adv, share, score]):
                    best_confidence = confidence
                    if pos:
                        positioning['industry_position'] = pos
                    if adv:
                        positioning['competitive_advantage'] = adv
                    if share:
                        positioning['market_share'] = str(share)
                    if score is not None:
                        positioning['positioning_score'] = score
    if not positioning:
        for key in ('industry_position', 'competitive_advantage', 'market_share', 'positioning_score'):
            val = ai_recommendations.get(key)
            if val is not None:
                positioning[key] = val
    return positioning
 def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract competitive advantages from AI recommendations.
    Scans all analysis types for advantage signals. Returns empty list if none found.
    Args:
        ai_recommendations: Dictionary containing AI analysis results
@@ -82,23 +129,40 @@ def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[D
    Returns:
        List of competitive advantages with impact and implementation status
    """
-    return [
+    advantages = []
-        {
+
-            'advantage': 'AI-powered content creation',
+    for analysis_type, recommendations in ai_recommendations.items():
-            'impact': 'High',
+        if not isinstance(recommendations, dict):
-            'implementation': 'In Progress'
+            continue
-        },
+        recs = recommendations.get('recommendations', [])
-        {
+        if not isinstance(recs, list):
-            'advantage': 'Data-driven strategy',
+            continue
-            'impact': 'Medium',
+        for r in recs:
-            'implementation': 'Complete'
+            if not isinstance(r, dict):
-        }
+                continue
-    ]
+            adv = r.get('advantage') or r.get('competitive_advantage')
            if adv:
                advantages.append({
                    'advantage': adv,
                    'impact': r.get('impact', 'Medium'),
                    'implementation': r.get('implementation', 'Planned')
                })
    seen = set()
    unique = []
    for a in advantages:
        key = a['advantage'].strip().lower()
        if key not in seen:
            seen.add(key)
            unique.append(a)
    return unique
 def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract strategic risks from AI recommendations.
    Scans all analysis types for risk signals. Returns empty list if none found.
    Args:
        ai_recommendations: Dictionary containing AI analysis results
@@ -106,23 +170,46 @@ def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str
    Returns:
        List of strategic risks with probability and impact assessment
    """
-    return [
+    risks = []
-        {
+
-            'risk': 'Content saturation in market',
+    for analysis_type, recommendations in ai_recommendations.items():
-            'probability': 'Medium',
+        if not isinstance(recommendations, dict):
-            'impact': 'High'
+            continue
-        },
+        recs = recommendations.get('recommendations', [])
-        {
+        if not isinstance(recs, list):
-            'risk': 'Algorithm changes affecting reach',
+            continue
-            'probability': 'High',
+        for r in recs:
-            'impact': 'Medium'
+            if not isinstance(r, dict):
-        }
+                continue
-    ]
+            risk_text = r.get('risk') or r.get('strategic_risk') or r.get('threat')
            if risk_text:
                risks.append({
                    'risk': risk_text,
                    'probability': r.get('probability', 'Medium'),
                    'impact': r.get('impact', 'Medium')
                })
        risks_list = recommendations.get('risks') or recommendations.get('strategic_risks')
        if isinstance(risks_list, list):
            for r in risks_list:
                if isinstance(r, dict) and r.get('risk'):
                    risks.append(r)
    seen = set()
    unique = []
    for r in risks:
        key = r['risk'].strip().lower()
        if key not in seen:
            seen.add(key)
            unique.append(r)
    return unique
 def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract opportunity analysis from AI recommendations.
    Scans all analysis types for opportunity signals. Returns empty list if none found.
    Args:
        ai_recommendations: Dictionary containing AI analysis results
@@ -130,18 +217,40 @@ def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dic
    Returns:
        List of opportunities with potential impact and implementation ease
    """
-    return [
+    opportunities = []
-        {
+
-            'opportunity': 'Video content expansion',
+    for analysis_type, recommendations in ai_recommendations.items():
-            'potential_impact': 'High',
+        if not isinstance(recommendations, dict):
-            'implementation_ease': 'Medium'
+            continue
-        },
+        recs = recommendations.get('recommendations', [])
-        {
+        if not isinstance(recs, list):
-            'opportunity': 'Social media engagement',
+            continue
-            'potential_impact': 'Medium',
+        for r in recs:
-            'implementation_ease': 'High'
+            if not isinstance(r, dict):
-        }
+                continue
-    ]
+            opp = r.get('opportunity') or r.get('growth_opportunity')
            if opp:
                opportunities.append({
                    'opportunity': opp,
                    'potential_impact': r.get('potential_impact', 'Medium'),
                    'implementation_ease': r.get('implementation_ease', 'Medium')
                })
        opps_list = recommendations.get('opportunities') or recommendations.get('growth_opportunities')
        if isinstance(opps_list, list):
            for o in opps_list:
                if isinstance(o, dict) and o.get('opportunity'):
                    opportunities.append(o)
    seen = set()
    unique = []
    for o in opportunities:
        key = o['opportunity'].strip().lower()
        if key not in seen:
            seen.add(key)
            unique.append(o)
    return unique
 def initialize_caches() -> Dict[str, Any]:
--- 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
		`@@ -1,2 +0,0 @@`
			`1). Replace Firecrawl with scrapy or crawlee : https://crawlee.dev/python/docs/introduction`