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Kunthawat Greethong
2026-01-08 22:39:53 +07:00
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backend/test/BILLING_SYSTEM_INTEGRATION.md Normal file
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# ALwrity Billing & Subscription System Integration
## Overview
The ALwrity backend now includes a comprehensive billing and subscription system that automatically tracks API usage, calculates costs, and manages subscription limits. This system is fully integrated into the startup process and provides real-time monitoring capabilities.
## 🚀 Quick Start
### 1. Start the Backend with Billing System
```bash
# From the backend directory
python start_alwrity_backend.py
```
The startup script will automatically:
- ✅ Create billing and subscription database tables
- ✅ Initialize default pricing and subscription plans
- ✅ Set up usage tracking middleware
- ✅ Verify all billing components are working
- ✅ Start the server with billing endpoints enabled
### 2. Verify Installation
```bash
# Run the comprehensive verification script
python verify_billing_setup.py
```
### 3. Test API Endpoints
```bash
# Get subscription plans
curl http://localhost:8000/api/subscription/plans
# Get user usage (replace 'demo' with actual user ID)
curl http://localhost:8000/api/subscription/usage/demo
# Get billing dashboard data
curl http://localhost:8000/api/subscription/dashboard/demo
# Get API pricing information
curl http://localhost:8000/api/subscription/pricing
```
## 📊 Database Tables
The billing system creates the following tables:
| Table Name | Purpose |
|------------|---------|
| `subscription_plans` | Available subscription tiers and pricing |
| `user_subscriptions` | User subscription assignments |
| `api_usage_logs` | Detailed API usage tracking |
| `usage_summaries` | Aggregated usage statistics |
| `api_provider_pricing` | Cost per token for each AI provider |
| `usage_alerts` | Usage limit warnings and notifications |
| `billing_history` | Historical billing records |
## 🔧 System Components
### 1. Database Models (`models/subscription_models.py`)
- **SubscriptionPlan**: Subscription tiers and pricing
- **UserSubscription**: User subscription assignments
- **APIUsageLog**: Detailed usage tracking
- **UsageSummary**: Aggregated statistics
- **APIProviderPricing**: Cost calculations
- **UsageAlert**: Limit notifications
### 2. Services
- **PricingService** (`services/pricing_service.py`): Cost calculations and plan management
- **UsageTrackingService** (`services/usage_tracking_service.py`): Usage monitoring and limits
- **SubscriptionExceptionHandler** (`services/subscription_exception_handler.py`): Error handling
### 3. API Endpoints (`api/subscription_api.py`)
- `GET /api/subscription/plans` - Available subscription plans
- `GET /api/subscription/usage/{user_id}` - User usage statistics
- `GET /api/subscription/dashboard/{user_id}` - Dashboard data
- `GET /api/subscription/pricing` - API pricing information
- `GET /api/subscription/trends/{user_id}` - Usage trends
### 4. Middleware Integration
- **Monitoring Middleware** (`middleware/monitoring_middleware.py`): Automatic usage tracking
- **Exception Handling**: Graceful error handling for billing issues
## 🎯 Frontend Integration
The billing system is fully integrated with the frontend dashboard:
### CompactBillingDashboard
- Real-time usage metrics
- Cost tracking
- System health monitoring
- Interactive tooltips and help text
### EnhancedBillingDashboard
- Detailed usage breakdowns
- Provider-specific costs
- Usage trends and analytics
- Alert management
## 📈 Usage Tracking
The system automatically tracks:
- **API Calls**: Number of requests to each provider
- **Token Usage**: Input and output tokens for each request
- **Costs**: Real-time cost calculations
- **Response Times**: Performance monitoring
- **Error Rates**: Failed request tracking
- **User Activity**: Per-user usage patterns
## 💰 Pricing Configuration
### Default AI Provider Pricing (per token)
| Provider | Model | Input Cost | Output Cost |
|----------|-------|------------|-------------|
| OpenAI | GPT-4 | $0.00003 | $0.00006 |
| OpenAI | GPT-3.5-turbo | $0.0000015 | $0.000002 |
| Gemini | Gemini Pro | $0.0000005 | $0.0000015 |
| Anthropic | Claude-3 | $0.000008 | $0.000024 |
| Mistral | Mistral-7B | $0.0000002 | $0.0000006 |
### Subscription Plans
| Plan | Monthly Price | Yearly Price | API Limits |
|------|---------------|--------------|------------|
| Free | $0 | $0 | 1,000 calls/month |
| Starter | $29 | $290 | 10,000 calls/month |
| Professional | $99 | $990 | 100,000 calls/month |
| Enterprise | $299 | $2,990 | Unlimited |
## 🔍 Monitoring & Alerts
### Real-time Monitoring
- Usage tracking for all API calls
- Cost calculations in real-time
- Performance metrics
- Error rate monitoring
### Alert System
- Usage approaching limits (80% threshold)
- Cost overruns
- System health issues
- Provider-specific problems
## 🛠️ Development Mode
For development with auto-reload:
```bash
# Development mode with auto-reload
python start_alwrity_backend.py --dev
# Or with explicit reload flag
python start_alwrity_backend.py --reload
```
## 📝 Configuration
### Environment Variables
The system uses the following environment variables:
```bash
# Database
DATABASE_URL=sqlite:///./alwrity.db
# API Keys (configured through onboarding)
OPENAI_API_KEY=your_key_here
GEMINI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
MISTRAL_API_KEY=your_key_here
# Server Configuration
HOST=0.0.0.0
PORT=8000
DEBUG=true
```
### Custom Pricing
To modify pricing, update the `PricingService.initialize_default_pricing()` method in `services/pricing_service.py`.
## 🧪 Testing
### Run Verification Script
```bash
python verify_billing_setup.py
```
### Test Individual Components
```bash
# Test subscription system
python test_subscription_system.py
# Test billing tables creation
python scripts/create_billing_tables.py
```
## 🚨 Troubleshooting
### Common Issues
1. **Tables not created**: Run `python scripts/create_billing_tables.py`
2. **Missing dependencies**: Run `pip install -r requirements.txt`
3. **Database errors**: Check `DATABASE_URL` in environment
4. **API key issues**: Verify API keys are configured
### Debug Mode
Enable debug logging by setting `DEBUG=true` in your environment.
## 📚 API Documentation
Once the server is running, access the interactive API documentation:
- **Swagger UI**: http://localhost:8000/api/docs
- **ReDoc**: http://localhost:8000/api/redoc
## 🔄 Updates and Maintenance
### Adding New Providers
1. Add provider to `APIProvider` enum in `models/subscription_models.py`
2. Update pricing in `PricingService.initialize_default_pricing()`
3. Add provider detection in middleware
4. Update frontend provider chips
### Modifying Plans
1. Update `PricingService.initialize_default_plans()`
2. Modify plan limits and pricing
3. Test with verification script
## 📞 Support
For issues or questions:
1. Check the verification script output
2. Review the startup logs
3. Test individual components
4. Check database table creation
## 🎉 Success Indicators
You'll know the billing system is working when:
- ✅ Startup script shows "Billing and subscription tables created successfully"
- ✅ Verification script passes all checks
- ✅ API endpoints return data
- ✅ Frontend dashboard shows usage metrics
- ✅ Usage tracking middleware is active
The billing system is now fully integrated and ready for production use!

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#!/usr/bin/env python3
"""
Database check and sample data creation script
"""
from services.database import get_db_session
from models.content_planning import ContentStrategy, ContentGapAnalysis, AIAnalysisResult
from sqlalchemy.orm import Session
import json
def check_database():
"""Check what data exists in the database"""
db = get_db_session()
try:
# Check strategies
strategies = db.query(ContentStrategy).all()
print(f"Found {len(strategies)} strategies")
for strategy in strategies:
print(f" Strategy {strategy.id}: {strategy.name} - {strategy.industry}")
# Check gap analyses
gap_analyses = db.query(ContentGapAnalysis).all()
print(f"Found {len(gap_analyses)} gap analyses")
# Check AI analytics
ai_analytics = db.query(AIAnalysisResult).all()
print(f"Found {len(ai_analytics)} AI analytics")
except Exception as e:
print(f"Error checking database: {e}")
finally:
db.close()
def create_sample_data():
"""Create sample data for Strategic Intelligence and Keyword Research tabs"""
db = get_db_session()
try:
# Create a sample strategy if none exists
existing_strategies = db.query(ContentStrategy).all()
if not existing_strategies:
sample_strategy = ContentStrategy(
name="Sample Content Strategy",
industry="Digital Marketing",
target_audience={"demographics": "Small to medium businesses", "interests": ["marketing", "technology"]},
content_pillars=["Educational Content", "Thought Leadership", "Case Studies"],
ai_recommendations={
"market_positioning": {
"score": 75,
"strengths": ["Strong brand voice", "Consistent content quality"],
"weaknesses": ["Limited video content", "Slow content production"]
},
"competitive_advantages": [
{"advantage": "AI-powered content creation", "impact": "High", "implementation": "In Progress"},
{"advantage": "Data-driven strategy", "impact": "Medium", "implementation": "Complete"}
],
"strategic_risks": [
{"risk": "Content saturation in market", "probability": "Medium", "impact": "High"},
{"risk": "Algorithm changes affecting reach", "probability": "High", "impact": "Medium"}
]
},
user_id=1
)
db.add(sample_strategy)
db.commit()
print("Created sample strategy")
# Create sample gap analysis
existing_gaps = db.query(ContentGapAnalysis).all()
if not existing_gaps:
sample_gap = ContentGapAnalysis(
website_url="https://example.com",
competitor_urls=["competitor1.com", "competitor2.com"],
target_keywords=["content marketing", "digital strategy", "SEO"],
analysis_results={
"gaps": ["Video content gap", "Local SEO opportunities"],
"opportunities": [
{"keyword": "AI content tools", "search_volume": "5K-10K", "competition": "Low", "cpc": "$2.50"},
{"keyword": "content marketing ROI", "search_volume": "1K-5K", "competition": "Medium", "cpc": "$4.20"}
]
},
recommendations=[
{
"type": "content",
"title": "Create video tutorials",
"description": "Address the video content gap",
"priority": "high"
},
{
"type": "seo",
"title": "Optimize for local search",
"description": "Target local keywords",
"priority": "medium"
}
],
user_id=1
)
db.add(sample_gap)
db.commit()
print("Created sample gap analysis")
# Create sample AI analytics
existing_ai = db.query(AIAnalysisResult).all()
if not existing_ai:
sample_ai = AIAnalysisResult(
analysis_type="strategic_intelligence",
insights=[
"Focus on video content to address market gap",
"Leverage AI tools for competitive advantage",
"Monitor algorithm changes closely"
],
recommendations=[
{
"type": "content",
"title": "Increase video content production",
"description": "Address the video content gap identified in analysis",
"priority": "high"
},
{
"type": "strategy",
"title": "Implement AI-powered content creation",
"description": "Leverage AI tools for competitive advantage",
"priority": "medium"
}
],
performance_metrics={
"content_engagement": 78.5,
"traffic_growth": 25.3,
"conversion_rate": 2.1
},
personalized_data_used={
"onboarding_data": True,
"user_preferences": True,
"historical_performance": True
},
processing_time=15.2,
ai_service_status="operational",
user_id=1
)
db.add(sample_ai)
db.commit()
print("Created sample AI analytics")
except Exception as e:
print(f"Error creating sample data: {e}")
db.rollback()
finally:
db.close()
if __name__ == "__main__":
print("Checking database...")
check_database()
print("\nCreating sample data...")
create_sample_data()
print("\nFinal database state:")
check_database()

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#!/usr/bin/env python3
"""
Debug Database Data
This script checks what data is actually in the database for debugging.
"""
import asyncio
import sys
import os
from loguru import logger
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
# Add the services directory to the path
services_dir = os.path.join(backend_dir, "services")
if services_dir not in sys.path:
sys.path.insert(0, services_dir)
async def debug_database_data():
"""Debug what data is in the database."""
try:
logger.info("🔍 Debugging database data")
# Initialize database
from services.database import init_database, get_db_session
try:
init_database()
logger.info("✅ Database initialized successfully")
except Exception as e:
logger.error(f"❌ Database initialization failed: {str(e)}")
return False
# Get database session
db_session = get_db_session()
if not db_session:
logger.error("❌ Failed to get database session")
return False
from services.content_planning_db import ContentPlanningDBService
db_service = ContentPlanningDBService(db_session)
# Check content strategies
logger.info("📋 Checking content strategies...")
strategies = await db_service.get_user_content_strategies(1)
logger.info(f"Found {len(strategies)} strategies for user 1")
for strategy in strategies:
logger.info(f"Strategy ID: {strategy.id}, Name: {strategy.name}")
logger.info(f" Content Pillars: {strategy.content_pillars}")
logger.info(f" Target Audience: {strategy.target_audience}")
# Check gap analyses
logger.info("📋 Checking gap analyses...")
gap_analyses = await db_service.get_user_content_gap_analyses(1)
logger.info(f"Found {len(gap_analyses)} gap analyses for user 1")
for gap_analysis in gap_analyses:
logger.info(f"Gap Analysis ID: {gap_analysis.id}")
logger.info(f" Website URL: {gap_analysis.website_url}")
logger.info(f" Analysis Results: {gap_analysis.analysis_results}")
logger.info(f" Recommendations: {gap_analysis.recommendations}")
logger.info(f" Opportunities: {gap_analysis.opportunities}")
# Check if analysis_results has content_gaps
if gap_analysis.analysis_results:
content_gaps = gap_analysis.analysis_results.get("content_gaps", [])
logger.info(f" Content Gaps in analysis_results: {len(content_gaps)} items")
for gap in content_gaps:
logger.info(f" - {gap}")
else:
logger.info(" Analysis Results is None or empty")
db_session.close()
logger.info("✅ Database debugging completed")
return True
except Exception as e:
logger.error(f"❌ Debug failed with error: {str(e)}")
return False
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the debug
success = asyncio.run(debug_database_data())
if success:
logger.info("✅ Debug completed successfully!")
sys.exit(0)
else:
logger.error("❌ Debug failed!")
sys.exit(1)

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#!/usr/bin/env python3
"""
Debug script for Step 8 (Daily Content Planning) to isolate data type issues.
"""
import asyncio
import logging
import sys
import os
# Add the project root to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_daily_content_planning.daily_schedule_generator import DailyScheduleGenerator
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s | %(levelname)-8s | %(name)s:%(funcName)s:%(lineno)d - %(message)s',
datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)
async def debug_step8():
"""Debug Step 8 with controlled test data."""
logger.info("🔍 Starting Step 8 Debug Session")
# Create test data with known types
test_weekly_themes = [
{
"title": "Week 1 Theme: AI Implementation",
"description": "Focus on AI tools and implementation",
"primary_pillar": "AI and Machine Learning",
"content_angles": ["AI tools", "Implementation guide", "Best practices"],
"target_platforms": ["LinkedIn", "Blog", "Twitter"],
"strategic_alignment": "High alignment with business goals",
"gap_addressal": "Addresses AI implementation gap",
"priority": "high",
"estimated_impact": "High",
"ai_confidence": 0.9,
"week_number": 1
},
{
"title": "Week 2 Theme: Digital Transformation",
"description": "Digital transformation strategies",
"primary_pillar": "Digital Transformation",
"content_angles": ["Strategy", "Case studies", "ROI"],
"target_platforms": ["LinkedIn", "Blog", "YouTube"],
"strategic_alignment": "Medium alignment with business goals",
"gap_addressal": "Addresses transformation gap",
"priority": "medium",
"estimated_impact": "Medium",
"ai_confidence": 0.8,
"week_number": 2
}
]
test_platform_strategies = {
"LinkedIn": {
"content_type": "professional",
"posting_frequency": "daily",
"engagement_strategy": "thought_leadership"
},
"Blog": {
"content_type": "educational",
"posting_frequency": "weekly",
"engagement_strategy": "seo_optimized"
},
"Twitter": {
"content_type": "conversational",
"posting_frequency": "daily",
"engagement_strategy": "community_building"
}
}
test_content_pillars = [
{
"name": "AI and Machine Learning",
"weight": 0.4,
"description": "AI tools and implementation"
},
{
"name": "Digital Transformation",
"weight": 0.3,
"description": "Digital strategy and transformation"
},
{
"name": "Business Strategy",
"weight": 0.3,
"description": "Strategic business insights"
}
]
test_calendar_framework = {
"type": "monthly",
"total_weeks": 4,
"posting_frequency": "daily",
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"industry": "technology",
"business_size": "sme"
}
test_posting_preferences = {
"preferred_times": ["09:00", "12:00", "15:00"],
"posting_frequency": "daily",
"content_count_per_day": 2
}
test_business_goals = [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership"
]
test_target_audience = {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {
"age_range": "25-45",
"location": "Global"
}
}
# Test data type validation
logger.info("🔍 Validating test data types:")
logger.info(f" weekly_themes: {type(test_weekly_themes)} (length: {len(test_weekly_themes)})")
logger.info(f" platform_strategies: {type(test_platform_strategies)}")
logger.info(f" content_pillars: {type(test_content_pillars)}")
logger.info(f" calendar_framework: {type(test_calendar_framework)}")
logger.info(f" posting_preferences: {type(test_posting_preferences)}")
logger.info(f" business_goals: {type(test_business_goals)}")
logger.info(f" target_audience: {type(test_target_audience)}")
# Validate weekly themes structure
for i, theme in enumerate(test_weekly_themes):
logger.info(f" Theme {i+1}: {type(theme)} - keys: {list(theme.keys())}")
if not isinstance(theme, dict):
logger.error(f"❌ Theme {i+1} is not a dictionary: {type(theme)}")
return
try:
# Initialize the daily schedule generator
generator = DailyScheduleGenerator()
logger.info("✅ DailyScheduleGenerator initialized successfully")
# Test the generate_daily_schedules method
logger.info("🚀 Testing generate_daily_schedules method...")
daily_schedules = await generator.generate_daily_schedules(
weekly_themes=test_weekly_themes,
platform_strategies=test_platform_strategies,
business_goals=test_business_goals,
target_audience=test_target_audience,
posting_preferences=test_posting_preferences,
calendar_duration=28 # 4 weeks * 7 days
)
logger.info(f"✅ Successfully generated {len(daily_schedules)} daily schedules")
# Log first few schedules for inspection
for i, schedule in enumerate(daily_schedules[:3]):
logger.info(f" Schedule {i+1}: {type(schedule)} - keys: {list(schedule.keys())}")
except Exception as e:
logger.error(f"❌ Error in Step 8 debug: {str(e)}")
logger.error(f"📋 Error type: {type(e)}")
import traceback
logger.error(f"📋 Traceback: {traceback.format_exc()}")
return
logger.info("🎉 Step 8 debug completed successfully!")
if __name__ == "__main__":
asyncio.run(debug_step8())

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#!/usr/bin/env python3
"""
Debug script to test AI response parsing in Step 8.
"""
import asyncio
import logging
import sys
import os
# Add the project root to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_daily_content_planning.daily_schedule_generator import DailyScheduleGenerator
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s | %(levelname)-8s | %(name)s:%(funcName)s:%(lineno)d - %(message)s',
datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)
async def debug_ai_response_parsing():
"""Debug AI response parsing in Step 8."""
logger.info("🔍 Starting AI Response Parsing Debug")
# Create test data
test_posting_day = {
"day_number": 1,
"date": "2025-09-01",
"day_name": "monday",
"posting_times": ["09:00", "12:00"],
"content_count": 2,
"week_number": 1
}
test_weekly_theme = {
"title": "Week 1 Theme: AI Implementation",
"description": "Focus on AI tools and implementation",
"content_angles": ["AI tools", "Implementation guide", "Best practices"]
}
test_platform_strategies = {
"LinkedIn": {"approach": "professional"},
"Blog": {"approach": "educational"}
}
# Test different AI response formats
test_responses = [
# Format 1: List of recommendations (correct format)
[
{
"type": "Content Creation Opportunity",
"title": "AI Implementation Guide",
"description": "A comprehensive guide to AI implementation"
},
{
"type": "Content Creation Opportunity",
"title": "AI Tools Overview",
"description": "Overview of AI tools for business"
}
],
# Format 2: Dictionary with recommendations key
{
"recommendations": [
{
"type": "Content Creation Opportunity",
"title": "AI Implementation Guide",
"description": "A comprehensive guide to AI implementation"
},
{
"type": "Content Creation Opportunity",
"title": "AI Tools Overview",
"description": "Overview of AI tools for business"
}
]
},
# Format 3: Float (the problematic case)
0.95,
# Format 4: String
"AI Implementation Guide",
# Format 5: None
None
]
generator = DailyScheduleGenerator()
for i, test_response in enumerate(test_responses):
logger.info(f"🔍 Testing AI response format {i+1}: {type(test_response)} = {test_response}")
try:
content_pieces = generator._parse_content_response(
ai_response=test_response,
posting_day=test_posting_day,
weekly_theme=test_weekly_theme,
platform_strategies=test_platform_strategies
)
logger.info(f"✅ Format {i+1} parsed successfully: {len(content_pieces)} content pieces")
for j, piece in enumerate(content_pieces):
logger.info(f" Piece {j+1}: {piece.get('title', 'No title')}")
except Exception as e:
logger.error(f"❌ Format {i+1} failed: {str(e)}")
logger.error(f"📋 Error type: {type(e)}")
import traceback
logger.error(f"📋 Traceback: {traceback.format_exc()}")
logger.info("🎉 AI Response Parsing Debug completed!")
if __name__ == "__main__":
asyncio.run(debug_ai_response_parsing())

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#!/usr/bin/env python3
"""
Step 8 Debug Script - Isolated Testing
=====================================
This script tests Step 8 (Daily Content Planning) in isolation with controlled inputs
to identify which specific parameter is causing the 'float' object has no attribute 'get' error.
The script will:
1. Set up Step 8 with fixed, known dictionary inputs
2. Test the daily content generation in isolation
3. Identify which specific parameter is coming through as a float
4. Help pinpoint whether the issue is in weekly_theme, posting_day, platform_strategies, or other data
"""
import asyncio
import sys
import os
import logging
from typing import Dict, List, Any
# Add the project root to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Import Step 8 components
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_daily_content_planning.step8_main import DailyContentPlanningStep
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_daily_content_planning.daily_schedule_generator import DailyScheduleGenerator
def create_controlled_test_data():
"""Create controlled test data with known types for Step 8 testing."""
# 1. Weekly themes from Step 7 (should be list of dictionaries)
weekly_themes = [
{
"title": "Week 1 Theme: AI Implementation Guide",
"description": "Comprehensive guide on AI implementation for businesses",
"primary_pillar": "AI and Machine Learning",
"secondary_pillars": ["Digital Transformation", "Innovation"],
"strategic_alignment": "high",
"audience_alignment": "high",
"week_number": 1,
"content_count": 5,
"priority": "high",
"estimated_impact": "High",
"ai_confidence": 0.9
},
{
"title": "Week 2 Theme: Digital Transformation Strategies",
"description": "Strategic approaches to digital transformation",
"primary_pillar": "Digital Transformation",
"secondary_pillars": ["Business Strategy", "Innovation"],
"strategic_alignment": "high",
"audience_alignment": "medium",
"week_number": 2,
"content_count": 4,
"priority": "medium",
"estimated_impact": "Medium",
"ai_confidence": 0.8
}
]
# 2. Platform strategies from Step 6 (should be dictionary)
platform_strategies = {
"linkedin": {
"content_types": ["articles", "posts", "videos"],
"posting_times": ["09:00", "12:00", "15:00"],
"content_adaptation": "professional tone, industry insights",
"engagement_strategy": "thought leadership content"
},
"twitter": {
"content_types": ["tweets", "threads", "images"],
"posting_times": ["08:00", "11:00", "14:00", "17:00"],
"content_adaptation": "concise, engaging, hashtag optimization",
"engagement_strategy": "conversation starters"
}
}
# 3. Content pillars from Step 5 (should be list)
content_pillars = [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
]
# 4. Calendar framework from Step 4 (should be dictionary)
calendar_framework = {
"duration_weeks": 4,
"content_frequency": "daily",
"posting_schedule": {
"monday": ["09:00", "12:00", "15:00"],
"tuesday": ["09:00", "12:00", "15:00"],
"wednesday": ["09:00", "12:00", "15:00"],
"thursday": ["09:00", "12:00", "15:00"],
"friday": ["09:00", "12:00", "15:00"]
},
"theme_structure": "weekly_themes",
"content_mix": {
"blog_posts": 0.4,
"social_media": 0.3,
"videos": 0.2,
"infographics": 0.1
}
}
# 5. Business goals from Step 1 (should be list)
business_goals = [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership"
]
# 6. Target audience from Step 1 (should be dictionary)
target_audience = {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {
"age_range": "25-45",
"location": "Global",
"interests": ["technology", "innovation", "business growth"]
}
}
# 7. Keywords from Step 2 (should be list)
keywords = [
"AI implementation",
"digital transformation",
"machine learning",
"business automation",
"technology trends"
]
return {
"weekly_themes": weekly_themes,
"platform_strategies": platform_strategies,
"content_pillars": content_pillars,
"calendar_framework": calendar_framework,
"business_goals": business_goals,
"target_audience": target_audience,
"keywords": keywords
}
def validate_data_types(data: Dict[str, Any], test_name: str):
"""Validate that all data has the expected types."""
logger.info(f"🔍 Validating data types for {test_name}")
expected_types = {
"weekly_themes": list,
"platform_strategies": dict,
"content_pillars": list,
"calendar_framework": dict,
"business_goals": list,
"target_audience": dict,
"keywords": list
}
for key, expected_type in expected_types.items():
if key in data:
actual_type = type(data[key])
if actual_type != expected_type:
logger.error(f"❌ Type mismatch for {key}: expected {expected_type.__name__}, got {actual_type.__name__}")
logger.error(f" Value: {data[key]}")
return False
else:
logger.info(f"✅ {key}: {actual_type.__name__} (correct)")
else:
logger.warning(f"⚠️ Missing key: {key}")
return True
async def test_daily_schedule_generator_isolated():
"""Test the DailyScheduleGenerator in isolation with controlled inputs."""
logger.info("🧪 Testing DailyScheduleGenerator in isolation")
# Create controlled test data
test_data = create_controlled_test_data()
# Validate data types
if not validate_data_types(test_data, "DailyScheduleGenerator"):
logger.error("❌ Data type validation failed")
return False
try:
# Create DailyScheduleGenerator instance
generator = DailyScheduleGenerator()
# Test the generate_daily_schedules method
logger.info("📅 Testing generate_daily_schedules method")
# Get posting preferences and calendar duration
posting_preferences = {
"preferred_times": ["09:00", "12:00", "15:00"],
"posting_frequency": "daily"
}
calendar_duration = test_data["calendar_framework"]["duration_weeks"] * 7
# Call the method with controlled inputs
daily_schedules = await generator.generate_daily_schedules(
test_data["weekly_themes"],
test_data["platform_strategies"],
test_data["content_pillars"],
test_data["calendar_framework"],
posting_preferences,
calendar_duration
)
logger.info(f"✅ DailyScheduleGenerator test successful")
logger.info(f" Generated {len(daily_schedules)} daily schedules")
# Validate the output
if isinstance(daily_schedules, list):
logger.info("✅ Output is a list (correct)")
for i, schedule in enumerate(daily_schedules[:3]): # Show first 3
logger.info(f" Schedule {i+1}: {type(schedule)} - {schedule.get('day_number', 'N/A')}")
else:
logger.error(f"❌ Output is not a list: {type(daily_schedules)}")
return False
return True
except Exception as e:
logger.error(f"❌ DailyScheduleGenerator test failed: {str(e)}")
logger.error(f" Exception type: {type(e).__name__}")
import traceback
logger.error(f" Traceback: {traceback.format_exc()}")
return False
async def test_step8_execute_method():
"""Test Step 8's execute method with controlled inputs."""
logger.info("🧪 Testing Step 8 execute method")
# Create controlled test data
test_data = create_controlled_test_data()
# Validate data types
if not validate_data_types(test_data, "Step 8 Execute"):
logger.error("❌ Data type validation failed")
return False
try:
# Create Step 8 instance
step8 = DailyContentPlanningStep()
# Create context with controlled data
context = {
"step_results": {
"step_07": {
"result": {
"weekly_themes": test_data["weekly_themes"]
}
},
"step_06": {
"result": {
"platform_strategies": test_data["platform_strategies"]
}
},
"step_05": {
"result": {
"content_pillars": test_data["content_pillars"]
}
},
"step_04": {
"result": {
"calendar_framework": test_data["calendar_framework"]
}
},
"step_01": {
"result": {
"business_goals": test_data["business_goals"],
"target_audience": test_data["target_audience"]
}
},
"step_02": {
"result": {
"keywords": test_data["keywords"]
}
}
},
"user_data": {
"business_goals": test_data["business_goals"],
"target_audience": test_data["target_audience"],
"keywords": test_data["keywords"]
}
}
# Test the execute method
logger.info("📅 Testing Step 8 execute method")
result = await step8.execute(context)
logger.info(f"✅ Step 8 execute test successful")
logger.info(f" Result type: {type(result)}")
logger.info(f" Result keys: {list(result.keys()) if isinstance(result, dict) else 'N/A'}")
return True
except Exception as e:
logger.error(f"❌ Step 8 execute test failed: {str(e)}")
logger.error(f" Exception type: {type(e).__name__}")
import traceback
logger.error(f" Traceback: {traceback.format_exc()}")
return False
async def test_specific_methods_with_debugging():
"""Test specific methods with detailed debugging to identify the float issue."""
logger.info("🔍 Testing specific methods with detailed debugging")
# Create controlled test data
test_data = create_controlled_test_data()
try:
# Create DailyScheduleGenerator instance
generator = DailyScheduleGenerator()
# Test _get_weekly_theme method specifically
logger.info("🔍 Testing _get_weekly_theme method")
for week_num in [1, 2]:
theme = generator._get_weekly_theme(test_data["weekly_themes"], week_num)
logger.info(f" Week {week_num} theme type: {type(theme)}")
logger.info(f" Week {week_num} theme: {theme}")
if not isinstance(theme, dict):
logger.error(f"❌ Week {week_num} theme is not a dictionary!")
return False
# Test _generate_daily_content method with controlled inputs
logger.info("🔍 Testing _generate_daily_content method")
# Create a controlled posting_day
posting_day = {
"day_number": 1,
"week_number": 1,
"content_count": 3,
"platforms": ["linkedin", "twitter"]
}
# Test with controlled weekly theme
weekly_theme = test_data["weekly_themes"][0] # First theme
# Test the method
content = await generator._generate_daily_content(
posting_day,
weekly_theme,
test_data["platform_strategies"],
test_data["content_pillars"],
test_data["calendar_framework"]
)
logger.info(f"✅ _generate_daily_content test successful")
logger.info(f" Content type: {type(content)}")
logger.info(f" Content: {content}")
return True
except Exception as e:
logger.error(f"❌ Specific method test failed: {str(e)}")
logger.error(f" Exception type: {type(e).__name__}")
import traceback
logger.error(f" Traceback: {traceback.format_exc()}")
return False
async def main():
"""Main debug function."""
logger.info("🚀 Starting Step 8 Debug Script")
logger.info("=" * 50)
# Test 1: DailyScheduleGenerator in isolation
logger.info("\n🧪 Test 1: DailyScheduleGenerator in isolation")
success1 = await test_daily_schedule_generator_isolated()
# Test 2: Step 8 execute method
logger.info("\n🧪 Test 2: Step 8 execute method")
success2 = await test_step8_execute_method()
# Test 3: Specific methods with debugging
logger.info("\n🧪 Test 3: Specific methods with debugging")
success3 = await test_specific_methods_with_debugging()
# Summary
logger.info("\n" + "=" * 50)
logger.info("📊 Debug Results Summary")
logger.info("=" * 50)
logger.info(f"✅ Test 1 (DailyScheduleGenerator): {'PASSED' if success1 else 'FAILED'}")
logger.info(f"✅ Test 2 (Step 8 Execute): {'PASSED' if success2 else 'FAILED'}")
logger.info(f"✅ Test 3 (Specific Methods): {'PASSED' if success3 else 'FAILED'}")
if success1 and success2 and success3:
logger.info("🎉 All tests passed! Step 8 is working correctly with controlled inputs.")
logger.info("💡 The issue might be in the data flow from previous steps.")
else:
logger.error("❌ Some tests failed. Check the logs above for specific issues.")
logger.info("=" * 50)
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
"""
Deployment script for the Persona System.
Sets up database tables and validates the complete system.
"""
import sys
import os
# Add the backend directory to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from loguru import logger
def deploy_persona_system():
"""Deploy the complete persona system."""
logger.info("🚀 Deploying Persona System")
try:
# Step 1: Create database tables
logger.info("📊 Step 1: Creating database tables...")
from scripts.create_persona_tables import create_persona_tables
create_persona_tables()
logger.info("✅ Database tables created")
# Step 2: Validate Gemini integration
logger.info("🤖 Step 2: Validating Gemini integration...")
from services.llm_providers.gemini_provider import gemini_structured_json_response
test_schema = {
"type": "object",
"properties": {
"status": {"type": "string"},
"timestamp": {"type": "string"}
},
"required": ["status"]
}
test_response = gemini_structured_json_response(
prompt="Return status='ready' and current timestamp",
schema=test_schema,
temperature=0.1,
max_tokens=1024
)
if "error" in test_response:
logger.warning(f"⚠️ Gemini test warning: {test_response['error']}")
else:
logger.info("✅ Gemini integration validated")
# Step 3: Test persona service
logger.info("🧠 Step 3: Testing persona service...")
from services.persona_analysis_service import PersonaAnalysisService
persona_service = PersonaAnalysisService()
logger.info("✅ Persona service initialized")
# Step 4: Test replication engine
logger.info("⚙️ Step 4: Testing replication engine...")
from services.persona_replication_engine import PersonaReplicationEngine
replication_engine = PersonaReplicationEngine()
logger.info("✅ Replication engine initialized")
# Step 5: Validate API endpoints
logger.info("🌐 Step 5: Validating API endpoints...")
from api.persona_routes import router
logger.info(f"✅ Persona router configured with {len(router.routes)} routes")
logger.info("🎉 Persona System deployed successfully!")
# Print deployment summary
print_deployment_summary()
return True
except Exception as e:
logger.error(f"❌ Deployment failed: {str(e)}")
return False
def print_deployment_summary():
"""Print deployment summary and next steps."""
logger.info("📋 PERSONA SYSTEM DEPLOYMENT SUMMARY")
logger.info("=" * 50)
logger.info("✅ Database Tables:")
logger.info(" - writing_personas")
logger.info(" - platform_personas")
logger.info(" - persona_analysis_results")
logger.info(" - persona_validation_results")
logger.info("✅ Services:")
logger.info(" - PersonaAnalysisService")
logger.info(" - PersonaReplicationEngine")
logger.info("✅ API Endpoints:")
logger.info(" - POST /api/personas/generate")
logger.info(" - GET /api/personas/user/{user_id}")
logger.info(" - GET /api/personas/platform/{platform}")
logger.info(" - GET /api/personas/export/{platform}")
logger.info("✅ Platform Support:")
logger.info(" - Twitter/X, LinkedIn, Instagram, Facebook")
logger.info(" - Blog, Medium, Substack")
logger.info("🔧 NEXT STEPS:")
logger.info("1. Complete onboarding with website analysis (Step 2)")
logger.info("2. Set research preferences (Step 3)")
logger.info("3. Generate persona in Final Step (Step 6)")
logger.info("4. Export hardened prompts for external AI systems")
logger.info("5. Use persona for consistent content generation")
logger.info("=" * 50)
def validate_deployment():
"""Validate that all components are working correctly."""
logger.info("🔍 Validating deployment...")
validation_results = {
"database": False,
"gemini": False,
"persona_service": False,
"replication_engine": False,
"api_routes": False
}
try:
# Test database
from services.database import get_db_session
session = get_db_session()
if session:
session.close()
validation_results["database"] = True
logger.info("✅ Database connection validated")
# Test Gemini
from services.llm_providers.gemini_provider import get_gemini_api_key
api_key = get_gemini_api_key()
if api_key and api_key != "your_gemini_api_key_here":
validation_results["gemini"] = True
logger.info("✅ Gemini API key configured")
else:
logger.warning("⚠️ Gemini API key not configured")
# Test services
from services.persona_analysis_service import PersonaAnalysisService
from services.persona_replication_engine import PersonaReplicationEngine
PersonaAnalysisService()
PersonaReplicationEngine()
validation_results["persona_service"] = True
validation_results["replication_engine"] = True
logger.info("✅ Services validated")
# Test API routes
from api.persona_routes import router
if len(router.routes) > 0:
validation_results["api_routes"] = True
logger.info("✅ API routes validated")
except Exception as e:
logger.error(f"❌ Validation error: {str(e)}")
# Summary
passed = sum(validation_results.values())
total = len(validation_results)
logger.info(f"📊 Validation Results: {passed}/{total} components validated")
if passed == total:
logger.info("🎉 All components validated successfully!")
return True
else:
logger.warning("⚠️ Some components failed validation")
for component, status in validation_results.items():
status_icon = "✅" if status else "❌"
logger.info(f" {status_icon} {component}")
return False
if __name__ == "__main__":
# Deploy system
deployment_success = deploy_persona_system()
if deployment_success:
# Validate deployment
validation_success = validate_deployment()
if validation_success:
logger.info("🎉 Persona System ready for production!")
sys.exit(0)
else:
logger.error("❌ Deployment validation failed")
sys.exit(1)
else:
logger.error("❌ Deployment failed")
sys.exit(1)

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#!/usr/bin/env python3
"""
Script to fix import paths in step files
"""
import os
import re
def fix_imports_in_file(file_path):
"""Fix import paths in a file."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
# Fix the base_step import path
# Change from ..base_step to ...base_step for subdirectories
if '/step9_content_recommendations/' in file_path or '/step10_performance_optimization/' in file_path or '/step11_strategy_alignment_validation/' in file_path or '/step12_final_calendar_assembly/' in file_path:
content = re.sub(r'from \.\.base_step import', 'from ...base_step import', content)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(content)
print(f"✅ Fixed imports in {file_path}")
return True
except Exception as e:
print(f"❌ Error fixing {file_path}: {e}")
return False
def main():
"""Main function to fix all import paths."""
base_path = "services/calendar_generation_datasource_framework/prompt_chaining/steps"
# Files that need fixing
files_to_fix = [
f"{base_path}/phase3/step9_content_recommendations/step9_main.py",
f"{base_path}/phase4/step10_performance_optimization/step10_main.py",
f"{base_path}/phase4/step11_strategy_alignment_validation/step11_main.py",
f"{base_path}/phase4/step12_final_calendar_assembly/step12_main.py",
]
for file_path in files_to_fix:
if os.path.exists(file_path):
fix_imports_in_file(file_path)
else:
print(f"⚠️ File not found: {file_path}")
if __name__ == "__main__":
main()

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backend/test/linkedin_keyword_test_report_20250914_223930.json Normal file
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{
"test_summary": {
"total_duration": 52.56023073196411,
"total_tests": 4,
"successful_tests": 4,
"failed_tests": 0,
"total_api_calls": 4
},
"test_results": [
{
"test_name": "Single Phrase Test (Should be preserved as-is)",
"keyword_phrase": "ALwrity content generation",
"success": true,
"duration": 8.364419937133789,
"api_calls": 1,
"error": null,
"content_length": 44,
"sources_count": 0,
"citations_count": 0,
"grounding_status": {
"status": "success",
"sources_used": 0,
"citation_coverage": 0,
"quality_score": 0.0
},
"generation_metadata": {
"model_used": "gemini-2.0-flash-001",
"generation_time": 0.002626,
"research_time": 0.000537,
"grounding_enabled": true
}
},
{
"test_name": "Comma-Separated Test (Should be split by commas)",
"keyword_phrase": "AI tools, content creation, marketing automation",
"success": true,
"duration": 12.616755723953247,
"api_calls": 1,
"error": null,
"content_length": 44,
"sources_count": 5,
"citations_count": 3,
"grounding_status": {
"status": "success",
"sources_used": 5,
"citation_coverage": 0.6,
"quality_score": 0.359
},
"generation_metadata": {
"model_used": "gemini-2.0-flash-001",
"generation_time": 0.009273,
"research_time": 0.000285,
"grounding_enabled": true
}
},
{
"test_name": "Another Single Phrase Test",
"keyword_phrase": "LinkedIn content strategy",
"success": true,
"duration": 11.366000652313232,
"api_calls": 1,
"error": null,
"content_length": 44,
"sources_count": 4,
"citations_count": 3,
"grounding_status": {
"status": "success",
"sources_used": 4,
"citation_coverage": 0.75,
"quality_score": 0.359
},
"generation_metadata": {
"model_used": "gemini-2.0-flash-001",
"generation_time": 0.008166,
"research_time": 0.000473,
"grounding_enabled": true
}
},
{
"test_name": "Another Comma-Separated Test",
"keyword_phrase": "social media, digital marketing, brand awareness",
"success": true,
"duration": 12.107932806015015,
"api_calls": 1,
"error": null,
"content_length": 44,
"sources_count": 0,
"citations_count": 0,
"grounding_status": {
"status": "success",
"sources_used": 0,
"citation_coverage": 0,
"quality_score": 0.0
},
"generation_metadata": {
"model_used": "gemini-2.0-flash-001",
"generation_time": 0.004575,
"research_time": 0.000323,
"grounding_enabled": true
}
}
],
"timestamp": "2025-09-14T22:39:30.220518"
}

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# Import content planning endpoints
from api.content_planning import router as content_planning_router

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"""Test script for ALwrity backend."""
import requests
import json
import time
def test_backend():
"""Test the backend endpoints."""
base_url = "http://localhost:8000"
print("🧪 Testing ALwrity Backend API...")
# Test 1: Health check
print("\n1. Testing health check...")
try:
response = requests.get(f"{base_url}/health")
if response.status_code == 200:
print("✅ Health check passed")
print(f" Response: {response.json()}")
else:
print(f"❌ Health check failed: {response.status_code}")
except Exception as e:
print(f"❌ Health check error: {e}")
return False
# Test 2: Get onboarding status
print("\n2. Testing onboarding status...")
try:
response = requests.get(f"{base_url}/api/onboarding/status")
if response.status_code == 200:
print("✅ Onboarding status passed")
data = response.json()
print(f" Current step: {data.get('current_step')}")
print(f" Completion: {data.get('completion_percentage')}%")
else:
print(f"❌ Onboarding status failed: {response.status_code}")
except Exception as e:
print(f"❌ Onboarding status error: {e}")
return False
# Test 3: Get onboarding config
print("\n3. Testing onboarding config...")
try:
response = requests.get(f"{base_url}/api/onboarding/config")
if response.status_code == 200:
print("✅ Onboarding config passed")
data = response.json()
print(f" Total steps: {data.get('total_steps')}")
else:
print(f"❌ Onboarding config failed: {response.status_code}")
except Exception as e:
print(f"❌ Onboarding config error: {e}")
return False
# Test 4: Get API keys
print("\n4. Testing API keys endpoint...")
try:
response = requests.get(f"{base_url}/api/onboarding/api-keys")
if response.status_code == 200:
print("✅ API keys endpoint passed")
data = response.json()
print(f" Configured keys: {data.get('total_configured')}")
else:
print(f"❌ API keys endpoint failed: {response.status_code}")
except Exception as e:
print(f"❌ API keys endpoint error: {e}")
return False
# Test 5: Save API key
print("\n5. Testing save API key...")
try:
test_key_data = {
"provider": "openai",
"api_key": "sk-test1234567890abcdef",
"description": "Test API key"
}
response = requests.post(
f"{base_url}/api/onboarding/api-keys",
json=test_key_data,
headers={"Content-Type": "application/json"}
)
if response.status_code == 200:
print("✅ Save API key passed")
data = response.json()
print(f" Message: {data.get('message')}")
else:
print(f"❌ Save API key failed: {response.status_code}")
print(f" Response: {response.text}")
except Exception as e:
print(f"❌ Save API key error: {e}")
return False
# Test 6: Complete step
print("\n6. Testing complete step...")
try:
step_data = {
"data": {"api_keys": ["openai"]},
"validation_errors": []
}
response = requests.post(
f"{base_url}/api/onboarding/step/1/complete",
json=step_data,
headers={"Content-Type": "application/json"}
)
if response.status_code == 200:
print("✅ Complete step passed")
data = response.json()
print(f" Message: {data.get('message')}")
else:
print(f"❌ Complete step failed: {response.status_code}")
print(f" Response: {response.text}")
except Exception as e:
print(f"❌ Complete step error: {e}")
return False
# Test 7: Get updated status
print("\n7. Testing updated status...")
try:
response = requests.get(f"{base_url}/api/onboarding/status")
if response.status_code == 200:
print("✅ Updated status passed")
data = response.json()
print(f" Current step: {data.get('current_step')}")
print(f" Completion: {data.get('completion_percentage')}%")
else:
print(f"❌ Updated status failed: {response.status_code}")
except Exception as e:
print(f"❌ Updated status error: {e}")
return False
print("\n🎉 All tests completed!")
return True
def test_api_docs():
"""Test if API documentation is accessible."""
base_url = "http://localhost:8000"
print("\n📚 Testing API documentation...")
try:
# Test Swagger docs
response = requests.get(f"{base_url}/api/docs")
if response.status_code == 200:
print("✅ Swagger docs accessible")
else:
print(f"❌ Swagger docs failed: {response.status_code}")
# Test ReDoc
response = requests.get(f"{base_url}/api/redoc")
if response.status_code == 200:
print("✅ ReDoc accessible")
else:
print(f"❌ ReDoc failed: {response.status_code}")
except Exception as e:
print(f"❌ API docs error: {e}")
if __name__ == "__main__":
print("🚀 Starting ALwrity Backend Tests")
print("=" * 50)
# Wait a moment for server to start
print("⏳ Waiting for server to be ready...")
time.sleep(2)
# Run tests
success = test_backend()
test_api_docs()
if success:
print("\n✅ All tests passed! Backend is working correctly.")
print("\n📖 You can now:")
print(" - View API docs at: http://localhost:8000/api/docs")
print(" - Test endpoints manually")
print(" - Integrate with React frontend")
else:
print("\n❌ Some tests failed. Please check the backend logs.")

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#!/usr/bin/env python3
"""
Test script for calendar generation API
"""
import asyncio
import aiohttp
import json
async def test_calendar_generation():
"""Test the calendar generation API."""
url = "http://localhost:8000/api/content-planning/calendar-generation/start"
payload = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme"
}
async with aiohttp.ClientSession() as session:
try:
async with session.post(url, json=payload) as response:
if response.status == 200:
result = await response.json()
print("✅ Calendar generation started successfully!")
print(f"Session ID: {result.get('session_id')}")
# Test progress endpoint
session_id = result.get('session_id')
if session_id:
print(f"\n🔄 Testing progress for session: {session_id}")
progress_url = f"http://localhost:8000/api/content-planning/calendar-generation/progress/{session_id}"
async with session.get(progress_url) as progress_response:
if progress_response.status == 200:
progress_data = await progress_response.json()
print("✅ Progress endpoint working!")
print(f"Status: {progress_data.get('status')}")
print(f"Current Step: {progress_data.get('current_step')}")
print(f"Overall Progress: {progress_data.get('overall_progress')}%")
# Check for Step 4 specifically
step_results = progress_data.get('step_results', {})
if 'step_04' in step_results:
step4_result = step_results['step_04']
print(f"\n📊 Step 4 Status: {step4_result.get('status')}")
print(f"Step 4 Quality: {step4_result.get('quality_score')}")
if step4_result.get('status') == 'error':
print(f"Step 4 Error: {step4_result.get('error_message')}")
else:
print("⚠️ Step 4 results not yet available")
else:
print(f"❌ Progress endpoint failed: {progress_response.status}")
else:
print(f"❌ Calendar generation failed: {response.status}")
error_text = await response.text()
print(f"Error: {error_text}")
except Exception as e:
print(f"❌ Error testing calendar generation: {e}")
if __name__ == "__main__":
asyncio.run(test_calendar_generation())

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#!/usr/bin/env python3
"""
Test Script for Calendar Generation Data Source Framework
Demonstrates the functionality of the scalable framework for evolving data sources
in calendar generation without architectural changes.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from services.calendar_generation_datasource_framework import (
DataSourceRegistry,
StrategyAwarePromptBuilder,
QualityGateManager,
DataSourceEvolutionManager,
ContentStrategyDataSource,
GapAnalysisDataSource,
KeywordsDataSource,
ContentPillarsDataSource,
PerformanceDataSource,
AIAnalysisDataSource
)
async def test_framework_initialization():
"""Test framework initialization and component setup."""
print("🧪 Testing Framework Initialization...")
try:
# Initialize registry
registry = DataSourceRegistry()
print("✅ DataSourceRegistry initialized successfully")
# Initialize data sources
content_strategy = ContentStrategyDataSource()
gap_analysis = GapAnalysisDataSource()
keywords = KeywordsDataSource()
content_pillars = ContentPillarsDataSource()
performance_data = PerformanceDataSource()
ai_analysis = AIAnalysisDataSource()
print("✅ All data sources initialized successfully")
# Register data sources
registry.register_source(content_strategy)
registry.register_source(gap_analysis)
registry.register_source(keywords)
registry.register_source(content_pillars)
registry.register_source(performance_data)
registry.register_source(ai_analysis)
print("✅ All data sources registered successfully")
# Initialize framework components
prompt_builder = StrategyAwarePromptBuilder(registry)
quality_manager = QualityGateManager()
evolution_manager = DataSourceEvolutionManager(registry)
print("✅ Framework components initialized successfully")
return registry, prompt_builder, quality_manager, evolution_manager
except Exception as e:
print(f"❌ Framework initialization failed: {e}")
return None, None, None, None
async def test_data_source_registry(registry):
"""Test data source registry functionality."""
print("\n🧪 Testing Data Source Registry...")
try:
# Test registry status
status = registry.get_registry_status()
print(f"✅ Registry status: {status['total_sources']} sources, {status['active_sources']} active")
# Test source retrieval
content_strategy = registry.get_source("content_strategy")
if content_strategy:
print(f"✅ Content strategy source retrieved: {content_strategy}")
# Test active sources
active_sources = registry.get_active_sources()
print(f"✅ Active sources: {len(active_sources)}")
# Test source types
strategy_sources = registry.get_sources_by_type("strategy")
print(f"✅ Strategy sources: {len(strategy_sources)}")
# Test priorities
critical_sources = registry.get_sources_by_priority(1)
print(f"✅ Critical priority sources: {len(critical_sources)}")
return True
except Exception as e:
print(f"❌ Registry test failed: {e}")
return False
async def test_data_source_validation(registry):
"""Test data source validation functionality."""
print("\n🧪 Testing Data Source Validation...")
try:
# Validate all sources
validation_results = await registry.validate_all_sources()
print(f"✅ Validation completed for {len(validation_results)} sources")
# Check validation results
for source_id, result in validation_results.items():
if hasattr(result, 'quality_score'):
print(f" - {source_id}: {result.quality_score:.2f} quality score")
else:
print(f" - {source_id}: {result.get('quality_score', 0):.2f} quality score")
return True
except Exception as e:
print(f"❌ Validation test failed: {e}")
return False
async def test_prompt_builder(prompt_builder):
"""Test strategy-aware prompt builder functionality."""
print("\n🧪 Testing Strategy-Aware Prompt Builder...")
try:
# Test available steps
available_steps = prompt_builder.get_available_steps()
print(f"✅ Available steps: {len(available_steps)}")
# Test step dependencies
step_1_deps = prompt_builder.get_step_dependencies("step_1_content_strategy_analysis")
print(f"✅ Step 1 dependencies: {step_1_deps}")
# Test step requirements validation
step_validation = prompt_builder.validate_step_requirements("step_1_content_strategy_analysis")
print(f"✅ Step 1 validation: {step_validation['is_ready']}")
# Test prompt building (simplified)
try:
prompt = await prompt_builder.build_prompt("step_1_content_strategy_analysis", 1, 1)
print(f"✅ Prompt built successfully (length: {len(prompt)} characters)")
except Exception as e:
print(f"⚠️ Prompt building failed (expected for test): {e}")
return True
except Exception as e:
print(f"❌ Prompt builder test failed: {e}")
return False
async def test_quality_gates(quality_manager):
"""Test quality gate functionality."""
print("\n🧪 Testing Quality Gates...")
try:
# Test quality gate info
gate_info = quality_manager.get_gate_info()
print(f"✅ Quality gates: {len(gate_info)} gates available")
# Test specific gate validation
sample_calendar_data = {
"content_items": [
{"title": "Sample Content 1", "type": "blog", "theme": "technology"},
{"title": "Sample Content 2", "type": "video", "theme": "marketing"}
]
}
# Test all gates validation
validation_results = await quality_manager.validate_all_gates(sample_calendar_data, "test_step")
print(f"✅ All gates validation: {len(validation_results)} gates validated")
# Test specific gate validation
content_uniqueness_result = await quality_manager.validate_specific_gate("content_uniqueness", sample_calendar_data, "test_step")
print(f"✅ Content uniqueness validation: {content_uniqueness_result['passed']}")
return True
except Exception as e:
print(f"❌ Quality gates test failed: {e}")
return False
async def test_evolution_manager(evolution_manager):
"""Test evolution manager functionality."""
print("\n🧪 Testing Evolution Manager...")
try:
# Test evolution status
status = evolution_manager.get_evolution_status()
print(f"✅ Evolution status for {len(status)} sources")
# Test evolution summary
summary = evolution_manager.get_evolution_summary()
print(f"✅ Evolution summary: {summary['sources_needing_evolution']} need evolution")
# Test evolution plan
plan = evolution_manager.get_evolution_plan("content_strategy")
print(f"✅ Content strategy evolution plan: {plan['is_ready_for_evolution']}")
# Test evolution (simplified)
try:
success = await evolution_manager.evolve_data_source("content_strategy", "2.5.0")
print(f"✅ Evolution test: {'Success' if success else 'Failed'}")
except Exception as e:
print(f"⚠️ Evolution test failed (expected for test): {e}")
return True
except Exception as e:
print(f"❌ Evolution manager test failed: {e}")
return False
async def test_framework_integration(registry, prompt_builder, quality_manager, evolution_manager):
"""Test framework integration and end-to-end functionality."""
print("\n🧪 Testing Framework Integration...")
try:
# Test comprehensive workflow
print("📊 Testing comprehensive workflow...")
# 1. Get data from sources
print(" 1. Retrieving data from sources...")
for source_id in ["content_strategy", "gap_analysis", "keywords"]:
try:
data = await registry.get_data_with_dependencies(source_id, 1, 1)
print(f" ✅ {source_id}: Data retrieved")
except Exception as e:
print(f" ⚠️ {source_id}: Data retrieval failed (expected)")
# 2. Build enhanced prompts
print(" 2. Building enhanced prompts...")
for step in ["step_1_content_strategy_analysis", "step_2_gap_analysis"]:
try:
base_prompt = await prompt_builder.build_prompt(step, 1, 1)
print(f" ✅ {step}: Prompt built")
except Exception as e:
print(f" ⚠️ {step}: Prompt building failed (expected)")
# 3. Check evolution readiness
print(" 3. Checking evolution readiness...")
for source_id in ["content_strategy", "gap_analysis", "keywords"]:
plan = evolution_manager.get_evolution_plan(source_id)
print(f" ✅ {source_id}: Ready for evolution: {plan['is_ready_for_evolution']}")
print("✅ Framework integration test completed")
return True
except Exception as e:
print(f"❌ Framework integration test failed: {e}")
return False
async def test_scalability_features(registry, evolution_manager):
"""Test scalability features of the framework."""
print("\n🧪 Testing Scalability Features...")
try:
# Test adding custom data source
print("📈 Testing custom data source addition...")
# Create a custom data source (simplified)
from services.calendar_generation_datasource_framework.interfaces import DataSourceInterface, DataSourceType, DataSourcePriority
class CustomDataSource(DataSourceInterface):
def __init__(self):
super().__init__("custom_source", DataSourceType.CUSTOM, DataSourcePriority.LOW)
async def get_data(self, user_id: int, strategy_id: int):
return {"custom_data": "test"}
async def validate_data(self, data):
return {"is_valid": True, "quality_score": 0.8}
async def enhance_data(self, data):
return {**data, "enhanced": True}
# Register custom source
custom_source = CustomDataSource()
registry.register_source(custom_source)
print("✅ Custom data source registered successfully")
# Test evolution config addition
custom_config = {
"current_version": "1.0.0",
"target_version": "1.5.0",
"enhancement_plan": ["Custom enhancement"],
"implementation_steps": ["Implement custom enhancement"],
"priority": "low",
"estimated_effort": "low"
}
evolution_manager.add_evolution_config("custom_source", custom_config)
print("✅ Custom evolution config added successfully")
# Test framework status with new source
status = registry.get_registry_status()
print(f"✅ Framework now has {status['total_sources']} sources")
return True
except Exception as e:
print(f"❌ Scalability test failed: {e}")
return False
async def main():
"""Run all framework tests."""
print("🚀 Starting Calendar Generation Data Source Framework Tests...")
print("=" * 80)
# Initialize framework
registry, prompt_builder, quality_manager, evolution_manager = await test_framework_initialization()
if not all([registry, prompt_builder, quality_manager, evolution_manager]):
print("❌ Framework initialization failed. Exiting.")
return False
# Run individual component tests
tests = [
("Data Source Registry", test_data_source_registry, registry),
("Data Source Validation", test_data_source_validation, registry),
("Prompt Builder", test_prompt_builder, prompt_builder),
("Quality Gates", test_quality_gates, quality_manager),
("Evolution Manager", test_evolution_manager, evolution_manager),
("Framework Integration", test_framework_integration, registry, prompt_builder, quality_manager, evolution_manager),
("Scalability Features", test_scalability_features, registry, evolution_manager)
]
results = []
for test_name, test_func, *args in tests:
try:
result = await test_func(*args)
results.append((test_name, result))
except Exception as e:
print(f"❌ {test_name} test failed with exception: {e}")
results.append((test_name, False))
# Print test summary
print("\n" + "=" * 80)
print("📋 Test Results Summary:")
passed = 0
total = len(results)
for test_name, result in results:
status = "✅ PASSED" if result else "❌ FAILED"
print(f" {status} - {test_name}")
if result:
passed += 1
print(f"\n🎯 Overall Results: {passed}/{total} tests passed")
if passed == total:
print("🎉 All tests passed! Framework is working correctly.")
print("\n✅ Framework Features Verified:")
print(" - Scalable data source management")
print(" - Strategy-aware prompt building")
print(" - Quality gate integration")
print(" - Evolution management")
print(" - Framework integration")
print(" - Scalability and extensibility")
return True
else:
print("⚠️ Some tests failed. Please check the implementation.")
return False
if __name__ == "__main__":
# Run the tests
success = asyncio.run(main())
sys.exit(0 if success else 1)

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#!/usr/bin/env python3
"""Test script for content planning services."""
import asyncio
from loguru import logger
# Import all content planning services
from services.content_gap_analyzer import ContentGapAnalyzer
from services.competitor_analyzer import CompetitorAnalyzer
from services.keyword_researcher import KeywordResearcher
from services.ai_engine_service import AIEngineService
from services.website_analyzer import WebsiteAnalyzer
async def test_content_planning_services():
"""Test all content planning services."""
logger.info("🧪 Testing Content Planning Services")
try:
# Test 1: Initialize all services
logger.info("1. Initializing services...")
content_gap_analyzer = ContentGapAnalyzer()
competitor_analyzer = CompetitorAnalyzer()
keyword_researcher = KeywordResearcher()
ai_engine = AIEngineService()
website_analyzer = WebsiteAnalyzer()
logger.info("✅ All services initialized successfully")
# Test 2: Test content gap analysis
logger.info("2. Testing content gap analysis...")
target_url = "https://alwrity.com"
competitor_urls = ["https://competitor1.com", "https://competitor2.com"]
target_keywords = ["content planning", "digital marketing", "seo strategy"]
gap_analysis = await content_gap_analyzer.analyze_comprehensive_gap(
target_url=target_url,
competitor_urls=competitor_urls,
target_keywords=target_keywords,
industry="technology"
)
if gap_analysis:
logger.info(f"✅ Content gap analysis completed: {len(gap_analysis.get('recommendations', []))} recommendations")
else:
logger.warning("⚠️ Content gap analysis returned empty results")
# Test 3: Test competitor analysis
logger.info("3. Testing competitor analysis...")
competitor_analysis = await competitor_analyzer.analyze_competitors(
competitor_urls=competitor_urls,
industry="technology"
)
if competitor_analysis:
logger.info(f"✅ Competitor analysis completed: {len(competitor_analysis.get('competitors', []))} competitors analyzed")
else:
logger.warning("⚠️ Competitor analysis returned empty results")
# Test 4: Test keyword research
logger.info("4. Testing keyword research...")
keyword_analysis = await keyword_researcher.analyze_keywords(
industry="technology",
url=target_url,
target_keywords=target_keywords
)
if keyword_analysis:
logger.info(f"✅ Keyword analysis completed: {len(keyword_analysis.get('opportunities', []))} opportunities found")
else:
logger.warning("⚠️ Keyword analysis returned empty results")
# Test 5: Test website analysis
logger.info("5. Testing website analysis...")
website_analysis = await website_analyzer.analyze_website(
url=target_url,
industry="technology"
)
if website_analysis:
logger.info(f"✅ Website analysis completed: {website_analysis.get('content_analysis', {}).get('total_pages', 0)} pages analyzed")
else:
logger.warning("⚠️ Website analysis returned empty results")
# Test 6: Test AI engine
logger.info("6. Testing AI engine...")
analysis_summary = {
'target_url': target_url,
'industry': 'technology',
'serp_opportunities': 5,
'expanded_keywords_count': 25,
'competitors_analyzed': 2,
'dominant_themes': ['content strategy', 'digital marketing', 'seo']
}
ai_insights = await ai_engine.analyze_content_gaps(analysis_summary)
if ai_insights:
logger.info(f"✅ AI insights generated: {len(ai_insights.get('strategic_insights', []))} insights")
else:
logger.warning("⚠️ AI insights returned empty results")
# Test 7: Test content quality analysis
logger.info("7. Testing content quality analysis...")
content_quality = await website_analyzer.analyze_content_quality(target_url)
if content_quality:
logger.info(f"✅ Content quality analysis completed: Score {content_quality.get('overall_quality_score', 0)}/10")
else:
logger.warning("⚠️ Content quality analysis returned empty results")
# Test 8: Test user experience analysis
logger.info("8. Testing user experience analysis...")
ux_analysis = await website_analyzer.analyze_user_experience(target_url)
if ux_analysis:
logger.info(f"✅ UX analysis completed: Score {ux_analysis.get('overall_ux_score', 0)}/10")
else:
logger.warning("⚠️ UX analysis returned empty results")
# Test 9: Test keyword expansion
logger.info("9. Testing keyword expansion...")
seed_keywords = ["content planning", "digital marketing"]
expanded_keywords = await keyword_researcher.expand_keywords(
seed_keywords=seed_keywords,
industry="technology"
)
if expanded_keywords:
logger.info(f"✅ Keyword expansion completed: {len(expanded_keywords.get('expanded_keywords', []))} keywords generated")
else:
logger.warning("⚠️ Keyword expansion returned empty results")
# Test 10: Test search intent analysis
logger.info("10. Testing search intent analysis...")
keywords = ["content planning guide", "digital marketing tips", "seo best practices"]
intent_analysis = await keyword_researcher.analyze_search_intent(keywords)
if intent_analysis:
logger.info(f"✅ Search intent analysis completed: {len(intent_analysis.get('keyword_intents', {}))} keywords analyzed")
else:
logger.warning("⚠️ Search intent analysis returned empty results")
logger.info("🎉 All content planning services tested successfully!")
return True
except Exception as e:
logger.error(f"❌ Error testing content planning services: {str(e)}")
return False
async def test_ai_engine_features():
"""Test specific AI engine features."""
logger.info("🤖 Testing AI Engine Features")
try:
ai_engine = AIEngineService()
# Test market position analysis
market_data = {
'competitors_analyzed': 3,
'avg_content_count': 150,
'avg_quality_score': 8.5,
'frequency_distribution': {'3x/week': 2, '2x/week': 1},
'industry': 'technology'
}
market_position = await ai_engine.analyze_market_position(market_data)
if market_position:
logger.info("✅ Market position analysis completed")
else:
logger.warning("⚠️ Market position analysis failed")
# Test content recommendations
analysis_data = {
'target_url': 'https://alwrity.com',
'industry': 'technology',
'keywords': ['content planning', 'digital marketing'],
'competitors': ['competitor1.com', 'competitor2.com']
}
recommendations = await ai_engine.generate_content_recommendations(analysis_data)
if recommendations:
logger.info(f"✅ Content recommendations generated: {len(recommendations)} recommendations")
else:
logger.warning("⚠️ Content recommendations failed")
# Test performance predictions
content_data = {
'content_type': 'blog_post',
'target_keywords': ['content planning'],
'industry': 'technology',
'content_length': 1500
}
predictions = await ai_engine.predict_content_performance(content_data)
if predictions:
logger.info("✅ Performance predictions generated")
else:
logger.warning("⚠️ Performance predictions failed")
# Test competitive intelligence
competitor_data = {
'competitors': ['competitor1.com', 'competitor2.com'],
'industry': 'technology',
'analysis_depth': 'comprehensive'
}
competitive_intelligence = await ai_engine.analyze_competitive_intelligence(competitor_data)
if competitive_intelligence:
logger.info("✅ Competitive intelligence analysis completed")
else:
logger.warning("⚠️ Competitive intelligence analysis failed")
# Test strategic insights
analysis_data = {
'industry': 'technology',
'target_audience': 'marketing professionals',
'business_goals': ['increase traffic', 'improve conversions'],
'current_performance': 'moderate'
}
strategic_insights = await ai_engine.generate_strategic_insights(analysis_data)
if strategic_insights:
logger.info(f"✅ Strategic insights generated: {len(strategic_insights)} insights")
else:
logger.warning("⚠️ Strategic insights failed")
# Test content quality analysis
content_data = {
'content_text': 'Sample content for analysis',
'target_keywords': ['content planning'],
'industry': 'technology'
}
quality_analysis = await ai_engine.analyze_content_quality(content_data)
if quality_analysis:
logger.info(f"✅ Content quality analysis completed: Score {quality_analysis.get('overall_quality_score', 0)}/10")
else:
logger.warning("⚠️ Content quality analysis failed")
logger.info("🎉 All AI engine features tested successfully!")
return True
except Exception as e:
logger.error(f"❌ Error testing AI engine features: {str(e)}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting Content Planning Services Test Suite")
# Test 1: Basic services
services_result = await test_content_planning_services()
# Test 2: AI engine features
ai_result = await test_ai_engine_features()
if services_result and ai_result:
logger.info("🎉 All tests passed! Content Planning Services are ready for Phase 1 implementation.")
else:
logger.error("❌ Some tests failed. Please check the logs above.")
logger.info("🏁 Test suite completed")
if __name__ == "__main__":
asyncio.run(main())

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"""
Test script for database functionality.
"""
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.database import init_database, get_db_session, close_database
from services.website_analysis_service import WebsiteAnalysisService
from models.onboarding import WebsiteAnalysis, OnboardingSession
def test_database_functionality():
"""Test database initialization and basic operations."""
try:
print("Testing database functionality...")
# Initialize database
init_database()
print("✅ Database initialized successfully")
# Get database session
db_session = get_db_session()
if not db_session:
print("❌ Failed to get database session")
return False
print("✅ Database session created successfully")
# Test website analysis service
analysis_service = WebsiteAnalysisService(db_session)
print("✅ Website analysis service created successfully")
# Test creating a session
session = OnboardingSession(user_id=1, current_step=2, progress=25.0)
db_session.add(session)
db_session.commit()
print(f"✅ Created onboarding session with ID: {session.id}")
# Test saving analysis
test_analysis_data = {
'style_analysis': {
'writing_style': {
'tone': 'professional',
'voice': 'active',
'complexity': 'moderate',
'engagement_level': 'high'
},
'target_audience': {
'demographics': ['professionals', 'business owners'],
'expertise_level': 'intermediate',
'industry_focus': 'technology',
'geographic_focus': 'global'
},
'content_type': {
'primary_type': 'blog',
'secondary_types': ['article', 'guide'],
'purpose': 'informational',
'call_to_action': 'subscribe'
},
'recommended_settings': {
'writing_tone': 'professional',
'target_audience': 'business professionals',
'content_type': 'blog posts',
'creativity_level': 'balanced',
'geographic_location': 'global'
}
},
'crawl_result': {
'content': 'Sample website content...',
'word_count': 1500
},
'style_patterns': {
'sentence_length': 'medium',
'paragraph_structure': 'well-organized'
},
'style_guidelines': {
'tone_guidelines': 'Maintain professional tone',
'structure_guidelines': 'Use clear headings'
}
}
analysis_id = analysis_service.save_analysis(
session_id=session.id,
website_url='https://example.com',
analysis_data=test_analysis_data
)
if analysis_id:
print(f"✅ Saved analysis with ID: {analysis_id}")
else:
print("❌ Failed to save analysis")
return False
# Test retrieving analysis
analysis = analysis_service.get_analysis(analysis_id)
if analysis:
print("✅ Retrieved analysis successfully")
print(f" Website URL: {analysis['website_url']}")
print(f" Writing Style: {analysis['writing_style']['tone']}")
else:
print("❌ Failed to retrieve analysis")
return False
# Test checking existing analysis
existing_check = analysis_service.check_existing_analysis(
session_id=session.id,
website_url='https://example.com'
)
if existing_check and existing_check.get('exists'):
print("✅ Existing analysis check works")
else:
print("❌ Existing analysis check failed")
return False
# Clean up
if analysis_id:
analysis_service.delete_analysis(analysis_id)
db_session.delete(session)
db_session.commit()
print("✅ Cleanup completed")
print("\n🎉 All database tests passed!")
return True
except Exception as e:
print(f"❌ Database test failed: {str(e)}")
return False
finally:
close_database()
if __name__ == "__main__":
success = test_database_functionality()
if success:
print("\n✅ Database functionality is working correctly!")
else:
print("\n❌ Database functionality has issues!")
sys.exit(1)

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import requests
import json
# Test the research endpoint with more detailed output
url = "http://localhost:8000/api/blog/research"
payload = {
"keywords": ["AI content generation", "blog writing"],
"topic": "ALwrity content generation",
"industry": "Technology",
"target_audience": "content creators"
}
try:
print("Sending request to research endpoint...")
response = requests.post(url, json=payload, timeout=60)
print(f"Status Code: {response.status_code}")
if response.status_code == 200:
data = response.json()
print("\n=== FULL RESPONSE ===")
print(json.dumps(data, indent=2))
# Check if we got the expected fields
expected_fields = ['success', 'sources', 'keyword_analysis', 'competitor_analysis', 'suggested_angles', 'search_widget', 'search_queries']
print(f"\n=== FIELD ANALYSIS ===")
for field in expected_fields:
value = data.get(field)
if field == 'sources':
print(f"{field}: {len(value) if value else 0} items")
elif field == 'search_queries':
print(f"{field}: {len(value) if value else 0} items")
elif field == 'search_widget':
print(f"{field}: {'Present' if value else 'Missing'}")
else:
print(f"{field}: {type(value).__name__} - {str(value)[:100]}...")
else:
print(f"Error Response: {response.text}")
except Exception as e:
print(f"Request failed: {e}")
import traceback
traceback.print_exc()

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#!/usr/bin/env python3
"""
Test Script for Enhanced LinkedIn Prompt Generation
This script demonstrates how the enhanced LinkedIn prompt generator analyzes
generated content and creates context-aware image prompts.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_path = Path(__file__).parent
sys.path.insert(0, str(backend_path))
from loguru import logger
# Configure logging
logger.remove()
logger.add(sys.stdout, colorize=True, format="<level>{level}</level>| {message}")
async def test_enhanced_prompt_generation():
"""Test the enhanced LinkedIn prompt generation with content analysis."""
logger.info("🧪 Testing Enhanced LinkedIn Prompt Generation")
logger.info("=" * 70)
try:
# Import the enhanced prompt generator
from services.linkedin.image_prompts import LinkedInPromptGenerator
# Initialize the service
prompt_generator = LinkedInPromptGenerator()
logger.success("✅ LinkedIn Prompt Generator initialized successfully")
# Test cases with different types of LinkedIn content
test_cases = [
{
'name': 'AI Marketing Post',
'content': {
'topic': 'AI in Marketing',
'industry': 'Technology',
'content_type': 'post',
'content': """🚀 Exciting news! Artificial Intelligence is revolutionizing how we approach marketing strategies.
Here are 3 game-changing ways AI is transforming the industry:
1️⃣ **Predictive Analytics**: AI algorithms can now predict customer behavior with 95% accuracy, allowing marketers to create hyper-personalized campaigns.
2️⃣ **Content Optimization**: Machine learning models analyze engagement patterns to optimize content timing, format, and messaging for maximum impact.
3️⃣ **Automated Personalization**: AI-powered tools automatically adjust marketing messages based on individual user preferences and behavior.
The future of marketing is here, and it's powered by AI! 🎯
What's your experience with AI in marketing? Share your thoughts below! 👇
#AIMarketing #DigitalTransformation #MarketingInnovation #TechTrends #FutureOfMarketing"""
}
},
{
'name': 'Leadership Article',
'content': {
'topic': 'Building High-Performance Teams',
'industry': 'Business',
'content_type': 'article',
'content': """Building High-Performance Teams: A Comprehensive Guide
In today's competitive business landscape, the ability to build and lead high-performance teams is not just a skill—it's a strategic imperative. After 15 years of leading teams across various industries, I've identified the key principles that consistently drive exceptional results.
**The Foundation: Clear Vision and Purpose**
Every high-performance team starts with a crystal-clear understanding of their mission. Team members need to know not just what they're doing, but why it matters. This creates intrinsic motivation that external rewards simply cannot match.
**Communication: The Lifeblood of Success**
Effective communication in high-performance teams goes beyond regular meetings. It involves creating an environment where feedback flows freely, ideas are shared without fear, and every voice is heard and valued.
**Trust and Psychological Safety**
High-performance teams operate in environments where team members feel safe to take risks, make mistakes, and learn from failures. This psychological safety is the bedrock of innovation and continuous improvement.
**Continuous Learning and Adaptation**
The best teams never rest on their laurels. They continuously seek new knowledge, adapt to changing circumstances, and evolve their approaches based on results and feedback.
**Results and Accountability**
While process matters, high-performance teams are ultimately measured by their results. Clear metrics, regular check-ins, and a culture of accountability ensure that the team stays focused on delivering value.
Building high-performance teams is both an art and a science. It requires patience, persistence, and a genuine commitment to developing people. The investment pays dividends not just in results, but in the satisfaction of seeing individuals grow and teams achieve what once seemed impossible.
What strategies have you found most effective in building high-performance teams? Share your insights in the comments below."""
}
},
{
'name': 'Data Analytics Carousel',
'content': {
'topic': 'Data-Driven Decision Making',
'industry': 'Finance',
'content_type': 'carousel',
'content': """📊 Data-Driven Decision Making: Your Competitive Advantage
Slide 1: The Power of Data
• 73% of companies using data-driven decision making report improved performance
• Data-driven organizations are 23x more likely to acquire customers
• 58% of executives say data analytics has improved their decision-making process
Slide 2: Key Metrics to Track
• Customer Acquisition Cost (CAC)
• Customer Lifetime Value (CLV)
• Conversion Rates
• Churn Rate
• Revenue Growth
Slide 3: Implementation Steps
1. Define clear objectives
2. Identify relevant data sources
3. Establish data quality standards
4. Build analytical capabilities
5. Create feedback loops
Slide 4: Common Pitfalls
• Analysis paralysis
• Ignoring qualitative insights
• Not validating assumptions
• Over-relying on historical data
• Poor data visualization
Slide 5: Success Stories
• Netflix: 75% of viewing decisions influenced by data
• Amazon: Dynamic pricing increases revenue by 25%
• Spotify: Personalized recommendations drive 40% of listening time
Slide 6: Getting Started
• Start small with key metrics
• Invest in data literacy training
• Use visualization tools
• Establish regular review cycles
• Celebrate data-driven wins
Ready to transform your decision-making process? Let's discuss your data strategy! 💬
#DataDriven #Analytics #BusinessIntelligence #DecisionMaking #Finance #Strategy"""
}
}
]
# Test each case
for i, test_case in enumerate(test_cases, 1):
logger.info(f"\n📝 Test Case {i}: {test_case['name']}")
logger.info("-" * 50)
# Generate prompts using the enhanced generator
prompts = await prompt_generator.generate_three_prompts(
test_case['content'],
aspect_ratio="1:1"
)
if prompts and len(prompts) >= 3:
logger.success(f"✅ Generated {len(prompts)} context-aware prompts")
# Display each prompt
for j, prompt in enumerate(prompts, 1):
logger.info(f"\n🎨 Prompt {j}: {prompt['style']}")
logger.info(f" Description: {prompt['description']}")
logger.info(f" Content Context: {prompt.get('content_context', 'N/A')}")
# Show a preview of the prompt
prompt_text = prompt['prompt']
if len(prompt_text) > 200:
prompt_text = prompt_text[:200] + "..."
logger.info(f" Prompt Preview: {prompt_text}")
# Validate prompt quality
quality_result = await prompt_generator.validate_prompt_quality(prompt)
if quality_result.get('valid'):
logger.success(f" ✅ Quality Score: {quality_result['overall_score']}/100")
else:
logger.warning(f" ⚠️ Quality Score: {quality_result.get('overall_score', 'N/A')}/100")
else:
logger.error(f"❌ Failed to generate prompts for {test_case['name']}")
# Test content analysis functionality directly
logger.info(f"\n🔍 Testing Content Analysis Functionality")
logger.info("-" * 50)
test_content = test_cases[0]['content']['content']
content_analysis = prompt_generator._analyze_content_for_image_context(
test_content,
test_cases[0]['content']['content_type']
)
logger.info("Content Analysis Results:")
for key, value in content_analysis.items():
logger.info(f" {key}: {value}")
logger.info("=" * 70)
logger.success("🎉 Enhanced LinkedIn Prompt Generation Test Completed Successfully!")
return True
except ImportError as e:
logger.error(f"❌ Import Error: {e}")
return False
except Exception as e:
logger.error(f"❌ Test Failed: {e}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting Enhanced LinkedIn Prompt Generation Tests")
success = await test_enhanced_prompt_generation()
if success:
logger.success("✅ All tests passed! The enhanced prompt generation is working correctly.")
sys.exit(0)
else:
logger.error("❌ Some tests failed. Please check the errors above.")
sys.exit(1)
if __name__ == "__main__":
# Run the async test
asyncio.run(main())

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#!/usr/bin/env python3
"""
Test script for Enhanced Strategy Data Processing
Verifies that the enhanced strategy data processing is working correctly.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from services.content_planning_db import ContentPlanningDBService
async def test_enhanced_strategy_processing():
"""Test the enhanced strategy data processing functionality."""
print("🧪 Testing Enhanced Strategy Data Processing...")
try:
# Initialize the database service
db_service = ContentPlanningDBService()
# Test with a sample strategy ID
strategy_id = 1 # You can change this to test with different strategies
print(f"📊 Testing strategy data retrieval for strategy ID: {strategy_id}")
# Test the enhanced strategy data retrieval
strategy_data = await db_service.get_strategy_data(strategy_id)
if strategy_data:
print("✅ Strategy data retrieved successfully!")
print(f"📈 Strategy data contains {len(strategy_data)} fields")
# Check for enhanced fields
enhanced_fields = [
"strategy_analysis",
"quality_indicators",
"data_completeness",
"strategic_alignment",
"quality_gate_data",
"prompt_chain_data"
]
print("\n🔍 Checking for enhanced strategy fields:")
for field in enhanced_fields:
if field in strategy_data:
print(f" ✅ {field}: Present")
if isinstance(strategy_data[field], dict):
print(f" Contains {len(strategy_data[field])} sub-fields")
else:
print(f" ❌ {field}: Missing")
# Check strategy analysis
if "strategy_analysis" in strategy_data:
analysis = strategy_data["strategy_analysis"]
print(f"\n📊 Strategy Analysis:")
print(f" - Completion Percentage: {analysis.get('completion_percentage', 0)}%")
print(f" - Filled Fields: {analysis.get('filled_fields', 0)}/{analysis.get('total_fields', 30)}")
print(f" - Data Quality Score: {analysis.get('data_quality_score', 0)}%")
print(f" - Strategy Coherence: {analysis.get('strategy_coherence', {}).get('overall_coherence', 0)}%")
# Check quality indicators
if "quality_indicators" in strategy_data:
quality = strategy_data["quality_indicators"]
print(f"\n🎯 Quality Indicators:")
print(f" - Data Completeness: {quality.get('data_completeness', 0)}%")
print(f" - Strategic Alignment: {quality.get('strategic_alignment', 0)}%")
print(f" - Market Relevance: {quality.get('market_relevance', 0)}%")
print(f" - Audience Alignment: {quality.get('audience_alignment', 0)}%")
print(f" - Content Strategy Coherence: {quality.get('content_strategy_coherence', 0)}%")
print(f" - Overall Quality Score: {quality.get('overall_quality_score', 0)}%")
# Check quality gate data
if "quality_gate_data" in strategy_data:
quality_gates = strategy_data["quality_gate_data"]
print(f"\n🚪 Quality Gate Data:")
for gate_name, gate_data in quality_gates.items():
if isinstance(gate_data, dict):
print(f" - {gate_name}: {len(gate_data)} fields")
else:
print(f" - {gate_name}: {type(gate_data).__name__}")
# Check prompt chain data
if "prompt_chain_data" in strategy_data:
prompt_chain = strategy_data["prompt_chain_data"]
print(f"\n🔗 Prompt Chain Data:")
for step_name, step_data in prompt_chain.items():
if isinstance(step_data, dict):
print(f" - {step_name}: {len(step_data)} sub-sections")
else:
print(f" - {step_name}: {type(step_data).__name__}")
print(f"\n✅ Enhanced Strategy Data Processing Test PASSED!")
return True
else:
print("❌ No strategy data retrieved")
return False
except Exception as e:
print(f"❌ Error during enhanced strategy data processing test: {str(e)}")
import traceback
traceback.print_exc()
return False
async def test_comprehensive_user_data():
"""Test the comprehensive user data retrieval with enhanced strategy data."""
print("\n🧪 Testing Comprehensive User Data with Enhanced Strategy...")
try:
# Initialize the database service
db_service = ContentPlanningDBService()
# Test with a sample user ID and strategy ID
user_id = 1
strategy_id = 1
print(f"📊 Testing comprehensive user data for user {user_id} with strategy {strategy_id}")
# Test the comprehensive user data retrieval
user_data = await calendar_service._get_comprehensive_user_data(user_id, strategy_id)
if user_data:
print("✅ Comprehensive user data retrieved successfully!")
print(f"📈 User data contains {len(user_data)} fields")
# Check for enhanced strategy fields in user data
enhanced_fields = [
"strategy_analysis",
"quality_indicators",
"data_completeness",
"strategic_alignment",
"quality_gate_data",
"prompt_chain_data"
]
print("\n🔍 Checking for enhanced strategy fields in user data:")
for field in enhanced_fields:
if field in user_data:
print(f" ✅ {field}: Present")
if isinstance(user_data[field], dict):
print(f" Contains {len(user_data[field])} sub-fields")
else:
print(f" ❌ {field}: Missing")
# Check strategy data quality
if "strategy_data" in user_data:
strategy_data = user_data["strategy_data"]
print(f"\n📊 Strategy Data Quality:")
print(f" - Strategy ID: {strategy_data.get('strategy_id', 'N/A')}")
print(f" - Strategy Name: {strategy_data.get('strategy_name', 'N/A')}")
print(f" - Industry: {strategy_data.get('industry', 'N/A')}")
print(f" - Content Pillars: {len(strategy_data.get('content_pillars', []))} pillars")
print(f" - Target Audience: {len(strategy_data.get('target_audience', {}))} audience fields")
print(f"\n✅ Comprehensive User Data Test PASSED!")
return True
else:
print("❌ No comprehensive user data retrieved")
return False
except Exception as e:
print(f"❌ Error during comprehensive user data test: {str(e)}")
import traceback
traceback.print_exc()
return False
async def main():
"""Run all tests for enhanced strategy data processing."""
print("🚀 Starting Enhanced Strategy Data Processing Tests...")
print("=" * 60)
# Test 1: Enhanced Strategy Data Processing
test1_passed = await test_enhanced_strategy_processing()
# Test 2: Comprehensive User Data
test2_passed = await test_comprehensive_user_data()
print("\n" + "=" * 60)
print("📋 Test Results Summary:")
print(f" ✅ Enhanced Strategy Data Processing: {'PASSED' if test1_passed else 'FAILED'}")
print(f" ✅ Comprehensive User Data: {'PASSED' if test2_passed else 'FAILED'}")
if test1_passed and test2_passed:
print("\n🎉 All Enhanced Strategy Data Processing Tests PASSED!")
print("✅ The enhanced strategy data processing is working correctly.")
print("✅ Ready for 12-step prompt chaining and quality gates integration.")
return True
else:
print("\n❌ Some tests failed. Please check the implementation.")
return False
if __name__ == "__main__":
# Run the tests
success = asyncio.run(main())
sys.exit(0 if success else 1)

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"""Test script for Facebook Writer API endpoints."""
import requests
import json
from typing import Dict, Any
# Base URL for the API
BASE_URL = "http://localhost:8000"
def test_health_check():
"""Test the health check endpoint."""
try:
response = requests.get(f"{BASE_URL}/api/facebook-writer/health")
print(f"Health Check: {response.status_code}")
if response.status_code == 200:
print(f"Response: {response.json()}")
return response.status_code == 200
except Exception as e:
print(f"Health check failed: {e}")
return False
def test_get_tools():
"""Test getting available tools."""
try:
response = requests.get(f"{BASE_URL}/api/facebook-writer/tools")
print(f"Get Tools: {response.status_code}")
if response.status_code == 200:
data = response.json()
print(f"Available tools: {data['total_count']}")
for tool in data['tools'][:3]: # Show first 3 tools
print(f" - {tool['name']}: {tool['description']}")
return response.status_code == 200
except Exception as e:
print(f"Get tools failed: {e}")
return False
def test_generate_post():
"""Test Facebook post generation."""
payload = {
"business_type": "Fitness coach",
"target_audience": "Fitness enthusiasts aged 25-35",
"post_goal": "Increase engagement",
"post_tone": "Inspirational",
"include": "Success story, workout tips",
"avoid": "Generic advice",
"media_type": "Image",
"advanced_options": {
"use_hook": True,
"use_story": True,
"use_cta": True,
"use_question": True,
"use_emoji": True,
"use_hashtags": True
}
}
try:
response = requests.post(
f"{BASE_URL}/api/facebook-writer/post/generate",
json=payload,
headers={"Content-Type": "application/json"}
)
print(f"Generate Post: {response.status_code}")
if response.status_code == 200:
data = response.json()
if data['success']:
print(f"Post generated successfully!")
print(f"Content preview: {data['content'][:100]}...")
if data.get('analytics'):
print(f"Expected reach: {data['analytics']['expected_reach']}")
else:
print(f"Generation failed: {data.get('error', 'Unknown error')}")
else:
print(f"Request failed: {response.text}")
return response.status_code == 200
except Exception as e:
print(f"Generate post failed: {e}")
return False
def test_generate_story():
"""Test Facebook story generation."""
payload = {
"business_type": "Fashion brand",
"target_audience": "Fashion enthusiasts aged 18-30",
"story_type": "Product showcase",
"story_tone": "Fun",
"include": "Behind the scenes",
"avoid": "Too much text",
"visual_options": {
"background_type": "Gradient",
"text_overlay": True,
"stickers": True,
"interactive_elements": True
}
}
try:
response = requests.post(
f"{BASE_URL}/api/facebook-writer/story/generate",
json=payload,
headers={"Content-Type": "application/json"}
)
print(f"Generate Story: {response.status_code}")
if response.status_code == 200:
data = response.json()
if data['success']:
print(f"Story generated successfully!")
print(f"Content preview: {data['content'][:100]}...")
if data.get('visual_suggestions'):
print(f"Visual suggestions: {len(data['visual_suggestions'])} items")
else:
print(f"Generation failed: {data.get('error', 'Unknown error')}")
return response.status_code == 200
except Exception as e:
print(f"Generate story failed: {e}")
return False
def test_generate_ad_copy():
"""Test Facebook ad copy generation."""
payload = {
"business_type": "E-commerce store",
"product_service": "Wireless headphones",
"ad_objective": "Conversions",
"ad_format": "Single image",
"target_audience": "Tech enthusiasts and music lovers",
"targeting_options": {
"age_group": "25-34",
"interests": "Technology, Music, Audio equipment",
"location": "United States"
},
"unique_selling_proposition": "Premium sound quality at affordable prices",
"offer_details": "20% off for first-time buyers",
"budget_range": "Medium"
}
try:
response = requests.post(
f"{BASE_URL}/api/facebook-writer/ad-copy/generate",
json=payload,
headers={"Content-Type": "application/json"}
)
print(f"Generate Ad Copy: {response.status_code}")
if response.status_code == 200:
data = response.json()
if data['success']:
print(f"Ad copy generated successfully!")
if data.get('primary_ad_copy'):
print(f"Headline: {data['primary_ad_copy'].get('headline', 'N/A')}")
if data.get('performance_predictions'):
print(f"Estimated reach: {data['performance_predictions']['estimated_reach']}")
else:
print(f"Generation failed: {data.get('error', 'Unknown error')}")
return response.status_code == 200
except Exception as e:
print(f"Generate ad copy failed: {e}")
return False
def test_analyze_engagement():
"""Test engagement analysis."""
payload = {
"content": "🚀 Ready to transform your fitness journey? Our new 30-day challenge is here! Join thousands who've already seen amazing results. What's your biggest fitness goal? 💪 #FitnessMotivation #Challenge #Transformation",
"content_type": "Post",
"analysis_type": "Performance prediction",
"business_type": "Fitness coach",
"target_audience": "Fitness enthusiasts"
}
try:
response = requests.post(
f"{BASE_URL}/api/facebook-writer/engagement/analyze",
json=payload,
headers={"Content-Type": "application/json"}
)
print(f"Analyze Engagement: {response.status_code}")
if response.status_code == 200:
data = response.json()
if data['success']:
print(f"Analysis completed successfully!")
print(f"Content score: {data.get('content_score', 'N/A')}/100")
if data.get('engagement_metrics'):
print(f"Predicted engagement: {data['engagement_metrics']['predicted_engagement_rate']}")
else:
print(f"Analysis failed: {data.get('error', 'Unknown error')}")
return response.status_code == 200
except Exception as e:
print(f"Analyze engagement failed: {e}")
return False
def main():
"""Run all tests."""
print("🧪 Testing Facebook Writer API Endpoints")
print("=" * 50)
tests = [
("Health Check", test_health_check),
("Get Tools", test_get_tools),
("Generate Post", test_generate_post),
("Generate Story", test_generate_story),
("Generate Ad Copy", test_generate_ad_copy),
("Analyze Engagement", test_analyze_engagement)
]
results = []
for test_name, test_func in tests:
print(f"\n🔍 Running {test_name}...")
try:
success = test_func()
results.append((test_name, success))
status = "✅ PASS" if success else "❌ FAIL"
print(f"{status}")
except Exception as e:
print(f"❌ FAIL - {e}")
results.append((test_name, False))
print(f"\n📊 Test Results Summary")
print("=" * 50)
passed = sum(1 for _, success in results if success)
total = len(results)
for test_name, success in results:
status = "✅ PASS" if success else "❌ FAIL"
print(f"{status} {test_name}")
print(f"\nOverall: {passed}/{total} tests passed ({passed/total*100:.1f}%)")
if passed == total:
print("🎉 All tests passed! Facebook Writer API is ready to use.")
else:
print("⚠️ Some tests failed. Check the server logs for details.")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Test the full 12-step calendar generation process to verify Step 5 fix.
"""
import asyncio
import time
from loguru import logger
import sys
import os
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
from services.calendar_generation_datasource_framework.prompt_chaining.orchestrator import PromptChainOrchestrator
async def test_full_12_step_process():
"""Test the complete 12-step process to verify Step 5 fix."""
try:
logger.info("🧪 Testing full 12-step calendar generation process")
# Create orchestrator
logger.info("✅ Creating orchestrator...")
orchestrator = PromptChainOrchestrator()
# Test parameters
user_id = 1
strategy_id = 1
calendar_type = "monthly"
industry = "technology"
business_size = "sme"
logger.info(f"🎯 Starting calendar generation for user {user_id}, strategy {strategy_id}")
logger.info(f"📋 Parameters: {calendar_type}, {industry}, {business_size}")
# Start the full process
start_time = time.time()
# Generate calendar using the orchestrator's main method
logger.info("🚀 Executing full 12-step process...")
final_calendar = await orchestrator.generate_calendar(
user_id=user_id,
strategy_id=strategy_id,
calendar_type=calendar_type,
industry=industry,
business_size=business_size
)
# Extract context from the result for analysis
context = {
"step_results": final_calendar.get("step_results", {}),
"quality_scores": final_calendar.get("quality_scores", {})
}
execution_time = time.time() - start_time
logger.info(f"✅ Full 12-step process completed in {execution_time:.2f} seconds")
# Analyze results
step_results = context.get("step_results", {})
quality_scores = context.get("quality_scores", {})
logger.info("📊 Step Results Analysis:")
logger.info(f" Total steps executed: {len(step_results)}")
# Check each step
for step_key in sorted(step_results.keys()):
step_result = step_results[step_key]
status = step_result.get("status", "unknown")
quality_score = step_result.get("quality_score", 0.0)
validation_passed = step_result.get("validation_passed", False)
logger.info(f" {step_key}: status={status}, quality={quality_score:.2f}, validation_passed={validation_passed}")
if status == "failed" or status == "error":
logger.error(f" ❌ {step_key} failed with status: {status}")
error_message = step_result.get("error_message", "No error message")
logger.error(f" Error: {error_message}")
# Check Step 5 specifically
step_05_result = step_results.get("step_05", {})
if step_05_result:
step_05_status = step_05_result.get("status", "unknown")
step_05_quality = step_05_result.get("quality_score", 0.0)
step_05_validation = step_05_result.get("validation_passed", False)
logger.info(f"🎯 Step 5 Analysis:")
logger.info(f" Status: {step_05_status}")
logger.info(f" Quality Score: {step_05_quality:.2f}")
logger.info(f" Validation Passed: {step_05_validation}")
if step_05_status == "completed" and step_05_validation:
logger.info("✅ Step 5 FIX VERIFIED - Working correctly in full process!")
else:
logger.error("❌ Step 5 still has issues in full process")
else:
logger.error("❌ Step 5 result not found in step_results")
# Overall quality
overall_quality = sum(quality_scores.values()) / len(quality_scores) if quality_scores else 0.0
logger.info(f"📊 Overall Quality Score: {overall_quality:.2f}")
# Success criteria
completed_steps = sum(1 for result in step_results.values() if result.get("status") == "completed")
total_steps = len(step_results)
logger.info(f"📊 Process Summary:")
logger.info(f" Completed Steps: {completed_steps}/{total_steps}")
logger.info(f" Success Rate: {(completed_steps/total_steps)*100:.1f}%")
logger.info(f" Overall Quality: {overall_quality:.2f}")
if completed_steps == total_steps and overall_quality > 0.8:
logger.info("🎉 SUCCESS: Full 12-step process completed successfully!")
return True
else:
logger.error("❌ FAILURE: Full 12-step process had issues")
return False
except Exception as e:
logger.error(f"❌ Error in full 12-step process test: {str(e)}")
import traceback
logger.error(f"📋 Traceback: {traceback.format_exc()}")
return False
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the test
success = asyncio.run(test_full_12_step_process())
if success:
print("\n🎉 Test completed successfully!")
sys.exit(0)
else:
print("\n❌ Test failed!")
sys.exit(1)

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import asyncio
from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
async def test_gemini_direct():
gemini = GeminiGroundedProvider()
prompt = """
Research the topic "AI content generation" in the Technology industry for content creators audience. Provide a comprehensive analysis including:
1. Current trends and insights (2024-2025)
2. Key statistics and data points with sources
3. Industry expert opinions and quotes
4. Recent developments and news
5. Market analysis and forecasts
6. Best practices and case studies
7. Keyword analysis: primary, secondary, and long-tail opportunities
8. Competitor analysis: top players and content gaps
9. Content angle suggestions: 5 compelling angles for blog posts
Focus on factual, up-to-date information from credible sources.
Include specific data points, percentages, and recent developments.
Structure your response with clear sections for each analysis area.
"""
try:
result = await gemini.generate_grounded_content(
prompt=prompt,
content_type="research",
max_tokens=2000
)
print("=== GEMINI RESULT ===")
print(f"Type: {type(result)}")
print(f"Keys: {list(result.keys()) if isinstance(result, dict) else 'Not a dict'}")
if isinstance(result, dict):
print(f"Sources count: {len(result.get('sources', []))}")
print(f"Search queries count: {len(result.get('search_queries', []))}")
print(f"Has search widget: {bool(result.get('search_widget'))}")
print(f"Content length: {len(result.get('content', ''))}")
print("\n=== FIRST SOURCE ===")
sources = result.get('sources', [])
if sources:
print(f"Source: {sources[0]}")
print("\n=== SEARCH QUERIES (First 3) ===")
queries = result.get('search_queries', [])
for i, query in enumerate(queries[:3]):
print(f"{i+1}. {query}")
else:
print(f"Result is not a dict: {result}")
except Exception as e:
print(f"Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(test_gemini_direct())

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"""
Unit tests for GroundingContextEngine.
Tests the enhanced grounding metadata utilization functionality.
"""
import pytest
from typing import List
from models.blog_models import (
GroundingMetadata,
GroundingChunk,
GroundingSupport,
Citation,
BlogOutlineSection,
BlogResearchResponse,
ResearchSource,
)
from services.blog_writer.outline.grounding_engine import GroundingContextEngine
class TestGroundingContextEngine:
"""Test cases for GroundingContextEngine."""
def setup_method(self):
"""Set up test fixtures."""
self.engine = GroundingContextEngine()
# Create sample grounding chunks
self.sample_chunks = [
GroundingChunk(
title="AI Research Study 2025: Machine Learning Breakthroughs",
url="https://research.university.edu/ai-study-2025",
confidence_score=0.95
),
GroundingChunk(
title="Enterprise AI Implementation Guide",
url="https://techcorp.com/enterprise-ai-guide",
confidence_score=0.88
),
GroundingChunk(
title="Machine Learning Algorithms Explained",
url="https://blog.datascience.com/ml-algorithms",
confidence_score=0.82
),
GroundingChunk(
title="AI Ethics and Responsible Development",
url="https://ethics.org/ai-responsible-development",
confidence_score=0.90
),
GroundingChunk(
title="Personal Opinion on AI Trends",
url="https://personal-blog.com/ai-opinion",
confidence_score=0.65
)
]
# Create sample grounding supports
self.sample_supports = [
GroundingSupport(
confidence_scores=[0.92, 0.89],
grounding_chunk_indices=[0, 1],
segment_text="Recent research shows that artificial intelligence is transforming enterprise operations with significant improvements in efficiency and decision-making capabilities.",
start_index=0,
end_index=150
),
GroundingSupport(
confidence_scores=[0.85, 0.78],
grounding_chunk_indices=[2, 3],
segment_text="Machine learning algorithms are becoming more sophisticated, enabling better pattern recognition and predictive analytics in business applications.",
start_index=151,
end_index=300
),
GroundingSupport(
confidence_scores=[0.45, 0.52],
grounding_chunk_indices=[4],
segment_text="Some people think AI is overhyped and won't deliver on its promises.",
start_index=301,
end_index=400
)
]
# Create sample citations
self.sample_citations = [
Citation(
citation_type="expert_opinion",
start_index=0,
end_index=50,
text="AI research shows significant improvements in enterprise operations",
source_indices=[0],
reference="Source 1"
),
Citation(
citation_type="statistical_data",
start_index=51,
end_index=100,
text="85% of enterprises report improved efficiency with AI implementation",
source_indices=[1],
reference="Source 2"
),
Citation(
citation_type="research_study",
start_index=101,
end_index=150,
text="University study demonstrates 40% increase in decision-making accuracy",
source_indices=[0],
reference="Source 1"
)
]
# Create sample grounding metadata
self.sample_grounding_metadata = GroundingMetadata(
grounding_chunks=self.sample_chunks,
grounding_supports=self.sample_supports,
citations=self.sample_citations,
search_entry_point="AI trends and enterprise implementation",
web_search_queries=[
"AI trends 2025 enterprise",
"machine learning business applications",
"AI implementation best practices"
]
)
# Create sample outline section
self.sample_section = BlogOutlineSection(
id="s1",
heading="AI Implementation in Enterprise",
subheadings=["Benefits of AI", "Implementation Challenges", "Best Practices"],
key_points=["Improved efficiency", "Cost reduction", "Better decision making"],
references=[],
target_words=400,
keywords=["AI", "enterprise", "implementation", "machine learning"]
)
def test_extract_contextual_insights(self):
"""Test extraction of contextual insights from grounding metadata."""
insights = self.engine.extract_contextual_insights(self.sample_grounding_metadata)
# Should have all insight categories
expected_categories = [
'confidence_analysis', 'authority_analysis', 'temporal_analysis',
'content_relationships', 'citation_insights', 'search_intent_insights',
'quality_indicators'
]
for category in expected_categories:
assert category in insights
# Test confidence analysis
confidence_analysis = insights['confidence_analysis']
assert 'average_confidence' in confidence_analysis
assert 'high_confidence_count' in confidence_analysis
assert confidence_analysis['average_confidence'] > 0.0
# Test authority analysis
authority_analysis = insights['authority_analysis']
assert 'average_authority' in authority_analysis
assert 'high_authority_sources' in authority_analysis
assert 'authority_distribution' in authority_analysis
def test_extract_contextual_insights_empty_metadata(self):
"""Test extraction with empty grounding metadata."""
insights = self.engine.extract_contextual_insights(None)
# Should return empty insights structure
assert insights['confidence_analysis']['average_confidence'] == 0.0
assert insights['authority_analysis']['high_authority_sources'] == 0
assert insights['temporal_analysis']['recent_content'] == 0
def test_analyze_confidence_patterns(self):
"""Test confidence pattern analysis."""
confidence_analysis = self.engine._analyze_confidence_patterns(self.sample_grounding_metadata)
assert 'average_confidence' in confidence_analysis
assert 'high_confidence_count' in confidence_analysis
assert 'confidence_distribution' in confidence_analysis
# Should have reasonable confidence values
assert 0.0 <= confidence_analysis['average_confidence'] <= 1.0
assert confidence_analysis['high_confidence_count'] >= 0
def test_analyze_source_authority(self):
"""Test source authority analysis."""
authority_analysis = self.engine._analyze_source_authority(self.sample_grounding_metadata)
assert 'average_authority' in authority_analysis
assert 'high_authority_sources' in authority_analysis
assert 'authority_distribution' in authority_analysis
# Should have reasonable authority values
assert 0.0 <= authority_analysis['average_authority'] <= 1.0
assert authority_analysis['high_authority_sources'] >= 0
def test_analyze_temporal_relevance(self):
"""Test temporal relevance analysis."""
temporal_analysis = self.engine._analyze_temporal_relevance(self.sample_grounding_metadata)
assert 'recent_content' in temporal_analysis
assert 'trending_topics' in temporal_analysis
assert 'evergreen_content' in temporal_analysis
assert 'temporal_balance' in temporal_analysis
# Should have reasonable temporal values
assert temporal_analysis['recent_content'] >= 0
assert temporal_analysis['evergreen_content'] >= 0
assert temporal_analysis['temporal_balance'] in ['recent_heavy', 'evergreen_heavy', 'balanced', 'unknown']
def test_analyze_content_relationships(self):
"""Test content relationship analysis."""
relationships = self.engine._analyze_content_relationships(self.sample_grounding_metadata)
assert 'related_concepts' in relationships
assert 'content_gaps' in relationships
assert 'concept_coverage' in relationships
assert 'gap_count' in relationships
# Should have reasonable relationship values
assert isinstance(relationships['related_concepts'], list)
assert isinstance(relationships['content_gaps'], list)
assert relationships['concept_coverage'] >= 0
assert relationships['gap_count'] >= 0
def test_analyze_citation_patterns(self):
"""Test citation pattern analysis."""
citation_analysis = self.engine._analyze_citation_patterns(self.sample_grounding_metadata)
assert 'citation_types' in citation_analysis
assert 'total_citations' in citation_analysis
assert 'citation_density' in citation_analysis
assert 'citation_quality' in citation_analysis
# Should have reasonable citation values
assert citation_analysis['total_citations'] == len(self.sample_citations)
assert citation_analysis['citation_density'] >= 0.0
assert 0.0 <= citation_analysis['citation_quality'] <= 1.0
def test_analyze_search_intent(self):
"""Test search intent analysis."""
intent_analysis = self.engine._analyze_search_intent(self.sample_grounding_metadata)
assert 'intent_signals' in intent_analysis
assert 'user_questions' in intent_analysis
assert 'primary_intent' in intent_analysis
# Should have reasonable intent values
assert isinstance(intent_analysis['intent_signals'], list)
assert isinstance(intent_analysis['user_questions'], list)
assert intent_analysis['primary_intent'] in ['informational', 'comparison', 'transactional']
def test_assess_quality_indicators(self):
"""Test quality indicator assessment."""
quality_indicators = self.engine._assess_quality_indicators(self.sample_grounding_metadata)
assert 'overall_quality' in quality_indicators
assert 'quality_factors' in quality_indicators
assert 'quality_grade' in quality_indicators
# Should have reasonable quality values
assert 0.0 <= quality_indicators['overall_quality'] <= 1.0
assert isinstance(quality_indicators['quality_factors'], list)
assert quality_indicators['quality_grade'] in ['A', 'B', 'C', 'D', 'F']
def test_calculate_chunk_authority(self):
"""Test chunk authority calculation."""
# Test high authority chunk
high_authority_chunk = self.sample_chunks[0] # Research study
authority_score = self.engine._calculate_chunk_authority(high_authority_chunk)
assert 0.0 <= authority_score <= 1.0
assert authority_score > 0.5 # Should be high authority
# Test low authority chunk
low_authority_chunk = self.sample_chunks[4] # Personal opinion
authority_score = self.engine._calculate_chunk_authority(low_authority_chunk)
assert 0.0 <= authority_score <= 1.0
assert authority_score < 0.7 # Should be lower authority
def test_get_authority_sources(self):
"""Test getting high-authority sources."""
authority_sources = self.engine.get_authority_sources(self.sample_grounding_metadata)
# Should return list of tuples
assert isinstance(authority_sources, list)
# Each item should be (chunk, score) tuple
for chunk, score in authority_sources:
assert isinstance(chunk, GroundingChunk)
assert isinstance(score, float)
assert 0.0 <= score <= 1.0
# Should be sorted by authority score (descending)
if len(authority_sources) > 1:
for i in range(len(authority_sources) - 1):
assert authority_sources[i][1] >= authority_sources[i + 1][1]
def test_get_high_confidence_insights(self):
"""Test getting high-confidence insights."""
insights = self.engine.get_high_confidence_insights(self.sample_grounding_metadata)
# Should return list of insights
assert isinstance(insights, list)
# Each insight should be a string
for insight in insights:
assert isinstance(insight, str)
assert len(insight) > 0
def test_enhance_sections_with_grounding(self):
"""Test section enhancement with grounding insights."""
sections = [self.sample_section]
insights = self.engine.extract_contextual_insights(self.sample_grounding_metadata)
enhanced_sections = self.engine.enhance_sections_with_grounding(
sections, self.sample_grounding_metadata, insights
)
# Should return same number of sections
assert len(enhanced_sections) == len(sections)
# Enhanced section should have same basic structure
enhanced_section = enhanced_sections[0]
assert enhanced_section.id == self.sample_section.id
assert enhanced_section.heading == self.sample_section.heading
# Should have enhanced content
assert len(enhanced_section.subheadings) >= len(self.sample_section.subheadings)
assert len(enhanced_section.key_points) >= len(self.sample_section.key_points)
assert len(enhanced_section.keywords) >= len(self.sample_section.keywords)
def test_enhance_sections_with_empty_grounding(self):
"""Test section enhancement with empty grounding metadata."""
sections = [self.sample_section]
enhanced_sections = self.engine.enhance_sections_with_grounding(
sections, None, {}
)
# Should return original sections unchanged
assert len(enhanced_sections) == len(sections)
assert enhanced_sections[0].subheadings == self.sample_section.subheadings
assert enhanced_sections[0].key_points == self.sample_section.key_points
assert enhanced_sections[0].keywords == self.sample_section.keywords
def test_find_relevant_chunks(self):
"""Test finding relevant chunks for a section."""
relevant_chunks = self.engine._find_relevant_chunks(
self.sample_section, self.sample_grounding_metadata
)
# Should return list of relevant chunks
assert isinstance(relevant_chunks, list)
# Each chunk should be a GroundingChunk
for chunk in relevant_chunks:
assert isinstance(chunk, GroundingChunk)
def test_find_relevant_supports(self):
"""Test finding relevant supports for a section."""
relevant_supports = self.engine._find_relevant_supports(
self.sample_section, self.sample_grounding_metadata
)
# Should return list of relevant supports
assert isinstance(relevant_supports, list)
# Each support should be a GroundingSupport
for support in relevant_supports:
assert isinstance(support, GroundingSupport)
def test_extract_insight_from_segment(self):
"""Test insight extraction from segment text."""
# Test with valid segment
segment = "This is a comprehensive analysis of AI trends in enterprise applications."
insight = self.engine._extract_insight_from_segment(segment)
assert insight == segment
# Test with short segment
short_segment = "Short"
insight = self.engine._extract_insight_from_segment(short_segment)
assert insight is None
# Test with long segment
long_segment = "This is a very long segment that exceeds the maximum length limit and should be truncated appropriately to ensure it fits within the expected constraints and provides comprehensive coverage of the topic while maintaining readability and clarity for the intended audience."
insight = self.engine._extract_insight_from_segment(long_segment)
assert insight is not None
assert len(insight) <= 203 # 200 + "..."
assert insight.endswith("...")
def test_get_confidence_distribution(self):
"""Test confidence distribution calculation."""
confidences = [0.95, 0.88, 0.82, 0.90, 0.65]
distribution = self.engine._get_confidence_distribution(confidences)
assert 'high' in distribution
assert 'medium' in distribution
assert 'low' in distribution
# Should have reasonable distribution
total = distribution['high'] + distribution['medium'] + distribution['low']
assert total == len(confidences)
def test_calculate_temporal_balance(self):
"""Test temporal balance calculation."""
# Test recent heavy
balance = self.engine._calculate_temporal_balance(8, 2)
assert balance == 'recent_heavy'
# Test evergreen heavy
balance = self.engine._calculate_temporal_balance(2, 8)
assert balance == 'evergreen_heavy'
# Test balanced
balance = self.engine._calculate_temporal_balance(5, 5)
assert balance == 'balanced'
# Test empty
balance = self.engine._calculate_temporal_balance(0, 0)
assert balance == 'unknown'
def test_extract_related_concepts(self):
"""Test related concept extraction."""
text_list = [
"Artificial Intelligence is transforming Machine Learning applications",
"Deep Learning algorithms are improving Neural Network performance",
"Natural Language Processing is advancing AI capabilities"
]
concepts = self.engine._extract_related_concepts(text_list)
# Should extract capitalized concepts
assert isinstance(concepts, list)
assert len(concepts) > 0
# Should contain expected concepts
expected_concepts = ['Artificial', 'Intelligence', 'Machine', 'Learning', 'Deep', 'Neural', 'Network']
for concept in expected_concepts:
assert concept in concepts
def test_identify_content_gaps(self):
"""Test content gap identification."""
text_list = [
"The research shows significant improvements in AI applications",
"However, there is a lack of comprehensive studies on AI ethics",
"The gap in understanding AI bias remains unexplored",
"Current research does not cover all aspects of AI implementation"
]
gaps = self.engine._identify_content_gaps(text_list)
# Should identify gaps
assert isinstance(gaps, list)
assert len(gaps) > 0
def test_assess_citation_quality(self):
"""Test citation quality assessment."""
quality = self.engine._assess_citation_quality(self.sample_citations)
# Should have reasonable quality score
assert 0.0 <= quality <= 1.0
assert quality > 0.0 # Should have some quality
def test_determine_primary_intent(self):
"""Test primary intent determination."""
# Test informational intent
intent = self.engine._determine_primary_intent(['informational', 'informational', 'comparison'])
assert intent == 'informational'
# Test empty signals
intent = self.engine._determine_primary_intent([])
assert intent == 'informational'
def test_get_quality_grade(self):
"""Test quality grade calculation."""
# Test A grade
grade = self.engine._get_quality_grade(0.95)
assert grade == 'A'
# Test B grade
grade = self.engine._get_quality_grade(0.85)
assert grade == 'B'
# Test C grade
grade = self.engine._get_quality_grade(0.75)
assert grade == 'C'
# Test D grade
grade = self.engine._get_quality_grade(0.65)
assert grade == 'D'
# Test F grade
grade = self.engine._get_quality_grade(0.45)
assert grade == 'F'
if __name__ == '__main__':
pytest.main([__file__])

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#!/usr/bin/env python3
"""
Test script to debug the grounding data flow
"""
import asyncio
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.linkedin_service import LinkedInService
from models.linkedin_models import LinkedInPostRequest, GroundingLevel
async def test_grounding_flow():
"""Test the grounding data flow"""
try:
print("🔍 Testing grounding data flow...")
# Initialize the service
service = LinkedInService()
print("✅ LinkedInService initialized")
# Create a test request
request = LinkedInPostRequest(
topic="AI in healthcare transformation",
industry="Healthcare",
grounding_level=GroundingLevel.ENHANCED,
include_citations=True,
research_enabled=True,
search_engine="google",
max_length=2000
)
print("✅ Test request created")
# Generate post
print("🚀 Generating LinkedIn post...")
response = await service.generate_linkedin_post(request)
if response.success:
print("✅ Post generated successfully!")
print(f"📊 Research sources count: {len(response.research_sources) if response.research_sources else 0}")
print(f"📝 Citations count: {len(response.data.citations) if response.data.citations else 0}")
print(f"🔗 Source list: {response.data.source_list[:200] if response.data.source_list else 'None'}")
if response.research_sources:
print(f"📚 First research source: {response.research_sources[0]}")
print(f"📚 Research source types: {[type(s) for s in response.research_sources[:3]]}")
if response.data.citations:
print(f"📝 First citation: {response.data.citations[0]}")
else:
print(f"❌ Post generation failed: {response.error}")
except Exception as e:
print(f"❌ Error during test: {str(e)}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(test_grounding_flow())

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backend/test/test_grounding_integration.py Normal file
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"""
Test script for LinkedIn grounding integration.
This script tests the integration of the new grounding services:
- GoogleSearchService
- GeminiGroundedProvider
- CitationManager
- ContentQualityAnalyzer
- Enhanced LinkedInService
"""
import asyncio
import os
from datetime import datetime
from loguru import logger
# Set up environment variables for testing
os.environ.setdefault('GOOGLE_SEARCH_API_KEY', 'test_key')
os.environ.setdefault('GOOGLE_SEARCH_ENGINE_ID', 'test_engine_id')
os.environ.setdefault('GEMINI_API_KEY', 'test_gemini_key')
from services.linkedin_service import LinkedInService
from models.linkedin_models import (
LinkedInPostRequest, LinkedInArticleRequest, LinkedInCarouselRequest,
LinkedInVideoScriptRequest, LinkedInCommentResponseRequest,
GroundingLevel, SearchEngine, LinkedInTone, LinkedInPostType
)
async def test_grounding_integration():
"""Test the complete grounding integration."""
logger.info("Starting LinkedIn grounding integration test")
try:
# Initialize the enhanced LinkedIn service
linkedin_service = LinkedInService()
logger.info("LinkedIn service initialized successfully")
# Test 1: Basic post generation with grounding disabled
logger.info("\n=== Test 1: Basic Post Generation (No Grounding) ===")
basic_request = LinkedInPostRequest(
topic="AI in Marketing",
industry="Marketing",
post_type=LinkedInPostType.PROFESSIONAL,
tone=LinkedInTone.PROFESSIONAL,
research_enabled=False,
grounding_level=GroundingLevel.NONE,
include_citations=False
)
basic_response = await linkedin_service.generate_linkedin_post(basic_request)
logger.info(f"Basic post generation: {'SUCCESS' if basic_response.success else 'FAILED'}")
if basic_response.success:
logger.info(f"Content length: {basic_response.data.character_count}")
logger.info(f"Grounding enabled: {basic_response.data.grounding_enabled}")
# Test 2: Enhanced post generation with grounding enabled
logger.info("\n=== Test 2: Enhanced Post Generation (With Grounding) ===")
enhanced_request = LinkedInPostRequest(
topic="Digital Transformation in Healthcare",
industry="Healthcare",
post_type=LinkedInPostType.THOUGHT_LEADERSHIP,
tone=LinkedInTone.AUTHORITATIVE,
research_enabled=True,
search_engine=SearchEngine.GOOGLE,
grounding_level=GroundingLevel.ENHANCED,
include_citations=True,
max_length=2000
)
enhanced_response = await linkedin_service.generate_linkedin_post(enhanced_request)
logger.info(f"Enhanced post generation: {'SUCCESS' if enhanced_response.success else 'FAILED'}")
if enhanced_response.success:
logger.info(f"Content length: {enhanced_response.data.character_count}")
logger.info(f"Grounding enabled: {enhanced_response.data.grounding_enabled}")
logger.info(f"Research sources: {len(enhanced_response.research_sources)}")
logger.info(f"Citations: {len(enhanced_response.data.citations)}")
if enhanced_response.data.quality_metrics:
logger.info(f"Quality score: {enhanced_response.data.quality_metrics.overall_score:.2f}")
if enhanced_response.grounding_status:
logger.info(f"Grounding status: {enhanced_response.grounding_status['status']}")
# Test 3: Article generation with grounding
logger.info("\n=== Test 3: Article Generation (With Grounding) ===")
article_request = LinkedInArticleRequest(
topic="Future of Remote Work",
industry="Technology",
tone=LinkedInTone.EDUCATIONAL,
research_enabled=True,
search_engine=SearchEngine.GOOGLE,
grounding_level=GroundingLevel.ENHANCED,
include_citations=True,
word_count=1500
)
article_response = await linkedin_service.generate_linkedin_article(article_request)
logger.info(f"Article generation: {'SUCCESS' if article_response.success else 'FAILED'}")
if article_response.success:
logger.info(f"Word count: {article_response.data.word_count}")
logger.info(f"Grounding enabled: {article_response.data.grounding_enabled}")
logger.info(f"Research sources: {len(article_response.research_sources)}")
logger.info(f"Citations: {len(article_response.data.citations)}")
# Test 4: Carousel generation with grounding
logger.info("\n=== Test 4: Carousel Generation (With Grounding) ===")
carousel_request = LinkedInCarouselRequest(
topic="Cybersecurity Best Practices",
industry="Technology",
tone=LinkedInTone.EDUCATIONAL,
research_enabled=True,
search_engine=SearchEngine.GOOGLE,
grounding_level=GroundingLevel.ENHANCED,
include_citations=True,
number_of_slides=5
)
carousel_response = await linkedin_service.generate_linkedin_carousel(carousel_request)
logger.info(f"Carousel generation: {'SUCCESS' if carousel_response.success else 'FAILED'}")
if carousel_response.success:
logger.info(f"Number of slides: {len(carousel_response.data.slides)}")
logger.info(f"Grounding enabled: {carousel_response.data.grounding_enabled}")
logger.info(f"Research sources: {len(carousel_response.research_sources)}")
# Test 5: Video script generation with grounding
logger.info("\n=== Test 5: Video Script Generation (With Grounding) ===")
video_request = LinkedInVideoScriptRequest(
topic="AI Ethics in Business",
industry="Technology",
tone=LinkedInTone.EDUCATIONAL,
research_enabled=True,
search_engine=SearchEngine.GOOGLE,
grounding_level=GroundingLevel.ENHANCED,
include_citations=True,
video_duration=90
)
video_response = await linkedin_service.generate_linkedin_video_script(video_request)
logger.info(f"Video script generation: {'SUCCESS' if video_response.success else 'FAILED'}")
if video_response.success:
logger.info(f"Grounding enabled: {video_response.data.grounding_enabled}")
logger.info(f"Research sources: {len(video_response.research_sources)}")
logger.info(f"Citations: {len(video_response.data.citations)}")
# Test 6: Comment response generation
logger.info("\n=== Test 6: Comment Response Generation ===")
comment_request = LinkedInCommentResponseRequest(
original_comment="Great insights on AI implementation!",
post_context="Post about AI transformation in healthcare",
industry="Healthcare",
tone=LinkedInTone.FRIENDLY,
response_length="medium",
include_questions=True,
research_enabled=False,
grounding_level=GroundingLevel.BASIC
)
comment_response = await linkedin_service.generate_linkedin_comment_response(comment_request)
logger.info(f"Comment response generation: {'SUCCESS' if comment_response.success else 'FAILED'}")
if comment_response.success:
logger.info(f"Response length: {len(comment_response.response) if comment_response.response else 0}")
logger.info(f"Grounding enabled: {comment_response.grounding_status['status'] if comment_response.grounding_status else 'N/A'}")
logger.info("\n=== Integration Test Summary ===")
logger.info("All tests completed successfully!")
except Exception as e:
logger.error(f"Integration test failed: {str(e)}")
raise
async def test_individual_services():
"""Test individual service components."""
logger.info("\n=== Testing Individual Service Components ===")
try:
# Test Google Search Service
from services.research import GoogleSearchService
google_search = GoogleSearchService()
logger.info("GoogleSearchService initialized successfully")
# Test Citation Manager
from services.citation import CitationManager
citation_manager = CitationManager()
logger.info("CitationManager initialized successfully")
# Test Content Quality Analyzer
from services.quality import ContentQualityAnalyzer
quality_analyzer = ContentQualityAnalyzer()
logger.info("ContentQualityAnalyzer initialized successfully")
# Test Gemini Grounded Provider
from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
gemini_grounded = GeminiGroundedProvider()
logger.info("GeminiGroundedProvider initialized successfully")
logger.info("All individual services initialized successfully!")
except Exception as e:
logger.error(f"Service component test failed: {str(e)}")
raise
async def main():
"""Main test function."""
logger.info("Starting LinkedIn Grounding Integration Tests")
logger.info(f"Test timestamp: {datetime.now().isoformat()}")
try:
# Test individual services first
await test_individual_services()
# Test complete integration
await test_grounding_integration()
logger.info("\n🎉 All tests completed successfully!")
except Exception as e:
logger.error(f"Test suite failed: {str(e)}")
logger.error("Please check the error details above and ensure all services are properly configured.")
return 1
return 0
if __name__ == "__main__":
# Run the tests
exit_code = asyncio.run(main())
exit(exit_code)

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#!/usr/bin/env python3
"""
Test script for the hallucination detector service.
This script tests the hallucination detector functionality
without requiring the full FastAPI server to be running.
"""
import asyncio
import os
import sys
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from services.hallucination_detector import HallucinationDetector
async def test_hallucination_detector():
"""Test the hallucination detector with sample text."""
print("🧪 Testing Hallucination Detector")
print("=" * 50)
# Initialize detector
detector = HallucinationDetector()
# Test text with various types of claims
test_text = """
The Eiffel Tower is located in Paris, France. It was built in 1889 and stands 330 meters tall.
The tower was designed by Gustave Eiffel and is one of the most visited monuments in the world.
Our company increased sales by 25% last quarter and launched three new products.
The weather today is sunny with a temperature of 22 degrees Celsius.
"""
print(f"📝 Test Text:\n{test_text.strip()}\n")
try:
# Test claim extraction
print("🔍 Testing claim extraction...")
claims = await detector._extract_claims(test_text)
print(f"✅ Extracted {len(claims)} claims:")
for i, claim in enumerate(claims, 1):
print(f" {i}. {claim}")
print()
# Test full hallucination detection
print("🔍 Testing full hallucination detection...")
result = await detector.detect_hallucinations(test_text)
print(f"✅ Analysis completed:")
print(f" Overall Confidence: {result.overall_confidence:.2f}")
print(f" Total Claims: {result.total_claims}")
print(f" Supported: {result.supported_claims}")
print(f" Refuted: {result.refuted_claims}")
print(f" Insufficient: {result.insufficient_claims}")
print()
# Display individual claims
print("📊 Individual Claim Analysis:")
for i, claim in enumerate(result.claims, 1):
print(f"\n Claim {i}: {claim.text}")
print(f" Assessment: {claim.assessment}")
print(f" Confidence: {claim.confidence:.2f}")
print(f" Supporting Sources: {len(claim.supporting_sources)}")
print(f" Refuting Sources: {len(claim.refuting_sources)}")
if claim.supporting_sources:
print(" Supporting Sources:")
for j, source in enumerate(claim.supporting_sources[:2], 1): # Show first 2
print(f" {j}. {source.get('title', 'Untitled')} (Score: {source.get('score', 0):.2f})")
if claim.refuting_sources:
print(" Refuting Sources:")
for j, source in enumerate(claim.refuting_sources[:2], 1): # Show first 2
print(f" {j}. {source.get('title', 'Untitled')} (Score: {source.get('score', 0):.2f})")
print("\n✅ Test completed successfully!")
except Exception as e:
print(f"❌ Test failed with error: {str(e)}")
import traceback
traceback.print_exc()
async def test_health_check():
"""Test the health check functionality."""
print("\n🏥 Testing Health Check")
print("=" * 30)
detector = HallucinationDetector()
# Check API availability
exa_available = bool(detector.exa_api_key)
openai_available = bool(detector.openai_api_key)
print(f"Exa.ai API Available: {'✅' if exa_available else '❌'}")
print(f"OpenAI API Available: {'✅' if openai_available else '❌'}")
if not exa_available:
print("⚠️ Exa.ai API key not found. Set EXA_API_KEY environment variable.")
if not openai_available:
print("⚠️ OpenAI API key not found. Set OPENAI_API_KEY environment variable.")
if exa_available and openai_available:
print("✅ All APIs are available for full functionality.")
elif openai_available:
print("⚠️ Limited functionality available (claim extraction only).")
else:
print("❌ No APIs available. Only fallback functionality will work.")
def main():
"""Main test function."""
print("🚀 Hallucination Detector Test Suite")
print("=" * 50)
# Check environment variables
print("🔧 Environment Check:")
exa_key = os.getenv('EXA_API_KEY')
openai_key = os.getenv('OPENAI_API_KEY')
print(f"EXA_API_KEY: {'✅ Set' if exa_key else '❌ Not set'}")
print(f"OPENAI_API_KEY: {'✅ Set' if openai_key else '❌ Not set'}")
print()
# Run tests
asyncio.run(test_health_check())
asyncio.run(test_hallucination_detector())
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Test script for LinkedIn Image Generation API endpoints
"""
import asyncio
import aiohttp
import json
async def test_image_generation_api():
"""Test the LinkedIn image generation API endpoints"""
base_url = "http://localhost:8000"
print("🧪 Testing LinkedIn Image Generation API...")
print("=" * 50)
# Test 1: Health Check
print("\n1️⃣ Testing Health Check...")
async with aiohttp.ClientSession() as session:
async with session.get(f"{base_url}/api/linkedin/image-generation-health") as response:
if response.status == 200:
health_data = await response.json()
print(f"✅ Health Check: {health_data['status']}")
print(f" Services: {health_data['services']}")
print(f" Test Prompts: {health_data['test_prompts_generated']}")
else:
print(f"❌ Health Check Failed: {response.status}")
return
# Test 2: Generate Image Prompts
print("\n2️⃣ Testing Image Prompt Generation...")
prompt_data = {
"content_type": "post",
"topic": "AI in Marketing",
"industry": "Technology",
"content": "This is a test LinkedIn post about AI in marketing. It demonstrates the image generation capabilities."
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{base_url}/api/linkedin/generate-image-prompts",
json=prompt_data
) as response:
if response.status == 200:
prompts = await response.json()
print(f"✅ Generated {len(prompts)} image prompts:")
for i, prompt in enumerate(prompts, 1):
print(f" {i}. {prompt['style']}: {prompt['description']}")
# Test 3: Generate Image from First Prompt
print("\n3️⃣ Testing Image Generation...")
image_data = {
"prompt": prompts[0]['prompt'],
"content_context": {
"topic": prompt_data["topic"],
"industry": prompt_data["industry"],
"content_type": prompt_data["content_type"],
"content": prompt_data["content"],
"style": prompts[0]['style']
},
"aspect_ratio": "1:1"
}
async with session.post(
f"{base_url}/api/linkedin/generate-image",
json=image_data
) as img_response:
if img_response.status == 200:
result = await img_response.json()
if result.get('success'):
print(f"✅ Image Generated Successfully!")
print(f" Image ID: {result.get('image_id')}")
print(f" Style: {result.get('style')}")
print(f" Aspect Ratio: {result.get('aspect_ratio')}")
else:
print(f"❌ Image Generation Failed: {result.get('error')}")
else:
print(f"❌ Image Generation Request Failed: {img_response.status}")
error_text = await img_response.text()
print(f" Error: {error_text}")
else:
print(f"❌ Prompt Generation Failed: {response.status}")
error_text = await response.text()
print(f" Error: {error_text}")
if __name__ == "__main__":
print("🚀 Starting LinkedIn Image Generation API Tests...")
try:
asyncio.run(test_image_generation_api())
print("\n🎉 All tests completed!")
except Exception as e:
print(f"\n💥 Test failed with error: {e}")
import traceback
traceback.print_exc()

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#!/usr/bin/env python3
"""
Simple test script to verify import issues are fixed.
This script tests that all the required services can be imported and initialized
without import errors.
Usage:
python test_imports.py
"""
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
def test_imports():
"""Test that all required modules can be imported."""
print("🧪 Testing Imports...")
try:
print("📦 Testing LinkedIn Models...")
from models.linkedin_models import (
LinkedInPostRequest, LinkedInPostResponse, PostContent, ResearchSource,
LinkedInArticleRequest, LinkedInArticleResponse, ArticleContent,
LinkedInCarouselRequest, LinkedInCarouselResponse, CarouselContent, CarouselSlide,
LinkedInVideoScriptRequest, LinkedInVideoScriptResponse, VideoScript,
LinkedInCommentResponseRequest, LinkedInCommentResponseResult,
HashtagSuggestion, ImageSuggestion, Citation, ContentQualityMetrics,
GroundingLevel
)
print("✅ LinkedIn Models imported successfully")
except Exception as e:
print(f"❌ LinkedIn Models import failed: {e}")
return False
try:
print("📦 Testing Research Service...")
from services.research import GoogleSearchService
print("✅ Research Service imported successfully")
except Exception as e:
print(f"❌ Research Service import failed: {e}")
return False
try:
print("📦 Testing Citation Service...")
from services.citation import CitationManager
print("✅ Citation Service imported successfully")
except Exception as e:
print(f"❌ Citation Service import failed: {e}")
return False
try:
print("📦 Testing Quality Service...")
from services.quality import ContentQualityAnalyzer
print("✅ Quality Service imported successfully")
except Exception as e:
print(f"❌ Quality Service import failed: {e}")
return False
try:
print("📦 Testing LLM Providers...")
from services.llm_providers.gemini_provider import gemini_structured_json_response, gemini_text_response
print("✅ LLM Providers imported successfully")
except Exception as e:
print(f"❌ LLM Providers import failed: {e}")
return False
try:
print("📦 Testing Gemini Grounded Provider...")
from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
print("✅ Gemini Grounded Provider imported successfully")
except Exception as e:
print(f"❌ Gemini Grounded Provider import failed: {e}")
return False
try:
print("📦 Testing LinkedIn Service...")
from services.linkedin_service import LinkedInService
print("✅ LinkedIn Service imported successfully")
except Exception as e:
print(f"❌ LinkedIn Service import failed: {e}")
return False
print("\n🎉 All imports successful!")
return True
def test_service_initialization():
"""Test that services can be initialized without errors."""
print("\n🔧 Testing Service Initialization...")
try:
print("📦 Initializing LinkedIn Service...")
from services.linkedin_service import LinkedInService
service = LinkedInService()
print("✅ LinkedIn Service initialized successfully")
# Check which services are available
print(f" - Google Search: {'✅' if service.google_search else '❌'}")
print(f" - Gemini Grounded: {'✅' if service.gemini_grounded else '❌'}")
print(f" - Citation Manager: {'✅' if service.citation_manager else '❌'}")
print(f" - Quality Analyzer: {'✅' if service.quality_analyzer else '❌'}")
print(f" - Fallback Provider: {'✅' if service.fallback_provider else '❌'}")
return True
except Exception as e:
print(f"❌ LinkedIn Service initialization failed: {e}")
return False
def main():
"""Main test function."""
print("🚀 Starting Import Tests")
print("=" * 50)
# Test imports
import_success = test_imports()
if import_success:
# Test service initialization
init_success = test_service_initialization()
if init_success:
print("\n🎉 SUCCESS: All tests passed!")
print("✅ Import issues have been resolved")
print("✅ Services can be initialized")
print("✅ Ready for testing native grounding")
else:
print("\n⚠️ PARTIAL SUCCESS: Imports work but initialization failed")
print("💡 This may be due to missing dependencies or configuration")
else:
print("\n❌ FAILURE: Import tests failed")
print("💡 There are still import issues to resolve")
sys.exit(1)
if __name__ == "__main__":
main()

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"""
Test script for LinkedIn content generation endpoints.
This script tests the LinkedIn content generation functionality
to ensure proper integration and validation.
"""
import asyncio
import json
import time
from typing import Dict, Any
import sys
import os
# Add the backend directory to Python path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from models.linkedin_models import (
LinkedInPostRequest, LinkedInArticleRequest, LinkedInCarouselRequest,
LinkedInVideoScriptRequest, LinkedInCommentResponseRequest
)
from services.linkedin_service import linkedin_service
from loguru import logger
# Configure logger
logger.remove()
logger.add(sys.stdout, level="INFO", format="<level>{level}</level> | {message}")
async def test_post_generation():
"""Test LinkedIn post generation."""
logger.info("🧪 Testing LinkedIn Post Generation")
try:
request = LinkedInPostRequest(
topic="Artificial Intelligence in Healthcare",
industry="Healthcare",
post_type="thought_leadership",
tone="professional",
target_audience="Healthcare executives and AI professionals",
key_points=["AI diagnostics", "Patient outcomes", "Cost reduction", "Implementation challenges"],
include_hashtags=True,
include_call_to_action=True,
research_enabled=True,
search_engine="metaphor",
max_length=2000
)
start_time = time.time()
response = await linkedin_service.generate_post(request)
duration = time.time() - start_time
logger.info(f"✅ Post generation completed in {duration:.2f} seconds")
logger.info(f"Success: {response.success}")
if response.success and response.data:
logger.info(f"Content length: {response.data.character_count} characters")
logger.info(f"Hashtags generated: {len(response.data.hashtags)}")
logger.info(f"Call-to-action: {response.data.call_to_action is not None}")
logger.info(f"Research sources: {len(response.research_sources)}")
# Preview content (first 200 chars)
content_preview = response.data.content[:200] + "..." if len(response.data.content) > 200 else response.data.content
logger.info(f"Content preview: {content_preview}")
else:
logger.error(f"Post generation failed: {response.error}")
return response.success
except Exception as e:
logger.error(f"❌ Error testing post generation: {str(e)}")
return False
async def test_article_generation():
"""Test LinkedIn article generation."""
logger.info("🧪 Testing LinkedIn Article Generation")
try:
request = LinkedInArticleRequest(
topic="Digital Transformation in Manufacturing",
industry="Manufacturing",
tone="professional",
target_audience="Manufacturing leaders and technology professionals",
key_sections=["Current challenges", "Technology solutions", "Implementation strategies", "Future outlook"],
include_images=True,
seo_optimization=True,
research_enabled=True,
search_engine="metaphor",
word_count=1500
)
start_time = time.time()
response = await linkedin_service.generate_article(request)
duration = time.time() - start_time
logger.info(f"✅ Article generation completed in {duration:.2f} seconds")
logger.info(f"Success: {response.success}")
if response.success and response.data:
logger.info(f"Word count: {response.data.word_count}")
logger.info(f"Sections: {len(response.data.sections)}")
logger.info(f"Reading time: {response.data.reading_time} minutes")
logger.info(f"Image suggestions: {len(response.data.image_suggestions)}")
logger.info(f"SEO metadata: {response.data.seo_metadata is not None}")
logger.info(f"Research sources: {len(response.research_sources)}")
# Preview title
logger.info(f"Article title: {response.data.title}")
else:
logger.error(f"Article generation failed: {response.error}")
return response.success
except Exception as e:
logger.error(f"❌ Error testing article generation: {str(e)}")
return False
async def test_carousel_generation():
"""Test LinkedIn carousel generation."""
logger.info("🧪 Testing LinkedIn Carousel Generation")
try:
request = LinkedInCarouselRequest(
topic="5 Ways to Improve Team Productivity",
industry="Business Management",
slide_count=8,
tone="professional",
target_audience="Team leaders and managers",
key_takeaways=["Clear communication", "Goal setting", "Tool optimization", "Regular feedback", "Work-life balance"],
include_cover_slide=True,
include_cta_slide=True,
visual_style="modern"
)
start_time = time.time()
response = await linkedin_service.generate_carousel(request)
duration = time.time() - start_time
logger.info(f"✅ Carousel generation completed in {duration:.2f} seconds")
logger.info(f"Success: {response.success}")
if response.success and response.data:
logger.info(f"Slide count: {len(response.data.slides)}")
logger.info(f"Carousel title: {response.data.title}")
logger.info(f"Design guidelines: {bool(response.data.design_guidelines)}")
# Preview first slide
if response.data.slides:
first_slide = response.data.slides[0]
logger.info(f"First slide title: {first_slide.title}")
else:
logger.error(f"Carousel generation failed: {response.error}")
return response.success
except Exception as e:
logger.error(f"❌ Error testing carousel generation: {str(e)}")
return False
async def test_video_script_generation():
"""Test LinkedIn video script generation."""
logger.info("🧪 Testing LinkedIn Video Script Generation")
try:
request = LinkedInVideoScriptRequest(
topic="Quick tips for remote team management",
industry="Human Resources",
video_length=90,
tone="conversational",
target_audience="Remote team managers",
key_messages=["Communication tools", "Regular check-ins", "Team building", "Performance tracking"],
include_hook=True,
include_captions=True
)
start_time = time.time()
response = await linkedin_service.generate_video_script(request)
duration = time.time() - start_time
logger.info(f"✅ Video script generation completed in {duration:.2f} seconds")
logger.info(f"Success: {response.success}")
if response.success and response.data:
logger.info(f"Hook: {bool(response.data.hook)}")
logger.info(f"Main content scenes: {len(response.data.main_content)}")
logger.info(f"Conclusion: {bool(response.data.conclusion)}")
logger.info(f"Thumbnail suggestions: {len(response.data.thumbnail_suggestions)}")
logger.info(f"Captions: {bool(response.data.captions)}")
# Preview hook
if response.data.hook:
hook_preview = response.data.hook[:100] + "..." if len(response.data.hook) > 100 else response.data.hook
logger.info(f"Hook preview: {hook_preview}")
else:
logger.error(f"Video script generation failed: {response.error}")
return response.success
except Exception as e:
logger.error(f"❌ Error testing video script generation: {str(e)}")
return False
async def test_comment_response_generation():
"""Test LinkedIn comment response generation."""
logger.info("🧪 Testing LinkedIn Comment Response Generation")
try:
request = LinkedInCommentResponseRequest(
original_post="Just published an article about AI transformation in healthcare. The potential for improving patient outcomes while reducing costs is incredible. Healthcare leaders need to start preparing for this shift now.",
comment="Great insights! How do you see this affecting smaller healthcare providers who might not have the resources for large AI implementations?",
response_type="value_add",
tone="professional",
include_question=True,
brand_voice="Expert but approachable, data-driven and helpful"
)
start_time = time.time()
response = await linkedin_service.generate_comment_response(request)
duration = time.time() - start_time
logger.info(f"✅ Comment response generation completed in {duration:.2f} seconds")
logger.info(f"Success: {response.success}")
if response.success and response.response:
logger.info(f"Primary response length: {len(response.response)} characters")
logger.info(f"Alternative responses: {len(response.alternative_responses)}")
logger.info(f"Tone analysis: {bool(response.tone_analysis)}")
# Preview response
response_preview = response.response[:150] + "..." if len(response.response) > 150 else response.response
logger.info(f"Response preview: {response_preview}")
if response.tone_analysis:
logger.info(f"Detected sentiment: {response.tone_analysis.get('sentiment', 'unknown')}")
else:
logger.error(f"Comment response generation failed: {response.error}")
return response.success
except Exception as e:
logger.error(f"❌ Error testing comment response generation: {str(e)}")
return False
async def test_error_handling():
"""Test error handling with invalid requests."""
logger.info("🧪 Testing Error Handling")
try:
# Test with empty topic
request = LinkedInPostRequest(
topic="", # Empty topic should trigger validation error
industry="Technology",
)
response = await linkedin_service.generate_post(request)
# Should still handle gracefully
if not response.success:
logger.info("✅ Error handling working correctly for invalid input")
return True
else:
logger.warning("⚠️ Expected error handling but got successful response")
return False
except Exception as e:
logger.error(f"❌ Error in error handling test: {str(e)}")
return False
async def run_all_tests():
"""Run all LinkedIn content generation tests."""
logger.info("🚀 Starting LinkedIn Content Generation Tests")
logger.info("=" * 60)
test_results = {}
# Run individual tests
test_results["post_generation"] = await test_post_generation()
logger.info("-" * 40)
test_results["article_generation"] = await test_article_generation()
logger.info("-" * 40)
test_results["carousel_generation"] = await test_carousel_generation()
logger.info("-" * 40)
test_results["video_script_generation"] = await test_video_script_generation()
logger.info("-" * 40)
test_results["comment_response_generation"] = await test_comment_response_generation()
logger.info("-" * 40)
test_results["error_handling"] = await test_error_handling()
logger.info("-" * 40)
# Summary
logger.info("📊 Test Results Summary")
logger.info("=" * 60)
passed = sum(test_results.values())
total = len(test_results)
for test_name, result in test_results.items():
status = "✅ PASSED" if result else "❌ FAILED"
logger.info(f"{test_name}: {status}")
logger.info(f"\nOverall: {passed}/{total} tests passed ({(passed/total)*100:.1f}%)")
if passed == total:
logger.info("🎉 All tests passed! LinkedIn content generation is working correctly.")
else:
logger.warning(f"⚠️ {total - passed} test(s) failed. Please check the implementation.")
return passed == total
if __name__ == "__main__":
# Run the tests
success = asyncio.run(run_all_tests())
if success:
logger.info("\n✅ LinkedIn content generation migration completed successfully!")
logger.info("The FastAPI endpoints are ready for use.")
else:
logger.error("\n❌ Some tests failed. Please review the implementation.")
# Print API endpoint information
logger.info("\n📡 Available LinkedIn Content Generation Endpoints:")
logger.info("- POST /api/linkedin/generate-post")
logger.info("- POST /api/linkedin/generate-article")
logger.info("- POST /api/linkedin/generate-carousel")
logger.info("- POST /api/linkedin/generate-video-script")
logger.info("- POST /api/linkedin/generate-comment-response")
logger.info("- GET /api/linkedin/health")
logger.info("- GET /api/linkedin/content-types")
logger.info("- GET /api/linkedin/usage-stats")

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#!/usr/bin/env python3
"""
Test Script for LinkedIn Image Generation Infrastructure
This script tests the basic functionality of the LinkedIn image generation services
to ensure they are properly initialized and can perform basic operations.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_path = Path(__file__).parent
sys.path.insert(0, str(backend_path))
from loguru import logger
# Configure logging
logger.remove()
logger.add(sys.stdout, colorize=True, format="<level>{level}</level>| {message}")
async def test_linkedin_image_infrastructure():
"""Test the LinkedIn image generation infrastructure."""
logger.info("🧪 Testing LinkedIn Image Generation Infrastructure")
logger.info("=" * 60)
try:
# Test 1: Import LinkedIn Image Services
logger.info("📦 Test 1: Importing LinkedIn Image Services...")
from services.linkedin.image_generation import (
LinkedInImageGenerator,
LinkedInImageEditor,
LinkedInImageStorage
)
from services.linkedin.image_prompts import LinkedInPromptGenerator
logger.success("✅ All LinkedIn image services imported successfully")
# Test 2: Initialize Services
logger.info("🔧 Test 2: Initializing LinkedIn Image Services...")
# Initialize services (without API keys for testing)
image_generator = LinkedInImageGenerator()
image_editor = LinkedInImageEditor()
image_storage = LinkedInImageStorage()
prompt_generator = LinkedInPromptGenerator()
logger.success("✅ All LinkedIn image services initialized successfully")
# Test 3: Test Prompt Generation (without API calls)
logger.info("📝 Test 3: Testing Prompt Generation Logic...")
# Test content context
test_content = {
'topic': 'AI in Marketing',
'industry': 'Technology',
'content_type': 'post',
'content': 'Exploring how artificial intelligence is transforming modern marketing strategies.'
}
# Test fallback prompt generation
fallback_prompts = prompt_generator._get_fallback_prompts(test_content, "1:1")
if len(fallback_prompts) == 3:
logger.success(f"✅ Fallback prompt generation working: {len(fallback_prompts)} prompts created")
for i, prompt in enumerate(fallback_prompts):
logger.info(f" Prompt {i+1}: {prompt['style']} - {prompt['description']}")
else:
logger.error(f"❌ Fallback prompt generation failed: expected 3, got {len(fallback_prompts)}")
# Test 4: Test Image Storage Directory Creation
logger.info("📁 Test 4: Testing Image Storage Directory Creation...")
# Check if storage directories were created
storage_path = image_storage.base_storage_path
if storage_path.exists():
logger.success(f"✅ Storage base directory created: {storage_path}")
# Check subdirectories
for subdir in ['images', 'metadata', 'temp']:
subdir_path = storage_path / subdir
if subdir_path.exists():
logger.info(f" ✅ {subdir} directory exists: {subdir_path}")
else:
logger.warning(f" ⚠️ {subdir} directory missing: {subdir_path}")
else:
logger.error(f"❌ Storage base directory not created: {storage_path}")
# Test 5: Test Service Methods
logger.info("⚙️ Test 5: Testing Service Method Signatures...")
# Test image generator methods
if hasattr(image_generator, 'generate_image'):
logger.success("✅ LinkedInImageGenerator.generate_image method exists")
else:
logger.error("❌ LinkedInImageGenerator.generate_image method missing")
if hasattr(image_editor, 'edit_image_conversationally'):
logger.success("✅ LinkedInImageEditor.edit_image_conversationally method exists")
else:
logger.error("❌ LinkedInImageEditor.edit_image_conversationally method missing")
if hasattr(image_storage, 'store_image'):
logger.success("✅ LinkedInImageStorage.store_image method exists")
else:
logger.error("❌ LinkedInImageStorage.store_image method missing")
if hasattr(prompt_generator, 'generate_three_prompts'):
logger.success("✅ LinkedInPromptGenerator.generate_three_prompts method exists")
else:
logger.error("❌ LinkedInPromptGenerator.generate_three_prompts method missing")
# Test 6: Test Prompt Enhancement
logger.info("🎨 Test 6: Testing Prompt Enhancement Logic...")
test_prompt = {
'style': 'Professional',
'prompt': 'Create a business image',
'description': 'Professional style'
}
enhanced_prompt = prompt_generator._enhance_prompt_for_linkedin(
test_prompt, test_content, "1:1", 0
)
if enhanced_prompt and 'enhanced_at' in enhanced_prompt:
logger.success("✅ Prompt enhancement working")
logger.info(f" Enhanced prompt length: {len(enhanced_prompt['prompt'])} characters")
else:
logger.error("❌ Prompt enhancement failed")
# Test 7: Test Image Validation Logic
logger.info("🔍 Test 7: Testing Image Validation Logic...")
# Test aspect ratio validation
valid_ratios = [(1024, 1024), (1600, 900), (1200, 1600)]
invalid_ratios = [(500, 500), (2000, 500)]
for width, height in valid_ratios:
if image_generator._is_aspect_ratio_suitable(width, height):
logger.info(f" ✅ Valid ratio {width}:{height} correctly identified")
else:
logger.warning(f" ⚠️ Valid ratio {width}:{height} incorrectly rejected")
for width, height in invalid_ratios:
if not image_generator._is_aspect_ratio_suitable(width, height):
logger.info(f" ✅ Invalid ratio {width}:{height} correctly rejected")
else:
logger.warning(f" ⚠️ Invalid ratio {width}:{height} incorrectly accepted")
logger.info("=" * 60)
logger.success("🎉 LinkedIn Image Generation Infrastructure Test Completed Successfully!")
return True
except ImportError as e:
logger.error(f"❌ Import Error: {e}")
logger.error("This usually means there's an issue with the module structure or dependencies")
return False
except Exception as e:
logger.error(f"❌ Test Failed: {e}")
logger.error(f"Error type: {type(e).__name__}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting LinkedIn Image Generation Infrastructure Tests")
success = await test_linkedin_image_infrastructure()
if success:
logger.success("✅ All tests passed! The infrastructure is ready for use.")
sys.exit(0)
else:
logger.error("❌ Some tests failed. Please check the errors above.")
sys.exit(1)
if __name__ == "__main__":
# Run the async test
asyncio.run(main())

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#!/usr/bin/env python3
"""
Test Script for LinkedIn Content Generation Keyword Fix
This script tests the fixed keyword processing by calling the LinkedIn content generation
endpoint directly and capturing detailed logs to analyze API usage patterns.
"""
import asyncio
import json
import time
import logging
from datetime import datetime
from typing import Dict, Any
import sys
import os
# Add the backend directory to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
# Configure detailed logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(f'test_linkedin_keyword_fix_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
# Import the LinkedIn service
from services.linkedin_service import LinkedInService
from models.linkedin_models import LinkedInPostRequest, LinkedInPostType, LinkedInTone, GroundingLevel, SearchEngine
class LinkedInKeywordTest:
"""Test class for LinkedIn keyword processing fix."""
def __init__(self):
self.linkedin_service = LinkedInService()
self.test_results = []
self.api_call_count = 0
self.start_time = None
def log_api_call(self, endpoint: str, duration: float, success: bool):
"""Log API call details."""
self.api_call_count += 1
logger.info(f"API Call #{self.api_call_count}: {endpoint} - Duration: {duration:.2f}s - Success: {success}")
async def test_keyword_phrase(self, phrase: str, test_name: str) -> Dict[str, Any]:
"""Test a specific keyword phrase."""
logger.info(f"\n{'='*60}")
logger.info(f"TESTING: {test_name}")
logger.info(f"KEYWORD PHRASE: '{phrase}'")
logger.info(f"{'='*60}")
test_start = time.time()
try:
# Create the request
request = LinkedInPostRequest(
topic=phrase,
industry="Technology",
post_type=LinkedInPostType.PROFESSIONAL,
tone=LinkedInTone.PROFESSIONAL,
grounding_level=GroundingLevel.ENHANCED,
search_engine=SearchEngine.GOOGLE,
research_enabled=True,
include_citations=True,
max_length=1000
)
logger.info(f"Request created: {request.topic}")
logger.info(f"Research enabled: {request.research_enabled}")
logger.info(f"Search engine: {request.search_engine}")
logger.info(f"Grounding level: {request.grounding_level}")
# Call the LinkedIn service
logger.info("Calling LinkedIn service...")
response = await self.linkedin_service.generate_linkedin_post(request)
test_duration = time.time() - test_start
self.log_api_call("LinkedIn Post Generation", test_duration, response.success)
# Analyze the response
result = {
"test_name": test_name,
"keyword_phrase": phrase,
"success": response.success,
"duration": test_duration,
"api_calls": self.api_call_count,
"error": response.error if not response.success else None,
"content_length": len(response.data.content) if response.success and response.data else 0,
"sources_count": len(response.research_sources) if response.success and response.research_sources else 0,
"citations_count": len(response.data.citations) if response.success and response.data and response.data.citations else 0,
"grounding_status": response.grounding_status if response.success else None,
"generation_metadata": response.generation_metadata if response.success else None
}
if response.success:
logger.info(f"✅ SUCCESS: Generated {result['content_length']} characters")
logger.info(f"📊 Sources: {result['sources_count']}, Citations: {result['citations_count']}")
logger.info(f"⏱️ Total duration: {test_duration:.2f}s")
logger.info(f"🔢 API calls made: {self.api_call_count}")
# Log content preview
if response.data and response.data.content:
content_preview = response.data.content[:200] + "..." if len(response.data.content) > 200 else response.data.content
logger.info(f"📝 Content preview: {content_preview}")
# Log grounding status
if response.grounding_status:
logger.info(f"🔍 Grounding status: {response.grounding_status}")
else:
logger.error(f"❌ FAILED: {response.error}")
return result
except Exception as e:
test_duration = time.time() - test_start
logger.error(f"❌ EXCEPTION in {test_name}: {str(e)}")
self.log_api_call("LinkedIn Post Generation", test_duration, False)
return {
"test_name": test_name,
"keyword_phrase": phrase,
"success": False,
"duration": test_duration,
"api_calls": self.api_call_count,
"error": str(e),
"content_length": 0,
"sources_count": 0,
"citations_count": 0,
"grounding_status": None,
"generation_metadata": None
}
async def run_comprehensive_test(self):
"""Run comprehensive tests for keyword processing."""
logger.info("🚀 Starting LinkedIn Keyword Processing Test Suite")
logger.info(f"Test started at: {datetime.now()}")
self.start_time = time.time()
# Test cases
test_cases = [
{
"phrase": "ALwrity content generation",
"name": "Single Phrase Test (Should be preserved as-is)"
},
{
"phrase": "AI tools, content creation, marketing automation",
"name": "Comma-Separated Test (Should be split by commas)"
},
{
"phrase": "LinkedIn content strategy",
"name": "Another Single Phrase Test"
},
{
"phrase": "social media, digital marketing, brand awareness",
"name": "Another Comma-Separated Test"
}
]
# Run all tests
for test_case in test_cases:
result = await self.test_keyword_phrase(
test_case["phrase"],
test_case["name"]
)
self.test_results.append(result)
# Reset API call counter for next test
self.api_call_count = 0
# Small delay between tests
await asyncio.sleep(2)
# Generate summary report
self.generate_summary_report()
def generate_summary_report(self):
"""Generate a comprehensive summary report."""
total_time = time.time() - self.start_time
logger.info(f"\n{'='*80}")
logger.info("📊 COMPREHENSIVE TEST SUMMARY REPORT")
logger.info(f"{'='*80}")
logger.info(f"🕐 Total test duration: {total_time:.2f} seconds")
logger.info(f"🧪 Total tests run: {len(self.test_results)}")
successful_tests = [r for r in self.test_results if r["success"]]
failed_tests = [r for r in self.test_results if not r["success"]]
logger.info(f"✅ Successful tests: {len(successful_tests)}")
logger.info(f"❌ Failed tests: {len(failed_tests)}")
if successful_tests:
avg_duration = sum(r["duration"] for r in successful_tests) / len(successful_tests)
avg_content_length = sum(r["content_length"] for r in successful_tests) / len(successful_tests)
avg_sources = sum(r["sources_count"] for r in successful_tests) / len(successful_tests)
avg_citations = sum(r["citations_count"] for r in successful_tests) / len(successful_tests)
logger.info(f"📈 Average generation time: {avg_duration:.2f}s")
logger.info(f"📝 Average content length: {avg_content_length:.0f} characters")
logger.info(f"🔍 Average sources found: {avg_sources:.1f}")
logger.info(f"📚 Average citations: {avg_citations:.1f}")
# Detailed results
logger.info(f"\n📋 DETAILED TEST RESULTS:")
for i, result in enumerate(self.test_results, 1):
status = "✅ PASS" if result["success"] else "❌ FAIL"
logger.info(f"{i}. {status} - {result['test_name']}")
logger.info(f" Phrase: '{result['keyword_phrase']}'")
logger.info(f" Duration: {result['duration']:.2f}s")
if result["success"]:
logger.info(f" Content: {result['content_length']} chars, Sources: {result['sources_count']}, Citations: {result['citations_count']}")
else:
logger.info(f" Error: {result['error']}")
# API Usage Analysis
logger.info(f"\n🔍 API USAGE ANALYSIS:")
total_api_calls = sum(r["api_calls"] for r in self.test_results)
logger.info(f"Total API calls across all tests: {total_api_calls}")
if successful_tests:
avg_api_calls = sum(r["api_calls"] for r in successful_tests) / len(successful_tests)
logger.info(f"Average API calls per successful test: {avg_api_calls:.1f}")
# Save detailed results to JSON file
report_data = {
"test_summary": {
"total_duration": total_time,
"total_tests": len(self.test_results),
"successful_tests": len(successful_tests),
"failed_tests": len(failed_tests),
"total_api_calls": total_api_calls
},
"test_results": self.test_results,
"timestamp": datetime.now().isoformat()
}
report_filename = f"linkedin_keyword_test_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
with open(report_filename, 'w') as f:
json.dump(report_data, f, indent=2, default=str)
logger.info(f"📄 Detailed report saved to: {report_filename}")
logger.info(f"{'='*80}")
async def main():
"""Main test execution function."""
try:
test_suite = LinkedInKeywordTest()
await test_suite.run_comprehensive_test()
except Exception as e:
logger.error(f"❌ Test suite failed: {str(e)}")
raise
if __name__ == "__main__":
print("🚀 Starting LinkedIn Keyword Processing Test Suite")
print("This will test the keyword fix and analyze API usage patterns...")
print("=" * 60)
asyncio.run(main())

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backend/test/test_linkedin_service.py Normal file
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#!/usr/bin/env python3
"""
Test script for LinkedIn service functionality.
This script tests that the LinkedIn service can be initialized and
basic functionality works without errors.
Usage:
python test_linkedin_service.py
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from loguru import logger
from models.linkedin_models import LinkedInPostRequest, GroundingLevel
from services.linkedin_service import LinkedInService
async def test_linkedin_service():
"""Test the LinkedIn service functionality."""
try:
logger.info("🧪 Testing LinkedIn Service Functionality")
# Initialize the service
logger.info("📦 Initializing LinkedIn Service...")
service = LinkedInService()
logger.info("✅ LinkedIn Service initialized successfully")
# Create a test request
test_request = LinkedInPostRequest(
topic="AI in Marketing",
industry="Technology",
tone="professional",
max_length=500,
target_audience="Marketing professionals",
key_points=["AI automation", "Personalization", "ROI improvement"],
research_enabled=True,
search_engine="google",
grounding_level=GroundingLevel.BASIC,
include_citations=True
)
logger.info("📝 Testing LinkedIn Post Generation...")
# Test post generation
response = await service.generate_linkedin_post(test_request)
if response.success:
logger.info("✅ LinkedIn post generation successful")
logger.info(f"📊 Content length: {len(response.data.content)} characters")
logger.info(f"🔗 Sources: {len(response.research_sources)}")
logger.info(f"📚 Citations: {len(response.data.citations)}")
logger.info(f"🏆 Quality score: {response.data.quality_metrics.overall_score if response.data.quality_metrics else 'N/A'}")
# Display a snippet of the generated content
content_preview = response.data.content[:200] + "..." if len(response.data.content) > 200 else response.data.content
logger.info(f"📄 Content preview: {content_preview}")
else:
logger.error(f"❌ LinkedIn post generation failed: {response.error}")
return False
logger.info("🎉 LinkedIn service test completed successfully!")
return True
except Exception as e:
logger.error(f"❌ LinkedIn service test failed: {str(e)}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting LinkedIn Service Test")
logger.info("=" * 50)
success = await test_linkedin_service()
if success:
logger.info("\n🎉 SUCCESS: LinkedIn service is working correctly!")
logger.info("✅ Service initialization successful")
logger.info("✅ Post generation working")
logger.info("✅ Ready for production use")
else:
logger.error("\n❌ FAILURE: LinkedIn service test failed")
sys.exit(1)
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(
sys.stderr,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
level="INFO"
)
# Run the test
asyncio.run(main())

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backend/test/test_native_grounding.py Normal file
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#!/usr/bin/env python3
"""
Test script for native Google Search grounding implementation.
This script tests the new GeminiGroundedProvider that uses native Google Search
grounding instead of custom search implementation.
Usage:
python test_native_grounding.py
"""
import asyncio
import os
import sys
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from loguru import logger
from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
async def test_native_grounding():
"""Test the native Google Search grounding functionality."""
try:
logger.info("🧪 Testing Native Google Search Grounding")
# Check if GEMINI_API_KEY is set
if not os.getenv('GEMINI_API_KEY'):
logger.error("❌ GEMINI_API_KEY environment variable not set")
logger.info("Please set GEMINI_API_KEY to test native grounding")
return False
# Initialize the grounded provider
logger.info("🔧 Initializing Gemini Grounded Provider...")
provider = GeminiGroundedProvider()
logger.info("✅ Provider initialized successfully")
# Test 1: Basic grounded content generation
logger.info("\n📝 Test 1: Basic LinkedIn Post Generation")
test_prompt = "Write a professional LinkedIn post about the latest AI trends in 2025"
result = await provider.generate_grounded_content(
prompt=test_prompt,
content_type="linkedin_post",
temperature=0.7,
max_tokens=500
)
if result and 'content' in result:
logger.info("✅ Content generated successfully")
logger.info(f"📊 Content length: {len(result['content'])} characters")
logger.info(f"🔗 Sources found: {len(result.get('sources', []))}")
logger.info(f"📚 Citations found: {len(result.get('citations', []))}")
# Display the generated content
logger.info("\n📄 Generated Content:")
logger.info("-" * 50)
logger.info(result['content'][:500] + "..." if len(result['content']) > 500 else result['content'])
logger.info("-" * 50)
# Display sources if available
if result.get('sources'):
logger.info("\n🔗 Sources:")
for i, source in enumerate(result['sources']):
logger.info(f" {i+1}. {source.get('title', 'Unknown')}")
logger.info(f" URL: {source.get('url', 'N/A')}")
# Display search queries if available
if result.get('search_queries'):
logger.info(f"\n🔍 Search Queries Used: {result['search_queries']}")
# Display grounding metadata info
if result.get('grounding_metadata'):
logger.info("✅ Grounding metadata found")
else:
logger.warning("⚠️ No grounding metadata found")
else:
logger.error("❌ Content generation failed")
if 'error' in result:
logger.error(f"Error: {result['error']}")
return False
# Test 2: Article generation
logger.info("\n📝 Test 2: LinkedIn Article Generation")
article_prompt = "Create a comprehensive article about sustainable business practices in tech companies"
article_result = await provider.generate_grounded_content(
prompt=article_prompt,
content_type="linkedin_article",
temperature=0.7,
max_tokens=1000
)
if article_result and 'content' in article_result:
logger.info("✅ Article generated successfully")
logger.info(f"📊 Article length: {len(article_result['content'])} characters")
logger.info(f"🔗 Sources: {len(article_result.get('sources', []))}")
# Check for article-specific processing
if 'title' in article_result:
logger.info(f"📰 Article title: {article_result['title']}")
if 'word_count' in article_result:
logger.info(f"📊 Word count: {article_result['word_count']}")
else:
logger.error("❌ Article generation failed")
return False
# Test 3: Content quality assessment
logger.info("\n📝 Test 3: Content Quality Assessment")
if result.get('content') and result.get('sources'):
quality_metrics = provider.assess_content_quality(
content=result['content'],
sources=result['sources']
)
logger.info("✅ Quality assessment completed")
logger.info(f"📊 Overall score: {quality_metrics.get('overall_score', 'N/A')}")
logger.info(f"🔗 Source coverage: {quality_metrics.get('source_coverage', 'N/A')}")
logger.info(f"🎯 Tone score: {quality_metrics.get('tone_score', 'N/A')}")
logger.info(f"📝 Word count: {quality_metrics.get('word_count', 'N/A')}")
logger.info(f"🏆 Quality level: {quality_metrics.get('quality_level', 'N/A')}")
# Test 4: Citation extraction
logger.info("\n📝 Test 4: Citation Extraction")
if result.get('content'):
citations = provider.extract_citations(result['content'])
logger.info(f"✅ Extracted {len(citations)} citations")
for i, citation in enumerate(citations):
logger.info(f" Citation {i+1}: {citation.get('reference', 'Unknown')}")
logger.info("\n🎉 All tests completed successfully!")
return True
except ImportError as e:
logger.error(f"❌ Import error: {str(e)}")
logger.info("💡 Make sure to install required dependencies:")
logger.info(" pip install google-genai loguru")
return False
except Exception as e:
logger.error(f"❌ Test failed with error: {str(e)}")
return False
async def test_individual_components():
"""Test individual components of the native grounding system."""
try:
logger.info("🔧 Testing Individual Components")
# Test 1: Provider initialization
logger.info("\n📋 Test 1: Provider Initialization")
if not os.getenv('GEMINI_API_KEY'):
logger.warning("⚠️ Skipping provider test - no API key")
return False
provider = GeminiGroundedProvider()
logger.info("✅ Provider initialized successfully")
# Test 2: Prompt building
logger.info("\n📋 Test 2: Prompt Building")
test_prompt = "Test prompt for LinkedIn post"
grounded_prompt = provider._build_grounded_prompt(test_prompt, "linkedin_post")
if grounded_prompt and len(grounded_prompt) > len(test_prompt):
logger.info("✅ Grounded prompt built successfully")
logger.info(f"📊 Original length: {len(test_prompt)}")
logger.info(f"📊 Enhanced length: {len(grounded_prompt)}")
else:
logger.error("❌ Prompt building failed")
return False
# Test 3: Content processing methods
logger.info("\n📋 Test 3: Content Processing Methods")
# Test post processing
test_content = "This is a test LinkedIn post #AI #Technology"
post_processing = provider._process_post_content(test_content)
if post_processing:
logger.info("✅ Post processing works")
logger.info(f"🔖 Hashtags found: {len(post_processing.get('hashtags', []))}")
# Test article processing
test_article = "# Test Article\n\nThis is test content for an article."
article_processing = provider._process_article_content(test_article)
if article_processing:
logger.info("✅ Article processing works")
logger.info(f"📊 Word count: {article_processing.get('word_count', 'N/A')}")
logger.info("✅ All component tests passed")
return True
except Exception as e:
logger.error(f"❌ Component test failed: {str(e)}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting Native Grounding Tests")
logger.info("=" * 60)
# Test individual components first
component_success = await test_individual_components()
if component_success:
# Test the full integration
integration_success = await test_native_grounding()
if integration_success:
logger.info("\n🎉 SUCCESS: All tests passed!")
logger.info("✅ Native Google Search grounding is working correctly")
logger.info("✅ Gemini API integration successful")
logger.info("✅ Grounding metadata processing working")
logger.info("✅ Content generation with sources successful")
else:
logger.error("\n❌ FAILURE: Integration tests failed")
sys.exit(1)
else:
logger.error("\n❌ FAILURE: Component tests failed")
sys.exit(1)
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(
sys.stderr,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
level="INFO"
)
# Run the tests
asyncio.run(main())

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#!/usr/bin/env python3
"""
Test script to verify the calendar generation progress endpoint.
This script tests the progress endpoint to ensure it returns the correct data structure.
"""
import asyncio
import sys
import os
import json
from datetime import datetime
# Add the current directory to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
def test_progress_endpoint():
"""Test the progress endpoint with a mock session."""
try:
from api.content_planning.services.calendar_generation_service import CalendarGenerationService
print("🧪 Testing Progress Endpoint")
print("=" * 50)
# Initialize service
service = CalendarGenerationService()
# Create a test session
session_id = f"test-session-{int(datetime.now().timestamp())}"
test_request_data = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme"
}
print(f"📋 Creating test session: {session_id}")
# Initialize session
success = service.initialize_orchestrator_session(session_id, test_request_data)
if not success:
print("❌ Failed to initialize session")
return False
print("✅ Session initialized successfully")
# Test progress retrieval
print(f"🔍 Testing progress retrieval for session: {session_id}")
progress = service.get_orchestrator_progress(session_id)
if not progress:
print("❌ No progress data returned")
return False
print("✅ Progress data retrieved successfully")
print(f"📊 Progress data structure:")
print(json.dumps(progress, indent=2, default=str))
# Verify required fields
required_fields = [
"status", "current_step", "step_progress", "overall_progress",
"step_results", "quality_scores", "errors", "warnings"
]
missing_fields = [field for field in required_fields if field not in progress]
if missing_fields:
print(f"❌ Missing required fields: {missing_fields}")
return False
print("✅ All required fields present")
# Test data types
if not isinstance(progress["current_step"], int):
print("❌ current_step should be int")
return False
if not isinstance(progress["step_progress"], (int, float)):
print("❌ step_progress should be numeric")
return False
if not isinstance(progress["overall_progress"], (int, float)):
print("❌ overall_progress should be numeric")
return False
print("✅ All data types correct")
# Test quality scores
quality_scores = progress["quality_scores"]
if not isinstance(quality_scores, dict):
print("❌ quality_scores should be dict")
return False
print("✅ Quality scores structure correct")
print("\n🎉 All tests passed! Progress endpoint is working correctly.")
return True
except Exception as e:
print(f"❌ Test failed with error: {str(e)}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
return False
if __name__ == "__main__":
success = test_progress_endpoint()
sys.exit(0 if success else 1)

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#!/usr/bin/env python3
"""
Real Database Integration Test for Steps 1-8
This script tests the calendar generation framework with real database services,
replacing all mock data with actual database operations.
"""
import asyncio
import sys
import os
from typing import Dict, Any
from loguru import logger
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
# Add the services directory to the path
services_dir = os.path.join(backend_dir, "services")
if services_dir not in sys.path:
sys.path.insert(0, services_dir)
async def test_real_database_integration():
"""Test Steps 1-8 with real database services."""
try:
logger.info("🚀 Starting real database integration test")
# Initialize database
logger.info("🗄️ Initializing database connection")
from services.database import init_database, get_db_session
try:
init_database()
logger.info("✅ Database initialized successfully")
except Exception as e:
logger.error(f"❌ Database initialization failed: {str(e)}")
return False
# Get database session
db_session = get_db_session()
if not db_session:
logger.error("❌ Failed to get database session")
return False
logger.info("✅ Database session created successfully")
# Test data
test_context = {
"user_id": 1,
"strategy_id": 1,
"calendar_duration": 7,
"posting_preferences": {
"posting_frequency": "daily",
"preferred_days": ["monday", "wednesday", "friday"],
"preferred_times": ["09:00", "12:00", "15:00"],
"content_per_day": 2
}
}
# Create test data in database
logger.info("📝 Creating test data in database")
await create_test_data(db_session, test_context)
# Test Step 1: Content Strategy Analysis with Real Database
logger.info("📋 Testing Step 1: Content Strategy Analysis (Real Database)")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import ContentStrategyAnalysisStep
from services.calendar_generation_datasource_framework.data_processing.strategy_data import StrategyDataProcessor
from services.content_planning_db import ContentPlanningDBService
# Create real database service
content_planning_db = ContentPlanningDBService(db_session)
# Create strategy processor with real database service
strategy_processor = StrategyDataProcessor()
strategy_processor.content_planning_db_service = content_planning_db
step1 = ContentStrategyAnalysisStep()
step1.strategy_processor = strategy_processor
result1 = await step1.execute(test_context)
logger.info(f"✅ Step 1 completed: {result1.get('status')}")
logger.info(f" Quality Score: {result1.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 1 failed: {str(e)}")
return False
# Test Step 2: Gap Analysis with Real Database
logger.info("📋 Testing Step 2: Gap Analysis (Real Database)")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import GapAnalysisStep
from services.calendar_generation_datasource_framework.data_processing.gap_analysis_data import GapAnalysisDataProcessor
# Create gap processor with real database service
gap_processor = GapAnalysisDataProcessor()
gap_processor.content_planning_db_service = content_planning_db
step2 = GapAnalysisStep()
step2.gap_processor = gap_processor
result2 = await step2.execute(test_context)
logger.info(f"✅ Step 2 completed: {result2.get('status')}")
logger.info(f" Quality Score: {result2.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 2 failed: {str(e)}")
return False
# Test Step 3: Audience & Platform Strategy with Real Database
logger.info("📋 Testing Step 3: Audience & Platform Strategy (Real Database)")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import AudiencePlatformStrategyStep
from services.calendar_generation_datasource_framework.data_processing.comprehensive_user_data import ComprehensiveUserDataProcessor
# Create comprehensive processor with real database service
comprehensive_processor = ComprehensiveUserDataProcessor(db_session)
comprehensive_processor.content_planning_db_service = content_planning_db
step3 = AudiencePlatformStrategyStep()
step3.comprehensive_processor = comprehensive_processor
result3 = await step3.execute(test_context)
logger.info(f"✅ Step 3 completed: {result3.get('status')}")
logger.info(f" Quality Score: {result3.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 3 failed: {str(e)}")
return False
# Test Steps 4-8 with Real Database
logger.info("📋 Testing Steps 4-8: Calendar Framework to Daily Content Planning (Real Database)")
try:
# Test Step 4: Calendar Framework
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step4_implementation import CalendarFrameworkStep
step4 = CalendarFrameworkStep()
result4 = await step4.execute(test_context)
logger.info(f"✅ Step 4 completed: {result4.get('status')}")
# Test Step 5: Content Pillar Distribution
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
step5 = ContentPillarDistributionStep()
result5 = await step5.execute(test_context)
logger.info(f"✅ Step 5 completed: {result5.get('status')}")
# Test Step 6: Platform-Specific Strategy
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step6_implementation import PlatformSpecificStrategyStep
step6 = PlatformSpecificStrategyStep()
result6 = await step6.execute(test_context)
logger.info(f"✅ Step 6 completed: {result6.get('status')}")
# Test Step 7: Weekly Theme Development
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step7_implementation import WeeklyThemeDevelopmentStep
step7 = WeeklyThemeDevelopmentStep()
result7 = await step7.execute(test_context)
logger.info(f"✅ Step 7 completed: {result7.get('status')}")
# Test Step 8: Daily Content Planning
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_implementation import DailyContentPlanningStep
step8 = DailyContentPlanningStep()
result8 = await step8.execute(test_context)
logger.info(f"✅ Step 8 completed: {result8.get('status')}")
except Exception as e:
logger.error(f"❌ Steps 4-8 failed: {str(e)}")
return False
# Clean up test data
logger.info("🧹 Cleaning up test data")
await cleanup_test_data(db_session, test_context)
# Close database session
db_session.close()
logger.info("🎉 All Steps 1-8 completed successfully with real database!")
logger.info("📝 Real database integration working perfectly!")
return True
except Exception as e:
logger.error(f"❌ Test failed with error: {str(e)}")
return False
async def create_test_data(db_session, test_context: Dict[str, Any]):
"""Create test data in the database."""
try:
from services.content_planning_db import ContentPlanningDBService
from models.content_planning import ContentStrategy, ContentGapAnalysis
db_service = ContentPlanningDBService(db_session)
# Create test content strategy
strategy_data = {
"user_id": test_context["user_id"],
"name": "Test Strategy - Real Database",
"industry": "technology",
"target_audience": {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {"age_range": "25-45", "location": "Global"}
},
"content_pillars": [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
],
"ai_recommendations": {
"strategic_insights": [
"Focus on deep-dive technical content",
"Emphasize practical implementation guides",
"Highlight ROI and business impact"
]
}
}
strategy = await db_service.create_content_strategy(strategy_data)
if strategy:
logger.info(f"✅ Created test strategy: {strategy.id}")
test_context["strategy_id"] = strategy.id
# Create test gap analysis
gap_data = {
"user_id": test_context["user_id"],
"website_url": "https://example.com",
"competitor_urls": ["https://competitor1.com", "https://competitor2.com"],
"target_keywords": [
{"keyword": "AI ethics in business", "search_volume": 5000, "competition": "low"},
{"keyword": "digital transformation ROI", "search_volume": 8000, "competition": "medium"}
],
"analysis_results": {
"content_gaps": [
{"topic": "AI Ethics", "priority": "high", "impact_score": 0.9},
{"topic": "Digital Transformation ROI", "priority": "medium", "impact_score": 0.7}
],
"keyword_opportunities": [
{"keyword": "AI ethics in business", "search_volume": 5000, "competition": "low"},
{"keyword": "digital transformation ROI", "search_volume": 8000, "competition": "medium"}
],
"competitor_insights": [
{"competitor": "Competitor A", "strength": "Technical content", "weakness": "Practical guides"},
{"competitor": "Competitor B", "strength": "Case studies", "weakness": "AI focus"}
],
"opportunities": [
{"type": "content", "topic": "AI Ethics", "priority": "high"},
{"type": "content", "topic": "ROI Analysis", "priority": "medium"}
]
},
"recommendations": [
"Create comprehensive AI ethics guide",
"Develop ROI calculator for digital transformation"
],
"opportunities": [
{"type": "content", "topic": "AI Ethics", "priority": "high"},
{"type": "content", "topic": "ROI Analysis", "priority": "medium"}
]
}
gap_analysis = await db_service.create_content_gap_analysis(gap_data)
if gap_analysis:
logger.info(f"✅ Created test gap analysis: {gap_analysis.id}")
logger.info("✅ Test data created successfully")
except Exception as e:
logger.error(f"❌ Error creating test data: {str(e)}")
raise
async def cleanup_test_data(db_session, test_context: Dict[str, Any]):
"""Clean up test data from the database."""
try:
from services.content_planning_db import ContentPlanningDBService
db_service = ContentPlanningDBService(db_session)
# Clean up gap analysis (get by user_id and delete)
gap_analyses = await db_service.get_user_content_gap_analyses(test_context["user_id"])
for gap_analysis in gap_analyses:
await db_service.delete_content_gap_analysis(gap_analysis.id)
# Clean up strategy
await db_service.delete_content_strategy(test_context["strategy_id"])
logger.info("✅ Test data cleaned up successfully")
except Exception as e:
logger.error(f"❌ Error cleaning up test data: {str(e)}")
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the test
success = asyncio.run(test_real_database_integration())
if success:
logger.info("✅ Real database integration test completed successfully!")
sys.exit(0)
else:
logger.error("❌ Real database integration test failed!")
sys.exit(1)

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import requests
import json
# Test the research endpoint
url = "http://localhost:8000/api/blog/research"
payload = {
"keywords": ["AI content generation", "blog writing"],
"topic": "ALwrity content generation",
"industry": "Technology",
"target_audience": "content creators"
}
try:
response = requests.post(url, json=payload)
print(f"Status Code: {response.status_code}")
print(f"Response Headers: {dict(response.headers)}")
if response.status_code == 200:
data = response.json()
print("\n=== RESEARCH RESPONSE ===")
print(f"Success: {data.get('success')}")
print(f"Sources Count: {len(data.get('sources', []))}")
print(f"Search Queries Count: {len(data.get('search_queries', []))}")
print(f"Has Search Widget: {bool(data.get('search_widget'))}")
print(f"Suggested Angles Count: {len(data.get('suggested_angles', []))}")
print("\n=== SOURCES ===")
for i, source in enumerate(data.get('sources', [])[:3]):
print(f"Source {i+1}: {source.get('title', 'No title')}")
print(f" URL: {source.get('url', 'No URL')}")
print(f" Type: {source.get('type', 'Unknown')}")
print("\n=== SEARCH QUERIES (First 5) ===")
for i, query in enumerate(data.get('search_queries', [])[:5]):
print(f"{i+1}. {query}")
print("\n=== SUGGESTED ANGLES ===")
for i, angle in enumerate(data.get('suggested_angles', [])[:3]):
print(f"{i+1}. {angle}")
print("\n=== KEYWORD ANALYSIS ===")
kw_analysis = data.get('keyword_analysis', {})
print(f"Primary: {kw_analysis.get('primary', [])}")
print(f"Secondary: {kw_analysis.get('secondary', [])}")
print(f"Search Intent: {kw_analysis.get('search_intent', 'Unknown')}")
print("\n=== SEARCH WIDGET (First 200 chars) ===")
widget = data.get('search_widget', '')
if widget:
print(widget[:200] + "..." if len(widget) > 200 else widget)
else:
print("No search widget provided")
else:
print(f"Error: {response.text}")
except Exception as e:
print(f"Request failed: {e}")

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import requests
import json
from datetime import datetime
# Test the research endpoint and capture full response
url = "http://localhost:8000/api/blog/research"
payload = {
"keywords": ["AI content generation", "blog writing"],
"topic": "ALwrity content generation",
"industry": "Technology",
"target_audience": "content creators"
}
try:
print("Sending request to research endpoint...")
response = requests.post(url, json=payload, timeout=120)
print(f"Status Code: {response.status_code}")
if response.status_code == 200:
data = response.json()
# Create analysis file with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"research_analysis_{timestamp}.json"
# Save full response to file
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data, f, indent=2, ensure_ascii=False)
print(f"\n=== RESEARCH RESPONSE ANALYSIS ===")
print(f"✅ Full response saved to: {filename}")
print(f"Success: {data.get('success')}")
print(f"Sources Count: {len(data.get('sources', []))}")
print(f"Search Queries Count: {len(data.get('search_queries', []))}")
print(f"Has Search Widget: {bool(data.get('search_widget'))}")
print(f"Suggested Angles Count: {len(data.get('suggested_angles', []))}")
print(f"\n=== SOURCES ANALYSIS ===")
sources = data.get('sources', [])
for i, source in enumerate(sources[:5]): # Show first 5
print(f"Source {i+1}: {source.get('title', 'No title')}")
print(f" URL: {source.get('url', 'No URL')[:100]}...")
print(f" Type: {source.get('type', 'Unknown')}")
print(f" Credibility: {source.get('credibility_score', 'N/A')}")
print(f"\n=== SEARCH QUERIES ANALYSIS ===")
queries = data.get('search_queries', [])
print(f"Total queries: {len(queries)}")
for i, query in enumerate(queries[:10]): # Show first 10
print(f"{i+1:2d}. {query}")
print(f"\n=== SEARCH WIDGET ANALYSIS ===")
widget = data.get('search_widget', '')
if widget:
print(f"Widget HTML length: {len(widget)} characters")
print(f"Contains Google branding: {'Google' in widget}")
print(f"Contains search chips: {'chip' in widget}")
print(f"Contains carousel: {'carousel' in widget}")
print(f"First 200 chars: {widget[:200]}...")
else:
print("No search widget provided")
print(f"\n=== KEYWORD ANALYSIS ===")
kw_analysis = data.get('keyword_analysis', {})
print(f"Primary keywords: {kw_analysis.get('primary', [])}")
print(f"Secondary keywords: {kw_analysis.get('secondary', [])}")
print(f"Long-tail keywords: {kw_analysis.get('long_tail', [])}")
print(f"Search intent: {kw_analysis.get('search_intent', 'Unknown')}")
print(f"Difficulty score: {kw_analysis.get('difficulty', 'N/A')}")
print(f"\n=== SUGGESTED ANGLES ===")
angles = data.get('suggested_angles', [])
for i, angle in enumerate(angles):
print(f"{i+1}. {angle}")
print(f"\n=== UI REPRESENTATION RECOMMENDATIONS ===")
print("Based on the response, here's what should be displayed in the Editor UI:")
print(f"1. Research Sources Panel: {len(sources)} real web sources")
print(f"2. Search Widget: Interactive Google search chips ({len(queries)} queries)")
print(f"3. Keyword Analysis: Primary/Secondary/Long-tail breakdown")
print(f"4. Content Angles: {len(angles)} suggested blog post angles")
print(f"5. Search Queries: {len(queries)} research queries for reference")
# Additional analysis for UI components
print(f"\n=== UI COMPONENT BREAKDOWN ===")
# Sources for UI
print("SOURCES FOR UI:")
for i, source in enumerate(sources[:3]):
print(f" - {source.get('title')} (Credibility: {source.get('credibility_score')})")
# Search widget for UI
print(f"\nSEARCH WIDGET FOR UI:")
print(f" - HTML length: {len(widget)} chars")
print(f" - Can be embedded directly in UI")
print(f" - Contains {len(queries)} search suggestions")
# Keywords for UI
print(f"\nKEYWORDS FOR UI:")
print(f" - Primary: {', '.join(kw_analysis.get('primary', []))}")
print(f" - Secondary: {', '.join(kw_analysis.get('secondary', []))}")
print(f" - Long-tail: {', '.join(kw_analysis.get('long_tail', []))}")
# Angles for UI
print(f"\nCONTENT ANGLES FOR UI:")
for i, angle in enumerate(angles[:3]):
print(f" - {angle}")
else:
print(f"Error: {response.text}")
except Exception as e:
print(f"Request failed: {e}")
import traceback
traceback.print_exc()

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"""
Unit tests for ResearchDataFilter.
Tests the filtering and cleaning functionality for research data.
"""
import pytest
from datetime import datetime, timedelta
from typing import List
from models.blog_models import (
BlogResearchResponse,
ResearchSource,
GroundingMetadata,
GroundingChunk,
GroundingSupport,
Citation,
)
from services.blog_writer.research.data_filter import ResearchDataFilter
class TestResearchDataFilter:
"""Test cases for ResearchDataFilter."""
def setup_method(self):
"""Set up test fixtures."""
self.filter = ResearchDataFilter()
# Create sample research sources
self.sample_sources = [
ResearchSource(
title="High Quality AI Article",
url="https://example.com/ai-article",
excerpt="This is a comprehensive article about artificial intelligence trends in 2024 with detailed analysis and expert insights.",
credibility_score=0.95,
published_at="2025-08-15",
index=0,
source_type="web"
),
ResearchSource(
title="Low Quality Source",
url="https://example.com/low-quality",
excerpt="This is a low quality source with very poor credibility score and outdated information from 2020.",
credibility_score=0.3,
published_at="2020-01-01",
index=1,
source_type="web"
),
ResearchSource(
title="PDF Document",
url="https://example.com/document.pdf",
excerpt="This is a PDF document with research data",
credibility_score=0.8,
published_at="2025-08-01",
index=2,
source_type="web"
),
ResearchSource(
title="Recent AI Study",
url="https://example.com/ai-study",
excerpt="A recent study on AI adoption shows significant growth in enterprise usage with detailed statistics and case studies.",
credibility_score=0.9,
published_at="2025-09-01",
index=3,
source_type="web"
)
]
# Create sample grounding metadata
self.sample_grounding_metadata = GroundingMetadata(
grounding_chunks=[
GroundingChunk(
title="High Confidence Chunk",
url="https://example.com/chunk1",
confidence_score=0.95
),
GroundingChunk(
title="Low Confidence Chunk",
url="https://example.com/chunk2",
confidence_score=0.5
),
GroundingChunk(
title="Medium Confidence Chunk",
url="https://example.com/chunk3",
confidence_score=0.8
)
],
grounding_supports=[
GroundingSupport(
confidence_scores=[0.9, 0.85],
grounding_chunk_indices=[0, 1],
segment_text="High confidence support text with expert insights"
),
GroundingSupport(
confidence_scores=[0.4, 0.3],
grounding_chunk_indices=[2, 3],
segment_text="Low confidence support text"
)
],
citations=[
Citation(
citation_type="expert_opinion",
start_index=0,
end_index=50,
text="Expert opinion on AI trends",
source_indices=[0],
reference="Source 1"
),
Citation(
citation_type="statistical_data",
start_index=51,
end_index=100,
text="Statistical data showing AI adoption rates",
source_indices=[1],
reference="Source 2"
),
Citation(
citation_type="inline",
start_index=101,
end_index=150,
text="Generic inline citation",
source_indices=[2],
reference="Source 3"
)
]
)
# Create sample research response
self.sample_research_response = BlogResearchResponse(
success=True,
sources=self.sample_sources,
keyword_analysis={
'primary': ['artificial intelligence', 'AI trends', 'machine learning'],
'secondary': ['AI adoption', 'enterprise AI', 'AI technology'],
'long_tail': ['AI trends 2024', 'enterprise AI adoption rates', 'AI technology benefits'],
'semantic_keywords': ['artificial intelligence', 'machine learning', 'deep learning'],
'trending_terms': ['AI 2024', 'generative AI', 'AI automation'],
'content_gaps': [
'AI ethics in small businesses',
'AI implementation guide for startups',
'AI cost-benefit analysis for SMEs',
'general overview', # Should be filtered out
'basics' # Should be filtered out
],
'search_intent': 'informational',
'difficulty': 7
},
competitor_analysis={
'top_competitors': ['Competitor A', 'Competitor B', 'Competitor C'],
'opportunities': ['Market gap 1', 'Market gap 2'],
'competitive_advantages': ['Advantage 1', 'Advantage 2'],
'market_positioning': 'Premium positioning'
},
suggested_angles=[
'AI trends in 2024',
'Enterprise AI adoption',
'AI implementation strategies'
],
search_widget="<div>Search widget HTML</div>",
search_queries=["AI trends 2024", "enterprise AI adoption"],
grounding_metadata=self.sample_grounding_metadata
)
def test_filter_sources_quality_filtering(self):
"""Test that sources are filtered by quality criteria."""
filtered_sources = self.filter.filter_sources(self.sample_sources)
# Should filter out low quality source (credibility < 0.6) and PDF document
assert len(filtered_sources) == 2 # Only high quality and recent AI study should pass
assert all(source.credibility_score >= 0.6 for source in filtered_sources)
# Should filter out sources with short excerpts
assert all(len(source.excerpt) >= 50 for source in filtered_sources)
def test_filter_sources_relevance_filtering(self):
"""Test that irrelevant sources are filtered out."""
filtered_sources = self.filter.filter_sources(self.sample_sources)
# Should filter out PDF document
pdf_sources = [s for s in filtered_sources if s.url.endswith('.pdf')]
assert len(pdf_sources) == 0
def test_filter_sources_recency_filtering(self):
"""Test that old sources are filtered out."""
filtered_sources = self.filter.filter_sources(self.sample_sources)
# Should filter out old source (2020)
old_sources = [s for s in filtered_sources if s.published_at == "2020-01-01"]
assert len(old_sources) == 0
def test_filter_sources_max_limit(self):
"""Test that sources are limited to max_sources."""
# Create more sources than max_sources
many_sources = self.sample_sources * 5 # 20 sources
filtered_sources = self.filter.filter_sources(many_sources)
assert len(filtered_sources) <= self.filter.max_sources
def test_filter_grounding_metadata_confidence_filtering(self):
"""Test that grounding metadata is filtered by confidence."""
filtered_metadata = self.filter.filter_grounding_metadata(self.sample_grounding_metadata)
assert filtered_metadata is not None
# Should filter out low confidence chunks
assert len(filtered_metadata.grounding_chunks) == 2
assert all(chunk.confidence_score >= 0.7 for chunk in filtered_metadata.grounding_chunks)
# Should filter out low confidence supports
assert len(filtered_metadata.grounding_supports) == 1
assert all(max(support.confidence_scores) >= 0.7 for support in filtered_metadata.grounding_supports)
# Should filter out irrelevant citations
assert len(filtered_metadata.citations) == 2
relevant_types = ['expert_opinion', 'statistical_data', 'recent_news', 'research_study']
assert all(citation.citation_type in relevant_types for citation in filtered_metadata.citations)
def test_clean_keyword_analysis(self):
"""Test that keyword analysis is cleaned and deduplicated."""
keyword_analysis = {
'primary': ['AI', 'artificial intelligence', 'AI', 'machine learning', ''],
'secondary': ['AI adoption', 'enterprise AI', 'ai adoption'], # Case duplicates
'long_tail': ['AI trends 2024', 'ai trends 2024', 'AI TRENDS 2024'], # Case duplicates
'search_intent': 'informational',
'difficulty': 7
}
cleaned_analysis = self.filter.clean_keyword_analysis(keyword_analysis)
# Should remove duplicates and empty strings (keywords are converted to lowercase)
assert len(cleaned_analysis['primary']) == 3
assert 'ai' in cleaned_analysis['primary']
assert 'artificial intelligence' in cleaned_analysis['primary']
assert 'machine learning' in cleaned_analysis['primary']
# Should handle case-insensitive deduplication
assert len(cleaned_analysis['secondary']) == 2
assert len(cleaned_analysis['long_tail']) == 1
# Should preserve other fields
assert cleaned_analysis['search_intent'] == 'informational'
assert cleaned_analysis['difficulty'] == 7
def test_filter_content_gaps(self):
"""Test that content gaps are filtered for quality and relevance."""
content_gaps = [
'AI ethics in small businesses',
'AI implementation guide for startups',
'general overview', # Should be filtered out
'basics', # Should be filtered out
'a', # Too short, should be filtered out
'AI cost-benefit analysis for SMEs'
]
filtered_gaps = self.filter.filter_content_gaps(content_gaps, self.sample_research_response)
# Should filter out generic and short gaps
assert len(filtered_gaps) >= 3 # At least the good ones should pass
assert 'AI ethics in small businesses' in filtered_gaps
assert 'AI implementation guide for startups' in filtered_gaps
assert 'AI cost-benefit analysis for SMEs' in filtered_gaps
assert 'general overview' not in filtered_gaps
assert 'basics' not in filtered_gaps
def test_filter_research_data_integration(self):
"""Test the complete filtering pipeline."""
filtered_research = self.filter.filter_research_data(self.sample_research_response)
# Should maintain success status
assert filtered_research.success is True
# Should filter sources
assert len(filtered_research.sources) < len(self.sample_research_response.sources)
assert len(filtered_research.sources) >= 0 # May be 0 if all sources are filtered out
# Should filter grounding metadata
if filtered_research.grounding_metadata:
assert len(filtered_research.grounding_metadata.grounding_chunks) < len(self.sample_grounding_metadata.grounding_chunks)
# Should clean keyword analysis
assert 'primary' in filtered_research.keyword_analysis
assert len(filtered_research.keyword_analysis['primary']) <= self.filter.max_keywords_per_category
# Should filter content gaps
assert len(filtered_research.keyword_analysis['content_gaps']) < len(self.sample_research_response.keyword_analysis['content_gaps'])
# Should preserve other fields
assert filtered_research.suggested_angles == self.sample_research_response.suggested_angles
assert filtered_research.search_widget == self.sample_research_response.search_widget
assert filtered_research.search_queries == self.sample_research_response.search_queries
def test_filter_with_empty_data(self):
"""Test filtering with empty or None data."""
empty_research = BlogResearchResponse(
success=True,
sources=[],
keyword_analysis={},
competitor_analysis={},
suggested_angles=[],
search_widget="",
search_queries=[],
grounding_metadata=None
)
filtered_research = self.filter.filter_research_data(empty_research)
assert filtered_research.success is True
assert len(filtered_research.sources) == 0
assert filtered_research.grounding_metadata is None
# keyword_analysis may contain content_gaps even if empty
assert 'content_gaps' in filtered_research.keyword_analysis
def test_parse_date_functionality(self):
"""Test date parsing functionality."""
# Test various date formats
test_dates = [
"2024-01-15",
"2024-01-15T10:30:00",
"2024-01-15T10:30:00Z",
"January 15, 2024",
"Jan 15, 2024",
"15 January 2024",
"01/15/2024",
"15/01/2024"
]
for date_str in test_dates:
parsed_date = self.filter._parse_date(date_str)
assert parsed_date is not None
assert isinstance(parsed_date, datetime)
# Test invalid date
invalid_date = self.filter._parse_date("invalid date")
assert invalid_date is None
# Test None date
none_date = self.filter._parse_date(None)
assert none_date is None
def test_clean_keyword_list_functionality(self):
"""Test keyword list cleaning functionality."""
keywords = [
'AI',
'artificial intelligence',
'AI', # Duplicate
'the', # Stop word
'machine learning',
'', # Empty
' ', # Whitespace only
'MACHINE LEARNING', # Case duplicate
'ai' # Case duplicate
]
cleaned_keywords = self.filter._clean_keyword_list(keywords)
# Should remove duplicates, stop words, and empty strings
assert len(cleaned_keywords) == 3
assert 'ai' in cleaned_keywords
assert 'artificial intelligence' in cleaned_keywords
assert 'machine learning' in cleaned_keywords
assert 'the' not in cleaned_keywords
assert '' not in cleaned_keywords
if __name__ == '__main__':
pytest.main([__file__])

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#!/usr/bin/env python3
"""
Test script for SEO analyzer integration
"""
import asyncio
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.comprehensive_seo_analyzer import ComprehensiveSEOAnalyzer
from services.database import init_database, get_db_session
from services.seo_analysis_service import SEOAnalysisService
from loguru import logger
async def test_seo_analyzer():
"""Test the SEO analyzer functionality."""
print("🔍 Testing SEO Analyzer Integration")
print("=" * 50)
try:
# Initialize database
print("📊 Initializing database...")
init_database()
print("✅ Database initialized successfully")
# Test URL
test_url = "https://example.com"
print(f"🌐 Testing with URL: {test_url}")
# Create analyzer
analyzer = ComprehensiveSEOAnalyzer()
# Run analysis
print("🔍 Running comprehensive SEO analysis...")
result = analyzer.analyze_url(test_url)
print(f"📈 Analysis Results:")
print(f" URL: {result.url}")
print(f" Overall Score: {result.overall_score}/100")
print(f" Health Status: {result.health_status}")
print(f" Critical Issues: {len(result.critical_issues)}")
print(f" Warnings: {len(result.warnings)}")
print(f" Recommendations: {len(result.recommendations)}")
# Test database storage
print("\n💾 Testing database storage...")
db_session = get_db_session()
if db_session:
try:
seo_service = SEOAnalysisService(db_session)
stored_analysis = seo_service.store_analysis_result(result)
if stored_analysis:
print(f"✅ Analysis stored in database with ID: {stored_analysis.id}")
# Test retrieval
retrieved_analysis = seo_service.get_latest_analysis(test_url)
if retrieved_analysis:
print(f"✅ Analysis retrieved from database")
print(f" Stored Score: {retrieved_analysis.overall_score}")
print(f" Stored Status: {retrieved_analysis.health_status}")
else:
print("❌ Failed to retrieve analysis from database")
else:
print("❌ Failed to store analysis in database")
except Exception as e:
print(f"❌ Database error: {str(e)}")
finally:
db_session.close()
else:
print("❌ Failed to get database session")
# Test statistics
print("\n📊 Testing statistics...")
db_session = get_db_session()
if db_session:
try:
seo_service = SEOAnalysisService(db_session)
stats = seo_service.get_analysis_statistics()
print(f"📈 Analysis Statistics:")
print(f" Total Analyses: {stats['total_analyses']}")
print(f" Total URLs: {stats['total_urls']}")
print(f" Average Score: {stats['average_score']}")
print(f" Health Distribution: {stats['health_distribution']}")
except Exception as e:
print(f"❌ Statistics error: {str(e)}")
finally:
db_session.close()
print("\n🎉 SEO Analyzer Integration Test Completed!")
except Exception as e:
print(f"❌ Test failed: {str(e)}")
logger.error(f"Test failed: {str(e)}")
if __name__ == "__main__":
asyncio.run(test_seo_analyzer())

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#!/usr/bin/env python3
"""
Test Script for AI SEO Tools API
This script tests all the migrated SEO tools endpoints to ensure
they are working correctly after migration to FastAPI.
"""
import asyncio
import aiohttp
import json
from datetime import datetime
BASE_URL = "http://localhost:8000"
async def test_endpoint(session, endpoint, method="GET", data=None):
"""Test a single endpoint"""
url = f"{BASE_URL}{endpoint}"
try:
if method == "POST":
async with session.post(url, json=data) as response:
result = await response.json()
return {
"endpoint": endpoint,
"status": response.status,
"success": response.status == 200,
"response": result
}
else:
async with session.get(url) as response:
result = await response.json()
return {
"endpoint": endpoint,
"status": response.status,
"success": response.status == 200,
"response": result
}
except Exception as e:
return {
"endpoint": endpoint,
"status": 0,
"success": False,
"error": str(e)
}
async def run_seo_tools_tests():
"""Run comprehensive tests for all SEO tools"""
print("🚀 Starting AI SEO Tools API Tests")
print("=" * 50)
async with aiohttp.ClientSession() as session:
# Test health endpoint
print("\n1. Testing Health Endpoints...")
health_tests = [
("/api/seo/health", "GET", None),
("/api/seo/tools/status", "GET", None)
]
for endpoint, method, data in health_tests:
result = await test_endpoint(session, endpoint, method, data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} {endpoint} - Status: {result['status']}")
# Test meta description generation
print("\n2. Testing Meta Description Generation...")
meta_desc_data = {
"keywords": ["SEO", "content marketing", "digital strategy"],
"tone": "Professional",
"search_intent": "Informational Intent",
"language": "English"
}
result = await test_endpoint(session, "/api/seo/meta-description", "POST", meta_desc_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} Meta Description Generation - Status: {result['status']}")
if result["success"]:
data = result["response"].get("data", {})
descriptions = data.get("meta_descriptions", [])
print(f" Generated {len(descriptions)} meta descriptions")
# Test PageSpeed analysis
print("\n3. Testing PageSpeed Analysis...")
pagespeed_data = {
"url": "https://example.com",
"strategy": "DESKTOP",
"categories": ["performance"]
}
result = await test_endpoint(session, "/api/seo/pagespeed-analysis", "POST", pagespeed_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} PageSpeed Analysis - Status: {result['status']}")
# Test sitemap analysis
print("\n4. Testing Sitemap Analysis...")
sitemap_data = {
"sitemap_url": "https://www.google.com/sitemap.xml",
"analyze_content_trends": False,
"analyze_publishing_patterns": False
}
result = await test_endpoint(session, "/api/seo/sitemap-analysis", "POST", sitemap_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} Sitemap Analysis - Status: {result['status']}")
# Test OpenGraph generation
print("\n5. Testing OpenGraph Generation...")
og_data = {
"url": "https://example.com",
"title_hint": "Test Page",
"description_hint": "Test description",
"platform": "General"
}
result = await test_endpoint(session, "/api/seo/opengraph-tags", "POST", og_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} OpenGraph Generation - Status: {result['status']}")
# Test on-page SEO analysis
print("\n6. Testing On-Page SEO Analysis...")
onpage_data = {
"url": "https://example.com",
"target_keywords": ["test", "example"],
"analyze_images": True,
"analyze_content_quality": True
}
result = await test_endpoint(session, "/api/seo/on-page-analysis", "POST", onpage_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} On-Page SEO Analysis - Status: {result['status']}")
# Test technical SEO analysis
print("\n7. Testing Technical SEO Analysis...")
technical_data = {
"url": "https://example.com",
"crawl_depth": 2,
"include_external_links": True,
"analyze_performance": True
}
result = await test_endpoint(session, "/api/seo/technical-seo", "POST", technical_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} Technical SEO Analysis - Status: {result['status']}")
# Test workflow endpoints
print("\n8. Testing Workflow Endpoints...")
# Website audit workflow
audit_data = {
"website_url": "https://example.com",
"workflow_type": "complete_audit",
"target_keywords": ["test", "example"]
}
result = await test_endpoint(session, "/api/seo/workflow/website-audit", "POST", audit_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} Website Audit Workflow - Status: {result['status']}")
# Content analysis workflow
content_data = {
"website_url": "https://example.com",
"workflow_type": "content_analysis",
"target_keywords": ["content", "strategy"]
}
result = await test_endpoint(session, "/api/seo/workflow/content-analysis", "POST", content_data)
status = "✅ PASS" if result["success"] else "❌ FAIL"
print(f" {status} Content Analysis Workflow - Status: {result['status']}")
print("\n" + "=" * 50)
print("🎉 SEO Tools API Testing Completed!")
print("\nNote: Some tests may show connection errors if the server is not running.")
print("Start the server with: uvicorn app:app --reload --host 0.0.0.0 --port 8000")
if __name__ == "__main__":
asyncio.run(run_seo_tools_tests())

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#!/usr/bin/env python3
"""
Test script to verify session management and duplicate prevention.
This script tests the session cleanup and duplicate prevention features.
"""
import asyncio
import sys
import os
import json
from datetime import datetime
# Add the current directory to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
def test_session_management():
"""Test session management features."""
try:
from api.content_planning.services.calendar_generation_service import CalendarGenerationService
print("🧪 Testing Session Management")
print("=" * 50)
# Initialize service
service = CalendarGenerationService(None) # No DB needed for this test
# Test 1: Initialize first session
print("\n📋 Test 1: Initialize first session")
request_data_1 = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme"
}
session_id_1 = f"test-session-{int(datetime.now().timestamp())}-1000"
success_1 = service.initialize_orchestrator_session(session_id_1, request_data_1)
print(f"✅ First session initialized: {success_1}")
print(f"📊 Available sessions: {list(service.orchestrator_sessions.keys())}")
# Test 2: Try to initialize second session for same user (should fail)
print("\n📋 Test 2: Try to initialize second session for same user")
session_id_2 = f"test-session-{int(datetime.now().timestamp())}-2000"
success_2 = service.initialize_orchestrator_session(session_id_2, request_data_1)
print(f"❌ Second session should fail: {success_2}")
print(f"📊 Available sessions: {list(service.orchestrator_sessions.keys())}")
# Test 3: Check active session for user
print("\n📋 Test 3: Check active session for user")
active_session = service._get_active_session_for_user(1)
print(f"✅ Active session for user 1: {active_session}")
# Test 4: Initialize session for different user (should succeed)
print("\n📋 Test 4: Initialize session for different user")
request_data_2 = {
"user_id": 2,
"strategy_id": 2,
"calendar_type": "weekly",
"industry": "finance",
"business_size": "enterprise"
}
session_id_3 = f"test-session-{int(datetime.now().timestamp())}-3000"
success_3 = service.initialize_orchestrator_session(session_id_3, request_data_2)
print(f"✅ Third session for different user: {success_3}")
print(f"📊 Available sessions: {list(service.orchestrator_sessions.keys())}")
# Test 5: Test session cleanup
print("\n📋 Test 5: Test session cleanup")
print(f"📊 Sessions before cleanup: {len(service.orchestrator_sessions)}")
service._cleanup_old_sessions(1)
print(f"📊 Sessions after cleanup: {len(service.orchestrator_sessions)}")
print("\n🎉 Session management tests completed successfully!")
except Exception as e:
print(f"❌ Test failed: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_session_management()

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#!/usr/bin/env python3
"""
Simple test script to verify basic grounding functionality.
This script tests the core components without triggering API overload.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from loguru import logger
from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
async def test_basic_functionality():
"""Test basic grounding functionality."""
try:
logger.info("🧪 Testing Basic Grounding Functionality")
# Initialize provider
provider = GeminiGroundedProvider()
logger.info("✅ Provider initialized successfully")
# Test prompt building
prompt = "Write a short LinkedIn post about AI trends"
grounded_prompt = provider._build_grounded_prompt(prompt, "linkedin_post")
logger.info(f"✅ Grounded prompt built: {len(grounded_prompt)} characters")
# Test content processing
test_content = "AI is transforming industries #AI #Technology"
processed = provider._process_post_content(test_content)
logger.info(f"✅ Content processed: {len(processed.get('hashtags', []))} hashtags found")
logger.info("🎉 Basic functionality test completed successfully!")
return True
except Exception as e:
logger.error(f"❌ Basic functionality test failed: {str(e)}")
return False
async def main():
"""Main test function."""
logger.info("🚀 Starting Simple Grounding Test")
logger.info("=" * 50)
success = await test_basic_functionality()
if success:
logger.info("\n🎉 SUCCESS: Basic grounding functionality is working!")
logger.info("✅ Provider initialization successful")
logger.info("✅ Prompt building working")
logger.info("✅ Content processing working")
logger.info("✅ Ready for API integration")
else:
logger.error("\n❌ FAILURE: Basic functionality test failed")
sys.exit(1)
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(
sys.stderr,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
level="INFO"
)
# Run the test
asyncio.run(main())

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backend/test/test_simple_schema.py Normal file
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import asyncio
from services.llm_providers.gemini_provider import gemini_structured_json_response
async def test_simple_schema():
"""Test with a very simple schema to see if structured output works at all"""
# Very simple schema
simple_schema = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"}
}
}
simple_prompt = "Generate a person with a name and age."
print("Testing simple schema...")
print(f"Schema: {simple_schema}")
print(f"Prompt: {simple_prompt}")
print("\n" + "="*50 + "\n")
try:
result = gemini_structured_json_response(
prompt=simple_prompt,
schema=simple_schema,
temperature=0.3,
max_tokens=100
)
print("Result:")
print(result)
except Exception as e:
print(f"Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(test_simple_schema())

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"""
Unit tests for SourceToSectionMapper.
Tests the intelligent source-to-section mapping functionality.
"""
import pytest
from typing import List
from models.blog_models import (
BlogOutlineSection,
ResearchSource,
BlogResearchResponse,
GroundingMetadata,
)
from services.blog_writer.outline.source_mapper import SourceToSectionMapper
class TestSourceToSectionMapper:
"""Test cases for SourceToSectionMapper."""
def setup_method(self):
"""Set up test fixtures."""
self.mapper = SourceToSectionMapper()
# Create sample research sources
self.sample_sources = [
ResearchSource(
title="AI Trends in 2025: Machine Learning Revolution",
url="https://example.com/ai-trends-2025",
excerpt="Comprehensive analysis of artificial intelligence trends in 2025, focusing on machine learning advancements, deep learning breakthroughs, and AI automation in enterprise environments.",
credibility_score=0.95,
published_at="2025-08-15",
index=0,
source_type="web"
),
ResearchSource(
title="Enterprise AI Implementation Guide",
url="https://example.com/enterprise-ai-guide",
excerpt="Step-by-step guide for implementing artificial intelligence solutions in enterprise environments, including best practices, challenges, and success stories from leading companies.",
credibility_score=0.9,
published_at="2025-08-01",
index=1,
source_type="web"
),
ResearchSource(
title="Machine Learning Algorithms Explained",
url="https://example.com/ml-algorithms",
excerpt="Detailed explanation of various machine learning algorithms including supervised learning, unsupervised learning, and reinforcement learning techniques with practical examples.",
credibility_score=0.85,
published_at="2025-07-20",
index=2,
source_type="web"
),
ResearchSource(
title="AI Ethics and Responsible Development",
url="https://example.com/ai-ethics",
excerpt="Discussion of ethical considerations in artificial intelligence development, including bias mitigation, transparency, and responsible AI practices for developers and organizations.",
credibility_score=0.88,
published_at="2025-07-10",
index=3,
source_type="web"
),
ResearchSource(
title="Deep Learning Neural Networks Tutorial",
url="https://example.com/deep-learning-tutorial",
excerpt="Comprehensive tutorial on deep learning neural networks, covering convolutional neural networks, recurrent neural networks, and transformer architectures with code examples.",
credibility_score=0.92,
published_at="2025-06-15",
index=4,
source_type="web"
)
]
# Create sample outline sections
self.sample_sections = [
BlogOutlineSection(
id="s1",
heading="Introduction to AI and Machine Learning",
subheadings=["What is AI?", "Types of Machine Learning", "AI Applications"],
key_points=["AI definition and scope", "ML vs traditional programming", "Real-world AI examples"],
references=[],
target_words=300,
keywords=["artificial intelligence", "machine learning", "AI basics", "introduction"]
),
BlogOutlineSection(
id="s2",
heading="Enterprise AI Implementation Strategies",
subheadings=["Planning Phase", "Implementation Steps", "Best Practices"],
key_points=["Strategic planning", "Technology selection", "Change management", "ROI measurement"],
references=[],
target_words=400,
keywords=["enterprise AI", "implementation", "strategies", "business"]
),
BlogOutlineSection(
id="s3",
heading="Machine Learning Algorithms Deep Dive",
subheadings=["Supervised Learning", "Unsupervised Learning", "Deep Learning"],
key_points=["Algorithm types", "Use cases", "Performance metrics", "Model selection"],
references=[],
target_words=500,
keywords=["machine learning algorithms", "supervised learning", "deep learning", "neural networks"]
),
BlogOutlineSection(
id="s4",
heading="AI Ethics and Responsible Development",
subheadings=["Ethical Considerations", "Bias and Fairness", "Transparency"],
key_points=["Ethical frameworks", "Bias detection", "Explainable AI", "Regulatory compliance"],
references=[],
target_words=350,
keywords=["AI ethics", "responsible AI", "bias", "transparency"]
)
]
# Create sample research response
self.sample_research = BlogResearchResponse(
success=True,
sources=self.sample_sources,
keyword_analysis={
'primary': ['artificial intelligence', 'machine learning', 'AI implementation'],
'secondary': ['enterprise AI', 'deep learning', 'AI ethics'],
'long_tail': ['AI trends 2025', 'enterprise AI implementation guide', 'machine learning algorithms explained'],
'semantic_keywords': ['AI', 'ML', 'neural networks', 'automation'],
'trending_terms': ['AI 2025', 'generative AI', 'AI automation'],
'search_intent': 'informational',
'content_gaps': ['AI implementation challenges', 'ML algorithm comparison']
},
competitor_analysis={
'top_competitors': ['TechCorp AI', 'DataScience Inc', 'AI Solutions Ltd'],
'opportunities': ['Enterprise market gap', 'SME AI adoption'],
'competitive_advantages': ['Comprehensive coverage', 'Practical examples']
},
suggested_angles=[
'AI trends in 2025',
'Enterprise AI implementation',
'Machine learning fundamentals',
'AI ethics and responsibility'
],
search_widget="<div>Search widget HTML</div>",
search_queries=["AI trends 2025", "enterprise AI implementation", "machine learning guide"],
grounding_metadata=GroundingMetadata(
grounding_chunks=[],
grounding_supports=[],
citations=[],
search_entry_point="AI trends and implementation",
web_search_queries=["AI trends 2025", "enterprise AI"]
)
)
def test_semantic_similarity_calculation(self):
"""Test semantic similarity calculation between sections and sources."""
section = self.sample_sections[0] # AI Introduction section
source = self.sample_sources[0] # AI Trends source
similarity = self.mapper._calculate_semantic_similarity(section, source)
# Should have high similarity due to AI-related content
assert 0.0 <= similarity <= 1.0
assert similarity > 0.3 # Should be reasonably high for AI-related content
def test_keyword_relevance_calculation(self):
"""Test keyword-based relevance calculation."""
section = self.sample_sections[1] # Enterprise AI section
source = self.sample_sources[1] # Enterprise AI Guide source
relevance = self.mapper._calculate_keyword_relevance(section, source, self.sample_research)
# Should have reasonable relevance due to enterprise AI keywords
assert 0.0 <= relevance <= 1.0
assert relevance > 0.1 # Should be reasonable for matching enterprise AI content
def test_contextual_relevance_calculation(self):
"""Test contextual relevance calculation."""
section = self.sample_sections[2] # ML Algorithms section
source = self.sample_sources[2] # ML Algorithms source
relevance = self.mapper._calculate_contextual_relevance(section, source, self.sample_research)
# Should have high relevance due to matching content angles
assert 0.0 <= relevance <= 1.0
assert relevance > 0.2 # Should be reasonable for matching content
def test_algorithmic_source_mapping(self):
"""Test the complete algorithmic mapping process."""
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
# Should have mapping results for all sections
assert len(mapping_results) == len(self.sample_sections)
# Each section should have some mapped sources
for section_id, sources in mapping_results.items():
assert isinstance(sources, list)
# Each source should be a tuple of (source, score)
for source, score in sources:
assert isinstance(source, ResearchSource)
assert isinstance(score, float)
assert 0.0 <= score <= 1.0
def test_source_mapping_quality(self):
"""Test that sources are mapped to relevant sections."""
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
# Enterprise AI section should have enterprise AI source
enterprise_section = mapping_results["s2"]
enterprise_source_titles = [source.title for source, score in enterprise_section]
assert any("Enterprise" in title for title in enterprise_source_titles)
# ML Algorithms section should have ML algorithms source
ml_section = mapping_results["s3"]
ml_source_titles = [source.title for source, score in ml_section]
assert any("Machine Learning" in title or "Algorithms" in title for title in ml_source_titles)
# AI Ethics section should have AI ethics source
ethics_section = mapping_results["s4"]
ethics_source_titles = [source.title for source, score in ethics_section]
assert any("Ethics" in title for title in ethics_source_titles)
def test_complete_mapping_pipeline(self):
"""Test the complete mapping pipeline from sections to mapped sections."""
mapped_sections = self.mapper.map_sources_to_sections(self.sample_sections, self.sample_research)
# Should return same number of sections
assert len(mapped_sections) == len(self.sample_sections)
# Each section should have mapped sources
for section in mapped_sections:
assert isinstance(section.references, list)
assert len(section.references) <= self.mapper.max_sources_per_section
# All references should be ResearchSource objects
for source in section.references:
assert isinstance(source, ResearchSource)
def test_mapping_with_empty_sources(self):
"""Test mapping behavior with empty sources list."""
empty_research = BlogResearchResponse(
success=True,
sources=[],
keyword_analysis={},
competitor_analysis={},
suggested_angles=[],
search_widget="",
search_queries=[],
grounding_metadata=None
)
mapped_sections = self.mapper.map_sources_to_sections(self.sample_sections, empty_research)
# Should return sections with empty references
for section in mapped_sections:
assert section.references == []
def test_mapping_with_empty_sections(self):
"""Test mapping behavior with empty sections list."""
mapped_sections = self.mapper.map_sources_to_sections([], self.sample_research)
# Should return empty list
assert mapped_sections == []
def test_meaningful_words_extraction(self):
"""Test extraction of meaningful words from text."""
text = "Artificial Intelligence and Machine Learning are transforming the world of technology and business applications."
words = self.mapper._extract_meaningful_words(text)
# Should extract meaningful words and remove stop words
assert "artificial" in words
assert "intelligence" in words
assert "machine" in words
assert "learning" in words
assert "the" not in words # Stop word should be removed
assert "and" not in words # Stop word should be removed
def test_phrase_similarity_calculation(self):
"""Test phrase similarity calculation."""
text1 = "machine learning algorithms"
text2 = "This article covers machine learning algorithms and their applications"
similarity = self.mapper._calculate_phrase_similarity(text1, text2)
# Should find phrase matches
assert similarity > 0.0
assert similarity <= 0.3 # Should be capped at 0.3
def test_intent_keywords_extraction(self):
"""Test extraction of intent-specific keywords."""
informational_keywords = self.mapper._get_intent_keywords("informational")
transactional_keywords = self.mapper._get_intent_keywords("transactional")
# Should return appropriate keywords for each intent
assert "what" in informational_keywords
assert "how" in informational_keywords
assert "guide" in informational_keywords
assert "buy" in transactional_keywords
assert "purchase" in transactional_keywords
assert "price" in transactional_keywords
def test_mapping_statistics(self):
"""Test mapping statistics calculation."""
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
stats = self.mapper.get_mapping_statistics(mapping_results)
# Should have valid statistics
assert stats['total_sections'] == len(self.sample_sections)
assert stats['total_mappings'] > 0
assert stats['sections_with_sources'] > 0
assert 0.0 <= stats['average_score'] <= 1.0
assert 0.0 <= stats['max_score'] <= 1.0
assert 0.0 <= stats['min_score'] <= 1.0
assert 0.0 <= stats['mapping_coverage'] <= 1.0
def test_source_quality_filtering(self):
"""Test that low-quality sources are filtered out."""
# Create a low-quality source
low_quality_source = ResearchSource(
title="Random Article",
url="https://example.com/random",
excerpt="This is a completely unrelated article about cooking recipes and gardening tips.",
credibility_score=0.3,
published_at="2025-08-01",
index=5,
source_type="web"
)
# Add to research data
research_with_low_quality = BlogResearchResponse(
success=True,
sources=self.sample_sources + [low_quality_source],
keyword_analysis=self.sample_research.keyword_analysis,
competitor_analysis=self.sample_research.competitor_analysis,
suggested_angles=self.sample_research.suggested_angles,
search_widget=self.sample_research.search_widget,
search_queries=self.sample_research.search_queries,
grounding_metadata=self.sample_research.grounding_metadata
)
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, research_with_low_quality)
# Low-quality source should not be mapped to any section
all_mapped_sources = []
for sources in mapping_results.values():
all_mapped_sources.extend([source for source, score in sources])
assert low_quality_source not in all_mapped_sources
def test_max_sources_per_section_limit(self):
"""Test that the maximum sources per section limit is enforced."""
# Create many sources
many_sources = self.sample_sources * 3 # 15 sources
research_with_many_sources = BlogResearchResponse(
success=True,
sources=many_sources,
keyword_analysis=self.sample_research.keyword_analysis,
competitor_analysis=self.sample_research.competitor_analysis,
suggested_angles=self.sample_research.suggested_angles,
search_widget=self.sample_research.search_widget,
search_queries=self.sample_research.search_queries,
grounding_metadata=self.sample_research.grounding_metadata
)
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, research_with_many_sources)
# Each section should have at most max_sources_per_section sources
for section_id, sources in mapping_results.items():
assert len(sources) <= self.mapper.max_sources_per_section
def test_ai_validation_prompt_building(self):
"""Test AI validation prompt building."""
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
prompt = self.mapper._build_validation_prompt(mapping_results, self.sample_research)
# Should contain key elements
assert "expert content strategist" in prompt
assert "Research Topic:" in prompt
assert "ALGORITHMIC MAPPING RESULTS" in prompt
assert "AVAILABLE SOURCES" in prompt
assert "VALIDATION TASK" in prompt
assert "RESPONSE FORMAT" in prompt
assert "overall_quality_score" in prompt
assert "section_improvements" in prompt
def test_ai_validation_response_parsing(self):
"""Test AI validation response parsing."""
# Mock AI response
mock_response = """
Here's my analysis of the source-to-section mapping:
```json
{
"overall_quality_score": 8,
"section_improvements": [
{
"section_id": "s1",
"current_sources": ["AI Trends in 2025: Machine Learning Revolution"],
"recommended_sources": ["AI Trends in 2025: Machine Learning Revolution", "Machine Learning Algorithms Explained"],
"reasoning": "Adding ML algorithms source provides more technical depth",
"confidence": 0.9
}
],
"summary": "Good mapping overall, minor improvements suggested"
}
```
"""
original_mapping = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
parsed_mapping = self.mapper._parse_validation_response(mock_response, original_mapping, self.sample_research)
# Should have improved mapping
assert "s1" in parsed_mapping
assert len(parsed_mapping["s1"]) > 0
# Should maintain other sections
assert len(parsed_mapping) == len(original_mapping)
def test_ai_validation_fallback_handling(self):
"""Test AI validation fallback when parsing fails."""
# Mock invalid AI response
invalid_response = "This is not a valid JSON response"
original_mapping = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
parsed_mapping = self.mapper._parse_validation_response(invalid_response, original_mapping, self.sample_research)
# Should fallback to original mapping
assert parsed_mapping == original_mapping
def test_ai_validation_with_missing_sources(self):
"""Test AI validation when recommended sources don't exist."""
# Mock AI response with non-existent source
mock_response = """
```json
{
"overall_quality_score": 7,
"section_improvements": [
{
"section_id": "s1",
"current_sources": ["AI Trends in 2025: Machine Learning Revolution"],
"recommended_sources": ["Non-existent Source", "Another Fake Source"],
"reasoning": "These sources would be better",
"confidence": 0.8
}
],
"summary": "Suggested improvements"
}
```
"""
original_mapping = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
parsed_mapping = self.mapper._parse_validation_response(mock_response, original_mapping, self.sample_research)
# Should fallback to original mapping for s1 since no valid sources found
assert parsed_mapping["s1"] == original_mapping["s1"]
def test_ai_validation_integration(self):
"""Test complete AI validation integration (with mocked LLM)."""
# This test would require mocking the LLM provider
# For now, we'll test that the method doesn't crash
mapping_results = self.mapper._algorithmic_source_mapping(self.sample_sections, self.sample_research)
# Test that AI validation method exists and can be called
# (In real implementation, this would call the actual LLM)
try:
# This will fail in test environment due to no LLM, but should not crash
validated_mapping = self.mapper._ai_validate_mapping(mapping_results, self.sample_research)
# If it doesn't crash, it should return the original mapping as fallback
assert validated_mapping == mapping_results
except Exception as e:
# Expected to fail in test environment, but should be handled gracefully
assert "AI validation failed" in str(e) or "Failed to get AI validation response" in str(e)
def test_format_sections_for_prompt(self):
"""Test formatting of sections for AI prompt."""
sections_info = [
{
'id': 's1',
'sources': [
{
'title': 'Test Source 1',
'algorithmic_score': 0.85
}
]
}
]
formatted = self.mapper._format_sections_for_prompt(sections_info)
assert "Section s1:" in formatted
assert "Test Source 1" in formatted
assert "0.85" in formatted
def test_format_sources_for_prompt(self):
"""Test formatting of sources for AI prompt."""
sources = [
{
'title': 'Test Source',
'url': 'https://example.com',
'credibility_score': 0.9,
'excerpt': 'This is a test excerpt for the source.'
}
]
formatted = self.mapper._format_sources_for_prompt(sources)
assert "Test Source" in formatted
assert "https://example.com" in formatted
assert "0.9" in formatted
assert "This is a test excerpt" in formatted
if __name__ == '__main__':
pytest.main([__file__])

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"""Test suite for Stability AI endpoints."""
import pytest
import asyncio
from fastapi.testclient import TestClient
from fastapi import FastAPI
import io
from PIL import Image
import json
import base64
from unittest.mock import Mock, AsyncMock, patch
from routers.stability import router
from services.stability_service import StabilityAIService
from models.stability_models import *
# Create test app
app = FastAPI()
app.include_router(router)
client = TestClient(app)
class TestStabilityEndpoints:
"""Test cases for Stability AI endpoints."""
def setup_method(self):
"""Set up test environment."""
self.test_image = self._create_test_image()
self.test_audio = self._create_test_audio()
def _create_test_image(self) -> bytes:
"""Create test image data."""
img = Image.new('RGB', (512, 512), color='red')
img_bytes = io.BytesIO()
img.save(img_bytes, format='PNG')
return img_bytes.getvalue()
def _create_test_audio(self) -> bytes:
"""Create test audio data."""
# Mock audio data
return b"fake_audio_data" * 1000
@patch('services.stability_service.StabilityAIService')
def test_generate_ultra_success(self, mock_service):
"""Test successful Ultra generation."""
# Mock service response
mock_service.return_value.__aenter__.return_value.generate_ultra = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/generate/ultra",
data={"prompt": "A beautiful landscape"},
files={}
)
assert response.status_code == 200
assert response.headers["content-type"].startswith("image/")
@patch('services.stability_service.StabilityAIService')
def test_generate_core_with_parameters(self, mock_service):
"""Test Core generation with various parameters."""
mock_service.return_value.__aenter__.return_value.generate_core = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/generate/core",
data={
"prompt": "A futuristic city",
"aspect_ratio": "16:9",
"style_preset": "digital-art",
"seed": 42
}
)
assert response.status_code == 200
@patch('services.stability_service.StabilityAIService')
def test_inpaint_with_mask(self, mock_service):
"""Test inpainting with mask."""
mock_service.return_value.__aenter__.return_value.inpaint = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/edit/inpaint",
data={"prompt": "A cat"},
files={
"image": ("test.png", self.test_image, "image/png"),
"mask": ("mask.png", self.test_image, "image/png")
}
)
assert response.status_code == 200
@patch('services.stability_service.StabilityAIService')
def test_upscale_fast(self, mock_service):
"""Test fast upscaling."""
mock_service.return_value.__aenter__.return_value.upscale_fast = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/upscale/fast",
files={"image": ("test.png", self.test_image, "image/png")}
)
assert response.status_code == 200
@patch('services.stability_service.StabilityAIService')
def test_control_sketch(self, mock_service):
"""Test sketch control."""
mock_service.return_value.__aenter__.return_value.control_sketch = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/control/sketch",
data={
"prompt": "A medieval castle",
"control_strength": 0.8
},
files={"image": ("sketch.png", self.test_image, "image/png")}
)
assert response.status_code == 200
@patch('services.stability_service.StabilityAIService')
def test_3d_generation(self, mock_service):
"""Test 3D model generation."""
mock_3d_data = b"fake_glb_data" * 100
mock_service.return_value.__aenter__.return_value.generate_3d_fast = AsyncMock(
return_value=mock_3d_data
)
response = client.post(
"/api/stability/3d/stable-fast-3d",
files={"image": ("test.png", self.test_image, "image/png")}
)
assert response.status_code == 200
assert response.headers["content-type"] == "model/gltf-binary"
@patch('services.stability_service.StabilityAIService')
def test_audio_generation(self, mock_service):
"""Test audio generation."""
mock_service.return_value.__aenter__.return_value.generate_audio_from_text = AsyncMock(
return_value=self.test_audio
)
response = client.post(
"/api/stability/audio/text-to-audio",
data={
"prompt": "Peaceful nature sounds",
"duration": 30
}
)
assert response.status_code == 200
assert response.headers["content-type"].startswith("audio/")
def test_health_check(self):
"""Test health check endpoint."""
response = client.get("/api/stability/health")
assert response.status_code == 200
assert response.json()["status"] == "healthy"
def test_models_info(self):
"""Test models info endpoint."""
response = client.get("/api/stability/models/info")
assert response.status_code == 200
data = response.json()
assert "generate" in data
assert "edit" in data
assert "upscale" in data
def test_supported_formats(self):
"""Test supported formats endpoint."""
response = client.get("/api/stability/supported-formats")
assert response.status_code == 200
data = response.json()
assert "image_input" in data
assert "image_output" in data
assert "audio_input" in data
def test_image_info_analysis(self):
"""Test image info utility endpoint."""
response = client.post(
"/api/stability/utils/image-info",
files={"image": ("test.png", self.test_image, "image/png")}
)
assert response.status_code == 200
data = response.json()
assert "width" in data
assert "height" in data
assert "format" in data
def test_prompt_validation(self):
"""Test prompt validation endpoint."""
response = client.post(
"/api/stability/utils/validate-prompt",
data={"prompt": "A beautiful landscape with mountains and lakes"}
)
assert response.status_code == 200
data = response.json()
assert "is_valid" in data
assert "suggestions" in data
def test_invalid_image_format(self):
"""Test error handling for invalid image format."""
response = client.post(
"/api/stability/generate/ultra",
data={"prompt": "Test prompt"},
files={"image": ("test.txt", b"not an image", "text/plain")}
)
# Should handle gracefully or return appropriate error
assert response.status_code in [400, 422]
def test_missing_required_parameters(self):
"""Test error handling for missing required parameters."""
response = client.post("/api/stability/generate/ultra")
assert response.status_code == 422 # Validation error
def test_outpaint_validation(self):
"""Test outpaint direction validation."""
response = client.post(
"/api/stability/edit/outpaint",
data={
"left": 0,
"right": 0,
"up": 0,
"down": 0
},
files={"image": ("test.png", self.test_image, "image/png")}
)
assert response.status_code == 400
assert "at least one outpaint direction" in response.json()["detail"]
@patch('services.stability_service.StabilityAIService')
def test_async_generation_response(self, mock_service):
"""Test async generation response format."""
mock_service.return_value.__aenter__.return_value.upscale_creative = AsyncMock(
return_value={"id": "test_generation_id"}
)
response = client.post(
"/api/stability/upscale/creative",
data={"prompt": "High quality upscale"},
files={"image": ("test.png", self.test_image, "image/png")}
)
assert response.status_code == 200
data = response.json()
assert "id" in data
@patch('services.stability_service.StabilityAIService')
def test_batch_comparison(self, mock_service):
"""Test model comparison endpoint."""
mock_service.return_value.__aenter__.return_value.generate_ultra = AsyncMock(
return_value=self.test_image
)
mock_service.return_value.__aenter__.return_value.generate_core = AsyncMock(
return_value=self.test_image
)
response = client.post(
"/api/stability/advanced/compare/models",
data={
"prompt": "A test image",
"models": json.dumps(["ultra", "core"]),
"seed": 42
}
)
assert response.status_code == 200
data = response.json()
assert "comparison_results" in data
class TestStabilityService:
"""Test cases for StabilityAIService class."""
@pytest.mark.asyncio
async def test_service_initialization(self):
"""Test service initialization."""
with patch.dict('os.environ', {'STABILITY_API_KEY': 'test_key'}):
service = StabilityAIService()
assert service.api_key == 'test_key'
def test_service_initialization_no_key(self):
"""Test service initialization without API key."""
with patch.dict('os.environ', {}, clear=True):
with pytest.raises(ValueError):
StabilityAIService()
@pytest.mark.asyncio
@patch('aiohttp.ClientSession')
async def test_make_request_success(self, mock_session):
"""Test successful API request."""
# Mock response
mock_response = AsyncMock()
mock_response.status = 200
mock_response.read.return_value = b"test_image_data"
mock_response.headers = {"Content-Type": "image/png"}
mock_session.return_value.__aenter__.return_value.request.return_value.__aenter__.return_value = mock_response
service = StabilityAIService(api_key="test_key")
async with service:
result = await service._make_request(
method="POST",
endpoint="/test",
data={"test": "data"}
)
assert result == b"test_image_data"
@pytest.mark.asyncio
async def test_image_preparation(self):
"""Test image preparation methods."""
service = StabilityAIService(api_key="test_key")
# Test bytes input
test_bytes = b"test_image_bytes"
result = await service._prepare_image_file(test_bytes)
assert result == test_bytes
# Test base64 input
test_b64 = base64.b64encode(test_bytes).decode()
result = await service._prepare_image_file(test_b64)
assert result == test_bytes
def test_dimension_validation(self):
"""Test image dimension validation."""
service = StabilityAIService(api_key="test_key")
# Valid dimensions
service._validate_image_requirements(1024, 1024)
# Invalid dimensions (too small)
with pytest.raises(ValueError):
service._validate_image_requirements(32, 32)
def test_aspect_ratio_validation(self):
"""Test aspect ratio validation."""
service = StabilityAIService(api_key="test_key")
# Valid aspect ratio
service._validate_aspect_ratio(1024, 1024)
# Invalid aspect ratio (too wide)
with pytest.raises(ValueError):
service._validate_aspect_ratio(3000, 500)
class TestStabilityModels:
"""Test cases for Pydantic models."""
def test_stable_image_ultra_request(self):
"""Test StableImageUltraRequest validation."""
# Valid request
request = StableImageUltraRequest(
prompt="A beautiful landscape",
aspect_ratio="16:9",
seed=42
)
assert request.prompt == "A beautiful landscape"
assert request.aspect_ratio == "16:9"
assert request.seed == 42
def test_invalid_seed_range(self):
"""Test invalid seed range validation."""
with pytest.raises(ValueError):
StableImageUltraRequest(
prompt="Test",
seed=5000000000 # Too large
)
def test_prompt_length_validation(self):
"""Test prompt length validation."""
# Too long prompt
with pytest.raises(ValueError):
StableImageUltraRequest(
prompt="x" * 10001 # Exceeds max length
)
# Empty prompt
with pytest.raises(ValueError):
StableImageUltraRequest(
prompt="" # Below min length
)
def test_outpaint_request(self):
"""Test OutpaintRequest validation."""
request = OutpaintRequest(
left=100,
right=200,
up=50,
down=150
)
assert request.left == 100
assert request.right == 200
def test_audio_request_validation(self):
"""Test audio request validation."""
request = TextToAudioRequest(
prompt="Peaceful music",
duration=60,
model="stable-audio-2.5"
)
assert request.duration == 60
assert request.model == "stable-audio-2.5"
class TestStabilityUtils:
"""Test cases for utility functions."""
def test_image_validator(self):
"""Test image validation utilities."""
from utils.stability_utils import ImageValidator
# Mock UploadFile
mock_file = Mock()
mock_file.content_type = "image/png"
mock_file.filename = "test.png"
result = ImageValidator.validate_image_file(mock_file)
assert result["is_valid"] is True
def test_prompt_optimizer(self):
"""Test prompt optimization utilities."""
from utils.stability_utils import PromptOptimizer
prompt = "A simple image"
result = PromptOptimizer.optimize_prompt(
prompt=prompt,
target_model="ultra",
target_style="photographic",
quality_level="high"
)
assert len(result["optimized_prompt"]) > len(prompt)
assert "optimizations_applied" in result
def test_parameter_validator(self):
"""Test parameter validation utilities."""
from utils.stability_utils import ParameterValidator
# Valid seed
seed = ParameterValidator.validate_seed(42)
assert seed == 42
# Invalid seed
with pytest.raises(HTTPException):
ParameterValidator.validate_seed(5000000000)
@pytest.mark.asyncio
async def test_image_analysis(self):
"""Test image content analysis."""
from utils.stability_utils import ImageValidator
result = await ImageValidator.analyze_image_content(self.test_image)
assert "width" in result
assert "height" in result
assert "total_pixels" in result
assert "quality_assessment" in result
class TestStabilityConfig:
"""Test cases for configuration."""
def test_stability_config_creation(self):
"""Test StabilityConfig creation."""
from config.stability_config import StabilityConfig
config = StabilityConfig(api_key="test_key")
assert config.api_key == "test_key"
assert config.base_url == "https://api.stability.ai"
def test_model_recommendations(self):
"""Test model recommendation logic."""
from config.stability_config import get_model_recommendations
recommendations = get_model_recommendations(
use_case="portrait",
quality_preference="premium"
)
assert "primary" in recommendations
assert "alternative" in recommendations
def test_image_validation_config(self):
"""Test image validation configuration."""
from config.stability_config import validate_image_requirements
# Valid image
result = validate_image_requirements(1024, 1024, "generate")
assert result["is_valid"] is True
# Invalid image (too small)
result = validate_image_requirements(32, 32, "generate")
assert result["is_valid"] is False
def test_cost_calculation(self):
"""Test cost calculation."""
from config.stability_config import calculate_estimated_cost
cost = calculate_estimated_cost("generate", "ultra")
assert cost == 8 # Ultra model cost
cost = calculate_estimated_cost("upscale", "fast")
assert cost == 2 # Fast upscale cost
class TestStabilityMiddleware:
"""Test cases for middleware."""
def test_rate_limit_middleware(self):
"""Test rate limiting middleware."""
from middleware.stability_middleware import RateLimitMiddleware
middleware = RateLimitMiddleware(requests_per_window=5, window_seconds=10)
# Test client identification
mock_request = Mock()
mock_request.headers = {"authorization": "Bearer test_api_key"}
client_id = middleware._get_client_id(mock_request)
assert len(client_id) == 8 # First 8 chars of API key
def test_monitoring_middleware(self):
"""Test monitoring middleware."""
from middleware.stability_middleware import MonitoringMiddleware
middleware = MonitoringMiddleware()
# Test operation extraction
operation = middleware._extract_operation("/api/stability/generate/ultra")
assert operation == "generate_ultra"
def test_caching_middleware(self):
"""Test caching middleware."""
from middleware.stability_middleware import CachingMiddleware
middleware = CachingMiddleware()
# Test cache key generation
mock_request = Mock()
mock_request.method = "GET"
mock_request.url.path = "/api/stability/health"
mock_request.query_params = {}
# This would need to be properly mocked for async
# cache_key = await middleware._generate_cache_key(mock_request)
# assert isinstance(cache_key, str)
class TestErrorHandling:
"""Test error handling scenarios."""
@patch('services.stability_service.StabilityAIService')
def test_api_error_handling(self, mock_service):
"""Test API error response handling."""
mock_service.return_value.__aenter__.return_value.generate_ultra = AsyncMock(
side_effect=HTTPException(status_code=400, detail="Invalid parameters")
)
response = client.post(
"/api/stability/generate/ultra",
data={"prompt": "Test"}
)
assert response.status_code == 400
@patch('services.stability_service.StabilityAIService')
def test_timeout_handling(self, mock_service):
"""Test timeout error handling."""
mock_service.return_value.__aenter__.return_value.generate_ultra = AsyncMock(
side_effect=asyncio.TimeoutError()
)
response = client.post(
"/api/stability/generate/ultra",
data={"prompt": "Test"}
)
assert response.status_code == 504
def test_file_size_validation(self):
"""Test file size validation."""
from utils.stability_utils import validate_file_size
# Mock large file
mock_file = Mock()
mock_file.size = 20 * 1024 * 1024 # 20MB
with pytest.raises(HTTPException) as exc_info:
validate_file_size(mock_file, max_size=10 * 1024 * 1024)
assert exc_info.value.status_code == 413
class TestWorkflowProcessing:
"""Test workflow and batch processing."""
@patch('services.stability_service.StabilityAIService')
def test_workflow_validation(self, mock_service):
"""Test workflow validation."""
from utils.stability_utils import WorkflowManager
# Valid workflow
workflow = [
{"operation": "generate_core", "parameters": {"prompt": "test"}},
{"operation": "upscale_fast", "parameters": {}}
]
errors = WorkflowManager.validate_workflow(workflow)
assert len(errors) == 0
# Invalid workflow
invalid_workflow = [
{"operation": "invalid_operation"}
]
errors = WorkflowManager.validate_workflow(invalid_workflow)
assert len(errors) > 0
def test_workflow_optimization(self):
"""Test workflow optimization."""
from utils.stability_utils import WorkflowManager
workflow = [
{"operation": "upscale_fast"},
{"operation": "generate_core"}, # Should be moved to front
{"operation": "inpaint"}
]
optimized = WorkflowManager.optimize_workflow(workflow)
# Generate operation should be first
assert optimized[0]["operation"] == "generate_core"
# ==================== INTEGRATION TESTS ====================
class TestStabilityIntegration:
"""Integration tests for full workflow."""
@pytest.mark.asyncio
@patch('aiohttp.ClientSession')
async def test_full_generation_workflow(self, mock_session):
"""Test complete generation workflow."""
# Mock successful API responses
mock_response = AsyncMock()
mock_response.status = 200
mock_response.read.return_value = b"test_image_data"
mock_response.headers = {"Content-Type": "image/png"}
mock_session.return_value.__aenter__.return_value.request.return_value.__aenter__.return_value = mock_response
service = StabilityAIService(api_key="test_key")
async with service:
# Test generation
result = await service.generate_ultra(
prompt="A beautiful landscape",
aspect_ratio="16:9",
seed=42
)
assert isinstance(result, bytes)
assert len(result) > 0
@pytest.mark.asyncio
@patch('aiohttp.ClientSession')
async def test_full_edit_workflow(self, mock_session):
"""Test complete edit workflow."""
# Mock successful API responses
mock_response = AsyncMock()
mock_response.status = 200
mock_response.read.return_value = b"test_edited_image_data"
mock_response.headers = {"Content-Type": "image/png"}
mock_session.return_value.__aenter__.return_value.request.return_value.__aenter__.return_value = mock_response
service = StabilityAIService(api_key="test_key")
async with service:
# Test inpainting
result = await service.inpaint(
image=b"test_image_data",
prompt="A cat in the scene",
grow_mask=10
)
assert isinstance(result, bytes)
assert len(result) > 0
# ==================== PERFORMANCE TESTS ====================
class TestStabilityPerformance:
"""Performance tests for Stability AI endpoints."""
@pytest.mark.asyncio
async def test_concurrent_requests(self):
"""Test handling of concurrent requests."""
from services.stability_service import StabilityAIService
async def mock_request():
service = StabilityAIService(api_key="test_key")
# Mock a quick operation
await asyncio.sleep(0.1)
return "success"
# Run multiple concurrent requests
tasks = [mock_request() for _ in range(10)]
results = await asyncio.gather(*tasks, return_exceptions=True)
# All should succeed
assert all(result == "success" for result in results)
def test_large_file_handling(self):
"""Test handling of large files."""
from utils.stability_utils import validate_file_size
# Test with various file sizes
mock_file = Mock()
# Valid size
mock_file.size = 5 * 1024 * 1024 # 5MB
validate_file_size(mock_file) # Should not raise
# Invalid size
mock_file.size = 15 * 1024 * 1024 # 15MB
with pytest.raises(HTTPException):
validate_file_size(mock_file)
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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#!/usr/bin/env python3
"""
Simple Test Script for Step 1 Only
This script tests only Step 1 to verify imports are working correctly.
"""
import asyncio
import sys
import os
from typing import Dict, Any
from loguru import logger
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
# Add the services directory to the path
services_dir = os.path.join(backend_dir, "services")
if services_dir not in sys.path:
sys.path.insert(0, services_dir)
async def test_step1_only():
"""Test only Step 1 to verify imports work."""
try:
logger.info("🚀 Starting test of Step 1 only")
# Test data
test_context = {
"user_id": 1,
"strategy_id": 1,
"calendar_duration": 7,
"posting_preferences": {
"posting_frequency": "daily",
"preferred_days": ["monday", "wednesday", "friday"],
"preferred_times": ["09:00", "12:00", "15:00"],
"content_per_day": 2
}
}
# Test Step 1: Content Strategy Analysis
logger.info("📋 Testing Step 1: Content Strategy Analysis")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import ContentStrategyAnalysisStep
from services.calendar_generation_datasource_framework.data_processing.strategy_data import StrategyDataProcessor
logger.info("✅ Imports successful")
# Create strategy processor with mock data for testing
strategy_processor = StrategyDataProcessor()
# Mock strategy data
mock_strategy_data = {
"strategy_id": 1,
"strategy_name": "Test Strategy",
"industry": "technology",
"target_audience": {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {"age_range": "25-45", "location": "Global"}
},
"content_pillars": [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
],
"business_objectives": [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership"
],
"target_metrics": {"awareness": "website_traffic", "leads": "lead_generation"},
"quality_indicators": {"data_completeness": 0.8, "strategic_alignment": 0.9}
}
# Mock the get_strategy_data method for testing
async def mock_get_strategy_data(strategy_id):
return mock_strategy_data
strategy_processor.get_strategy_data = mock_get_strategy_data
# Mock the validate_data method
async def mock_validate_data(data):
return {
"quality_score": 0.85,
"missing_fields": [],
"recommendations": []
}
strategy_processor.validate_data = mock_validate_data
step1 = ContentStrategyAnalysisStep()
step1.strategy_processor = strategy_processor
result1 = await step1.execute(test_context)
logger.info(f"✅ Step 1 completed: {result1.get('status')}")
logger.info(f" Quality Score: {result1.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 1 failed: {str(e)}")
return False
logger.info("🎉 Step 1 test completed successfully!")
return True
except Exception as e:
logger.error(f"❌ Test failed with error: {str(e)}")
return False
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the test
success = asyncio.run(test_step1_only())
if success:
logger.info("✅ Test completed successfully!")
sys.exit(0)
else:
logger.error("❌ Test failed!")
sys.exit(1)

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#!/usr/bin/env python3
"""
Test script for Step 2 specifically
"""
import asyncio
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step2_implementation import Step2Implementation
async def test_step2():
"""Test Step 2 implementation."""
print("🧪 Testing Step 2: Gap Analysis & Opportunity Identification")
# Create test context
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme",
"user_data": {
"onboarding_data": {
"posting_preferences": {
"daily": 2,
"weekly": 10,
"monthly": 40
},
"posting_days": [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"
],
"optimal_times": [
"09:00", "12:00", "15:00", "18:00", "20:00"
]
},
"strategy_data": {
"industry": "technology",
"target_audience": {
"primary": "Tech professionals",
"secondary": "Business leaders"
},
"business_objectives": [
"Increase brand awareness",
"Generate leads",
"Establish thought leadership"
]
}
},
"step_results": {},
"quality_scores": {}
}
try:
# Create Step 2 instance
step2 = Step2Implementation()
print("✅ Step 2 instance created successfully")
# Test Step 2 execution
print("🔄 Executing Step 2...")
result = await step2.run(context)
if result:
print("✅ Step 2 executed successfully!")
print(f"Status: {result.get('status')}")
print(f"Quality Score: {result.get('quality_score')}")
print(f"Execution Time: {result.get('execution_time')}")
if result.get('status') == 'error':
print(f"❌ Step 2 Error: {result.get('error_message')}")
else:
print("❌ Step 2 returned None")
except Exception as e:
print(f"❌ Error testing Step 2: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step2())

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#!/usr/bin/env python3
"""
Test script for Step 4 data
"""
import asyncio
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
async def test_step4_data():
"""Test Step 4 data processing."""
print("🧪 Testing Step 4: Calendar Framework & Timeline Data")
try:
# Test comprehensive user data
from services.calendar_generation_datasource_framework.data_processing.comprehensive_user_data import ComprehensiveUserDataProcessor
processor = ComprehensiveUserDataProcessor()
data = await processor.get_comprehensive_user_data(1, 1)
print("✅ Comprehensive user data retrieved successfully")
print(f"📊 Data keys: {list(data.keys())}")
onboarding_data = data.get('onboarding_data', {})
print(f"📋 Onboarding data keys: {list(onboarding_data.keys())}")
posting_preferences = onboarding_data.get('posting_preferences')
posting_days = onboarding_data.get('posting_days')
optimal_times = onboarding_data.get('optimal_times')
print(f"📅 Posting preferences: {posting_preferences}")
print(f"📅 Posting days: {posting_days}")
print(f"⏰ Optimal times: {optimal_times}")
if posting_preferences and posting_days:
print("✅ Step 4 data requirements met!")
else:
print("❌ Step 4 data requirements NOT met!")
except Exception as e:
print(f"❌ Error testing Step 4 data: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step4_data())

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#!/usr/bin/env python3
"""
Debug Step 4 data issues - check what data is available from database.
"""
import asyncio
import time
from loguru import logger
import sys
import os
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
async def test_step4_data_sources():
"""Test what data Step 4 is actually receiving."""
try:
logger.info("🧪 Testing Step 4 data sources")
# Test 1: Onboarding Data Service
logger.info("📋 Test 1: Onboarding Data Service")
try:
from services.onboarding_data_service import OnboardingDataService
onboarding_service = OnboardingDataService()
# Test with user_id = 1
onboarding_data = onboarding_service.get_personalized_ai_inputs(1)
logger.info(f"📊 Onboarding data keys: {list(onboarding_data.keys()) if onboarding_data else 'None'}")
if onboarding_data:
# Check for posting preferences
posting_prefs = onboarding_data.get("posting_preferences")
posting_days = onboarding_data.get("posting_days")
optimal_times = onboarding_data.get("optimal_times")
logger.info(f"📅 Posting preferences: {posting_prefs}")
logger.info(f"📅 Posting days: {posting_days}")
logger.info(f"📅 Optimal times: {optimal_times}")
# Check website analysis
website_analysis = onboarding_data.get("website_analysis", {})
logger.info(f"🌐 Website analysis keys: {list(website_analysis.keys())}")
# Check competitor analysis
competitor_analysis = onboarding_data.get("competitor_analysis", {})
logger.info(f"🏢 Competitor analysis keys: {list(competitor_analysis.keys())}")
# Check keyword analysis
keyword_analysis = onboarding_data.get("keyword_analysis", {})
logger.info(f"🔍 Keyword analysis keys: {list(keyword_analysis.keys())}")
else:
logger.error("❌ No onboarding data returned")
except Exception as e:
logger.error(f"❌ Onboarding service error: {str(e)}")
# Test 2: Comprehensive User Data Processor
logger.info("\n📋 Test 2: Comprehensive User Data Processor")
try:
from services.calendar_generation_datasource_framework.data_processing.comprehensive_user_data import ComprehensiveUserDataProcessor
processor = ComprehensiveUserDataProcessor()
comprehensive_data = await processor.get_comprehensive_user_data(1, 1)
logger.info(f"📊 Comprehensive data keys: {list(comprehensive_data.keys()) if comprehensive_data else 'None'}")
if comprehensive_data:
# Check onboarding data
onboarding_data = comprehensive_data.get("onboarding_data", {})
logger.info(f"👤 Onboarding data keys: {list(onboarding_data.keys())}")
# Check for posting preferences (Step 4 requirement)
posting_prefs = onboarding_data.get("posting_preferences")
posting_days = onboarding_data.get("posting_days")
optimal_times = onboarding_data.get("optimal_times")
logger.info(f"📅 Posting preferences: {posting_prefs}")
logger.info(f"📅 Posting days: {posting_days}")
logger.info(f"📅 Optimal times: {optimal_times}")
# Check strategy data
strategy_data = comprehensive_data.get("strategy_data", {})
logger.info(f"🎯 Strategy data keys: {list(strategy_data.keys())}")
# Check gap analysis
gap_analysis = comprehensive_data.get("gap_analysis", {})
logger.info(f"📊 Gap analysis keys: {list(gap_analysis.keys())}")
else:
logger.error("❌ No comprehensive data returned")
except Exception as e:
logger.error(f"❌ Comprehensive data processor error: {str(e)}")
# Test 3: Database Connection
logger.info("\n📋 Test 3: Database Connection")
try:
from services.database import get_db_session
from models.onboarding import OnboardingSession, WebsiteAnalysis
session = get_db_session()
# Check for onboarding sessions
onboarding_sessions = session.query(OnboardingSession).all()
logger.info(f"📊 Found {len(onboarding_sessions)} onboarding sessions")
if onboarding_sessions:
for i, session_data in enumerate(onboarding_sessions):
logger.info(f" Session {i+1}: user_id={session_data.user_id}, created={session_data.created_at}")
# Check for website analysis
website_analyses = session.query(WebsiteAnalysis).filter(
WebsiteAnalysis.session_id == session_data.id
).all()
logger.info(f" Website analyses: {len(website_analyses)}")
else:
logger.warning("⚠️ No onboarding sessions found in database")
except Exception as e:
logger.error(f"❌ Database connection error: {str(e)}")
# Test 4: Step 4 Direct Test
logger.info("\n📋 Test 4: Step 4 Direct Test")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step4_implementation import CalendarFrameworkStep
step4 = CalendarFrameworkStep()
# Create mock context
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme"
}
# Try to execute Step 4
logger.info("🔄 Executing Step 4...")
result = await step4.execute(context)
logger.info(f"✅ Step 4 executed successfully")
logger.info(f"📊 Result keys: {list(result.keys()) if result else 'None'}")
except Exception as e:
logger.error(f"❌ Step 4 execution error: {str(e)}")
import traceback
logger.error(f"📋 Traceback: {traceback.format_exc()}")
logger.info("\n🎯 Step 4 Data Debug Complete")
except Exception as e:
logger.error(f"❌ Error in data debug test: {str(e)}")
import traceback
logger.error(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the test
asyncio.run(test_step4_data_sources())

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#!/usr/bin/env python3
"""
Test script for Step 4 execution
"""
import asyncio
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
async def test_step4_execution():
"""Test Step 4 execution directly."""
print("🧪 Testing Step 4: Calendar Framework & Timeline Execution")
try:
# Import Step 4
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step4_implementation import CalendarFrameworkStep
# Create test context
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme",
"step_results": {},
"quality_scores": {}
}
# Create Step 4 instance
step4 = CalendarFrameworkStep()
print("✅ Step 4 instance created successfully")
# Test Step 4 execution
print("🔄 Executing Step 4...")
result = await step4.run(context)
if result:
print("✅ Step 4 executed successfully!")
print(f"Status: {result.get('status')}")
print(f"Quality Score: {result.get('quality_score')}")
print(f"Execution Time: {result.get('execution_time')}")
if result.get('status') == 'error':
print(f"❌ Step 4 Error: {result.get('error_message')}")
else:
print("📊 Step 4 Results:")
print(f" - Calendar Structure: {result.get('calendar_structure', {}).get('type')}")
print(f" - Timeline Config: {result.get('timeline_config', {}).get('total_weeks')} weeks")
print(f" - Duration Control: {result.get('duration_control', {}).get('validation_passed')}")
else:
print("❌ Step 4 returned None")
except Exception as e:
print(f"❌ Error testing Step 4 execution: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step4_execution())

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#!/usr/bin/env python3
"""
Test Script for Step 4 Implementation
This script tests the Step 4 (Calendar Framework and Timeline) implementation
to ensure it works correctly with real AI services and data processing.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add the backend directory to the Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.phase2_steps import CalendarFrameworkStep
from services.calendar_generation_datasource_framework.data_processing import ComprehensiveUserDataProcessor
async def test_step4_implementation():
"""Test Step 4 implementation with real data processing."""
print("🧪 Testing Step 4: Calendar Framework and Timeline Implementation")
try:
# Initialize Step 4
step4 = CalendarFrameworkStep()
print("✅ Step 4 initialized successfully")
# Initialize data processor
data_processor = ComprehensiveUserDataProcessor()
print("✅ Data processor initialized successfully")
# Test context data
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme"
}
print(f"📊 Testing with context: {context}")
# Execute Step 4
print("🔄 Executing Step 4...")
result = await step4.execute(context)
# Validate results
print("📋 Step 4 Results:")
print(f" - Step Number: {result.get('stepNumber')}")
print(f" - Step Name: {result.get('stepName')}")
print(f" - Quality Score: {result.get('qualityScore', 0):.2f}")
print(f" - Execution Time: {result.get('executionTime')}")
print(f" - Data Sources Used: {result.get('dataSourcesUsed')}")
# Validate calendar structure
calendar_structure = result.get('results', {}).get('calendarStructure', {})
print(f" - Calendar Type: {calendar_structure.get('type')}")
print(f" - Total Weeks: {calendar_structure.get('totalWeeks')}")
print(f" - Content Distribution: {calendar_structure.get('contentDistribution')}")
# Validate timeline configuration
timeline_config = result.get('results', {}).get('timelineConfiguration', {})
print(f" - Start Date: {timeline_config.get('startDate')}")
print(f" - End Date: {timeline_config.get('endDate')}")
print(f" - Total Days: {timeline_config.get('totalDays')}")
print(f" - Posting Days: {timeline_config.get('postingDays')}")
# Validate quality gates
duration_control = result.get('results', {}).get('durationControl', {})
strategic_alignment = result.get('results', {}).get('strategicAlignment', {})
print(f" - Duration Accuracy: {duration_control.get('accuracyScore', 0):.1%}")
print(f" - Strategic Alignment: {strategic_alignment.get('alignmentScore', 0):.1%}")
# Validate insights and recommendations
insights = result.get('insights', [])
recommendations = result.get('recommendations', [])
print(f" - Insights Count: {len(insights)}")
print(f" - Recommendations Count: {len(recommendations)}")
# Quality validation
quality_score = result.get('qualityScore', 0)
if quality_score >= 0.85:
print(f"✅ Quality Score: {quality_score:.2f} (Excellent)")
elif quality_score >= 0.75:
print(f"✅ Quality Score: {quality_score:.2f} (Good)")
else:
print(f"⚠️ Quality Score: {quality_score:.2f} (Needs Improvement)")
print("✅ Step 4 implementation test completed successfully!")
return True
except Exception as e:
print(f"❌ Error testing Step 4: {str(e)}")
import traceback
print(f"Traceback: {traceback.format_exc()}")
return False
async def test_step4_integration():
"""Test Step 4 integration with the orchestrator."""
print("\n🧪 Testing Step 4 Integration with Orchestrator")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.orchestrator import PromptChainOrchestrator
# Initialize orchestrator
orchestrator = PromptChainOrchestrator()
print("✅ Orchestrator initialized successfully")
# Check if Step 4 is properly registered
step4 = orchestrator.steps.get("step_04")
if step4 and step4.name == "Calendar Framework & Timeline":
print("✅ Step 4 properly registered in orchestrator")
else:
print("❌ Step 4 not properly registered in orchestrator")
return False
# Test context initialization
context = await orchestrator._initialize_context(
user_id=1,
strategy_id=1,
calendar_type="monthly",
industry="technology",
business_size="sme"
)
print("✅ Context initialization successful")
# Test Step 4 execution through orchestrator
print("🔄 Testing Step 4 execution through orchestrator...")
step_result = await step4.execute(context)
if step_result and step_result.get('stepNumber') == 4:
print("✅ Step 4 execution through orchestrator successful")
print(f" - Quality Score: {step_result.get('qualityScore', 0):.2f}")
else:
print("❌ Step 4 execution through orchestrator failed")
return False
print("✅ Step 4 integration test completed successfully!")
return True
except Exception as e:
print(f"❌ Error testing Step 4 integration: {str(e)}")
import traceback
print(f"Traceback: {traceback.format_exc()}")
return False
async def test_step4_data_processing():
"""Test Step 4 data processing capabilities."""
print("\n🧪 Testing Step 4 Data Processing")
try:
from services.calendar_generation_datasource_framework.data_processing import ComprehensiveUserDataProcessor
# Initialize data processor
data_processor = ComprehensiveUserDataProcessor()
print("✅ Data processor initialized successfully")
# Test comprehensive user data retrieval
print("🔄 Testing comprehensive user data retrieval...")
user_data = await data_processor.get_comprehensive_user_data(1, 1)
if user_data:
print("✅ Comprehensive user data retrieved successfully")
print(f" - User ID: {user_data.get('user_id')}")
print(f" - Strategy ID: {user_data.get('strategy_id')}")
print(f" - Industry: {user_data.get('industry')}")
# Check for required data sections
required_sections = ['onboarding_data', 'strategy_data', 'gap_analysis', 'ai_analysis']
for section in required_sections:
if section in user_data:
print(f" - {section}: Available")
else:
print(f" - {section}: Missing")
else:
print("❌ Failed to retrieve comprehensive user data")
return False
print("✅ Step 4 data processing test completed successfully!")
return True
except Exception as e:
print(f"❌ Error testing Step 4 data processing: {str(e)}")
import traceback
print(f"Traceback: {traceback.format_exc()}")
return False
async def main():
"""Main test function."""
print("🚀 Starting Step 4 Implementation Tests")
print("=" * 50)
# Run all tests
tests = [
test_step4_implementation(),
test_step4_integration(),
test_step4_data_processing()
]
results = await asyncio.gather(*tests, return_exceptions=True)
# Summarize results
print("\n" + "=" * 50)
print("📊 Test Results Summary")
print("=" * 50)
test_names = [
"Step 4 Implementation",
"Step 4 Integration",
"Step 4 Data Processing"
]
passed = 0
total = len(results)
for i, result in enumerate(results):
if isinstance(result, Exception):
print(f"❌ {test_names[i]}: Failed - {str(result)}")
elif result:
print(f"✅ {test_names[i]}: Passed")
passed += 1
else:
print(f"❌ {test_names[i]}: Failed")
print(f"\n🎯 Overall Results: {passed}/{total} tests passed")
if passed == total:
print("🎉 All tests passed! Step 4 implementation is ready for production.")
return True
else:
print("⚠️ Some tests failed. Please review the implementation.")
return False
if __name__ == "__main__":
success = asyncio.run(main())
sys.exit(0 if success else 1)

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#!/usr/bin/env python3
"""
Test script for Step 5 debugging
"""
import asyncio
import sys
import os
import time
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
async def test_step5_debug():
"""Debug Step 5 execution specifically."""
print("🧪 Debugging Step 5: Content Pillar Distribution")
try:
# Import Step 5
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
# Create test context with data from previous steps
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme",
"previous_step_results": {
4: {
"results": {
"calendarStructure": {
"type": "monthly",
"total_weeks": 4,
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"posting_frequency": {
"daily": 2,
"weekly": 10,
"monthly": 40
}
}
}
}
},
"step_results": {},
"quality_scores": {}
}
# Create Step 5 instance
print("✅ Creating Step 5 instance...")
step5 = ContentPillarDistributionStep()
print("✅ Step 5 instance created successfully")
# Test Step 5 execution with timing
print("🔄 Executing Step 5...")
start_time = time.time()
result = await step5.run(context)
execution_time = time.time() - start_time
print(f"⏱️ Step 5 execution time: {execution_time:.2f} seconds")
if result:
print("✅ Step 5 executed successfully!")
print(f"Status: {result.get('status', 'unknown')}")
print(f"Quality Score: {result.get('quality_score', 0)}")
print(f"Execution Time: {result.get('execution_time', 'unknown')}")
if result.get('status') == 'error':
print(f"❌ Step 5 Error: {result.get('error_message', 'Unknown error')}")
else:
print("📊 Step 5 Results:")
results = result.get('results', {})
print(f" - Pillar Mapping: {results.get('pillarMapping', {}).get('distribution_balance', 0):.1%} balance")
print(f" - Theme Development: {results.get('themeDevelopment', {}).get('variety_score', 0):.1%} variety")
print(f" - Strategic Validation: {results.get('strategicValidation', {}).get('alignment_score', 0):.1%} alignment")
print(f" - Diversity Assurance: {results.get('diversityAssurance', {}).get('diversity_score', 0):.1%} diversity")
else:
print("❌ Step 5 returned None")
except Exception as e:
print(f"❌ Error testing Step 5: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step5_debug())

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#!/usr/bin/env python3
"""
Test script for Step 5 with orchestrator context structure
"""
import asyncio
import sys
import os
import time
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
async def test_step5_orchestrator_context():
"""Test Step 5 with orchestrator context structure."""
print("🧪 Testing Step 5 with orchestrator context structure")
try:
# Import Step 5
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
# Create context exactly as the orchestrator does
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme",
"user_data": {
"user_id": 1,
"strategy_id": 1,
"industry": "technology",
"onboarding_data": {
"posting_preferences": {
"daily": 2,
"weekly": 10,
"monthly": 40
},
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"optimal_times": ["09:00", "12:00", "15:00", "18:00", "20:00"]
},
"strategy_data": {
"content_pillars": [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
],
"business_objectives": [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership in AI/ML space"
]
}
},
"step_results": {
"step_04": {
"stepNumber": 4,
"stepName": "Calendar Framework & Timeline",
"results": {
"calendarStructure": {
"type": "monthly",
"total_weeks": 4,
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"posting_frequency": {
"daily": 2,
"weekly": 10,
"monthly": 40
},
"industry": "technology",
"business_size": "sme"
}
},
"qualityScore": 1.0,
"executionTime": "2.9s"
}
},
"quality_scores": {},
"current_step": 5,
"phase": "phase_2_structure"
}
# Create Step 5 instance
print("✅ Creating Step 5 instance...")
step5 = ContentPillarDistributionStep()
print("✅ Step 5 instance created successfully")
# Test Step 5 execution with timing
print("🔄 Executing Step 5...")
start_time = time.time()
result = await step5.run(context)
execution_time = time.time() - start_time
print(f"⏱️ Step 5 execution time: {execution_time:.2f} seconds")
if result:
print("✅ Step 5 executed successfully!")
print(f"Status: {result.get('status', 'unknown')}")
print(f"Quality Score: {result.get('quality_score', 0)}")
print(f"Execution Time: {result.get('execution_time', 'unknown')}")
if result.get('status') == 'error':
print(f"❌ Step 5 Error: {result.get('error_message', 'Unknown error')}")
else:
print("📊 Step 5 Results:")
results = result.get('results', {})
print(f" - Pillar Mapping: {results.get('pillarMapping', {}).get('distribution_balance', 0):.1%} balance")
print(f" - Theme Development: {results.get('themeDevelopment', {}).get('variety_score', 0):.1%} variety")
print(f" - Strategic Validation: {results.get('strategicValidation', {}).get('alignment_score', 0):.1%} alignment")
print(f" - Diversity Assurance: {results.get('diversityAssurance', {}).get('diversity_score', 0):.1%} diversity")
else:
print("❌ Step 5 returned None")
except Exception as e:
print(f"❌ Error testing Step 5: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step5_orchestrator_context())

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#!/usr/bin/env python3
"""
Test script for Step 5 with orchestrator's direct step execution
"""
import asyncio
import sys
import os
import time
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
async def test_step5_orchestrator_direct():
"""Test Step 5 with orchestrator's direct step execution."""
print("🧪 Testing Step 5 with orchestrator's direct step execution")
try:
# Import orchestrator and Step 5
from services.calendar_generation_datasource_framework.prompt_chaining.orchestrator import PromptChainOrchestrator
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
# Create orchestrator
print("✅ Creating orchestrator...")
orchestrator = PromptChainOrchestrator()
print("✅ Orchestrator created successfully")
# Get Step 5 from orchestrator
step5 = orchestrator.steps["step_05"]
print(f"✅ Got Step 5 from orchestrator: {type(step5)}")
# Create context exactly as the orchestrator does
context = {
"user_id": 1,
"strategy_id": 1,
"calendar_type": "monthly",
"industry": "technology",
"business_size": "sme",
"user_data": {
"user_id": 1,
"strategy_id": 1,
"industry": "technology",
"onboarding_data": {
"posting_preferences": {
"daily": 2,
"weekly": 10,
"monthly": 40
},
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"optimal_times": ["09:00", "12:00", "15:00", "18:00", "20:00"]
},
"strategy_data": {
"content_pillars": [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
],
"business_objectives": [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership in AI/ML space"
]
}
},
"step_results": {
"step_04": {
"stepNumber": 4,
"stepName": "Calendar Framework & Timeline",
"results": {
"calendarStructure": {
"type": "monthly",
"total_weeks": 4,
"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"posting_frequency": {
"daily": 2,
"weekly": 10,
"monthly": 40
},
"industry": "technology",
"business_size": "sme"
}
},
"qualityScore": 1.0,
"executionTime": "2.9s"
}
},
"quality_scores": {},
"current_step": 5,
"phase": "phase_2_structure"
}
# Test Step 5 execution with timing
print("🔄 Executing Step 5 with orchestrator's step...")
start_time = time.time()
result = await step5.run(context)
execution_time = time.time() - start_time
print(f"⏱️ Step 5 execution time: {execution_time:.2f} seconds")
if result:
print("✅ Step 5 executed successfully!")
print(f"Status: {result.get('status', 'unknown')}")
print(f"Quality Score: {result.get('quality_score', 0)}")
print(f"Execution Time: {result.get('execution_time', 'unknown')}")
if result.get('status') == 'error':
print(f"❌ Step 5 Error: {result.get('error_message', 'Unknown error')}")
else:
print("📊 Step 5 Results:")
step_result = result.get('result', {})
print(f" - Pillar Mapping: {step_result.get('pillarMapping', {}).get('distribution_balance', 0):.1%} balance")
print(f" - Theme Development: {step_result.get('themeDevelopment', {}).get('variety_score', 0):.1%} variety")
print(f" - Strategic Validation: {step_result.get('strategicValidation', {}).get('alignment_score', 0):.1%} alignment")
print(f" - Diversity Assurance: {step_result.get('diversityAssurance', {}).get('diversity_score', 0):.1%} diversity")
else:
print("❌ Step 5 returned None")
except Exception as e:
print(f"❌ Error testing Step 5: {e}")
import traceback
print(f"📋 Traceback: {traceback.format_exc()}")
if __name__ == "__main__":
asyncio.run(test_step5_orchestrator_direct())

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#!/usr/bin/env python3
"""
Test Script for Steps 1-8 of Calendar Generation Framework
This script tests the first 8 steps of the calendar generation process
with real data sources and no fallbacks.
"""
import asyncio
import sys
import os
from typing import Dict, Any
from loguru import logger
# Add the backend directory to the path
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
# Add the services directory to the path
services_dir = os.path.join(backend_dir, "services")
if services_dir not in sys.path:
sys.path.insert(0, services_dir)
async def test_steps_1_8():
"""Test Steps 1-8 of the calendar generation framework."""
try:
logger.info("🚀 Starting test of Steps 1-8")
# Test data
test_context = {
"user_id": 1,
"strategy_id": 1,
"calendar_duration": 7, # 1 week
"posting_preferences": {
"posting_frequency": "daily",
"preferred_days": ["monday", "wednesday", "friday"],
"preferred_times": ["09:00", "12:00", "15:00"],
"content_per_day": 2
}
}
# Test Step 1: Content Strategy Analysis
logger.info("📋 Testing Step 1: Content Strategy Analysis")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import ContentStrategyAnalysisStep
from services.calendar_generation_datasource_framework.data_processing.strategy_data import StrategyDataProcessor
# Create strategy processor with mock data for testing
strategy_processor = StrategyDataProcessor()
# For testing, we'll create a simple mock strategy data
# In a real scenario, this would come from the database
mock_strategy_data = {
"strategy_id": 1,
"strategy_name": "Test Strategy",
"industry": "technology",
"target_audience": {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {"age_range": "25-45", "location": "Global"}
},
"content_pillars": [
"AI and Machine Learning",
"Digital Transformation",
"Innovation and Technology Trends",
"Business Strategy and Growth"
],
"business_objectives": [
"Increase brand awareness by 40%",
"Generate 500 qualified leads per month",
"Establish thought leadership"
],
"target_metrics": {"awareness": "website_traffic", "leads": "lead_generation"},
"quality_indicators": {"data_completeness": 0.8, "strategic_alignment": 0.9}
}
# Mock the get_strategy_data method for testing
async def mock_get_strategy_data(strategy_id):
return mock_strategy_data
strategy_processor.get_strategy_data = mock_get_strategy_data
# Mock the validate_data method
async def mock_validate_data(data):
return {
"quality_score": 0.85,
"missing_fields": [],
"recommendations": []
}
strategy_processor.validate_data = mock_validate_data
step1 = ContentStrategyAnalysisStep()
step1.strategy_processor = strategy_processor
result1 = await step1.execute(test_context)
logger.info(f"✅ Step 1 completed: {result1.get('status')}")
logger.info(f" Quality Score: {result1.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 1 failed: {str(e)}")
return False
# Test Step 2: Gap Analysis
logger.info("📋 Testing Step 2: Gap Analysis")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import GapAnalysisStep
from services.calendar_generation_datasource_framework.data_processing.gap_analysis_data import GapAnalysisDataProcessor
# Create gap processor with mock data for testing
gap_processor = GapAnalysisDataProcessor()
# Mock gap analysis data
mock_gap_data = {
"content_gaps": [
{"topic": "AI Ethics", "priority": "high", "impact_score": 0.9},
{"topic": "Digital Transformation ROI", "priority": "medium", "impact_score": 0.7},
{"topic": "Cloud Migration Strategies", "priority": "high", "impact_score": 0.8}
],
"keyword_opportunities": [
{"keyword": "AI ethics in business", "search_volume": 5000, "competition": "low"},
{"keyword": "digital transformation ROI", "search_volume": 8000, "competition": "medium"},
{"keyword": "cloud migration guide", "search_volume": 12000, "competition": "high"}
],
"competitor_insights": {
"top_competitors": ["Competitor A", "Competitor B"],
"content_gaps": ["AI Ethics", "Practical ROI"],
"opportunities": ["Case Studies", "Implementation Guides"]
},
"opportunities": [
{"type": "content", "topic": "AI Ethics", "priority": "high"},
{"type": "content", "topic": "ROI Analysis", "priority": "medium"}
],
"recommendations": [
"Create comprehensive AI ethics guide",
"Develop ROI calculator for digital transformation",
"Publish case studies on successful implementations"
]
}
# Mock the get_gap_analysis_data method
async def mock_get_gap_analysis_data(user_id):
return mock_gap_data
gap_processor.get_gap_analysis_data = mock_get_gap_analysis_data
step2 = GapAnalysisStep()
step2.gap_processor = gap_processor
result2 = await step2.execute(test_context)
logger.info(f"✅ Step 2 completed: {result2.get('status')}")
logger.info(f" Quality Score: {result2.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 2 failed: {str(e)}")
return False
# Test Step 3: Audience & Platform Strategy
logger.info("📋 Testing Step 3: Audience & Platform Strategy")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase1.phase1_steps import AudiencePlatformStrategyStep
from services.calendar_generation_datasource_framework.data_processing.comprehensive_user_data import ComprehensiveUserDataProcessor
# Create comprehensive processor with mock data for testing
comprehensive_processor = ComprehensiveUserDataProcessor()
# Mock comprehensive user data
mock_user_data = {
"user_id": 1,
"onboarding_data": {
"industry": "technology",
"business_size": "enterprise",
"target_audience": {
"primary": "Tech professionals",
"secondary": "Business leaders",
"demographics": {"age_range": "25-45", "location": "Global"}
},
"platform_preferences": {
"LinkedIn": {"priority": "high", "content_focus": "professional"},
"Twitter": {"priority": "medium", "content_focus": "news"},
"Blog": {"priority": "high", "content_focus": "in-depth"}
}
},
"performance_data": {
"LinkedIn": {"engagement_rate": 0.08, "reach": 10000},
"Twitter": {"engagement_rate": 0.05, "reach": 5000},
"Blog": {"engagement_rate": 0.12, "reach": 8000}
},
"strategy_data": mock_strategy_data
}
# Mock the get_comprehensive_user_data method
async def mock_get_comprehensive_user_data(user_id, strategy_id):
return mock_user_data
comprehensive_processor.get_comprehensive_user_data = mock_get_comprehensive_user_data
step3 = AudiencePlatformStrategyStep()
step3.comprehensive_processor = comprehensive_processor
result3 = await step3.execute(test_context)
logger.info(f"✅ Step 3 completed: {result3.get('status')}")
logger.info(f" Quality Score: {result3.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 3 failed: {str(e)}")
return False
# Test Step 4: Calendar Framework
logger.info("📋 Testing Step 4: Calendar Framework")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step4_implementation import CalendarFrameworkStep
step4 = CalendarFrameworkStep()
result4 = await step4.execute(test_context)
logger.info(f"✅ Step 4 completed: {result4.get('status')}")
logger.info(f" Quality Score: {result4.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 4 failed: {str(e)}")
return False
# Test Step 5: Content Pillar Distribution
logger.info("📋 Testing Step 5: Content Pillar Distribution")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
step5 = ContentPillarDistributionStep()
result5 = await step5.execute(test_context)
logger.info(f"✅ Step 5 completed: {result5.get('status')}")
logger.info(f" Quality Score: {result5.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 5 failed: {str(e)}")
return False
# Test Step 6: Platform-Specific Strategy
logger.info("📋 Testing Step 6: Platform-Specific Strategy")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step6_implementation import PlatformSpecificStrategyStep
step6 = PlatformSpecificStrategyStep()
result6 = await step6.execute(test_context)
logger.info(f"✅ Step 6 completed: {result6.get('status')}")
logger.info(f" Quality Score: {result6.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 6 failed: {str(e)}")
return False
# Test Step 7: Weekly Theme Development
logger.info("📋 Testing Step 7: Weekly Theme Development")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step7_implementation import WeeklyThemeDevelopmentStep
step7 = WeeklyThemeDevelopmentStep()
result7 = await step7.execute(test_context)
logger.info(f"✅ Step 7 completed: {result7.get('status')}")
logger.info(f" Quality Score: {result7.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 7 failed: {str(e)}")
return False
# Test Step 8: Daily Content Planning
logger.info("📋 Testing Step 8: Daily Content Planning")
try:
from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase3.step8_implementation import DailyContentPlanningStep
step8 = DailyContentPlanningStep()
result8 = await step8.execute(test_context)
logger.info(f"✅ Step 8 completed: {result8.get('status')}")
logger.info(f" Quality Score: {result8.get('quality_score')}")
except Exception as e:
logger.error(f"❌ Step 8 failed: {str(e)}")
return False
logger.info("🎉 All Steps 1-8 completed successfully!")
logger.info("📝 Note: This test uses mock data for database services.")
logger.info("📝 In production, real database services would be used.")
return True
except Exception as e:
logger.error(f"❌ Test failed with error: {str(e)}")
return False
if __name__ == "__main__":
# Configure logging
logger.remove()
logger.add(sys.stderr, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
# Run the test
success = asyncio.run(test_steps_1_8())
if success:
logger.info("✅ Test completed successfully!")
sys.exit(0)
else:
logger.error("❌ Test failed!")
sys.exit(1)

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"""
Test script to verify strategy data structure matches frontend expectations
"""
import asyncio
import json
from api.content_planning.services.strategy_service import StrategyService
async def test_strategy_data_structure():
"""Test the strategy data structure to ensure it matches frontend expectations."""
print("🧪 Testing Strategy Data Structure")
print("=" * 50)
# Initialize service
service = StrategyService()
# Get strategies
result = await service.get_strategies(user_id=1)
print("📊 Backend Response Structure:")
print(json.dumps(result, indent=2, default=str))
# Check if strategies array exists
if "strategies" in result and len(result["strategies"]) > 0:
strategy = result["strategies"][0]
print("\n✅ Frontend Expected Structure Check:")
print("-" * 40)
# Check for ai_recommendations
if "ai_recommendations" in strategy:
ai_rec = strategy["ai_recommendations"]
print(f"✅ ai_recommendations: Present")
# Check market_score
if "market_score" in ai_rec:
print(f"✅ market_score: {ai_rec['market_score']}")
else:
print("❌ market_score: Missing")
# Check strengths
if "strengths" in ai_rec:
print(f"✅ strengths: {len(ai_rec['strengths'])} items")
else:
print("❌ strengths: Missing")
# Check weaknesses
if "weaknesses" in ai_rec:
print(f"✅ weaknesses: {len(ai_rec['weaknesses'])} items")
else:
print("❌ weaknesses: Missing")
# Check competitive_advantages
if "competitive_advantages" in ai_rec:
print(f"✅ competitive_advantages: {len(ai_rec['competitive_advantages'])} items")
else:
print("❌ competitive_advantages: Missing")
# Check strategic_risks
if "strategic_risks" in ai_rec:
print(f"✅ strategic_risks: {len(ai_rec['strategic_risks'])} items")
else:
print("❌ strategic_risks: Missing")
else:
print("❌ ai_recommendations: Missing")
# Check for required strategy fields
required_fields = ["id", "name", "industry", "target_audience", "content_pillars"]
for field in required_fields:
if field in strategy:
print(f"✅ {field}: Present")
else:
print(f"❌ {field}: Missing")
print("\n🎯 Frontend Data Mapping Validation:")
print("-" * 40)
# Validate the specific structure expected by frontend
if "ai_recommendations" in strategy:
ai_rec = strategy["ai_recommendations"]
# Check market positioning structure
if "market_score" in ai_rec:
print(f"✅ Frontend can access: strategy.ai_recommendations.market_score")
# Check strengths structure
if "strengths" in ai_rec and isinstance(ai_rec["strengths"], list):
print(f"✅ Frontend can access: strategy.ai_recommendations.strengths")
# Check weaknesses structure
if "weaknesses" in ai_rec and isinstance(ai_rec["weaknesses"], list):
print(f"✅ Frontend can access: strategy.ai_recommendations.weaknesses")
# Check competitive advantages structure
if "competitive_advantages" in ai_rec and isinstance(ai_rec["competitive_advantages"], list):
print(f"✅ Frontend can access: strategy.ai_recommendations.competitive_advantages")
# Check strategic risks structure
if "strategic_risks" in ai_rec and isinstance(ai_rec["strategic_risks"], list):
print(f"✅ Frontend can access: strategy.ai_recommendations.strategic_risks")
print("\n🎉 Data Structure Validation Complete!")
print("=" * 50)
return True
else:
print("❌ No strategies found in response")
return False
if __name__ == "__main__":
success = asyncio.run(test_strategy_data_structure())
if success:
print("✅ All tests passed! Backend data structure matches frontend expectations.")
else:
print("❌ Tests failed! Backend data structure needs adjustment.")

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"""
Test Script for Subscription System
Tests the core functionality of the usage-based subscription system.
"""
import sys
import os
from pathlib import Path
import asyncio
import json
# Add the backend directory to Python path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from sqlalchemy.orm import sessionmaker
from loguru import logger
from services.database import engine
from services.pricing_service import PricingService
from services.usage_tracking_service import UsageTrackingService
from models.subscription_models import APIProvider, SubscriptionTier
async def test_pricing_service():
"""Test the pricing service functionality."""
logger.info("🧪 Testing Pricing Service...")
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
db = SessionLocal()
try:
pricing_service = PricingService(db)
# Test cost calculation
cost_data = pricing_service.calculate_api_cost(
provider=APIProvider.GEMINI,
model_name="gemini-2.5-flash",
tokens_input=1000,
tokens_output=500,
request_count=1
)
logger.info(f"✅ Cost calculation: {cost_data}")
# Test user limits
limits = pricing_service.get_user_limits("test_user")
logger.info(f"✅ User limits: {limits}")
# Test usage limit checking
can_proceed, message, usage_info = pricing_service.check_usage_limits(
user_id="test_user",
provider=APIProvider.GEMINI,
tokens_requested=100
)
logger.info(f"✅ Usage check: {can_proceed} - {message}")
logger.info(f" Usage info: {usage_info}")
return True
except Exception as e:
logger.error(f"❌ Pricing service test failed: {e}")
return False
finally:
db.close()
async def test_usage_tracking():
"""Test the usage tracking service."""
logger.info("🧪 Testing Usage Tracking Service...")
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
db = SessionLocal()
try:
usage_service = UsageTrackingService(db)
# Test tracking an API usage
result = await usage_service.track_api_usage(
user_id="test_user",
provider=APIProvider.GEMINI,
endpoint="/api/generate",
method="POST",
model_used="gemini-2.5-flash",
tokens_input=500,
tokens_output=300,
response_time=1.5,
status_code=200
)
logger.info(f"✅ Usage tracking result: {result}")
# Test getting usage stats
stats = usage_service.get_user_usage_stats("test_user")
logger.info(f"✅ Usage stats: {json.dumps(stats, indent=2)}")
# Test usage trends
trends = usage_service.get_usage_trends("test_user", 3)
logger.info(f"✅ Usage trends: {json.dumps(trends, indent=2)}")
return True
except Exception as e:
logger.error(f"❌ Usage tracking test failed: {e}")
return False
finally:
db.close()
async def test_limit_enforcement():
"""Test usage limit enforcement."""
logger.info("🧪 Testing Limit Enforcement...")
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
db = SessionLocal()
try:
usage_service = UsageTrackingService(db)
# Test multiple API calls to approach limits
for i in range(5):
result = await usage_service.track_api_usage(
user_id="test_user_limits",
provider=APIProvider.GEMINI,
endpoint="/api/generate",
method="POST",
model_used="gemini-2.5-flash",
tokens_input=1000,
tokens_output=800,
response_time=2.0,
status_code=200
)
logger.info(f"Call {i+1}: {result}")
# Check if limits are being enforced
can_proceed, message, usage_info = await usage_service.enforce_usage_limits(
user_id="test_user_limits",
provider=APIProvider.GEMINI,
tokens_requested=5000
)
logger.info(f"✅ Limit enforcement: {can_proceed} - {message}")
logger.info(f" Usage info: {usage_info}")
return True
except Exception as e:
logger.error(f"❌ Limit enforcement test failed: {e}")
return False
finally:
db.close()
def test_database_tables():
"""Test that all subscription tables exist."""
logger.info("🧪 Testing Database Tables...")
try:
from sqlalchemy import text
with engine.connect() as conn:
# Check for subscription tables
tables_query = text("""
SELECT name FROM sqlite_master
WHERE type='table' AND (
name LIKE '%subscription%' OR
name LIKE '%usage%' OR
name LIKE '%pricing%' OR
name LIKE '%billing%'
)
ORDER BY name
""")
result = conn.execute(tables_query)
tables = result.fetchall()
expected_tables = [
'api_provider_pricing',
'api_usage_logs',
'billing_history',
'subscription_plans',
'usage_alerts',
'usage_summaries',
'user_subscriptions'
]
found_tables = [t[0] for t in tables]
logger.info(f"Found tables: {found_tables}")
missing_tables = [t for t in expected_tables if t not in found_tables]
if missing_tables:
logger.error(f"❌ Missing tables: {missing_tables}")
return False
# Check table data
for table in ['subscription_plans', 'api_provider_pricing']:
count_query = text(f"SELECT COUNT(*) FROM {table}")
result = conn.execute(count_query)
count = result.fetchone()[0]
logger.info(f"✅ {table}: {count} records")
return True
except Exception as e:
logger.error(f"❌ Database tables test failed: {e}")
return False
async def run_comprehensive_test():
"""Run comprehensive test suite."""
logger.info("🚀 Starting Subscription System Comprehensive Test")
logger.info("="*60)
test_results = {}
# Test 1: Database Tables
logger.info("\n1. Testing Database Tables...")
test_results['database_tables'] = test_database_tables()
# Test 2: Pricing Service
logger.info("\n2. Testing Pricing Service...")
test_results['pricing_service'] = await test_pricing_service()
# Test 3: Usage Tracking
logger.info("\n3. Testing Usage Tracking...")
test_results['usage_tracking'] = await test_usage_tracking()
# Test 4: Limit Enforcement
logger.info("\n4. Testing Limit Enforcement...")
test_results['limit_enforcement'] = await test_limit_enforcement()
# Summary
logger.info("\n" + "="*60)
logger.info("TEST RESULTS SUMMARY")
logger.info("="*60)
passed = sum(1 for result in test_results.values() if result)
total = len(test_results)
for test_name, result in test_results.items():
status = "✅ PASS" if result else "❌ FAIL"
logger.info(f"{test_name.upper().replace('_', ' ')}: {status}")
logger.info(f"\nOverall: {passed}/{total} tests passed")
if passed == total:
logger.info("🎉 All tests passed! Subscription system is ready.")
logger.info("\n" + "="*60)
logger.info("NEXT STEPS:")
logger.info("="*60)
logger.info("1. Start the FastAPI server:")
logger.info(" cd backend && python start_alwrity_backend.py")
logger.info("\n2. Test the API endpoints:")
logger.info(" GET http://localhost:8000/api/subscription/plans")
logger.info(" GET http://localhost:8000/api/subscription/pricing")
logger.info(" GET http://localhost:8000/api/subscription/usage/test_user")
logger.info("\n3. Integrate with your frontend dashboard")
logger.info("4. Set up user authentication/identification")
logger.info("5. Configure payment processing (Stripe, etc.)")
logger.info("="*60)
return True
else:
logger.error("❌ Some tests failed. Please check the errors above.")
return False
if __name__ == "__main__":
# Run the comprehensive test
success = asyncio.run(run_comprehensive_test())
if not success:
sys.exit(1)
logger.info("✅ Test completed successfully!")

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#!/usr/bin/env python3
"""
Test script for user data service
"""
import sys
import os
# Add the backend directory to the path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from services.database import init_database, get_db_session
from services.user_data_service import UserDataService
from loguru import logger
def test_user_data():
"""Test the user data service functionality."""
print("👤 Testing User Data Service")
print("=" * 50)
try:
# Initialize database
print("📊 Initializing database...")
init_database()
print("✅ Database initialized successfully")
# Test fetching user website URL
print("\n🌐 Testing website URL fetching...")
db_session = get_db_session()
if db_session:
try:
user_data_service = UserDataService(db_session)
website_url = user_data_service.get_user_website_url()
if website_url:
print(f"✅ Found website URL: {website_url}")
else:
print("⚠️ No website URL found in database")
print(" This is expected if no onboarding has been completed yet")
# Test getting full onboarding data
print("\n📋 Testing full onboarding data...")
onboarding_data = user_data_service.get_user_onboarding_data()
if onboarding_data:
print("✅ Found onboarding data:")
print(f" Session ID: {onboarding_data['session']['id']}")
print(f" Current Step: {onboarding_data['session']['current_step']}")
print(f" Progress: {onboarding_data['session']['progress']}")
if onboarding_data['website_analysis']:
print(f" Website URL: {onboarding_data['website_analysis']['website_url']}")
print(f" Analysis Status: {onboarding_data['website_analysis']['status']}")
else:
print(" No website analysis found")
print(f" API Keys: {len(onboarding_data['api_keys'])} configured")
if onboarding_data['research_preferences']:
print(" Research preferences configured")
else:
print(" No research preferences found")
else:
print("⚠️ No onboarding data found")
print(" This is expected if no onboarding has been completed yet")
except Exception as e:
print(f"❌ Database error: {str(e)}")
finally:
db_session.close()
else:
print("❌ Failed to get database session")
print("\n🎉 User Data Service Test Completed!")
except Exception as e:
print(f"❌ Test failed: {str(e)}")
logger.error(f"Test failed: {str(e)}")
if __name__ == "__main__":
test_user_data()

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#!/usr/bin/env python3
"""
Database validation script for billing system
"""
import sqlite3
from datetime import datetime
def validate_database():
conn = sqlite3.connect('alwrity.db')
cursor = conn.cursor()
print('=== BILLING DATABASE VALIDATION ===')
print(f'Validation timestamp: {datetime.now()}')
print()
# Check subscription-related tables
cursor.execute("""
SELECT name FROM sqlite_master
WHERE type='table' AND (
name LIKE '%subscription%' OR
name LIKE '%usage%' OR
name LIKE '%billing%' OR
name LIKE '%pricing%' OR
name LIKE '%alert%'
)
ORDER BY name
""")
tables = cursor.fetchall()
print('=== SUBSCRIPTION TABLES ===')
for table in tables:
table_name = table[0]
print(f'\nTable: {table_name}')
# Get table schema
cursor.execute(f'PRAGMA table_info({table_name})')
columns = cursor.fetchall()
print(' Schema:')
for col in columns:
col_id, name, type_name, not_null, default, pk = col
constraints = []
if pk:
constraints.append('PRIMARY KEY')
if not_null:
constraints.append('NOT NULL')
if default:
constraints.append(f'DEFAULT {default}')
constraint_str = f' ({", ".join(constraints)})' if constraints else ''
print(f' {name}: {type_name}{constraint_str}')
# Get row count
cursor.execute(f'SELECT COUNT(*) FROM {table_name}')
count = cursor.fetchone()[0]
print(f' Row count: {count}')
# Sample data for non-empty tables
if count > 0 and count <= 10:
cursor.execute(f'SELECT * FROM {table_name} LIMIT 3')
rows = cursor.fetchall()
print(' Sample data:')
for i, row in enumerate(rows):
print(f' Row {i+1}: {row}')
# Check for user-specific data
print('\n=== USER DATA VALIDATION ===')
# Check if we have user-specific usage data
cursor.execute("SELECT DISTINCT user_id FROM usage_summary LIMIT 5")
users = cursor.fetchall()
print(f'Users with usage data: {[u[0] for u in users]}')
# Check user subscriptions
cursor.execute("SELECT DISTINCT user_id FROM user_subscriptions LIMIT 5")
user_subs = cursor.fetchall()
print(f'Users with subscriptions: {[u[0] for u in user_subs]}')
# Check API usage logs
cursor.execute("SELECT COUNT(*) FROM api_usage_logs")
api_logs_count = cursor.fetchone()[0]
print(f'Total API usage logs: {api_logs_count}')
if api_logs_count > 0:
cursor.execute("SELECT DISTINCT user_id FROM api_usage_logs LIMIT 5")
api_users = cursor.fetchall()
print(f'Users with API usage logs: {[u[0] for u in api_users]}')
conn.close()
print('\n=== VALIDATION COMPLETE ===')
if __name__ == '__main__':
validate_database()

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backend/test/validate_linkedin_structure.py Normal file
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"""
Simple validation script for LinkedIn content generation structure.
This script validates the code structure without requiring external dependencies.
"""
import os
import sys
import ast
import traceback
from pathlib import Path
def validate_file_syntax(file_path: str) -> bool:
"""Validate Python file syntax."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
ast.parse(content)
print(f"✅ {file_path}: Syntax valid")
return True
except SyntaxError as e:
print(f"❌ {file_path}: Syntax error - {e}")
return False
except Exception as e:
print(f"❌ {file_path}: Error - {e}")
return False
def validate_import_structure(file_path: str) -> bool:
"""Validate import structure without actually importing."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
tree = ast.parse(content)
imports = []
for node in ast.walk(tree):
if isinstance(node, ast.Import):
for alias in node.names:
imports.append(alias.name)
elif isinstance(node, ast.ImportFrom):
module = node.module or ""
for alias in node.names:
imports.append(f"{module}.{alias.name}")
print(f"✅ {file_path}: Found {len(imports)} imports")
return True
except Exception as e:
print(f"❌ {file_path}: Import validation error - {e}")
return False
def check_class_structure(file_path: str, expected_classes: list) -> bool:
"""Check if expected classes are defined."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
tree = ast.parse(content)
found_classes = []
for node in ast.walk(tree):
if isinstance(node, ast.ClassDef):
found_classes.append(node.name)
missing_classes = set(expected_classes) - set(found_classes)
if missing_classes:
print(f"⚠️ {file_path}: Missing classes: {missing_classes}")
else:
print(f"✅ {file_path}: All expected classes found")
print(f" Found classes: {found_classes}")
return len(missing_classes) == 0
except Exception as e:
print(f"❌ {file_path}: Class validation error - {e}")
return False
def check_function_structure(file_path: str, expected_functions: list) -> bool:
"""Check if expected functions are defined."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
tree = ast.parse(content)
found_functions = []
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
found_functions.append(node.name)
elif isinstance(node, ast.AsyncFunctionDef):
found_functions.append(node.name)
missing_functions = set(expected_functions) - set(found_functions)
if missing_functions:
print(f"⚠️ {file_path}: Missing functions: {missing_functions}")
else:
print(f"✅ {file_path}: All expected functions found")
return len(missing_functions) == 0
except Exception as e:
print(f"❌ {file_path}: Function validation error - {e}")
return False
def validate_linkedin_models():
"""Validate LinkedIn models file."""
print("\n🔍 Validating LinkedIn Models")
print("-" * 40)
file_path = "models/linkedin_models.py"
if not os.path.exists(file_path):
print(f"❌ {file_path}: File does not exist")
return False
# Check syntax
syntax_ok = validate_file_syntax(file_path)
# Check imports
imports_ok = validate_import_structure(file_path)
# Check expected classes
expected_classes = [
"LinkedInPostRequest", "LinkedInArticleRequest", "LinkedInCarouselRequest",
"LinkedInVideoScriptRequest", "LinkedInCommentResponseRequest",
"LinkedInPostResponse", "LinkedInArticleResponse", "LinkedInCarouselResponse",
"LinkedInVideoScriptResponse", "LinkedInCommentResponseResult",
"PostContent", "ArticleContent", "CarouselContent", "VideoScript"
]
classes_ok = check_class_structure(file_path, expected_classes)
return syntax_ok and imports_ok and classes_ok
def validate_linkedin_service():
"""Validate LinkedIn service file."""
print("\n🔍 Validating LinkedIn Service")
print("-" * 40)
file_path = "services/linkedin_service.py"
if not os.path.exists(file_path):
print(f"❌ {file_path}: File does not exist")
return False
# Check syntax
syntax_ok = validate_file_syntax(file_path)
# Check imports
imports_ok = validate_import_structure(file_path)
# Check expected classes
expected_classes = ["LinkedInContentService"]
classes_ok = check_class_structure(file_path, expected_classes)
# Check expected methods
expected_functions = [
"generate_post", "generate_article", "generate_carousel",
"generate_video_script", "generate_comment_response"
]
functions_ok = check_function_structure(file_path, expected_functions)
return syntax_ok and imports_ok and classes_ok and functions_ok
def validate_linkedin_router():
"""Validate LinkedIn router file."""
print("\n🔍 Validating LinkedIn Router")
print("-" * 40)
file_path = "routers/linkedin.py"
if not os.path.exists(file_path):
print(f"❌ {file_path}: File does not exist")
return False
# Check syntax
syntax_ok = validate_file_syntax(file_path)
# Check imports
imports_ok = validate_import_structure(file_path)
# Check expected functions (endpoints)
expected_functions = [
"health_check", "generate_post", "generate_article",
"generate_carousel", "generate_video_script", "generate_comment_response",
"get_content_types", "get_usage_stats"
]
functions_ok = check_function_structure(file_path, expected_functions)
return syntax_ok and imports_ok and functions_ok
def check_file_exists(file_path: str) -> bool:
"""Check if file exists."""
exists = os.path.exists(file_path)
status = "✅" if exists else "❌"
print(f"{status} {file_path}: {'Exists' if exists else 'Missing'}")
return exists
def validate_file_structure():
"""Validate the overall file structure."""
print("\n🔍 Validating File Structure")
print("-" * 40)
required_files = [
"models/linkedin_models.py",
"services/linkedin_service.py",
"routers/linkedin.py",
"test_linkedin_endpoints.py"
]
all_exist = True
for file_path in required_files:
if not check_file_exists(file_path):
all_exist = False
return all_exist
def main():
"""Run all validations."""
print("🚀 LinkedIn Content Generation Structure Validation")
print("=" * 60)
results = {}
# Validate file structure
results["file_structure"] = validate_file_structure()
# Validate individual components
results["models"] = validate_linkedin_models()
results["service"] = validate_linkedin_service()
results["router"] = validate_linkedin_router()
# Summary
print("\n📊 Validation Results")
print("=" * 40)
passed = sum(results.values())
total = len(results)
for component, result in results.items():
status = "✅ PASSED" if result else "❌ FAILED"
print(f"{component}: {status}")
print(f"\nOverall: {passed}/{total} validations passed ({(passed/total)*100:.1f}%)")
if passed == total:
print("\n🎉 All structure validations passed!")
print("The LinkedIn content generation migration is structurally complete.")
print("\nNext steps:")
print("1. Install required dependencies (fastapi, pydantic, etc.)")
print("2. Configure API keys (GEMINI_API_KEY)")
print("3. Start the FastAPI server")
print("4. Test the endpoints")
else:
print(f"\n⚠️ {total - passed} validation(s) failed. Please review the implementation.")
return passed == total
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

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backend/test/verify_billing_setup.py Normal file
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"""
Comprehensive verification script for billing and subscription system setup.
Checks that all files are created, tables exist, and the system is properly integrated.
"""
import os
import sys
from pathlib import Path
def check_file_exists(file_path, description):
"""Check if a file exists and report status."""
if os.path.exists(file_path):
print(f"✅ {description}: {file_path}")
return True
else:
print(f"❌ {description}: {file_path} - NOT FOUND")
return False
def check_file_content(file_path, search_terms, description):
"""Check if file contains expected content."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
missing_terms = []
for term in search_terms:
if term not in content:
missing_terms.append(term)
if not missing_terms:
print(f"✅ {description}: All expected content found")
return True
else:
print(f"❌ {description}: Missing content - {missing_terms}")
return False
except Exception as e:
print(f"❌ {description}: Error reading file - {e}")
return False
def check_database_tables():
"""Check if billing database tables exist."""
print("\n🗄️ Checking Database Tables:")
print("-" * 30)
try:
# Add backend to path
backend_dir = Path(__file__).parent
sys.path.insert(0, str(backend_dir))
from services.database import get_db_session, DATABASE_URL
from sqlalchemy import text
session = get_db_session()
if not session:
print("❌ Could not get database session")
return False
# Check for billing tables
tables_query = text("""
SELECT name FROM sqlite_master
WHERE type='table' AND (
name LIKE '%subscription%' OR
name LIKE '%usage%' OR
name LIKE '%billing%' OR
name LIKE '%pricing%' OR
name LIKE '%alert%'
)
ORDER BY name
""")
result = session.execute(tables_query)
tables = result.fetchall()
expected_tables = [
'api_provider_pricing',
'api_usage_logs',
'subscription_plans',
'usage_alerts',
'usage_summaries',
'user_subscriptions'
]
found_tables = [t[0] for t in tables]
print(f"Found tables: {found_tables}")
missing_tables = [t for t in expected_tables if t not in found_tables]
if missing_tables:
print(f"❌ Missing tables: {missing_tables}")
return False
# Check table data
for table in ['subscription_plans', 'api_provider_pricing']:
count_query = text(f"SELECT COUNT(*) FROM {table}")
result = session.execute(count_query)
count = result.fetchone()[0]
print(f"✅ {table}: {count} records")
session.close()
return True
except Exception as e:
print(f"❌ Database check failed: {e}")
return False
def main():
"""Main verification function."""
print("🔍 ALwrity Billing & Subscription System Setup Verification")
print("=" * 70)
backend_dir = Path(__file__).parent
# Files to check
files_to_check = [
(backend_dir / "models" / "subscription_models.py", "Subscription Models"),
(backend_dir / "services" / "pricing_service.py", "Pricing Service"),
(backend_dir / "services" / "usage_tracking_service.py", "Usage Tracking Service"),
(backend_dir / "services" / "subscription_exception_handler.py", "Exception Handler"),
(backend_dir / "api" / "subscription_api.py", "Subscription API"),
(backend_dir / "scripts" / "create_billing_tables.py", "Billing Migration Script"),
(backend_dir / "scripts" / "create_subscription_tables.py", "Subscription Migration Script"),
(backend_dir / "start_alwrity_backend.py", "Backend Startup Script"),
]
# Check file existence
print("\n📁 Checking File Existence:")
print("-" * 30)
files_exist = 0
for file_path, description in files_to_check:
if check_file_exists(file_path, description):
files_exist += 1
# Check content of key files
print("\n📝 Checking File Content:")
print("-" * 30)
content_checks = [
(
backend_dir / "models" / "subscription_models.py",
["SubscriptionPlan", "APIUsageLog", "UsageSummary", "APIProviderPricing"],
"Subscription Models Content"
),
(
backend_dir / "services" / "pricing_service.py",
["calculate_api_cost", "check_usage_limits", "initialize_default_pricing"],
"Pricing Service Content"
),
(
backend_dir / "services" / "usage_tracking_service.py",
["track_api_usage", "get_user_usage_stats", "enforce_usage_limits"],
"Usage Tracking Content"
),
(
backend_dir / "api" / "subscription_api.py",
["get_user_usage", "get_subscription_plans", "get_dashboard_data"],
"API Endpoints Content"
),
(
backend_dir / "start_alwrity_backend.py",
["setup_billing_tables", "verify_billing_tables"],
"Backend Startup Integration"
)
]
content_valid = 0
for file_path, search_terms, description in content_checks:
if os.path.exists(file_path):
if check_file_content(file_path, search_terms, description):
content_valid += 1
else:
print(f"❌ {description}: File not found")
# Check database tables
database_ok = check_database_tables()
# Check middleware integration
print("\n🔧 Checking Middleware Integration:")
print("-" * 30)
middleware_file = backend_dir / "middleware" / "monitoring_middleware.py"
middleware_terms = [
"UsageTrackingService",
"detect_api_provider",
"track_api_usage",
"check_usage_limits_middleware"
]
middleware_ok = check_file_content(
middleware_file,
middleware_terms,
"Middleware Integration"
)
# Check app.py integration
print("\n🚀 Checking FastAPI Integration:")
print("-" * 30)
app_file = backend_dir / "app.py"
app_terms = [
"from api.subscription_api import router as subscription_router",
"app.include_router(subscription_router)"
]
app_ok = check_file_content(
app_file,
app_terms,
"FastAPI App Integration"
)
# Check database service integration
print("\n💾 Checking Database Integration:")
print("-" * 30)
db_file = backend_dir / "services" / "database.py"
db_terms = [
"from models.subscription_models import Base as SubscriptionBase",
"SubscriptionBase.metadata.create_all(bind=engine)"
]
db_ok = check_file_content(
db_file,
db_terms,
"Database Service Integration"
)
# Summary
print("\n" + "=" * 70)
print("📊 VERIFICATION SUMMARY")
print("=" * 70)
total_files = len(files_to_check)
total_content = len(content_checks)
print(f"Files Created: {files_exist}/{total_files}")
print(f"Content Valid: {content_valid}/{total_content}")
print(f"Database Tables: {'✅' if database_ok else '❌'}")
print(f"Middleware Integration: {'✅' if middleware_ok else '❌'}")
print(f"FastAPI Integration: {'✅' if app_ok else '❌'}")
print(f"Database Integration: {'✅' if db_ok else '❌'}")
# Overall status
all_checks = [
files_exist == total_files,
content_valid == total_content,
database_ok,
middleware_ok,
app_ok,
db_ok
]
if all(all_checks):
print("\n🎉 ALL CHECKS PASSED!")
print("✅ Billing and subscription system setup is complete and ready to use.")
print("\n" + "=" * 70)
print("🚀 NEXT STEPS:")
print("=" * 70)
print("1. Start the backend server:")
print(" python start_alwrity_backend.py")
print("\n2. Test the API endpoints:")
print(" GET http://localhost:8000/api/subscription/plans")
print(" GET http://localhost:8000/api/subscription/usage/demo")
print(" GET http://localhost:8000/api/subscription/dashboard/demo")
print(" GET http://localhost:8000/api/subscription/pricing")
print("\n3. Access the frontend billing dashboard")
print("4. Monitor usage through the API monitoring middleware")
print("5. Set up user identification for production use")
print("=" * 70)
else:
print("\n❌ SOME CHECKS FAILED!")
print("Please review the errors above and fix any issues.")
return False
return True
if __name__ == "__main__":
success = main()
if not success:
sys.exit(1)

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backend/test/verify_subscription_setup.py Normal file
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"""
Simple verification script for subscription system setup.
Checks that all files are created and properly structured.
"""
import os
import sys
from pathlib import Path
def check_file_exists(file_path, description):
"""Check if a file exists and report status."""
if os.path.exists(file_path):
print(f"✅ {description}: {file_path}")
return True
else:
print(f"❌ {description}: {file_path} - NOT FOUND")
return False
def check_file_content(file_path, search_terms, description):
"""Check if file contains expected content."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
missing_terms = []
for term in search_terms:
if term not in content:
missing_terms.append(term)
if not missing_terms:
print(f"✅ {description}: All expected content found")
return True
else:
print(f"❌ {description}: Missing content - {missing_terms}")
return False
except Exception as e:
print(f"❌ {description}: Error reading file - {e}")
return False
def main():
"""Main verification function."""
print("🔍 ALwrity Subscription System Setup Verification")
print("=" * 60)
backend_dir = Path(__file__).parent
# Files to check
files_to_check = [
(backend_dir / "models" / "subscription_models.py", "Subscription Models"),
(backend_dir / "services" / "pricing_service.py", "Pricing Service"),
(backend_dir / "services" / "usage_tracking_service.py", "Usage Tracking Service"),
(backend_dir / "services" / "subscription_exception_handler.py", "Exception Handler"),
(backend_dir / "api" / "subscription_api.py", "Subscription API"),
(backend_dir / "scripts" / "create_subscription_tables.py", "Migration Script"),
(backend_dir / "test_subscription_system.py", "Test Script"),
(backend_dir / "SUBSCRIPTION_SYSTEM_README.md", "Documentation")
]
# Check file existence
print("\n📁 Checking File Existence:")
print("-" * 30)
files_exist = 0
for file_path, description in files_to_check:
if check_file_exists(file_path, description):
files_exist += 1
# Check content of key files
print("\n📝 Checking File Content:")
print("-" * 30)
content_checks = [
(
backend_dir / "models" / "subscription_models.py",
["SubscriptionPlan", "APIUsageLog", "UsageSummary", "APIProvider"],
"Subscription Models Content"
),
(
backend_dir / "services" / "pricing_service.py",
["calculate_api_cost", "check_usage_limits", "APIProvider.GEMINI"],
"Pricing Service Content"
),
(
backend_dir / "services" / "usage_tracking_service.py",
["track_api_usage", "get_user_usage_stats", "enforce_usage_limits"],
"Usage Tracking Content"
),
(
backend_dir / "api" / "subscription_api.py",
["get_user_usage", "get_subscription_plans", "get_dashboard_data"],
"API Endpoints Content"
)
]
content_valid = 0
for file_path, search_terms, description in content_checks:
if os.path.exists(file_path):
if check_file_content(file_path, search_terms, description):
content_valid += 1
else:
print(f"❌ {description}: File not found")
# Check middleware integration
print("\n🔧 Checking Middleware Integration:")
print("-" * 30)
middleware_file = backend_dir / "middleware" / "monitoring_middleware.py"
middleware_terms = [
"UsageTrackingService",
"detect_api_provider",
"track_api_usage",
"check_usage_limits_middleware"
]
middleware_ok = check_file_content(
middleware_file,
middleware_terms,
"Middleware Integration"
)
# Check app.py integration
print("\n🚀 Checking FastAPI Integration:")
print("-" * 30)
app_file = backend_dir / "app.py"
app_terms = [
"from api.subscription_api import router as subscription_router",
"app.include_router(subscription_router)"
]
app_ok = check_file_content(
app_file,
app_terms,
"FastAPI App Integration"
)
# Check database service integration
print("\n💾 Checking Database Integration:")
print("-" * 30)
db_file = backend_dir / "services" / "database.py"
db_terms = [
"from models.subscription_models import Base as SubscriptionBase",
"SubscriptionBase.metadata.create_all(bind=engine)"
]
db_ok = check_file_content(
db_file,
db_terms,
"Database Service Integration"
)
# Summary
print("\n" + "=" * 60)
print("📊 VERIFICATION SUMMARY")
print("=" * 60)
total_files = len(files_to_check)
total_content = len(content_checks)
print(f"Files Created: {files_exist}/{total_files}")
print(f"Content Valid: {content_valid}/{total_content}")
print(f"Middleware Integration: {'✅' if middleware_ok else '❌'}")
print(f"FastAPI Integration: {'✅' if app_ok else '❌'}")
print(f"Database Integration: {'✅' if db_ok else '❌'}")
# Overall status
all_checks = [
files_exist == total_files,
content_valid == total_content,
middleware_ok,
app_ok,
db_ok
]
if all(all_checks):
print("\n🎉 ALL CHECKS PASSED!")
print("✅ Subscription system setup is complete and ready to use.")
print("\n" + "=" * 60)
print("🚀 NEXT STEPS:")
print("=" * 60)
print("1. Install dependencies (if not already done):")
print(" pip install sqlalchemy loguru fastapi")
print("\n2. Run the migration script:")
print(" python scripts/create_subscription_tables.py")
print("\n3. Test the system:")
print(" python test_subscription_system.py")
print("\n4. Start the server:")
print(" python start_alwrity_backend.py")
print("\n5. Test API endpoints:")
print(" GET http://localhost:8000/api/subscription/plans")
print(" GET http://localhost:8000/api/subscription/pricing")
else:
print("\n❌ SOME CHECKS FAILED!")
print("Please review the errors above and fix any issues.")
return False
return True
if __name__ == "__main__":
success = main()
if not success:
sys.exit(1)