Production-ready weather mashup application with multi-source data aggregation,
historical tracking, intelligent recommendations, and premium glassmorphism UI.

Project Overview:
- Real-time weather data aggregation from multiple APIs
- Historical weather tracking with SQLite database
- Smart activity and clothing recommendations
- Modern web dashboard with glassmorphism design
- Automated setup and deployment scripts
- Comprehensive documentation

Core Features:
✓ Multi-source weather API aggregation (Open-Meteo + WeatherAPI.com fallback)
✓ Historical data persistence with SQLite
✓ Statistical analysis and trend visualization
✓ Activity recommendations based on weather conditions
✓ Clothing advisor with feels-like temperature
✓ 24-hour and 7-day forecast charts
✓ Auto-refresh dashboard (5-minute intervals)
✓ Browser geolocation support
✓ Premium glassmorphism UI design
✓ Responsive layout for all devices

Technology Stack:
- Backend: Python 3.8+, Flask 3.1.0, SQLite
- Frontend: HTML5, Bootstrap 5, Chart.js, Font Awesome
- APIs: Open-Meteo (primary), WeatherAPI.com (optional)
- Caching: Flask-Caching (5-minute TTL)

Project Structure:
- aggregator/ - Business logic and API clients (11 modules)
- models/ - Database models and persistence layer
- templates/ - Jinja2 HTML templates
- tests/ - Unit tests with pytest
- utils/ - Configuration and utilities
- data/ - SQLite database (auto-created, gitignored)

API Endpoints:
- GET /api/metrics - Complete weather data + recommendations
- GET /api/history - Historical weather records
- GET /api/statistics - Statistical analysis (min/max/avg)
- GET /api/trends - Hourly aggregated trends
- GET /api/current - Raw weather data
- GET /api/forecast/hourly - 24-hour forecast
- GET /api/forecast/daily - 7-day forecast
- GET /dashboard - Main HTML dashboard
- GET / - Homepage

Setup & Deployment:
- Automated setup scripts (setup.bat/setup.sh)
- One-click launch scripts (run.bat/run.sh)
- Environment-based configuration (.env)
- Cross-platform support (Windows, Linux, Mac)

Documentation:
- README.md - Complete user guide
- QUICKSTART.md - Simple setup guide
- TECHNICAL_DOCUMENTATION.md - Technical reference
- Inline code documentation (Google-style docstrings)

Key Technical Features:
- Retry mechanism with exponential backoff
- Multi-source fallback for reliability
- Comprehensive error handling and logging
- Database indexing for performance
- Caching to reduce API calls
- Type hints throughout codebase
- Unit tests with >80% coverage

External APIs:
- Open-Meteo API (free, no key required) - Primary source
- WeatherAPI.com (free tier, 1M calls/month) - Optional fallback

Configuration:
- Default location: Geneva, Switzerland (46.2°N, 6.15°E)
- Customizable via .env file
- Optional multi-source fallback with WEATHERAPI_KEY
- Configurable cache timeout and logging level

Database Schema:
- weather_records table with 14 columns
- Indexed on timestamp for fast queries
- Automatic data persistence on every API call
- Support for historical analysis and trends

UI/UX:
- Modern glassmorphism design with backdrop blur
- Inter font family from Google Fonts
- Pastel gradient backgrounds (purple/cyan)
- Smooth animations and transitions
- Responsive grid layout (6 activities displayed)
- Interactive charts with Chart.js
- Loading states and error handling

Testing:
- Unit tests for API endpoints
- Aggregator logic tests
- Weather client tests
- Error handling tests
- Run with: pytest --cov

Security:
- API keys in .env (gitignored)
- Input validation for coordinates
- Proper error handling
- HTTPS ready (use reverse proxy)

Performance:
- 5-minute caching on all endpoints
- Database indexing for fast queries
- Non-blocking history saving
- Connection pooling
- Lazy loading for charts

Dependencies:
- Flask==3.1.0
- requests==2.32.3
- Flask-Caching==2.3.0
- python-dotenv==1.0.1
- pytest (dev)
- pytest-cov (dev)

Project Status: Production Ready ✓
Version: 1.0.0
Educational Project: University of Geneva - Mashups TP3
Contributors: Yohan, Marwan, Marc