This commit introduces major enhancements to Talk2Me:
## Database Integration
- PostgreSQL support with SQLAlchemy ORM
- Redis integration for caching and real-time analytics
- Automated database initialization scripts
- Migration support infrastructure
## User Authentication System
- JWT-based API authentication
- Session-based web authentication
- API key authentication for programmatic access
- User roles and permissions (admin/user)
- Login history and session tracking
- Rate limiting per user with customizable limits
## Admin Dashboard
- Real-time analytics and monitoring
- User management interface (create, edit, delete users)
- System health monitoring
- Request/error tracking
- Language pair usage statistics
- Performance metrics visualization
## Key Features
- Dual authentication support (token + user accounts)
- Graceful fallback for missing services
- Non-blocking analytics middleware
- Comprehensive error handling
- Session management with security features
## Bug Fixes
- Fixed rate limiting bypass for admin routes
- Added missing email validation method
- Improved error handling for missing database tables
- Fixed session-based authentication for API endpoints
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This adds a complete production deployment setup using Gunicorn as the WSGI server, replacing Flask's development server.
Key components:
- Gunicorn configuration with optimized worker settings
- Support for sync, threaded, and async (gevent) workers
- Automatic worker recycling to prevent memory leaks
- Increased timeouts for audio processing
- Production-ready logging and monitoring
Deployment options:
1. Docker/Docker Compose for containerized deployment
2. Systemd service for traditional deployment
3. Nginx reverse proxy configuration
4. SSL/TLS support
Production features:
- wsgi.py entry point for WSGI servers
- gunicorn_config.py with production settings
- Dockerfile with multi-stage build
- docker-compose.yml with full stack (Redis, PostgreSQL)
- nginx.conf with caching and security headers
- systemd service with security hardening
- deploy.sh automated deployment script
Configuration:
- .env.production template with all settings
- Support for environment-based configuration
- Separate requirements-prod.txt
- Prometheus metrics endpoint (/metrics)
Monitoring:
- Health check endpoints for liveness/readiness
- Prometheus-compatible metrics
- Structured logging
- Memory usage tracking
- Request counting
Security:
- Non-root user in Docker
- Systemd security restrictions
- Nginx security headers
- File permission hardening
- Resource limits
Documentation:
- Comprehensive PRODUCTION_DEPLOYMENT.md
- Scaling strategies
- Performance tuning guide
- Troubleshooting section
Also fixed memory_manager.py GC stats collection error.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This comprehensive fix addresses memory leaks in both backend and frontend that could cause server crashes after extended use.
Backend fixes:
- MemoryManager class monitors process and GPU memory usage
- Automatic cleanup when thresholds exceeded (4GB process, 2GB GPU)
- Whisper model reloading to clear GPU memory fragmentation
- Aggressive temporary file cleanup based on age
- Context manager for audio processing with guaranteed cleanup
- Integration with session manager for resource tracking
- Background monitoring thread runs every 30 seconds
Frontend fixes:
- MemoryManager singleton tracks all browser resources
- SafeMediaRecorder wrapper ensures stream cleanup
- AudioBlobHandler manages blob lifecycle and object URLs
- Automatic cleanup of closed AudioContexts
- Proper MediaStream track stopping
- Periodic cleanup of orphaned resources
- Cleanup on page unload
Admin features:
- GET /admin/memory - View memory statistics
- POST /admin/memory/cleanup - Trigger manual cleanup
- Real-time metrics including GPU usage and temp files
- Model reload tracking
Key improvements:
- AudioContext properly closed after use
- Object URLs revoked after use
- MediaRecorder streams properly stopped
- Audio chunks cleared after processing
- GPU cache cleared after each transcription
- Temp files tracked and cleaned aggressively
This prevents the gradual memory increase that could lead to out-of-memory errors or performance degradation after hours of use.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
