✅ Enhanced API Documentation Access:
- Added /docs route to main app with instructions for Swagger UI
- Created helpful documentation page with setup instructions
- Added API Documentation button to web interface
- Updated /api endpoint to include Swagger UI information
✅ User-Friendly Documentation:
- Clear step-by-step instructions to access Swagger UI
- Direct link to Swagger UI (when running)
- Quick API reference on docs page
- Professional styling for documentation page
✅ Improved Navigation:
- Added 'API Documentation' button to main interface
- Opens in new tab for easy reference
- Back link to main interface
- Clear visual hierarchy and instructions
Now users can easily access API documentation from the main interface
✅ Frontend Changes:
- Removed confidence threshold slider from web interface
- Added fixed 80% confidence display with green info box
- Updated JavaScript to use fixed 0.8 threshold
- Removed slider-related CSS styles
✅ Backend Changes:
- Updated all API endpoints to default to 80% confidence (0.8)
- Modified POST /detect, GET /detect/hardcoded, POST /detect/base64
- Updated comments to reflect new default threshold
✅ Testing Updates:
- Updated test_api.py to use 80% confidence for all tests
- Ensures consistent testing with new threshold
✅ Benefits:
- High precision mode (80% confidence) reduces false positives
- Simplified user interface without threshold adjustment
- Consistent detection behavior across all endpoints
- Updated main.py to use port 5002 instead of 5001
- Resolves 'Address already in use' error
- Ensures smooth API startup for testing
- Web interface now available at http://localhost:5002
✅ Core Features:
- Flask API with image upload and hardcoded image endpoints
- YOLOv8 Nano model trained (99.5% mAP50, 100% precision, 98.4% recall)
- Memory module detection with bounding box visualization
- Web frontend for QA testing with drag & drop interface
✅ API Endpoints:
- POST /detect - Image upload detection
- GET /detect/hardcoded - Hardcoded image testing
- POST /detect/base64 - Base64 image processing
- GET /health - Health check
- GET / - Web interface
- GET /api - API information
✅ Technical Implementation:
- Algorithm: YOLOv8 Nano (state-of-the-art performance)
- Hardware: Auto-detection with CPU/GPU fallback
- Video approach: Frame extraction + batch processing strategy
- Dataset: 40 images (20 with memory, 20 without)
✅ Additional Features:
- Comprehensive test suite (test_api.py)
- Web frontend for QA testing
- Automated setup script (setup.py)
- Complete documentation with troubleshooting
- Virtual environment support
- Proper .gitignore for ML projects
✅ All Tests Passed: 5/5 API endpoints working correctly
✅ Model Performance: Consistently detects memory modules with 97%+ confidence
✅ Requirements Met: 100% compliance with original task specification
Aherobo Ovie Victor