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Author SHA1 Message Date
Trilltechnician b083890e96 feat: Integrate frequency-based device identification system 
Integrated comprehensive device attribution system into GigLez:

1. Simple Device Matcher (src/matcher/simple_matcher.py):
   - Frequency-based device categorization (315/433/868/915 MHz)
   - Protocol-specific identification (Princeton, EV1527, Oregon Scientific, etc.)
   - Modulation + frequency matching (OOK/FSK/ASK)
   - Confidence scoring (0.4-0.95 range)
   - 50+ device types covered

2. API Integration (src/api/main_simple.py):
   - Device matching in upload pipeline
   - Added device fields: device_name, device_category, match_confidence, match_method, device_description
   - Top 5 alternative matches stored per capture
   - New endpoint: GET /api/v1/captures/{id} for detail view

3. Frontend Implementation:
   - Detail modal with comprehensive device information
   - Device identification section with confidence bars
   - Alternative matches display
   - Signal, location, and metadata sections
   - Keyboard (ESC) and click-outside modal closing

4. UI Enhancements (static/css/main.css):
   - Modal overlay with backdrop blur
   - Animated modal slide-in
   - Confidence visualization (green/yellow/red bars)
   - Responsive detail grid layout
   - Device match cards with categories

5. JavaScript Integration:
   - detail-modal.js: Comprehensive detail view renderer
   - Updated map.js and search.js to use detail modal
   - Removed placeholder functions

6. Utilities:
   - scripts/rematch_captures.py: Re-run matcher on existing data
   - Successfully re-matched 20 existing captures

Device Categories Supported:
- Consumer RF (remotes, sensors)
- Automotive (key fobs, TPMS)
- Home Automation (garage/gate openers, blinds)
- Sensors (weather stations, temperature)
- Security (door/window sensors, alarms)
- IoT (smart meters, LoRa devices)
- Industrial (SCADA, telemetry, RFID)

Match Methods:
- Protocol matching (highest confidence: 0.7-0.95)
- Frequency matching (0.4-0.7)
- Modulation + frequency matching (0.5-0.7)

Frontend now displays:
- Device name and category on map markers
- Confidence percentage
- Detailed device information modal
- Alternative device matches
- Match method explanation

All existing captures successfully identified with 60-70% confidence.
 2026-01-14 10:51:46 -08:00
Trilltechnician f5d92f1d36 feat: Add wardriving data import system and cleanup tools 
Major Features:
- Multi-format GPS data import (Wigle CSV, GPS JSON, filename GPS)
- Data source tracking (test/mock/production)
- Database cleanup tools for managing test data
- JSON file persistence for simplified server
- UI improvements for map controls

Backend:
- Added wardriving_importer.py with WigleCSVImporter, GPSJSONImporter, BatchImporter
- Added data_source and session_id tracking to captures
- New API endpoints: DELETE /api/v1/admin/cleanup
- Auto-save/load functionality for captures_simple.json
- Updated stats endpoint to show data source breakdown

CLI Tools:
- scripts/import_wardriving_data.py - Batch import with --data-source flag
- scripts/cleanup_database.py - Clean by source, session, or all
- scripts/create_test_dataset.py - Generate GPS-tagged test data
- scripts/test_wardriving_import.py - Test suite for importers

Frontend:
- Fixed map controls z-index and positioning issues
- Moved controls to top-right to avoid zoom button overlap
- Fixed Leaflet zoom controls rendering over header
- Changed controls to position:fixed for persistent visibility
- Export map object to window.map for proper invalidateSize

Documentation:
- docs/WARDRIVING_IMPORT.md - Complete import guide
- docs/DATA_CLEANUP_GUIDE.md - Cleanup system documentation
- docs/TEST_LOCATIONS.md - Test GPS coordinates reference
- TEST_RESULTS_SUMMARY.md - Format testing results

Test Data:
- Created test_dataset_gps with 15 captures across 5 US cities
- All test data marked with data_source="test" for easy cleanup

Testing:
- Verified Wigle CSV import (1 capture from West LA)
- Verified GPS filename import (3 captures from UCLA area)
- Verified GPS JSON companion files (15 captures, 5 cities)
- Verified cleanup functionality (deleted 15 test captures)
- Verified data persistence across server restarts

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-01-14 07:48:01 -08:00
Trilltechnician 48fcb00241 Phase 3 Complete: Web Interface MVP 
Major Achievements:
- ✅ Full web interface (1,520+ lines of frontend code)
- ✅ Interactive Leaflet.js map with marker clustering
- ✅ Drag-and-drop upload system with GPS input
- ✅ Search & filter UI with multi-criteria
- ✅ Statistics dashboard with Chart.js
- ✅ Responsive mobile-friendly design

Backend:
- ✅ FastAPI static file serving
- ✅ Simplified server mode (main_simple.py)
- ✅ Improved startup script with port auto-selection
- ✅ PostgreSQL schema ready (requires setup)

Database:
- ✅ SQLite populated with 85 Flipper Zero signatures
- ✅ Device matching system operational
- ✅ Frequency-based search working

Documentation:
- ✅ PHASE_3_COMPLETE.md - Technical summary
- ✅ WEB_INTERFACE_README.md - User guide
- ✅ WEBAPP_STARTUP_GUIDE.md - Troubleshooting
- ✅ POSTGRESQL_SETUP_EXPLANATION.md - DB setup guide
- ✅ DATABASE_POPULATION_SUCCESS.md - Import report
- ✅ DEVICE_IDENTIFICATION_REPORT.md - Matching analysis

Files Created:
- templates/index.html (260 lines)
- static/css/main.css (500 lines)
- static/js/*.js (760 lines total)
- src/api/main_simple.py (simplified server)
- start_web.sh (auto port selection)

Status: Production MVP Ready
Next: Phase 4 - API & Integration

🛰️ Generated with Claude Code
https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-01-12 18:21:11 -08:00