Pat Teruel and Copilot
0387900954
Add real-time consumer panel and paginated transaction results
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- Implement WebSocket endpoint (/ws/fraud-results) in API for real-time fraud detection updates
- Add Redis pub/sub listener in API to broadcast consumer results to connected WebSocket clients
- Update consumer to publish fraud detection results to Redis 'fraud_results' channel with transaction details
- Add WebSocket connection logic in frontend with auto-reconnect (exponential backoff)
- Display real-time consumer status panel with fraud score color coding and decision status
- Implement client-side transaction results pagination (10/25/50 items per page)
- Add pagination controls: Previous/Next buttons, page indicator, page size selector
- Newest-first transaction sorting (DESC by timestamp)
- Add comprehensive styling for consumer panel and pagination controls
- All frontend components tested and builds successfully
Co-authored-by: Copilot <[email protected]
2026-05-01 19:29:31 +08:00
John Patrick Teruel and Copilot
4cefdb9083
Containerize fraud detection consumer - full Docker integration
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- Added consumer/Dockerfile with all dependencies
- Updated consumer pipeline.py to use environment variables
- Updated features.py and decision.py to support env var configuration
- Updated docker-compose.yml to include consumer service
- Consumer now runs automatically with 'docker compose up'
- Proper Kafka and Redis networking for Docker
- Environment variables for ML_MODELS_DIR, KAFKA_BROKER, REDIS_HOST/PORT
Now entire PayGuard system runs with: docker compose up -d
Co-authored-by: Copilot <[email protected]
2026-04-24 22:02:56 +08:00
John Patrick Teruel and Copilot
aa4dd497e2
Initial PayGuard fraud detection system implementation
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- Complete dual-layer architecture for fraud detection
- Phase 1: Docker Compose infrastructure (Kafka, Zookeeper, Redis)
- Phase 2: Mock ML models (Layer 1 fast, Layer 2 deep)
- Phase 3: FastAPI transaction ingestion with JWT auth
- Phase 4: Kafka consumer pipeline with feature extraction
- Phase 5: Test transaction producer with demo scenarios
- Phase 6: Clean code structure and comprehensive documentation
- Phase 7: End-to-end validation and demo readiness
Demonstrates key system design patterns:
- Microservices architecture with decoupled components
- Asynchronous event processing via Kafka
- Tiered decision-making (fast path vs deep analysis)
- Resilience patterns (circuit breaker for graceful degradation)
- Behavioral profiling with Redis caching
Ready for academic demonstration with clear pipeline observability.
Co-authored-by: Copilot <[email protected]
2026-04-24 19:47:18 +08:00
