MohamedELfaidy/FluSentinel-Egypt

# 🦅 FluSentinel Egypt > AI-powered early warning platform for Avian Influenza in Egypt ## Quick start (5 minutes) ```bash # 1. Clone and configure git clone cd flusentinel cp .env.local .env.local # already configured for local dev # 2. Start everything make up # 3. In a second terminal — first-time setup make migrate make seed make generate-data make train # Or run all at once: make setup ``` **Open:** - API docs → localhost - Frontend → localhost - MinIO console → localhost (user: `flusentinel`, pass: `flusentinel_dev`) --- ## Project structure ``` flusentinel/ ├── main.py # FastAPI app entry point ├── core/ # Config, DB, security │ ├── config.py │ ├── database.py │ ├── security.py │ └── deps.py ├── models/ │ └── tables.py # All SQLAlchemy ORM models ├── routers/ # One file per route group │ ├── auth.py │ ├── farms.py │ ├── surveillance.py │ ├── climate.py │ ├── risk.py │ ├── genomic.py │ ├── alerts.py │ ├── governorates.py │ ├── reports.py │ └── websocket.py ├── services/ # Business logic │ ├── feature_engineering.py │ ├── ml_predictor.py │ ├── alert_dispatcher.py │ ├── climate_etl.py │ ├── genomic_pipeline.py │ ├── genomic_qc.py │ ├── mutation_scanner.py │ ├── genomic_risk_scorer.py │ ├── variant_comparator.py │ ├── report_generator.py │ └── genbank_fetcher.py ├── tasks/ │ └── celery_app.py # Celery tasks + beat schedule ├── training/ │ ├── train_xgboost.py # XGBoost model training │ ├── train_lstm.py # LSTM seasonal model │ └── export_training_data.py # DB → CSV export ├── scripts/ │ ├── init.sql # PostGIS + TimescaleDB extensions │ ├── seed_govs.py # Seed 27 governorates │ └── generate_training_data.py # Synthetic training data ├── tests/ │ ├── test_auth.py │ ├── test_risk.py │ └── test_surveil …