Apichain-Kenya/ApiChain--Backend

ApiChain Backend Backend service for the Geo-AI and Blockchain-enabled Honey Traceability System. This backend powers the core functionality for ApiChain, including farmer and aggregator onboarding, secure authentication, geo-spatial farm data management, document verification, and the honey traceability pipeline leveraging Geo-AI and blockchain technologies. The Geo-AI module predicts expected honey physicochemical properties based on apiary location, harvest season, and local flowering species, then validates those predictions against actual lab results to produce an authenticity score. Tech Stack Backend Framework: FastAPI Database: PostgreSQL + PostGIS ORM & Migrations: SQLAlchemy + Alembic Geospatial: GeoAlchemy2 Authentication: JWT-based authentication Containerization: Docker Setup Instructions 1. Clone the repository git clone github.com cd ApiChain--Backend 2. Create a virtual environment python -m venv .venv Activate the virtual environment: Windows (PowerShell): .venv\Scripts\Activate Linux/Mac: source .venv/bin/activate 3. Install dependencies pip install -r backend/requirements.txt 4. Set up environment variables Create a .env file inside backend/ 5. Set up the database Make sure PostgreSQL is running and has the PostGIS extension installed. 6. Set up Geo-AI models The machine learning models are **not stored in this repository** (binary files, ~50MB). You must download and extract them before the Geo-AI endpoints will work. **Step 1 — Download the model zip** Download `ml_models.zip` from the shared team drive: **Step 2 — Create the models folder** ```bash mkdir backend/app/ml_models ``` **Step 3 — Extract the zip into that folder** The folder must contain exactly these files: backend/app/ml_models/ scaler.pkl le_region.pkl le_season.pkl le_veg.pkl feature_cols.json flowering_calendar.pkl ensemble_moisture_content.pkl ensemble_sucrose_level.pkl ensemble_hmf_level.pkl 7. Start the serv …