Yassine-Mhirsi/somalia-agrifood-exploration

# Somalia Agrifood Exploration A small end-to-end prototype that integrates Somalia agrifood datasets (food prices, crop production, and food security indicators), enriches them with a light AI step, and serves the unified data to an interactive dashboard. ## What this project does - Runs an ETL pipeline that merges multiple HDX-style datasets into a single SQLite database. - Uses an AI step to help map region names and filter relevant commodities. - Exposes the integrated dataset through a FastAPI backend. - Visualizes trends, regional comparisons, and indicators in a Next.js dashboard. ## Project journey For a detailed write-up of the approach, decisions, and AI usage, see JOURNEY.md. ## Prerequisites - Python 3.11+ - Node.js 20+ and pnpm If pnpm is not installed: ``` npm install -g pnpm@latest-10 ``` - Docker + Docker Compose (for containerized runs) - **Google API key** for the AI steps Get one here: aistudio.google.com ## Run with Docker 1. Create a `.env` file in the repo root: ``` GOOGLE_API_KEY=your_actual_key_here ``` 2. Build and run all services: ``` docker compose up --build ``` 3. Open the dashboard: - Frontend: localhost - Backend API: localhost ## CI/CD & Cloud Deployment (Render) This project includes a full CI/CD pipeline: - **CI** – GitHub Actions runs linting (`ruff`) and Docker build checks on every push and PR to `main`. - **CD** – Render auto-deploys the backend and frontend from `main` using the `render.yaml` blueprint. ### Live URLs (after deployment) | Service | URL | |----------|-----| | Backend | `somalia-agrifood-backend.on…` | | Frontend | `somalia-agrifood-frontend.o…` | ### Deploy to Render (one-time setup) 1. Push `render.yaml` to the `main` branch. 2. Go to render.com and sign in with GitHub. 3. Click **New** > **Blueprint** and connect this repository. 4. Render detects `render.yaml` and creates both services automatically. 5. In the **so …