Drisa-Infotech/FarmDepot_ai

FarmDepot.ai App is a multilingual AI-powered classified ads platform for agricultural products. Users can post, search, and register using voice commands in English, Hausa, Yoruba, and Igbo. # README.md # FarmDepot.ai Classified App A multilingual AI-powered classified ads platform for agricultural products. Users can post, search, and register using voice commands in English, Hausa, Yoruba, and Igbo. ## Features - Voice-enabled ad posting & search - Multilingual support - CrewAI agents with FastAPI backend - Streamlit frontend with voice recording - User registration and dashboard ## Project Structure ``` FarmDepot_ai/ ├── backend/ # Backend agents & FastAPI API │ ├── agents/ # AI agents for voice, post, search, etc. │ ├── tasks/ # Agent tasks │ ├── db/ # Database models │ └── main.py # FastAPI entrypoint ├── frontend/ # Streamlit frontend │ └── app.py # Streamlit app ├── static/audios/ # Uploaded voice files ├── templates/ # HTML templates (if needed) ├── requirements.txt # Dependencies ├── Procfile # Railway deployment ├── railway.json # Railway config └── README.md ``` ## Setup Locally 1. Clone the repo: ```bash git clone github.com cd agric-voice-classified ``` 2. Create virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Set environment variables: Create `.env` file in the root: ``` DATABASE_URL=postgresql://user:pass@host:port/dbname SECRET_KEY=your-secret ``` 5. Run backend: ```bash uvicorn backend.main:app --reload ``` 6. Run frontend: ```bash streamlit run frontend/app.py ``` ## Deploy to Railway 1. Push code to GitHub 2. Connect Railway to the repo 3. Add environment variables in Railway Dashboard 4. Click "Deploy" ## License MIT