DataPilot-AI-Agent / docs /DEPLOYMENT.md
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Deploy DataPilot AI production Docker Space
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# Deployment Guide
## Streamlit Community Cloud
1. Push the repository to GitHub.
2. Create a Streamlit app with `streamlit_app.py` as the entrypoint.
3. Use Python 3.12.
4. Add `GEMINI_API_KEY` only if optional LLM narration is desired.
5. Keep the default SQLite/local artifact mode for a stateless public demo.
The app is fully functional without an LLM key.
## Render API
`render.yaml` defines a Docker-backed FastAPI service. Configure:
- `DATABASE_URL`: Neon, Supabase or another PostgreSQL URL.
- `GEMINI_API_KEY`: optional.
- persistent/object storage if generated artifacts must survive redeployments.
## Docker Compose
```bash
docker compose up --build
```
- Streamlit: <http://localhost:8501>
- FastAPI: <http://localhost:8000/docs>
The isolated worker has no exposed port and no network.
## Production topology
For heavier datasets, make `/v1/analyze/*` enqueue jobs to a separate worker and return
`202 Accepted`. Persist state in PostgreSQL, place artifacts in S3-compatible storage, and
stream progress through server-sent events or polling.