Deployment notes for Hugging Face Spaces

1) HF_TOKEN secret
- Create a Hugging Face token at https://huggingface.co/settings/tokens
- Token should have repository write permissions (to create and push Spaces)
- In GitHub, go to Settings -> Secrets -> Actions -> New repository secret
  - Name: HF_TOKEN
  - Value: <your_token_here>

2) Streamlit compatibility
- The workflow creates the Space with `space_sdk='streamlit'` so it will run as a Streamlit app.
- Hugging Face Spaces will run `streamlit_app.py` or `app.py` by default; this repo contains `streamlit_app.py` to be explicit.

3) System dependencies
- Some OCR engines require system packages (e.g., Tesseract binary, system libs for PaddlePaddle). Hugging Face's Streamlit SDK does not allow installing system packages.
- If you need system packages, use a Docker-based Space (set `space_sdk='docker'` and add a Dockerfile that installs required system packages).

4) LLM / Ollama
- The app optionally uses `ollama` for LLM features. Ollama is not installed by default in Spaces; LLM features will be disabled if `ollama` isn't present.

5) Tesseract
- Ensure Tesseract is available in the environment or use the Docker approach to install it.

6) Running CI/CD
- After pushing to `main` and setting `HF_TOKEN` secret, the GitHub Actions workflow `.github/workflows/deploy_to_hf.yml` will create the Space and upload the repository.

Note: This repository includes a `Dockerfile` and the CI workflow is configured to create a Docker-based Space (`space_sdk='docker'`). The Dockerfile installs system dependencies such as Tesseract so the OCR engines can run inside the Space container.

7) Troubleshooting
- If the deployment fails, open the Actions run logs to see the error and adjust the workflow or repository accordingly.