openspec/changes/hf-spaces-deployment/tasks.md

1. Model Upload Script

• 1.1 Create scripts/upload_models_to_hub.py with parse_args() function that accepts --repo-id CLI argument and validates it is provided (exits with usage message if missing)
• 1.2 Implement filter_inference_files(model_dir: Path) -> list[Path] that returns only root-level files (skipping any path under a checkpoint-* subdir and any .DS_Store file)
• 1.3 Implement upload_models(repo_id: str) -> None that iterates over domain_classifier/, evidence_ner/, and next_action/ under models/, calls filter_inference_files() for each, and uploads via huggingface_hub.upload_file() — logging a warning and continuing if a model directory does not exist locally

2. Dockerfile

• 2.1 Create Dockerfile at repo root: base python:3.11-slim, install tesseract-ocr and git via apt-get, copy requirements.txt and run pip install
• 2.2 Add RUN python -m spacy download en_core_web_lg layer after pip install
• 2.3 Add ARG HF_MODEL_REPO and RUN huggingface-cli download $HF_MODEL_REPO --local-dir models layer to pull weights at build time; add COPY . /app, WORKDIR /app, EXPOSE 7860, ENV ANTHROPIC_API_KEY="", and CMD ["python", "start.py", "--no-train"]

3. README Update

• 3.1 Add a "Deploying to HF Spaces" section to README.md documenting: (a) upload script usage with --repo-id, (b) Dockerfile build ARG HF_MODEL_REPO, (c) setting ANTHROPIC_API_KEY in HF Spaces Secrets UI, and (d) the two-server port layout (FastAPI :8000 internal, Gradio :7860 public)