## Why

G.U.I.D.E. runs locally today with no public access. Deploying to Hugging Face Spaces makes the academic demo accessible to evaluators without requiring a local setup, while keeping the existing two-server architecture (FastAPI + Gradio) intact.

## What Changes

- New `scripts/upload_models_to_hub.py` — uploads only inference-needed model files to HF Model Hub, filtering out `checkpoint-*` training artifacts
- New `Dockerfile` — HF Spaces Dockerfile Space entry point; downloads weights from Hub at build time, starts both servers with `--no-train`
- Updated `README.md` — deployment instructions and HF Spaces architecture note

## Capabilities

### New Capabilities

- `hf-spaces-deployment`: Containerised deployment to Hugging Face Spaces using a Dockerfile Space; FastAPI on `:8000` (internal) and Gradio on `:7860` (public-facing)
- `model-hub-upload`: One-time script to push inference weights for `domain_classifier`, `evidence_ner`, and `next_action` models to HF Model Hub, filtering training-only artifacts

### Modified Capabilities

## Non-goals

- No changes to predict.py files, start.py, or ui/app.py
- No single-process refactor of the two-server design
- No CI/CD pipeline for automated re-deployment
- No user-facing API key input in the UI (key is a HF Space Secret)

## Impact

- New files: `Dockerfile`, `scripts/upload_models_to_hub.py`
- Modified files: `README.md`
- New dependency at build time: `huggingface_hub` CLI (already in requirements via `huggingface_hub` transitive dep from `transformers`)
- `ANTHROPIC_API_KEY` must be set as an HF Spaces Secret before the Space goes live