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Deploying the synthetic demo

The hosted dashboard runs on fully synthetic data — the real ~11.4M-loan warehouse is gitignored and is never deployed (SIGN-007). In demo mode the app builds a small fictitious warehouse on first launch (relief_probe.demo), runs the real production detectors over it, and serves the five tabs.

What's in the repo for this

  • src/relief_probe/demo/ — the synthetic-warehouse builder (ensure_demo_warehouse).
  • app/dashboard.py — when RELIEF_PROBE_DEMO=1, it builds the demo warehouse and shows a "Demo mode — fully synthetic data" banner.
  • requirements.txt — what Streamlit Community Cloud installs (-e . + pinned deps for the viz + vision + embeddings-lite extras). Regenerate after changing deps: uv pip compile pyproject.toml --extra viz --extra vision --extra embeddings-lite -o requirements.txt
  • .streamlit/config.toml — theme + server settings (no secrets).

Try it locally first

RELIEF_PROBE_DEMO=1 uv run --extra viz --extra vision --extra embeddings-lite \
  streamlit run app/dashboard.py

The first load builds the synthetic warehouse into your data/ dir (instant). It will not overwrite an existing non-empty warehouse, so this is safe to run even if you have the real data ingested — but use a clean checkout or RELIEF_PROBE_DATA_DIR=/tmp/rp_demo to be certain.

Deploy to Streamlit Community Cloud

  1. Push the repo to GitHub (public or private — free tier supports both).
  2. Go to https://share.streamlit.io → New app → pick the repo + branch.
  3. Set Main file path to app/dashboard.py.
  4. Open Advanced settings → Secrets and add: 
    RELIEF_PROBE_DEMO = "1"
# Optional — enables the "Explain this cluster (LLM)" button on Similar cases:
# ANTHROPIC_API_KEY = "sk-ant-..."
    Streamlit Cloud exposes secrets as environment variables, so this is what flips the app into demo mode.
  5. Deploy. First boot installs deps and builds the synthetic warehouse; you get a public *.streamlit.app URL.
  6. Paste that URL into the README's "Try the synthetic demo" line (replace the <!-- TODO --> placeholder).

Notes

  • No keys required. Every tab loads offline; the LLM explain button degrades gracefully to a deterministic summary when ANTHROPIC_API_KEY is absent.
  • Document authenticity tab. It needs a locally-trained ELA model (data/models/doc_authenticity.joblib), which isn't built in demo mode, so that tab shows a "no trained model" note. To enable it, commit a model trained via relief-probe vision-demo, or leave the note — the four data tabs are the demo.
  • Base rate. The demo's prosecution base rate is intentionally high (small population) so the tabs have something to show; it is not the real 0.034%.