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Demo guide

How to run all three pieces locally and record a walkthrough.

Full setup instructions are in the README; this file covers the demo flow itself.

Before you record

Register a face. Face recognition only matches people enrolled on the machine doing the recording. Without this step the demo will show "no one recognized" for its entire duration.

  1. Start the vision service and open http://localhost:8000/static/register.html.
  2. Enter a name and relationship — "Self" is fine for a solo demo.
  3. Capture 3–5 photos from slightly different angles, then submit.
  4. Confirm: curl http://localhost:8000/people should list the name.

This stores a face embedding on your machine. See PRIVACY.md.

Decide on Gemini or mock mode. The free Gemini tier allows roughly 5 requests per minute, which a live demo can exhaust. Running with MOCK_LLM=true gives canned responses, no network calls, and no rate limit — the architecture and every response shape are identical. Mock cards are labelled (mock) so it is obvious on camera.

Match the profile to your script. patient_profile.json ships with a fictional patient named Arthur. If you plan to ask "what does Arthur like?", either keep it as-is or edit the file first — the answers come from it.

Flow (3–5 minutes)

  1. Sketch the architecture. Camera → vision service (8000) → backend (8001) → browser page. Mention that face recognition runs locally and only text is sent to Gemini.

  2. Show the live scene. Open frontend/index.html, point the camera at the registered face plus a detectable object — a cup, a bottle, a phone. The memory card fills in with the name, relationship, and note; the object chips update below it.

  3. Hold still, then move. Worth calling out: the card does not regenerate every second. The backend hashes the scene and only calls the LLM when it actually changes. Step out of frame and back to trigger a fresh card.

  4. Ask a caregiver question. Type something like "How do I keep him calm at dinner?" into the question box and send. The answer appears in its own panel — the patient's card above is untouched, which is deliberate.

  5. Show the analysis endpoint.

    curl -s -X POST http://localhost:8001/api/caregiver/analyze \
  -H "Content-Type: application/json" \
  -d '{"message":"Arthur got agitated and started pacing before dinner."}'

    Returns a structured BehavioralAnalysis: category, observed triggers, rationale, a non-pharmacological intervention, and a crisis flag. Note that the rationale is model-generated text, not a clinical assessment — see DISCLAIMER.md.

  6. Close on the data source. Open patient_profile.json and explain that the backend retrieves matching lines from it and passes only those to the model. Every card and answer in the demo traces back to this file.

URLs to keep open

URL Shows
http://localhost:8000/health Vision service is up
http://localhost:8000/people Registered faces
http://localhost:8000/static/register.html Registration UI
http://localhost:8001/health Backend is up
http://localhost:8001/latest Current memory card, as JSON
frontend/index.html The demo page

Troubleshooting

"No one recognized" throughout. The face on camera was never registered on this machine. See "Before you record" above.

Gemini errors mentioning quota or 429. The free tier is about 5 requests per minute. Wait ~15 seconds, or restart the backend with MOCK_LLM=true.

No card appears. Check that both services are running and the browser console is clean. The page polls http://localhost:8001/latest every 2 seconds — hitting that URL directly tells you whether the backend or the page is at fault.

No webcam. Run python backend/mock_vision.py instead of the real vision service. It serves the same websocket contract with synthetic scenes, so the backend and frontend behave normally.

Camera is black or permission was denied. Browsers only grant camera access on localhost or HTTPS, and only one process can hold the camera. Close other apps using it, then reload.