# Timmy the Talking Turd

A working mobile-first bowel diary with an optional **photo-first AI assist**: “Your intelligent pooping pal.”

## Run with the self-hosted open-weight path

```bash
npm install

git clone --depth 1 https://github.com/ggml-org/llama.cpp /root/model-spikes/llama.cpp
cmake -S /root/model-spikes/llama.cpp -B /root/model-spikes/llama.cpp/build -DCMAKE_BUILD_TYPE=Release
cmake --build /root/model-spikes/llama.cpp/build -j4 --target llama-server

hf download ggml-org/SmolVLM2-2.2B-Instruct-GGUF \
  SmolVLM2-2.2B-Instruct-Q4_K_M.gguf \
  mmproj-SmolVLM2-2.2B-Instruct-Q8_0.gguf \
  --local-dir /root/model-spikes/models/smolvlm2-2.2b

TIMMY_MODEL_PORT=8080 scripts/run_selfhost_smolvlm.sh

# In another terminal
TIMMY_VISION_PROFILE=selfhost npm start
# open http://localhost:4173
```

The selected bootstrap model is `SmolVLM2-2.2B-Instruct` using the official Apache-2.0 GGUF conversion. The default self-hosted endpoint is loopback-only at `http://127.0.0.1:8080/v1`. Override paths, host, port, threads, model ID, or endpoint with the `TIMMY_MODEL_*` and `TIMMY_VISION_*` environment variables.

## Run with a hosted provider

```bash
hermes proxy start --provider nous --host 127.0.0.1 --port 8645
npm start
```

Hosted remains the default compatibility profile. To make it explicit, set `TIMMY_VISION_PROFILE=hosted`. Credentials stay server-side:

```bash
TIMMY_VISION_BASE_URL=https://your-openai-compatible-provider/v1 \
TIMMY_VISION_API_KEY=... \
TIMMY_VISION_MODEL=your-vision-model \
npm start
```

Set `TIMMY_VISION_ENABLED=0` to disable uploads and retain manual-only operation.

## Verify

```bash
npm test
npm run test:ui       # server required on port 4173
npm run test:photo    # mocked positive suggestion through the real browser UX
npm audit --audit-level=high
```

## What works

- Photo-first camera/file capture
- Client-side image compression before transfer
- Explicit consent before one-time provider analysis
- Structured AI suggestions for **visible Bristol form and color only**
- Confidence display, low-confidence abstention, and user confirmation
- Urgency, discomfort, notes, and symptoms remain strictly user-reported
- Manual logging that never uploads
- Red-flag symptom escalation
- Local browser ledger, calendar, pattern summary, JSON portability, and delete-all
- Installable/offline PWA shell for the manual and saved-ledger paths

## What is deliberately not faked

The positive photo-prefill interface is covered by a deterministic provider fixture. A live self-hosted SmolVLM2-2.2B model was exercised against a CC-licensed real Type 4 stool photograph. It accepted the content and identified stool, but predicted the wrong type with low confidence; Timmy correctly abstained. **The open model removes provider moderation from the path, but its Bristol accuracy is not clinically established.**

Timmy does not diagnose disease, identify bleeding, infer pain/urgency, recommend treatment, or clear foods. The selected VLM is a bootstrap worker, not the final classifier. See [research/SELF-HOSTED-STOOL-VISION.md](research/SELF-HOSTED-STOOL-VISION.md) for the measured receipts, hardware gate, consented-data pipeline, specialist-classifier plan, and sources.

## Architecture

```text
Browser PWA
├── app.js                    photo-first UX, local persistence, compression
├── src/domain.js             tested health/safety and summary rules
├── src/analysis.js           strict AI schema, validation, visual-only merge
├── server.mjs                static server + bounded /api/analyze route
├── src/vision-service.js     server-side OpenAI-compatible provider adapter
├── src/vision-config.js      hosted/self-hosted profiles and readiness probe
├── scripts/run_selfhost_smolvlm.sh
├── scripts/ingest_training_photo.py
├── localStorage              saved ledger and attached photos
└── llama.cpp / hosted API    selected per server-side profile
```

The server accepts JPEG/PNG/WebP data URLs up to 4 MB, uses a bounded 60-second hosted or 120-second self-hosted timeout, never accepts an API key from browser input, does not write inference images to disk, and returns only validated visual suggestions. The separate dataset ingester runs only for explicit, consented contributions.
