| --- |
| title: Metabolic Forensics |
| emoji: 🩸 |
| colorFrom: red |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 5.49.1 |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: N-of-1 biosignal evidence engine. Forensics, not coaching. |
| tags: |
| - build-small-hackathon |
| - backyard-ai |
| --- |
| |
| # 🩸 Metabolic Forensics |
|
|
| **An N-of-1 biosignal *evidence engine* — forensics, not coaching.** |
|
|
| Point it at your own wearable history (CGM glucose, HRV, recovery, sleep RHR, |
| steps, morning alertness) and ask forensic questions: |
|
|
| - *"What reliably precedes my glucose spikes?"* |
| - *"What's different about my bad-recovery mornings?"* |
|
|
| The **system** does the hard part deterministically — temporal alignment, |
| event-window search, personal baselines, and **counterexample retrieval** |
| (does the pattern actually hold, or are there days it breaks?). A small local |
| model only **narrates** the evidence it's handed. It cannot invent findings. |
|
|
| Every answer follows the same honest shape: |
|
|
| > **observed association → counterexamples → one next experiment to run** |
|
|
| Never a diagnosis. This is associations in *your* data, not medical advice. |
|
|
| ## Why this isn't "just prompting Gemma" |
|
|
| The model is a commodity ≤32B model. The moat is the evidence pipeline: |
| event detection, window alignment, baseline/variance estimation, and |
| counterexample mining. The LLM is handed structured evidence and narrates it — |
| it is structurally prevented from hallucinating a pattern that the data doesn't |
| support. |
|
|
| ## Status |
|
|
| 🚧 Skeleton running on **synthetic demo data**. The real engine + local model |
| (llama.cpp, off-grid) plug in at the marked points in `app.py`. |
|
|
| Built for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon). |
|
|
