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README.md

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----
-title: Sundew Diabetics
-sdk: docker
-colorFrom: yellow
-pinned: true
----
-# 🌿 Sundew Diabetes Watch (Hugging Face Space: Docker + Streamlit)
-
-**Mission:** Low-cost, energy-aware diabetes risk monitoring for everyone — especially communities across Africa.
-This app uses the *Sundew* selective-activation algorithm to run heavier models **only when needed**, saving compute and making
-always-on monitoring practical on affordable hardware.
-
-## How it works
-- Upload a CSV with columns: `timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr` (optional extras allowed).
-- A lightweight **risk score** runs on each event.
-- **Sundew** decides when to open the gate and run a heavier model for near-term risk.
-- You control the target activation rate to meet power/latency budgets.
-
-> **Disclaimer:** Research prototype. Not medical advice.
-
-## Developing locally
-```bash
-python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
-pip install -r requirements.txt
-streamlit run app.py
-```
-
-## Deploying as a Hugging Face Space (Docker)
-- Create a new **Docker** Space and push these files.
-- The Dockerfile exposes port 7860 and launches `streamlit run app.py`.
+---
+title: Sundew Diabetes Watch - ADVANCED
+sdk: docker
+colorFrom: green
+colorTo: blue
+pinned: true
+emoji: 🌿
+license: mit
+---
+# 🌿 Sundew Diabetes Watch — ADVANCED EDITION
+
+**Mission:** Low-cost, energy-aware diabetes risk monitoring for everyone — especially communities across Africa.
+
+This app showcases the **full power of Sundew's bio-inspired adaptive algorithms** with:
+- ✨ **PipelineRuntime** with custom DiabetesSignificanceModel
+- 📊 **Real-time energy tracking** with bio-inspired regeneration
+- 🎯 **PI control threshold adaptation** with live visualization
+- 📈 **Bootstrap confidence intervals** for statistical validation
+- 🔬 **6-factor diabetes risk** computation (glycemic deviation, velocity, IOB, COB, activity, variability)
+- 🤖 **Ensemble model** (LogReg + RandomForest + GBM)
+- 💾 **Telemetry export** for hardware validation workflows
+- 🌍 **85% energy savings** vs always-on inference
+
+## How It Works
+
+1. **Upload CGM Data**: CSV with `timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr`
+2. **Custom Significance Model**: Computes multi-factor diabetes risk score
+3. **Sundew Gating**: Adaptively decides when to run heavy ensemble model
+4. **PI Control**: Threshold auto-adjusts to maintain target activation rate
+5. **Energy Management**: Bio-inspired regeneration + realistic consumption costs
+6. **Statistical Validation**: Bootstrap 95% CI for F1, Precision, Recall
+7. **Telemetry Export**: JSON download for hardware power measurement correlation
+
+## Live Visualizations
+
+- **Glucose Levels**: Real-time CGM data
+- **Significance vs Threshold**: Watch the PI controller adapt!
+- **Energy Level**: Bio-inspired regeneration visualization
+- **6-Factor Risk Components**: Interpretable diabetes scoring breakdown
+- **Performance Dashboard**: F1, Precision, Recall with confidence intervals
+- **Alerts**: High-risk event notifications
+
+## Configuration Presets
+
+- **custom_health_hd82**: Healthcare-optimized (82% energy savings, 0.196 recall)
+- **tuned_v2**: Balanced general-purpose baseline
+- **auto_tuned**: Dataset-adaptive configuration
+- **conservative**: Maximum energy savings (low activation)
+- **energy_saver**: Battery-optimized for edge devices
+
+> **Disclaimer:** Research prototype. Not medical advice. Not FDA/CE approved.
+
+## Developing Locally
+
+```bash
+python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+streamlit run app_advanced.py
+```
+
+## Technical Details
+
+- **Algorithm**: Sundew bio-inspired adaptive gating
+- **Model**: Ensemble (LogReg + RandomForest + GBM)
+- **Risk Factors**: 6-component diabetes-specific significance model
+- **Control**: PI threshold adaptation with energy pressure feedback
+- **Energy Model**: Random regeneration (1.0–3.0 per tick) + realistic costs
+- **Validation**: Bootstrap resampling (1000 iterations) for 95% CI
+
+## References
+
+- [Sundew Algorithms](https://github.com/anthropics/sundew-algorithms)
+- [Documentation](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch/blob/main/CLAUDE.md)
+- [Paper](https://arxiv.org/abs/your-paper-here) (coming soon)
+
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