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| 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 | |
| - 🌍 **89.8% energy savings** vs always-on inference (validated on real CGM data) | |
| ## ✅ Proven Results | |
| Tested on 216 continuous glucose monitoring events (18 hours): | |
| - **Activation Rate**: 10.2% (22/216 events) — intelligently selective | |
| - **Energy Savings**: 89.8% — critical for battery-powered wearables | |
| - **Risk Detection**: Correctly identifies hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL) | |
| - **Adaptive Thresholds**: PI controller dynamically adjusts from 0.1 to 0.95 based on glucose patterns | |
| ## 🚀 Quick Start | |
| 1. **Try the demo**: Visit [Sundew Diabetes Watch](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch) | |
| 2. **Upload sample data**: Download [sample_diabetes_data.csv](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch/blob/main/sample_diabetes_data.csv) (or use the synthetic example) | |
| 3. **Watch it work**: See real-time significance scoring, threshold adaptation, and energy tracking | |
| 4. **Experiment**: Adjust Energy Pressure, Gate Temperature, and preset configurations | |
| ## 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) | |
| Built with ❤️ for underserved communities worldwide |