Update README.md

README.md

@@ -7,85 +7,90 @@ pinned: true
 emoji: 🌿
 license: mit
 ---
-# 🌿 Sundew Diabetes Watch — ADVANCED EDITION
+# 🌿 Sundew Diabetes Watch — Advanced Edition
 
-**Mission:** Low-cost, energy-aware diabetes risk monitoring for everyone — especially communities across Africa.
+**Mission:** Deliver low-cost, energy-aware diabetes risk monitoring for everyone — with a special focus on communities across Africa.
 
-This app showcases the **full power of Sundew's bio-inspired adaptive algorithms** with:
-- ✨ **PipelineRuntime** with custom DiabetesSignificanceModel
+This app demonstrates the **full capabilities of Sundew’s bio-inspired adaptive algorithms**, including:
+
+- ✨ **PipelineRuntime** with a custom `DiabetesSignificanceModel`
 - 📊 **Real-time energy tracking** with bio-inspired regeneration
-- 🎯 **PI control threshold adaptation** with live visualization
+- 🎯 **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)
+- 🔬 **Six-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)
+- 🌍 **89.8% energy savings** versus 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
+Tested on 216 continuous glucose monitoring events (≈18 hours):
+
+- **Activation rate:** 10.2% (22/216 events) — intelligently selective  
+- **Energy savings:** 89.8% — essential for battery-powered wearables  
+- **Risk detection:** Correctly identifies hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL)  
+- **Adaptive thresholds:** PI controller dynamically adjusts from 0.10 → 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
+1. **Try the demo:** [Sundew Diabetes Watch](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch)  
+2. **Upload data:** Use your CSV or the [sample_diabetes_data.csv](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch/blob/main/sample_diabetes_data.csv)  
+3. **Observe:** Real-time significance scoring, threshold adaptation, and energy tracking  
+4. **Experiment:** Tweak Energy Pressure, Gate Temperature, and presets
 
-## How It Works
+## 🛠️ 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
+1. **Upload CGM data** with columns: `timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr`  
+2. **Custom significance model** computes a multi-factor diabetes risk score  
+3. **Sundew gating** decides when to run the heavy ensemble model  
+4. **PI control** auto-adjusts thresholds to maintain target activation  
+5. **Energy management** uses bio-inspired regeneration and realistic costs  
+6. **Statistical validation** via bootstrap 95% CIs (F1, Precision, Recall)  
+7. **Telemetry export** (JSON) for power-measurement correlation
 
-## Live Visualizations
+## 📺 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
+- **Glucose levels:** Continuous CGM stream  
+- **Significance vs. threshold:** See the PI controller adapt in real time  
+- **Energy level:** Bio-inspired regeneration over time  
+- **Risk components (×6):** Interpretable breakdown of the score  
+- **Performance dashboard:** F1, Precision, Recall with confidence intervals  
+- **Alerts:** High-risk notifications
 
-## Configuration Presets
+## 🔧 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
+- **custom_health_hd82:** Healthcare-optimized (≈82% energy savings, ~0.196 recall)  
+- **tuned_v2:** Balanced general-purpose baseline  
+- **auto_tuned:** Dataset-adaptive configuration  
+- **conservative:** Maximum savings (lower activation)  
+- **energy_saver:** Battery-optimized for edge devices
 
 > **Disclaimer:** Research prototype. Not medical advice. Not FDA/CE approved.
 
-## Developing Locally
+## 💻 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: Six-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
 
-## Technical Details
+Validation: Bootstrap resampling (1,000 iterations) for 95% CI
 
-- **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
 
-## References
+Documentation
 
-- [Sundew Algorithms](https://github.com/oluwafemidiakhoa/sundew_algorithms)
-- [Documentation](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch/edit/main/README.md)
-- [Paper](https://arxiv.org/abs/your-paper-here) (coming soon)
+Paper (coming soon)
 
-Built with ❤️ for underserved communities worldwide
+Built with ❤️ for underserved communities worldwide.