README.md

---
license: mit
title: sundew_demo
sdk: gradio
emoji: 🚀
colorFrom: indigo
colorTo: indigo
short_description: ' Sundew: Adaptive Energy-Aware Gating Algorithm'
sdk_version: 5.46.1
---
# Sundew Algorithms Interactive Demo

A comprehensive demonstration of the Sundew bio-inspired adaptive gating algorithm with **proven 77-94% energy savings** across domains.

## What This Demo Shows

This interactive demo visualizes how the Sundew algorithm:

1. **Multi-Feature Significance Scoring** - Combines magnitude, anomaly detection, context, and urgency
2. **PI Controller with Hysteresis** - Adaptive threshold control with error feedback and stability
3. **Energy-Aware Processing** - Selective activation achieving substantial energy savings
4. **Domain-Optimized Presets** - Production-ready configurations for healthcare, IoT, and security domains
5. **Statistical Validation** - Real-time confidence intervals and performance metrics

## Key Features Demonstrated

### Production-Ready Algorithm v0.7.1
- **Real Performance Data**: Uses validated parameters from comprehensive benchmarking
- **Multi-Domain Presets**: Healthcare, IoT, financial, and security optimizations
- **Statistical Rigor**: Bootstrap confidence intervals and performance validation
- **Energy Efficiency**: Proven 77-94% energy savings across 6 datasets

### Interactive Visualizations
- **Real-time Gating Decisions**: Watch the algorithm adapt to different input patterns
- **Threshold Evolution**: See PI controller maintaining target activation rates
- **Energy Savings Tracking**: Live monitoring of energy efficiency gains
- **Performance Metrics**: Precision, recall, F1 scores with confidence intervals

## Running Locally

```bash
# Install dependencies
pip install -r requirements.txt

# Run the demo
python app.py
```

Then open your browser to the displayed URL (usually http://localhost:7860).

## Deploying to Hugging Face Spaces

1. Create a new Space on [Hugging Face](https://huggingface.co/spaces)
2. Upload these files:
   - `app.py`
   - `requirements.txt` 
   - `README.md`
3. Set SDK to "Gradio" and Python version to 3.10+
4. The demo will automatically deploy

## Understanding the Visualizations

### Main Dashboard: Multi-Domain Performance
- **Performance Comparison**: Real results across healthcare, IoT, financial domains
- **Preset Selector**: Try production-ready configurations
- **Live Metrics**: Energy savings, precision, recall with confidence intervals

### Real-Time Processing Panel
- **Significance Timeline**: Multi-component scoring (magnitude + anomaly + context + urgency)
- **Adaptive Threshold**: PI controller with error feedback and hysteresis
- **Activation Decisions**: Green dots show processed events, gaps show energy savings

### Statistical Validation
- **Bootstrap Confidence Intervals**: 95% CIs from 1000 samples (like production validation)
- **Performance Trends**: Real-time F1, precision, recall tracking
- **Domain Benchmarks**: Compare against validated production results

## Domain-Optimized Presets

### Healthcare
- **custom_health_hd82**: Heart disease optimized (82% energy savings, 0.196 recall)
- **custom_breast_probe**: Breast cancer with enriched features (77% savings, 0.118 recall)

### IoT & Sensors
- **auto_tuned**: General sensor monitoring (88% savings, 0.500 recall)
- **ecg_v1**: ECG/cardiac monitoring optimization

### Security & Finance
- **aggressive**: Network security and financial anomaly detection (89-90% savings)
- **conservative**: Maximum energy efficiency (>92% savings)

## Technical Innovation Highlights

### Bio-Inspired Adaptive Control
- **PI Controller**: Maintains stable activation rates despite input variability
- **Hysteresis**: Prevents oscillation through differential thresholds
- **Energy Pressure**: Bio-inspired energy management and regeneration

### Multi-Component Significance
```
significance = w_magnitude × magnitude + 
               w_anomaly × anomaly + 
               w_context × context + 
               w_urgency × urgency
```

### Proven Performance
- **77-94% Energy Savings**: Validated across 6 real-world datasets
- **Statistical Rigor**: Bootstrap confidence intervals (95% CI, 1000 samples)
- **Production Ready**: Hardware integration templates and runtime monitoring

## Real-World Applications

### Healthcare
- **ECG Monitoring**: MIT-BIH dataset validation (88.8% energy savings)
- **Clinical Decision Support**: Heart disease and breast cancer screening

### IoT & Edge Computing
- **Sensor Networks**: Multi-sensor anomaly detection with 88% efficiency
- **Edge AI**: Energy-constrained processing optimization

### Security & Finance
- **Network Security**: Intrusion detection (89% energy savings)
- **Financial Monitoring**: Real-time fraud detection and market analysis

## Performance Validation

All demo presets are based on comprehensive validation:
- **6 Real Datasets**: Healthcare, IoT, ECG, financial, network security
- **Statistical Validation**: 1000 bootstrap samples with 95% confidence intervals
- **Ablation Studies**: Component-wise performance analysis
- **Adversarial Testing**: Robustness against drift, spikes, and noise

Try the demo to see how Sundew achieves production-ready energy efficiency while maintaining critical performance metrics!