Update README.md

README.md

@@ -1,60 +1,69 @@
-# Sundew Algorithm Demo
-
-A simple, interactive demonstration of the Sundew adaptive gating algorithm.
-
-## What This Demo Shows
-
-This demo visualizes how the Sundew algorithm:
-1. **Scores input significance** based on multiple features
-2. **Adapts the activation threshold** to maintain target processing rates  
-3. **Saves energy** by skipping low-importance inputs
-4. **Maintains stability** using hysteresis to prevent oscillation
-
-## Running Locally
-
-```bash
-pip install -r requirements.txt
-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"
-4. The demo will automatically deploy
-
-## Understanding the Visualization
-
-### Top Chart: Significance vs Threshold
-- **Blue line**: Significance score for each input (0-1)
-- **Red line**: Adaptive threshold that adjusts over time
-- **Green dots**: Inputs that were processed (activated)
-
-### Middle Chart: Activation Pattern  
-- Shows which samples were processed (green) vs skipped (white)
-- Gives a clear view of the selective processing pattern
-
-### Bottom Chart: Energy Savings
-- Real-time percentage of energy saved
-- Orange dashed line shows the target based on processing rate
-
-## Key Parameters
-
-- **Target Processing Rate**: What percentage of inputs to process
-- **Number of Samples**: How many data points to simulate
-- **Anomaly Rate**: Percentage of high-importance events in the stream
-
-## Technical Innovation
-
-The algorithm uses a PI controller with hysteresis to maintain stable activation rates while adapting to changing input patterns. This prevents oscillation while enabling efficient energy management.
-
-Typical results:
-- 70-85% energy savings
-- ±3% accuracy in maintaining target rates
+---
+license: mit
+title: sundew_demo
+sdk: gradio
+emoji: 🚀
+colorFrom: indigo
+colorTo: indigo
+short_description: ' Sundew: Adaptive Energy-Aware Gating Algorithm'
+---
+# Sundew Algorithm Demo
+
+A simple, interactive demonstration of the Sundew adaptive gating algorithm.
+
+## What This Demo Shows
+
+This demo visualizes how the Sundew algorithm:
+1. **Scores input significance** based on multiple features
+2. **Adapts the activation threshold** to maintain target processing rates  
+3. **Saves energy** by skipping low-importance inputs
+4. **Maintains stability** using hysteresis to prevent oscillation
+
+## Running Locally
+
+```bash
+pip install -r requirements.txt
+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"
+4. The demo will automatically deploy
+
+## Understanding the Visualization
+
+### Top Chart: Significance vs Threshold
+- **Blue line**: Significance score for each input (0-1)
+- **Red line**: Adaptive threshold that adjusts over time
+- **Green dots**: Inputs that were processed (activated)
+
+### Middle Chart: Activation Pattern  
+- Shows which samples were processed (green) vs skipped (white)
+- Gives a clear view of the selective processing pattern
+
+### Bottom Chart: Energy Savings
+- Real-time percentage of energy saved
+- Orange dashed line shows the target based on processing rate
+
+## Key Parameters
+
+- **Target Processing Rate**: What percentage of inputs to process
+- **Number of Samples**: How many data points to simulate
+- **Anomaly Rate**: Percentage of high-importance events in the stream
+
+## Technical Innovation
+
+The algorithm uses a PI controller with hysteresis to maintain stable activation rates while adapting to changing input patterns. This prevents oscillation while enabling efficient energy management.
+
+Typical results:
+- 70-85% energy savings
+- ±3% accuracy in maintaining target rates
 - Stable operation across varying input patterns