Update README.md

README.md

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 ---
 title: Sensor Placement Explorer
-emoji: ⚡
-colorFrom: green
-colorTo: indigo
+emoji: 🎯
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎯 Risk-Aware Sensor Placement Explorer
+
+An interactive educational tool to understand **optimal sensor placement** using Log-Gaussian Cox Process (LGCP) models.
+
+## What You'll Learn
+
+1. **Detection Formula**: How sensors detect targets based on distance
+2. **Intensity Functions**: Where targets are expected to appear
+3. **Uncertainty**: How variance affects decision-making
+4. **Mean vs Conservative**: When to use each placement strategy
+
+## Features
+
+- 🎮 **Interactive sliders** to adjust all parameters
+- 📊 **Real-time visualizations** of sensor coverage
+- 🔬 **Monte Carlo simulation** to test placement strategies
+- 📚 **Educational summaries** of key concepts
+
+## How to Use
+
+1. **Tab 1**: Learn the detection probability formula
+2. **Tab 2**: Play with a single sensor
+3. **Tab 3**: Understand intensity and uncertainty
+4. **Tab 4**: Run full analysis comparing strategies
+5. **Tab 5**: Review key concepts
+
+## The Core Question
+
+> "Should we place sensors where we EXPECT the most intruders (mean-based) 
+> or prepare for WORSE than expected (conservative/Q90-based)?"
+
+**Spoiler**: For high-stakes security, conservative placement wins! 🏆
+
+## License
+
+MIT License - Feel free to use and modify!