SurroundShield is an advanced AI-powered chatbot that offers real-time, personalized safety insights based on a user's location, weather conditions, and health profile (BMI). Designed with a sleek UI and powered by cutting-edge AI models, it acts as a digital guardian—helping users stay ahead of environmental hazards, community risks, and natural disasters.
Built for Chubb’s innovation challenge, SurroundShield combines multi-source data, robust backend services, and a fine-tuned LLM (LLaMA 3.3 - 70B) to deliver immediate, actionable, and context-aware guidance.
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📍 Location-Aware Risk Detection
Detects nearby environmental and community hazards based on geolocation -
🌤️ Weather & Environmental Analysis
Assesses real-time weather, pollution, UV levels, and natural disaster alerts -
💪 Health-Profile Awareness
Integrates user BMI to personalize safety advice -
💬 AI Chatbot Assistant
Fine-tuned LLaMA 3.3 (70B) model guiding users with risk responses and safety tips -
🧾 Prompted for Chubb Use Case
Tailored to Chubb’s mission of Harnessing AI for Community Risk Awareness Prompt
| Layer | Technology |
|---|---|
| Frontend | React.js, Bootstrap, HTML/CSS |
| Backend | Node.js, Express.js |
| AI/ML | Flask API, Databricks, LLaMA 3.3 (70B) |
| Database | MongoDB |
| Hosting/Infra | Databricks |
Clean, modern, responsive UI – optimized for desktop and mobile devices
https://www.youtube.com/watch?v=prmbRBFXmvA (Checkout the Project Demo)
- User Input: User enters their location and BMI while registering. Later he can ask queries to the Chatbot related to community awareness, surroundings, environmental disasters, etc
- Real-Time Data Fetch: System pulls current weather, pollution, disaster alerts
- Risk Assessment: Data is sent to Flask AI backend where the fine-tuned LLaMA 3.3 LLM evaluates the threat level
- Chatbot Response: The AI chatbot provides personalized advice or safety warnings
- Node.js & npm
- Python 3.10+
- MongoDB (local/cloud)
- Databricks Workspace
- API Keys for Weather/Pollution
# Clone the repo
git clone https://github.com/yashdd/SurroundShield.git
cd SurroundShield
# Setup frontend
cd client
npm install
npm start
# Setup backend
cd ../server
npm install
npm run dev
# Start Flask AI service
cd ../ai-model
pip install -r requirements.txt
python app.py
# Fork the repo
# Create your feature branch (git checkout -b feature/AmazingFeature)
# Commit your changes (git commit -m 'Add amazing feature')
# Push to the branch (git push origin feature/AmazingFeature)
# Open a Pull Request