🔥 RapidResponseAI - Automated Emergency Response Intelligence

RapidResponseAI is an AI-powered emergency response platform that generates comprehensive emergency plans in under 60 seconds using real-time satellite data and multi-agent AI analysis.

🎯 The Problem

Emergency managers currently spend 2-3 hours manually analyzing disasters to create response plans. In emergencies, every minute counts.

💡 Our Solution

An automated intelligence pipeline that:

• Detects wildfires using NASA satellite data
• Analyzes impact using 5 specialized AI agents
• Generates complete response plans via LLM synthesis
• Updates every 15 minutes with real-time data

Result: 60 seconds vs 2-3 hours = Lives saved

🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 16+
• API Keys: NASA FIRMS, OpenWeather, Mapbox, OpenRouter

Setup & Run

Backend:

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
# Edit .env with your API keys
python app.py

Frontend:

cd frontend
npm install
cp .env.example .env
# Edit .env with your REACT_APP_MAPBOX_TOKEN
npm start

Access at http://localhost:3000

🏗️ Architecture

React Dashboard
      ↓ WebSocket/REST
Flask Backend
      ↓
Orchestrator (LLM-powered)
      ↓
5 AI Agents (parallel processing)
├─ Damage Assessment
├─ Population Impact
├─ Routing & Evacuation
├─ Resource Allocation
└─ Prediction Modeling
      ↓
Real-time Data Sources
├─ NASA FIRMS (satellite)
├─ OpenWeather (weather)
└─ OpenStreetMap (infrastructure)

✨ Key Features

• Proactive Detection: Automatic wildfire identification via satellite
• Multi-Agent AI: Parallel specialized analysis
• 60-Second Plans: Complete emergency response in under a minute
• Real-time Updates: Continuous monitoring every 15 minutes
• Interactive Dashboard: Map visualization with danger zones & evacuation routes
• Demo Mode: Pre-cached historical scenarios for reliable demonstrations

🎮 Demo Mode

For demonstrations without live API calls:

1. Set USE_CACHED_RESPONSES=True in backend/.env
2. Restart backend server
3. System will use pre-generated July 2020 Brampton fire scenario

📊 Tech Stack

Backend: Python, Flask, Flask-SocketIO, Geopandas, Shapely
Frontend: React, Mapbox GL JS, Socket.IO, Axios, Chart.js
AI: OpenRouter API (LLM orchestration)
Data: NASA FIRMS, OpenWeather, OpenStreetMap

📚 Documentation

• SETUP.md - Detailed installation & configuration
• ARCHITECTURE.md - System design & data flow
• CONTRIBUTING.md - Development guidelines

🧪 Testing

# Backend tests
cd backend
pytest tests/

# Frontend tests
cd frontend
npm test

📄 License

MIT License - see LICENSE file for details

🙏 Acknowledgments

• NASA FIRMS for satellite fire data
• OpenWeather for weather APIs
• Brampton GeoHub for local infrastructure data

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Built with ❤️ for emergency responders everywhere