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SafeNav 🌊🚗

SafeNav is an intelligent, ML-powered waterlogging precaution and monitoring system designed to help users navigate safely during monsoon seasons and flood-prone conditions. It provides real-time risk assessment, smart route planning, and live weather updates to ensure safe travel.

🚀 Features

  • 🗺️ Smart Route Planning: Find alternative routes that avoid flood-prone areas and waterlogged streets in real-time using advanced pathfinding algorithms.
  • 📊 Area Risk Insights: Get detailed flood risk assessments for specific locations before you travel.
  • 🌦️ Live Weather Integration: Real-time weather updates and monsoon safety warnings powered by OpenWeather API.
  • 🤖 ML-Powered Risk Assessment: Utilizes machine learning models (Scikit-learn) to classify flood risk and predict severity based on environmental factors.
  • 🧠 AI Assistant: Integrated Google Gemini AI for intelligent insights and assistance.
  • 📢 Community Reporting: Report waterlogging issues to help the community stay safe.

🛠️ Tech Stack

Frontend

  • Framework: Next.js 13 (App Router)
  • Language: TypeScript
  • Styling: Tailwind CSS, Shadcn/ui
  • Maps: Leaflet / React-Leaflet
  • State Management: React Hooks
  • Auth: Supabase Auth / Clerk

Backend

  • Framework: FastAPI (Python)
  • Machine Learning: Scikit-learn (Joblib for model serialization)
  • AI Integration: Google Gemini API
  • Data Processing: Pandas, NumPy

Infrastructure

  • Database: Supabase
  • Deployment: Netlify (Frontend)
  • IDE: Kiro Specs and Vibe for Dijkstra's Algorithm

📋 Prerequisites

Before you begin, ensure you have the following installed:

  • Node.js (v18 or higher)
  • Python (v3.9 or higher)
  • npm or yarn

⚙️ Installation & Setup

1. Clone the Repository

git clone https://github.com/yourusername/SafeNav.git
cd SafeNav

2. Frontend Setup

Navigate to the root directory to set up the Next.js frontend.

# Install dependencies
npm install

# Start the development server
npm run dev

The frontend will be available at http://localhost:3000.

3. Backend Setup

Navigate to the backend directory to set up the FastAPI server.

cd backend

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install Python dependencies
pip install fastapi uvicorn joblib requests python-dotenv google-generativeai aiofiles

# Start the backend server
uvicorn main:app --reload

The backend API will be available at http://localhost:8000.

🔐 Environment Variables

Create a .env.local file in the root directory with the following variables:

# Backend
OPENWEATHER_API_KEY=your_openweather_api_key
GEMINI_API_KEY=your_gemini_api_key

# Frontend (Supabase/Clerk)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
# Add other necessary keys based on your auth provider

📂 Project Structure

SafeNav/
├── app/                    # Next.js App Router pages
│   ├── auth/               # Authentication pages
│   ├── home/               # Main dashboard
│   └── page.tsx            # Landing page
├── backend/                # FastAPI Backend
│   ├── main.py             # API Entry point
│   ├── models/             # ML Models (.pkl files)
│   └── uploads/            # User uploaded content
├── components/             # React Components
│   ├── ui/                 # Shadcn UI components
│   ├── RiskMap.tsx         # Map visualization
│   └── RouteMap.tsx        # Navigation component
├── lib/                    # Utility functions
├──public/                  # Static assets
├── .kiro/                  # Kiro AI assistant configurations
└── .bolt/                  # Bolt.new configurations

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the project
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.