Carbon Footprint Optimizer - Supply Chain Logistics

An AI-powered application that optimizes transportation routes for minimal environmental impact in supply chain logistics. Features a web-based dashboard built with Streamlit.

Features

• Interactive Web Dashboard with visual analytics
• Route Optimization based on carbon emissions, weather, and cargo
• Multiple Route Alternatives comparison (Eco-Friendly, Fastest, Balanced)
• Deep Learning Model for carbon footprint prediction
• Sample Data Generation for immediate experimentation
• Interactive Maps with Folium visualization
• Performance Metrics tracking emission reductions

Tech Stack

• Frontend: Streamlit, Plotly, Folium
• Backend: TensorFlow/Keras, Scikit-learn, Pandas
• Mapping: OSRM, GeoPy
• Optimization: Custom route optimization algorithms

Requirements

• Python 3.8+
• Internet connection

Setup Instructions

1. Navigate to project directory:

   cd CARBON-FOOTPRINT-OPTIMIZER

2. Create and activate a virtual environment:

   python -m venv .venv

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Streamlit:

   streamlit run main.py

5. Open the Browser:

   Visit: http://localhost:8501

Project Structure

CFO/
├── main.py              # Main Streamlit application entry 
├── requirements.txt         # Python dependencies
├── gui/
│   └── main_gui.py         # Streamlit interface
├── models/
│   └── carbon_model.py     # AI model 
├── utils/
│   └── route_optimizer.py  # Route optimization logic
├── data/
│   └── data_handler.py     # Data processing utilities
└── README.md               

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

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