/AI-Environmental-Impact-Analyzer

This full-stack application leverages cutting-edge AI to help consumers understand the environmental impact of the products they buy. The app analyzes product descriptions, manufacturing details, and supply chain information to generate an Environmental Footprint Score to guide users toward more sustainable choices.

MIT LicenseMIT

🌍 AI Environmental Impact Analyzer 🌱

Welcome to the AI Environmental Impact Analyzer project! This full-stack application leverages cutting-edge AI to help consumers understand the environmental impact of the products they buy. The app analyzes product descriptions, manufacturing details, and supply chain information to generate an Environmental Footprint Score to guide users toward more sustainable choices.

🚀 Features

1. Product Analysis with AI 🤖

  • AI-Powered Impact Assessment: Analyze product descriptions, manufacturing processes, and supply chain data using advanced NLP models (BERT, GPT-4).
  • Environmental Footprint Score: A comprehensive score indicating the product’s environmental impact, categorized by:
    • Carbon Footprint (CO₂ emissions)
    • Water Usage 💧
    • Waste Generation ♻️
    • Energy Consumption ⚡

2. Data Sources 🌐

  • External API Integrations: Pulls sustainability data from public APIs and partner databases to enhance product assessments.
  • User-Submitted Data: Allow users to input product details manually if data is unavailable.

3. Sustainability Dashboard 📊

  • Product Overview: Visual representation of product data, including eco-friendly features, sustainability score, and impact breakdown.
  • Charts & Graphs: Graphical display of product's environmental footprint using interactive charts and progress bars.
  • Recommendations: Get eco-friendly alternatives based on product comparisons and sustainability goals.

4. User Accounts and History 👤

  • Personal Profiles: Create and manage user accounts to save products, track environmental footprints, and set sustainability goals.
  • Impact Tracking: View historical data and monitor the progress of reducing carbon footprints over time.

5. Shopping Alternatives 🌱

  • Eco-Friendly Product Suggestions: Get smart recommendations for products with a lower environmental footprint based on the user’s past searches.
  • Sustainability Tips: Personalized advice on reducing environmental impact by choosing greener products.

6. Advanced Features 🔮

  • Blockchain for Transparency: Integrate blockchain to verify the authenticity of sustainability claims and product supply chain transparency.
  • Real-Time Sustainability Updates: Keep track of products as their environmental scores evolve with new data (e.g., updated manufacturing practices).
  • AI Model Training & Continuous Improvement: Continuously train the AI model to improve accuracy based on user feedback and new sustainability data.

🧑‍💻 Project Structure

AI-Environmental-Impact-Analyzer/
├── backend/
│   ├── ai_model/                # AI model and data processing scripts
│   ├── app/                     # Django app for API and logic
│   ├── migrations/              # Django database migrations
│   ├── manage.py                # Django management script
│   ├── requirements.txt         # Backend dependencies
│   └── settings.py              # Django project settings
├── frontend/
│   ├── public/                  # Public files (e.g., images, favicon)
│   ├── src/                     # React or Vue.js components
│   │   ├── components/          # Reusable components (e.g., product card, chart)
│   │   ├── views/               # Page components (e.g., dashboard, product page)
│   │   ├── services/            # API service calls
│   │   └── App.js               # Main app file
│   ├── package.json             # Frontend dependencies
│   ├── tailwind.config.js       # Tailwind CSS configuration (if used)
│   └── index.html               # HTML template
├── database/
│   └── product_data.sql         # Database schema and example data
├── .gitignore                   # Git ignore file
└── README.md                    # Project documentation

Backend:

  • Django Framework: Powers the backend APIs and models.
  • TensorFlow/PyTorch: For AI model development and inference.
  • PostgreSQL/MySQL: Database for storing product and user data.

Frontend:

  • React.js / Vue.js: Used for building a dynamic user interface.
  • Tailwind CSS/Material UI: Styling libraries for responsive, modern UI design.
  • Chart.js / D3.js: For displaying environmental impact scores in an intuitive, graphical way.

AI Model:

  • NLP Models (BERT, GPT-4): Used to process product descriptions and manufacturing data.
  • Training Data: Custom dataset for training the AI model (product details and environmental scores).

⚙️ Installation and Setup

1. Clone the Repository

git clone https://github.com/yourusername/AI-Environmental-Impact-Analyzer.git
cd AI-Environmental-Impact-Analyzer

2. Set Up Backend (Django)

Install Python dependencies:

cd backend
pip install -r requirements.txt

Set Up Database:

python manage.py migrate

Start the Django server:

python manage.py runserver

3. Set Up Frontend (React/Vue.js)

Install Node.js dependencies:

cd frontend
npm install

Start the frontend server:

npm start

🌍 Contributing

We welcome contributions to make this project better! If you have suggestions, bug fixes, or new features to propose, feel free to fork this repository and submit a pull request.

Steps to Contribute:

  1. Fork this repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Make your changes.
  4. Commit your changes (git commit -am 'Add new feature').
  5. Push to the branch (git push origin feature-branch).
  6. Open a pull request.

📝 License

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

📱 Contact

For any inquiries or further information, feel free to contact us at: