🌍 EcoScan - Clothing Carbon Footprint Scanner

📜 Overview

EcoScan is a web application designed to help users understand the environmental impact of their clothing. By uploading or capturing images of clothing items, users can see estimated carbon scores, earn eco-reward points, and redeem sustainability-focused offers. This project is a full-stack solution aimed at promoting sustainable practices.

🔧 Tech Stack

• Frontend: React.js, TailwindCSS
• Backend: Node.js, Express.js, TypeScript
• Image Recognition: Mock image analysis for development; integration-ready for AI/ML-based APIs.

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🚀 Setup Instructions

1. Clone the Repository
   Clone the repository and navigate into the project directory:

   git clone https://github.com/KshitijTodkar48/Eco-Scan.git
   cd Eco-Scan

2. Install Dependencies
   Install dependencies for both frontend and backend:

    cd frontend
    npm install

    cd backend
    npm install

3. Add environment variables

   • Create a '.env' file in the client directory.
   • Add the following variable in it.

      VITE_SERVER_URL = http://localhost:3000

4. Run the Application

   • Backend: Start the backend server:

      cd backend
      npm run dev

   • Frontend: Start the frontend application:

      cd frontend
      npm run start

🌱 Carbon Score Assumptions

To calculate the environmental impact of each clothing item, we have assigned approximate carbon scores based on item type. These scores are stored in an in-memory dictionary for quick access.

👕 Item	🌍 Estimated Carbon Score (kg CO₂)
T-shirt	5
Jeans	10
Sweater	7
Jacket	15
Socks	2
Shoes	12
Scarf	4
Hat	3

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🌟 Product & Technical Enhancements

In this section, suggest possible improvements that could make EcoScan a more effective and scalable solution.

1. 🌐Scaling:

• Introduce a robust database like MongoDB for storing user-uploaded image data and eco-reward points.
• Implement caching (e.g., Redis) for frequent queries to optimize performance.
• Scale horizontally by deploying the backend on a cloud platform like AWS or Google Cloud using load balancers.
• Utilize a CDN (Content Delivery Network) for serving static assets to reduce load times.
• Migrate to a microservices architecture to isolate modules like image processing and eco-reward calculations for better fault tolerance.
• Use containerization (e.g., Docker) and orchestration tools like Kubernetes to ensure smooth scaling and deployment.

2. 📊 Enhanced Eco-Score Model:

• Include additional parameters like clothing material, manufacturing origin, and user-input brand data.
• Use external APIs for real-time carbon impact data.

3. ✨ User Experience Improvements:

• Add interactive visualizations for carbon impact comparisons (e.g., bar graphs, pie charts).
• Allow users to track their sustainability progress over time with personal dashboards.
• Introduce gamification elements like badges and milestones for eco-reward achievements.
• Create an "Eco Tips" section with actionable advice on reducing carbon footprints.
• Implement a dark mode for better accessibility and user comfort.
• Allow users to share their eco-savings on social media to inspire others.

4. 🔌 API Integrations:

• Integrate with services like OpenAI’s Vision API or TensorFlow for advanced image recognition.

📲 Deployment

If deployed, include a link here to access the live version of EcoScan.

• URL: Deployment Link

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Thank you for building a greener future with EcoScan! 🌍💚