GreenTag: AI-Powered Sustainability Platform - Repository

Overview

This repository houses the codebase for GreenTag, an innovative platform that empowers businesses to optimize their environmental, social, and governance (ESG) performance. GreenTag leverages AI, data analytics, and a modern web stack to transform raw product tag data into actionable sustainability insights.

Table of Contents

• Features
• Technology Stack
• AI Pipeline
• Data Model
• Prerequisites
• Installation
• Configuration
• Deployment
• Contributing
• License

Features

• AI-Powered Tag Analysis: Extracts material composition, recyclability, and certification information from product tag images using the Gemini AI model.
• Sustainability Scoring: Generates a standardized Sustainability Score for each item, enabling consistent measurement and comparison.
• Intelligent Routing: Recommends optimal routing decisions for returned items (donate, resell, recycle) based on AI analysis and business rules.
• ESG Performance Dashboard: Visualizes key environmental, social, and governance metrics:
  • Returns Overview (item disposition distribution).
  • ESG Sustainability Score.
  • ESG Metric Tracker (recycled products percentage, landfill diversion rate, etc.).
  • Industry Overview (GreenTag vs. industry benchmarks).
• Returns Page: List of data for current items, with routing
• User-Friendly Interface: Intuitive web interface built with React and Next.js.
• RESTful API: Provides programmatic access to data and functionality.
• Scalable Data Storage: Utilizes Google Cloud Firestore for reliable data persistence.
• Responsive Design: Accessible on various devices and screen sizes.

Technology Stack

• AI:

  • Gemini AI (Google AI models): Powers OCR and data analysis.

• Frontend:

  • React: JavaScript library for building user interfaces.
  • Next.js: React framework for server-side rendering (SSR) and static site generation (SSG).
  • Tailwind CSS: Utility-first CSS framework for rapid UI development.
  • D3.js: JavaScript library for data visualization.
  • React Icons: Library of pre-built React icons.

• Backend:

  • Flask: Python microframework for building REST APIs.
  • Google Cloud Firestore: NoSQL document database for storing data.

AI Pipeline

1. Image Upload: The user uploads an image of a product tag.
2. OCR with Gemini AI: The Gemini AI model performs Optical Character Recognition (OCR) to extract text from the image.
3. Data Analysis with Gemini AI LLM: A Gemini AI Large Language Model (LLM) analyzes the extracted text to identify material composition, recyclability, and certifications.
4. Sustainability Scoring: An algorithm generates a Sustainability Score based on the analyzed data.
5. Routing Decision: The AI suggests an optimal routing decision (donate, resell, recycle).
6. Data Storage: The extracted data, Sustainability Score, and routing decision are stored in Firestore.

Data Model

Data is stored in Google Cloud Firestore using the following key collections:

• items:
  • Each document represents an item with fields like:
    • id (string): Unique item identifier.
    • date (string): Date of processing (ISO 8601 format).
    • status (string): Routing decision (e.g., "Donate", "Resell", "Recycle").
    • score (number): Sustainability Score.
    • composition (object): Material composition details (e.g., {"Acrylic": 20, "Mohair": 80}).

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js: JavaScript runtime environment.
• npm or Yarn: Package managers for JavaScript.
• Python: Programming language for the backend.
• Google Cloud SDK: Command-line tool for interacting with Google Cloud services.
• Firebase tools npm install -g firebase-tools

Installation

1. Clone the repository:

   git clone <repository_url>
   cd GreenTag

2. Install frontend dependencies:

   cd client
   npm install  # or yarn install

3. Install backend dependencies:

   cd server
   pip install -r requirements.txt

Configuration

1. Firebase Project Setup:

   • Create a new project in the Firebase console.
   • Enable Firestore Database in your project.
   • Create a service account and download the service account key JSON file.

2. Environment Variables:

   • Frontend (Client):
     • Create a .env.local file in the client/ directory (if you need to store client-side environment variables). Important: Never store sensitive information (like API keys) in client-side environment variables.
   • Backend (Server):
     • Set the following environment variables in your server environment (e.g., Heroku, Google Cloud Functions):
       • FIRESTORE_CREDENTIALS: The path to your Firebase service account key JSON file.
       • GEMINI_API_KEY: Your Gemini AI API key (if required).
       • Other specific env var

3. Configure the APIs so that they match your current infrastructure and versions

Deployment

Detailed deployment instructions will be included at a later time, however you need to follow how the database is used, and set all of those parameters in that file.

Contributing

Contributions to the GreenTag ESG Dashboard are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive commit messages.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.

License

This project is licensed under the MIT License.