AI Sustainability Tracker

Project: AI Sustainability Tracker for Textile Factories & Eco-Brands
Author: Saba Saleem
Repo: (https://github.com/CodePokemon-p/ai_sustainability_tracker)

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Project Overview

The AI Sustainability Tracker is a comprehensive tool designed for textile factories and eco-conscious brands to track, analyze, and optimize environmental impact. The platform monitors key sustainability metrics such as CO₂ emissions, water consumption, energy usage, waste generation, and pollutants.

It also integrates AI-driven analytics, predictive alerts, and interactive tools for improved decision-making and sustainable production practices.

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Key Features

Tracker Tool

• Track CO₂ emissions, water consumption, energy consumption, waste, and pollutants.
• Provides professional analysis with primary driver, risk, and actionable recommendations.
• Supports multiple product types: Recycled Poly, Organic Cotton, Synthetic Blend, Microfiber.
• Multi-line, concise dashboard analytics.
• 
• Run via (python app.py)

AI Pattern

Layout Optimizer

• Uses a Genetic Algorithm (GA) and Computer Vision (CV) to optimize fabric layouts.
• Maximizes utilization (80–90%) and reduces fabric waste.
• Generates DXF files for production.
• Run via (python pattern_app.py) Figure 1: Fronted dashboard showing real-time optimization result.

Report Analyzer

• Upload CSV or PDF reports to analyze environmental metrics.
• Generates automated analytics with clear sustainability insights.
• Run via Python analyzer.py.

MessageAlert

• Allows managers to send messages/alerts to employees or brand members.
• Messages appear in the Alert Center for real-time communication.
• Run via Python app.py in the respective Flask folder.

EcoBoot AI Chatbot

• Interactive AI chatbot for discussing sustainability practices.
• Provides guidance and answers questions related to environmental impact.
• Run backend with: uvicorn eco_bot:app --reload --port 8000

📦 Additional Project Resources

Some project components (due to large size) are hosted externally.

You can download them here:
🔗 Google Drive Link: [Click to access backend, model, and environment folders](https://drive.google.com/drive/folders/10suWg5OIHMBZnAicmI9WiYjzEHoeb1QX?usp=sharing+ +)

Included in Drive:

• backend/ folder (Node + Flask backend)
• flan-env/ (Python virtual environment)
• ml-model/ (trained machine learning model files)
• node_modules/ (local dependencies)

These files are excluded from GitHub due to size limits.
Please extract them in the root project directory after download.

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⚙️ Security+ Threshold

• JWT authentication, secure hosting, and model input validation
• Ensures 10% tolerance for prediction/utilization deviation
• Enterprise-level security, reproducibility, and data integrity

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🧠 Tech Stack

Frontend: React, Tailwind CSS, Framer Motion, AOS, React Router
Backend: Node.js / Express, MongoDB, JWT Auth
AI / ML Microservices: Python / Flask, Gemini-based RLCV Regression Model
File Management: Git LFS for large files (.pkl, .csv, .zip, .dxf)

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🚀 Quick Setup Steps

1️⃣ Clone repository with Git LFS:

git lfs install
git clone https://github.com/CodePokemon-p/ai_sustainability_tracker.git
cd ai_sustainability_tracker
2️⃣ Install Python dependencies:
pip install -r requirements.txt
3️⃣ Install Node.js dependencies:

# Frontend
cd frontend
npm install

# Backend
cd ../backend
npm install
🧩 Running the Project
Frontend


cd frontend
npm run dev
Backend (Node.js)


cd backend
node server.js
ML / Flask Services


cd ml-model

# Run Tracker or Pattern Optimizer
python app.py
python pattern_app.py

# Run Report Analyzer
python analyzer.py

# Run MessageAlert or EcoBot AI Chatbot
uvicorn eco_bot:app --reload --port 8000
🌱 Usage
Access the Tracker Dashboard via frontend

Upload CSV / PDF reports to analyze sustainability metrics

Use Pattern Layout Optimizer to generate efficient fabric layouts

Interact with EcoBot AI Chatbot for sustainability guidance

Managers can send alerts/messages visible to employees in the Alert Center

🤝 Contributing
Fork the repository and create a new branch for your feature

Track large files using Git LFS

Submit pull requests with clear descriptions of your changes

📜 License
This project is licensed under the MIT License

📝 Notes
Large files (.pkl, .csv, .zip, .dxf) are managed using Git LFS

For local development, always ensure:

git lfs install
git pull
Security+ Threshold ensures enterprise-grade protection and reproducibility