🌿 NeuraGreen - AI-Powered Carbon Footprint Analyzer

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📌 Overview:

NeuraGreen is a full-stack web application that:

1. Lets users input or scrape Amazon product data
2. Uses Gemini AI to analyze the product's carbon footprint
3. Recommends top 3 lower-carbon alternatives based on the analysis

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🛠️ Tech Stack

Component	Technology	Purpose
Frontend	React + Vite	Fast UI & SPA structure
Backend	Python + FastAPI	API layer for scraping & AI analysis
AI Model	Gemini API (Google)	Carbon footprint estimation
Scraper	BeautifulSoup/Selenium	Amazon product scraping
CSV Logging	Pandas	Save scraped product analysis

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🔧 Installation

🔁 Clone & Setup

git clone https://github.com/yourusername/NeuraGreen.git
cd NeuraGreen

📦 Backend Setup

cd server
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

💻 Frontend Setup

cd ../
npm install

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🔐 Configuration

Create a .env file in the root directory:

VITE_GEMINI_API_KEY="your_gemini_api_key"    #🤖 Gemini API Key → Get your key for Google AI (https://makersuite.google.com/app/apikey)
VITE_API_URL=http://localhost:8000
VITE_CLERK_PUBLISHABLE_KEY="your_clerk_api_key"   #🔑 Get your Clerk API keys at the [Clerk Dashboard](https://dashboard.clerk.dev)  
→ Go to your application → API Keys section

You can also configure backend environment variables (if needed) in server/.env.

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🚀 Running the App

🖥️ Start Frontend (React)

npm run dev

🧠 Start Backend (FastAPI)

cd server
uvicorn main:app --reload

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📂 Project Structure

NeuraGreen/
├── public/
│   └── vite.svg
├── src/
│   ├── api/                 # API services
│   ├── assets/              # Static images/icons
│   ├── components/          # UI Components
│   ├── pages/               # Route Pages (Home, Results)
│   ├── App.jsx
│   ├── main.jsx
│   └── index.css
├── server/
│   ├── routers/
│   │   ├── analyze.py       # API route for analysis
│   ├── services/
│   │   ├── gemini.py        # Gemini API integration
│   │   ├── scraper.py       # Amazon scraping logic
│   ├── main.py              # FastAPI entry point
│   ├── requirements.txt
├── Output/
│   └── scraped_products.csv # CSV file with outputs
├── .env
├── index.html
├── package.json
├── vite.config.js
└── README.md

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🌟 Key Features

🔍 Product Scraping Logic (scraper.py)

def scrape_product_data(url: str) -> dict:
    # Extracts title, price, and product attributes
    soup = BeautifulSoup(requests.get(url).text, 'html.parser')
    title = soup.find("span", {"id": "productTitle"}).text.strip()
    return {"title": title}

🤖 AI-Based Carbon Analysis (gemini.py)

def analyze_product_footprint(title: str) -> str:
    prompt = f"Estimate carbon footprint of {title}"
    response = gemini.generate_content(prompt)
    return response.text

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🧪 Example Usage

Frontend calls:

// src/api/productApi.js
export const analyzeProduct = async (title) => {
  const response = await fetch("/api/analyze", {
    method: "POST",
    body: JSON.stringify({ title }),
  });
  return await response.json();
};

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🧪 Output:

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📜 License

MIT © [Tanishq Shinde]

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Crafted with 🌱 by Tanishq using React, FastAPI, and Gemini AI