README.md

metadata

title: Environmental Impact Analyzer
emoji: ⚡
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
short_description: Gives Carbon Footprint Score for products description or url

Enviromental Impact Analyzer

This project, "Environmental Impact Analyzer," is a Gradio-based application designed to assess the environmental footprint of products. Users can input either a product description or a URL to a product page, and the application will extract relevant information, calculate its carbon footprint, generate an environmental sustainability score, and provide actionable recommendations for improvement.

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Features:

• Product Information Extraction: Automatically extracts key environmental factors from text descriptions or web pages (via URL scraping).
• Carbon Footprint Calculation: Integrates with the Climatiq API to estimate the carbon emissions associated with the product's manufacturing and transportation.
• Environmental Scoring: Leverages advanced Large Language Models (LLMs) to generate a comprehensive sustainability score (0-100) based on multiple environmental criteria.
• Improvement Recommendations: Provides specific and actionable suggestions to reduce the product's environmental impact. Intuitive Gradio Interface: Offers an easy-to-use web interface for seamless interaction.

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How It Works

The core of the Environmental Impact Analyzer is built using LangGraph, enabling a stateful, multi-step agentic workflow:

1. Input Classification: Determines if the user input is a direct product description or a URL.
2. Information Extraction:
   • If a URL, it scrapes the product information from the web page.
   • If a description, it directly processes the provided text.
   • An LLM (deepseek-ai/DeepSeek-R1) is then used to extract structured environmental data (e.g., material composition, manufacturing location, weight, transport distance, recyclability).
3. Carbon Footprint Calculation: The extracted data is sent to the Climatiq API to calculate the estimated carbon footprint (in kg CO2e) based on factors like materials and transportation.
4. Environmental Scoring & Recommendations:
   • An LLM (meta-llama/Meta-Llama-3.1-70B-Instruct) evaluates the extracted data and carbon footprint.
   • It generates an overall environmental score (0-100), considering factors like carbon emissions, material sustainability, manufacturing practices, transport, longevity, and end-of-life. The scoring is calibrated to recognize positive environmental efforts.
   • It also provides a category breakdown of the score and actionable recommendations for improving the product's environmental impact.

Note: You can also change the Models for your liking to increse Speed or more accuracy.but make sure hyperbolic supports it.

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Tech Stack

Category	Technology	Purpose
Framework	Gradio	Building the interactive web user interface.
Agentic Workflow	LangGraph	Orchestrating multi-step agent logic and state management.
LLM Integration	Hugging Face Inference Client	Interacting with Large Language Models hosted on Hugging Face Hub.
Core LLM	deepseek-ai/DeepSeek-R1	Generates environmental scores, category breakdowns, and recommendations.
External APIs	Climatiq API	Calculates carbon footprint based on product data.
Web Scraping	requests, BeautifulSoup (via scrapper.py)	Fetching and parsing HTML content from product URLs.
Prompting	langchain.prompts.ChatPromptTemplate	Structuring and templating LLM prompts for consistent input.
Environment Mgt.	python-dotenv	Loading environment variables from .env files for secure key management.
Data Typing	typing, typing_extensions (TypedDict)	Enhancing code readability and maintainability with type hints.

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Setup and Installation

To run this project locally, follow these steps:

1. Clone the Repository

git clone [https://huggingface.co/spaces/Agents-MCP-Hackathon/Environmental-Impact-Analyzer](https://huggingface.co/spaces/Agents-MCP-Hackathon/Environmental-Impact-Analyzer)
cd Environmental-Impact-Analyzer

2. Create a Virtual Environment (Recommended)

python -m venv .venv
# On Windows
.venv\Scripts\activate
# On macOS/Linux
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. 4. Configure Environment Variables

Create a .env file in the root directory of your project and add the following:

HF_API_KEY="hf_YOUR_HUGGING_FACE_ACCESS_TOKEN"
CLIMATIQ_API_KEY="YOUR_CLIMATIQ_API_KEY"

5. Run the Application

python app.py