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Sripadh Sujith
Sripadh Sujith

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🌍CarbonSnap: AI-Powered Image Recognition for Real-Time Carbon Footprint Insights

#devchallenge #weekendchallenge #ai #python

DEV Weekend Challenge: Earth Day

This is a submission for Weekend Challenge: Earth Day Edition

What I Built

CarbonSnap is an AI-powered web application that helps users instantly understand the environmental impact of everyday objects. As part of Earth day I decisively want to build something which is useful and interactive.

With a simple snap or upload of an image, CarbonSnap:

  • Identifies the object using image classification
  • Estimates its carbon footprint
  • Provides eco-friendly alternatives
  • Offers insights into its environmental impact

🎯 Goal

The goal of CarbonSnap is to make sustainability awareness instant, intuitive, and accessible.

Instead of requiring users to research environmental data manually, CarbonSnap delivers meaningful insights in seconds with just a snap

Live Demo

🌐CarbonSnap-Make smarter sustainable choices→

Code

Github Repository

GitHub logo Sripadh-Sujith / CarbonSnap

An innovative approach to discover carbon footprints of objects around you using AI

🌍 CarbonSnap

Snap an object. Instantly understand its carbon footprint—and make smarter, sustainable choices.

🚀 Overview

CarbonSnap is an AI-powered web application that analyzes everyday objects using image recognition and estimates their environmental impact in real time.

By combining computer vision with large language models, CarbonSnap transforms a simple image into meaningful environmental insights—helping users make more sustainable decisions effortlessly.

✨ Features

  • 📸 Capture or upload images using camera or file input
  • 🧠 AI-powered object detection using Hugging Face models
  • 🌍 Real-time carbon footprint estimation
  • 💡 Eco-friendly alternative recommendations
  • 🔮 Environmental impact insights and future implications
  • ⚡ Fast AI reasoning powered by Groq API
  • 🎨 Clean and modern Streamlit interface

🧠 How It Works

  1. Image Input
    User captures or uploads an image

  2. Object Detection
    Hugging Face model identifies the object

  3. AI Processing
    Detected object is sent to a Groq-powered LLM

  4. Insight Generation
    The AI generates:

    • Carbon footprint estimation
    • Impact level
    • …
View on GitHub

How I Built It

  1. Image Input

    • Users capture or upload an image via a Streamlit interface

  2. Object Detection

    • Image is processed using Hugging Face Inference API
    • A pre-trained image classification model identifies the object

  3. AI Reasoning

    • The detected object is passed to a Groq-powered LLM (LLaMA)
    • The model generates:
      • Carbon footprint estimation
      • Environmental impact level
      • Sustainable alternatives
      • Additional insights

  4. Output UI

    • Results are displayed in a structured Streamlit interface

⚙️ Tech Stack

  • Frontend / App Layer: Streamlit
  • Image Classification: Hugging Face Inference API
  • LLM Reasoning: Groq API (LLaMA models)
  • Backend Logic: Python
  • Image Processing: PIL

Key Decisions

  • Used API-based models instead of local inference

    → Reduced compute requirements and made deployment lightweight

  • Focused on UX simplicity

    → One input → complete sustainability insight

Final Note

Thank you for taking the time to explore CarbonSnap.

This project is a small step toward a larger vision—making sustainability more accessible through technology. I hope it sparks thought, awareness, and even small changes in everyday choices.

I truly appreciate the opportunity to be part of this challenge and learn alongside such an inspiring community.

🌍 Together, even simple actions can create meaningful impact.

Thank You 💖

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