TejasAI-2.o - A CO₂ Footprint Tracker Web Application

Track, reduce, and compete in lowering your CO₂ footprint with personalized suggestions, image verification, and weekly contests. Join the movement for a greener future, one activity at a time!

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🌍 TejasAI-2.0 - CO₂ Footprint Tracker Web Application

## Inspiration 🌱

The rising concerns about climate change and the urgent need to reduce carbon footprints inspired the creation of TejasAI-2.0. This web application empowers individuals to track and reduce their CO₂ emissions, promoting collective action toward a sustainable planet. 🌎✨

💡 What it does

TejasAI-2.0 helps users:

  • Log daily activities and calculate their CO₂ footprint.
  • Track progress with visual insights like dashboards and leaderboards.
  • Earn points for sustainable actions verified through image recognition (e.g., tree planting, recycling).
  • Participate in weekly contests to earn bonus points for environmental tasks.

💡 Additionally, personalized CO₂ reduction suggestions powered by AWS Bedrock guide users toward making greener choices. 🌱

🛠 How We Built It

Frontend:

  • Developed using Next.js for a modern, responsive UI.

Backend:

  • Express.js for server-side logic and API handling.

Database:

  • MongoDB Atlas for scalable, cloud-based data storage.

Authentication:

  • Cleark for robust user authentication.

Image Recognition:

  • AWS Rekognition verifies user-uploaded activities (e.g., recycling).

AI-Powered Suggestions:

  • AWS Bedrock and Cohere's LLM suggest personalized CO₂ reduction actions.

Storage:

  • User-uploaded images are securely stored in AWS S3.

🚧 Challenges We Faced

  • Image Recognition Accuracy: Fine-tuning AWS Rekognition to correctly identify activities like tree planting and recycling, minimizing false positives or negatives.
  • Integrating AI Models: Using AWS Bedrock's LLM to match user activity logs with relevant CO₂ reduction tips.
  • Handling Large User Data: Optimizing MongoDB Atlas and AWS S3 to efficiently store and retrieve large volumes of user content.
  • Lambda Integration Issues: While integrating AWS Lambda with our Next.js application for certain serverless functionalities, we encountered deployment configuration issues.

AWS Lambda Integration Issue

🏆 Accomplishments That We Are Proud Of

  • Successfully implemented real-time image recognition using AWS Rekognition.
  • Integrated AWS Bedrock's LLM to provide personalized CO₂ reduction tips.
  • Developed a fully functional leaderboard system to engage users.
  • Designed an intuitive dashboard with clear visual insights (e.g., pie charts) for CO₂ footprint tracking.
  • Hosted dynamic weekly contests, encouraging proactive environmental actions.

🎓 What We Learned

-Seemless integration of Cleark Authentication for our application.

  • Fine-tuning cloud services like AWS Rekognition for image verification.
  • Seamless integration of AI models to provide actionable, personalized suggestions.
  • Scaling cloud infrastructure with MongoDB Atlas and AWS S3.
  • Designing engaging user experiences through leaderboards and contests.

🚀 What’s Next for TejasAI-2.0

  • Expand Contests: Introduce more dynamic weekly challenges to keep users engaged.
  • OCR for co2 Extraction from user Bill: use OCR Technology for effecient dedection of co2 emmision from bills.
  • Mobile App: Launch a mobile app to make tracking accessible anytime, anywhere.
  • Community Features: Enable users to share their progress and tips with peers(Social Media Features).
  • Dynamic User Activity Logging: Feature for the user to log his daily activity dynamically as and when required.
  • Badges and Redeem Reward Feature: Enable users to access their badges for posting their achievement and a redeem feature for their achievement.

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