EcoLens AI

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Inspiration

EcoLens AI was inspired by the everyday confusion around waste disposal. Many people want to recycle correctly but struggle with unclear rules, leading to “wish-cycling” and contamination. We wanted to create a tool that removes the guesswork and makes sustainable decisions simple, fast, and accessible to everyone.

What it does

EcoLens AI uses real-time computer vision and AI to identify waste items and provide clear, localized instructions on how to reduce, reuse, or recycle them. Instead of guessing, users get instant, actionable guidance—helping them make smarter and more sustainable choices.

How we built it

We built EcoLens AI as a mobile-first web app using a React + Vite frontend with Tailwind CSS for styling. We integrated AI using Google’s Gemini API to analyze items and generate intelligent recommendations. The app uses environment variables to securely manage API keys and connects user input with AI-generated outputs in real time.

Challenges we ran into

One of our biggest challenges was integrating the AI API smoothly while keeping the application fast and responsive. We also faced issues with deployment and environment variable management, especially when moving from local development to production. Additionally, ensuring that AI responses were clear, useful, and not overly generic required careful prompt design.

Accomplishments that we're proud of

We successfully built a working AI-powered prototype that can identify items and provide meaningful sustainability guidance in real time. We’re especially proud of combining computer vision with LLM-generated suggestions to go beyond simple classification and actually guide user behavior.

What we learned

We learned how to integrate AI APIs into a full-stack application, manage environment variables securely, and design systems that balance usability with technical complexity. We also gained insight into how AI can be used not just for automation, but for influencing real-world habits and decisions.

What's next for EcoLens AI

Next, we plan to improve classification accuracy, integrate location-based recycling databases, and add a gamified rewards system to encourage consistent sustainable behavior. We also want to move AI processing to a secure backend and explore partnerships with eco-friendly brands to scale impact.