This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

🌿 EcoSnap: From Waste to Action

What I Built

EcoSnap is an AI-powered waste identification tool designed to turn a simple photo into an opportunity to take action for the environment.

We see litter on roadsides, in parks, and around our neighborhoods every day. We know waste is a problem, but sometimes we don't know what kind of waste we're looking at or how to dispose of it responsibly.

I built EcoSnap to make that first step easier.

With EcoSnap, users can upload or capture a photo of waste and use AI to identify its likely category and get guidance on responsible disposal.

The idea is simple: instead of just scrolling through environmental awareness posts, people can step outside, observe their surroundings, and learn how to make a small difference.

EcoSnap is designed for students, communities, and anyone who wants to become more conscious of the waste around them.

I wanted to build something that connects technology with the physical world. AI doesn't have to keep us staring at screens. It can help us understand what's happening around us and encourage us to do something about it.

The goal isn't just to recognize waste. It's to turn recognition into action.

🌱 EcoSnap in Action

Here's EcoSnap analyzing a waste image using the LLaVA 7B vision-language model. The goal is to help people identify waste and make more informed disposal decisions.

Demo

🌎 Try EcoSnap: https://ecosnap-ai-environme-hks8.bolt.host

The demo lets you explore the EcoSnap interface and try the waste-scanning experience.

Code

💻 GitHub Repository: https://github.com/aryamore05/EcoSnap

I'd love for others to explore the project, suggest improvements, and contribute ideas for making environmental action more accessible.

How I Built It

I built EcoSnap with a focus on open-source AI and a straightforward architecture.

The project uses:

• React, TypeScript, and Vite for the frontend.
• Python and FastAPI for the backend API.
• Ollama to run an AI model locally.
• LLaVA 7B, an open-weight vision-language model, to analyze images.
• SQLite for lightweight data storage.

The core workflow is:

1. A user uploads or captures a photo of waste.
2. The frontend sends the image to the backend.
3. The backend passes the image to the vision AI model.
4. The model analyzes the image and helps identify the likely waste category.
5. EcoSnap presents the result and relevant disposal guidance.

I used Bolt to help build the application and bring the frontend and backend together.

One of my goals was to make the project practical rather than unnecessarily complicated. I wanted a working application that demonstrates how an open-weight AI model can be integrated into a real-world environmental use case.

Why Does Open Innovation Matter?

Open innovation matters because useful AI should not be limited to people who have access to expensive APIs or proprietary platforms.

For EcoSnap, using an open-weight vision model gave me an opportunity to experiment with image understanding while running inference locally through Ollama. I could explore the AI workflow, connect it to my own backend, and build around a model I could run and test myself.

This also gave me more control over the architecture and helped me understand how the pieces fit together: image input, model inference, backend logic, and the user experience.

Beyond the technical benefits, open innovation makes experimentation more accessible. Students and independent developers can build projects around real problems without having to start with a large budget or a closed AI ecosystem.

EcoSnap is a small project, but it represents something I believe in: open-source AI can help people build practical tools for problems in their own communities.

And when the problem is environmental waste, the best outcome isn't another hour spent online. It's someone learning something useful and taking that knowledge into the real world.