# 🌿 EcoQuest AI — Less Scrolling, More Wonder
Live Website: https://mahiiverma351-gif.github.io/EcoQuest-AI/
GitHub Repository: https://github.com/mahiiverma351-gif/EcoQuest-AI.git
What I Built
EcoQuest AI is an AI-powered nature companion designed to encourage people to spend less time on screens and more time exploring the outdoors.
Built using HTML, CSS, and vanilla JavaScript, the application combines an AI-powered image scanner with outdoor challenges, a personal nature journal, activity statistics, and an outdoor break timer.
The goal is to use AI as a tool for real-world discovery rather than another reason to stay online.
Key Features
- AI Nature Scanner: Upload a photo and receive image classification predictions from an open-weight AI model running in the browser.
- Daily Outdoor Quests: Complete activities such as observing leaf shapes, listening to birdsong, and watching clouds.
- Nature Journal: Save outdoor activities and AI-assisted discoveries locally in your browser.
- Outdoor Break Timer: Take a ten-minute break and track your outdoor activity.
- Activity Dashboard: View recorded outdoor minutes, saved discoveries, and activity streaks.
- Responsive Design: Explore the application on desktop and mobile devices.
Tech Stack
- HTML5
- CSS3
- Vanilla JavaScript
- Hugging Face Transformers.js
- MobileNetV4 image classification model
- ONNX Runtime Web
- Browser LocalStorage
How Open-Source AI Powers the Project
EcoQuest AI uses Transformers.js and a compatible open-weight image classification model to run AI inference directly in the browser.
Rather than sending every image to a proprietary AI API, the application processes selected photos on the user's device. This reduces dependence on remote inference services and helps keep personal images private.
The open model approach also makes it possible to experiment with alternative models and build more specialized nature-recognition features in the future.
The model and its required assets must be downloaded initially, so the current version is not yet fully offline.
Why I Built It
I wanted to explore how AI could encourage healthier digital habits and create a stronger connection with the natural world.
Many applications compete for our attention. EcoQuest AI takes a different approach: it gives users a small reason to go outside, notice their surroundings, and learn something new.
The idea behind the project is simple: technology should help us experience more of the world, not just spend more time looking at a screen.
What I Learned
Building EcoQuest AI gave me an opportunity to combine frontend development with browser-based AI inference.
I explored how to integrate a machine-learning model into a JavaScript application, manage user-uploaded images, save data locally, and create interactive features without requiring a dedicated application backend.
Future Improvements
- Add specialized plant and bird identification models.
- Improve offline functionality by caching model assets.
- Add richer AI-powered explanations of nature discoveries.
- Introduce location-aware nature trails and recommendations.
- Improve journal storage and accessibility.
- Test the experience during real outdoor activities.
Try It Out
🌐 Live demo: https://mahiiverma351-gif.github.io/EcoQuest-AI/
💻 Source code: https://github.com/mahiiverma351-gif/EcoQuest-AI.git
Take a quest, step outside, and discover something new.
EcoQuest AI — Built with curiosity and open-weight AI. 🌱
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