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

What I Built

For Hacktoberfest Week 1 — "Touch Grass" — I built SnapNature, a mobile-first Progressive Web App that gets people out of their screens and into nature.

Demo

Code

# Create virtual environment python -m venv venv venv\Scripts\activate # Windows # source venv/bin/activate # Linux/Mac # Install dependencies pip install -r requirements.txt # Run the app uvicorn main:app --reload --port 8000

docker build -t snapnature . docker run -p 8000:8000 snapnature

curl -X POST "http://127.0.0.1:8000/predict" \ -F "file=@path/to/image.jpg"

curl "http://127.0.0.1:8000/predict_url?url=https://example.com/image.jpg"

curl "

How I Built It

• 📸 Take a photo of any plant, animal, or nature scene
• 🤖 Identify it using an open-source HuggingFace Vision Transformer (ViT) model — google/vit-base-patch16-224
• 🌱 See results with confidence scores and fun education facts
• 📝 Save to journal — offline observations stored in your browser
• 📱 Works offline via Service Worker — go to a trail, no signal needed

Key Features

• Open-source AI at core — Uses open-weight HuggingFace models, not a locked vendor API
• Works on any device — PWA installs to home screen, responsive design
• Privacy-first — Photos saved locally, nothing sent to cloud databases
• Swap the model — Replace with fine-tuned regional biodiversity models easily

The Challenge: "Touch Grass"

The theme is simple: get outside. SnapNature encourages that directly — open the app, go find something interesting, take a photo. The app then teaches you about it. It's exploration + education combined.

Why Does Open Innovation Matter?

SnapNature demonstrates why open weight models matter:

• No vendor lock-in — The ViT model is open; swap it anytime
• Runs without expensive APIs — Works with free HuggingFace inference
• Fine-tuning ready — Train on local bird/plant datasets for your region
• Data stays yours — All observations in localStorage; your data, your control
• Offline by design — Service worker caches assets; works without internet after first visit

Closed APIs don't count as core. Open innovation does.

System Design

Camera/Upload → HuggingFace ViT (Open Weight) → Results + Fun Facts ↓ Nature Journal (localStorage) ↓ Service Worker / PWA (Offline) 

Project Structure

snapnature/ ├── index.html # Mobile-first PWA UI ├── js/app.js # Camera, AI integration, journal ├── css/style.css # Responsive design ├── sw.js # Offline service worker ├── manifest.json # PWA install config └── main.py # FastAPI backend (optional Python API mode) 

Tech Stack

• Frontend: Vanilla HTML/CSS/JS (no framework needed for speed)
• AI: HuggingFace Inference API with google/vit-base-patch16-224
• Storage: browser localStorage for offline journal
• PWA: Service worker + Web App Manifest
• Backend (optional): FastAPI + PyTorch ResNet50

Where to Try

Live: https://github.com/nashdev97/snapnature

Clone and run locally:

git clone https://github.com/nashdev97/snapnature.git cd snapnature python -m http.server 8080 # Open http://localhost:8080 

My Agent Session

Agent session - Claude and Omniroute.

Prize Categories

• Best Use of Open-Source AI / Open Weight Models
• Best Mobile / PWA Implementation
• Best "Touch Grass" Theme Execution
• Best Offline / On-Device Innovation

Built with open-source tools for Hacktoberfest 2026. Let's get outside. 🌿