Food Calorie Estimation App (VeganSnap)

VeganSnap lets busy vegans snap a photo of their meal to instantly get calorie and nutrient info—no logging, just smart, plant-based tracking on the go. Tagline: Snap Your Meal. Know Your Nutrients

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Inspiration

As someone passionate about wellness and technology, I noticed that many people, especially those on plant-based diets, struggle with tracking the nutritional content of their meals. Traditional calorie-tracking apps are tedious and often don't recognize homemade vegan meals. VeganSnap was born out of the desire to make nutrition tracking easier, smarter, and more accessible, simply by taking a photo.

What it does

VeganSnap is an AI-powered food analysis app that allows users to:

  • 📸 Upload or take a photo of a vegan meal
  • 🧠 Use AI (Clarifai) to recognize the food item(s)
  • 📊 Automatically fetch nutrition data from the USDA API
  • 📥 Store images and data securely with Supabase
  • 💳 Subscribe to premium features using Stripe integration
  • 🌐 Use the app on a custom, secure domain: www.vegansnap.food

How we built it

  • Built with Bolt on StackBlitz for a cloud-based development experience
  • Backend and data handled via Supabase (Auth, Edge Functions, Storage, Database)
  • Clarifai API powers the image recognition component
  • USDA FoodData Central API is used to fetch nutrient details
  • Stripe Checkout is integrated for handling subscription payments
  • Deployed on Netlify, connected to a custom domain from Namecheap
  • Designed for ease of use and responsiveness

Challenges we ran into

  • 🔧 Debugging Supabase Edge Functions locally via Docker
  • 🕵️‍♂️ Troubleshooting empty nutrition results from USDA API for uncommon foods
  • 🎥 Browser camera permission issues for photo capture
  • 🔐 Securing the API keys and environment variables in Bolt
  • 🌐 Configuring Netlify DNS and SSL for the custom domain
  • 🐞 Handling WebContainer and deployment environment timeouts

Accomplishments that we're proud of

  • 🎯 Achieved a working MVP that connects photo input to real-time nutrition analysis
  • 🌐 Successfully deployed with a custom .food domain and SSL
  • 💡 Solved multiple integration challenges across APIs and platforms
  • 💳 Integrated Stripe payments for premium access
  • 🔄 Implemented a smooth user experience from image upload to data output

What we learned

  • How to use and deploy Supabase Edge Functions
  • Integrating AI image recognition with third-party APIs
  • Managing state, permissions, and errors in a cloud-based IDE (Bolt)
  • How to set up custom domains with HTTPS and DNS management
  • Importance of error handling and fallback messages when data isn't available
  • How to work across multiple tools in a constrained hackathon timeline

What's next for Food Calorie Estimation App (VeganSnap)

  • 🔍 Improve accuracy for multiple food items in a single image
  • 📱 Build a mobile version for Android/iOS
  • 📊 Add user dashboards with nutrition history tracking
  • 👥 Create community features for sharing and meal logging
  • 📈 Partner with nutritionists to validate data and improve AI accuracy
  • 🌍 Expand food recognition to local and cultural vegan meals
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