🥦 CalQulate

Smart Food Insights from Your Grocery Cart

CalQulate is an AI-powered nutrition assistant that helps users instantly analyze food label photos. With a simple upload, CalQulate extracts nutritional values, performs a health check, and uses AI to provide personalized dietary recommendations — all with a focus on privacy and simplicity.

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🚀 Features

• 📸 Upload food label images (snap a photo or drag & drop)
• 🧠 AI-powered extraction and analysis of nutrients
• 📊 Health analysis with nutrient checks (sugar, sodium, fat, fiber, etc.)
• 🤖 LLM-based insights using Groq LLaMA 3
• 🔐 Privacy-first: no data is stored or shared
• 💡 Tailored suggestions and warnings for dietary goals

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🖥️ Tech Stack

Layer	Technology
Frontend	Next.js 14 + Tailwind CSS
UI Components	Custom + shadcn/ui style system
AI Layer	Groq LLaMA 3 (llama3-70b-vision)
Styling	CSS Modules, Tailwind
Hosting	Vercel (or any Node-compatible environment)

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📸 Demo

Coming soon...

🌐 Live URL: https://your-calqulate-app.vercel.app

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📂 Project Structure

calqulate/
├── app/
│   ├── page.tsx           # Home page
│   ├── upload/page.tsx    # Upload interface + AI results
│   ├── about/page.tsx     # About and usage details
├── components/
│   ├── Navbar.tsx         # Responsive navigation bar
│   ├── ui/button.tsx      # Reusable styled button
├── public/                # Static assets
├── styles/
│   └── globals.css        # Tailwind + base styles
├── .env.local             # Your API keys (excluded from Git)
├── .gitignore
└── README.md

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🛠️ Getting Started

1. Clone the repository

git clone https://github.com/your-username/calqulate.git
cd calqulate

2. Install dependencies

npm install
# or
yarn install

3. Create a .env.local file

# .env.local

NEXT_PUBLIC_API_URL=https://your-backend.com/
NEXT_PUBLIC_GROQ_API_URL=your-groq-api-key

✅ Note: All public client-side env vars must start with NEXT_PUBLIC_

4. Start the development server

npm run dev
# or
yarn dev

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💡 How It Works

1. User uploads a photo of a nutrition label
2. The app sends the image to an OCR backend (API_URL)
3. Extracted data is:
   • Checked for health score (calories, fat, sugar, fiber, etc.)
   • Sent to Groq LLM for AI-generated benefits/risks/recommendations
4. Results are displayed in a beautiful and easy-to-read UI

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🧪 Example Use Case

• Upload a cereal box label
• CalQulate highlights high sugar and low fiber
• Groq suggests healthier alternatives and reasons to moderate use

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✅ Deployment

This app can be deployed on:

• Vercel (best for Next.js)
• Netlify
• AWS Amplify / S3 + Lambda
• Dockerized container on any cloud

To deploy on Vercel:

vercel login
vercel

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📦 Future Improvements

• 🍎 Add barcode scanning via camera
• 🧠 GPT-4 or Claude 3 integration for deeper insights
• 📊 Trend tracking (history of food uploads)
• 🗃️ Database of scanned items per user (optional account system)
• 🌍 Multilingual support

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🙌 Credits

• Built by [Your Name] with ❤️
• Inspired by the need for accessible and instant food transparency
• Uses:
  • Groq for LLaMA 3 models
  • Tailwind CSS
  • Next.js

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