Cal AI - Calorie Tracker App

A full-stack calorie tracking application built with React, NestJS, and Google AI (Gemini) for intelligent meal recognition.

🚀 Features

• AI-Powered Meal Recognition: Take a photo of your meal and get instant nutritional analysis
• Real-time Calorie Tracking: See your daily targets, consumed calories, and remaining macros
• Macro Tracking: Track protein, carbs, and fats with visual progress bars
• Daily Summary: View all your meals and progress for the day

📁 Project Structure

cal-ai/
├── backend/          # NestJS backend API
│   ├── src/
│   │   ├── ai/       # Google AI integration
│   │   ├── meals/    # Meals module (controller, service, DTOs)
│   │   └── config/   # Configuration module
│   └── .env          # Environment variables
├── frontend/         # React frontend
│   ├── src/
│   │   ├── components/  # React components
│   │   └── services/    # API service
│   └── package.json
└── shared/           # Shared TypeScript types
    └── types.ts

🛠️ Setup Instructions

Prerequisites

• Node.js 18+ and npm
• Google AI Studio API key (Get it here)

Backend Setup

1. Navigate to backend directory:

cd backend

2. Install dependencies:

npm install

3. Create .env file (copy from .env.example):

cp .env.example .env

4. Add your Google AI API key to .env:

GOOGLE_AI_API_KEY=your_api_key_here
PORT=3001

5. Start the backend server:

npm run start:dev

The backend will run on http://localhost:3001

Frontend Setup

1. Navigate to frontend directory:

cd frontend

2. Install dependencies:

npm install

3. Start the development server:

npm run dev

The frontend will run on http://localhost:3000

📡 API Endpoints

GET /api/meals/daily-summary

Get today's summary including targets, consumed, remaining, and meals.

Query Parameters:

• date (optional): Date in YYYY-MM-DD format

Response:

{
  "date": "2024-01-15",
  "targets": {
    "calories": 2000,
    "protein": 150,
    "carbs": 250,
    "fats": 65
  },
  "consumed": {
    "calories": 500,
    "protein": 45,
    "carbs": 60,
    "fats": 20
  },
  "remaining": {
    "calories": 1500,
    "protein": 105,
    "carbs": 190,
    "fats": 45
  },
  "meals": [...]
}

POST /api/meals/analyze

Analyze a meal image using Google AI.

Request:

• Form data with image file

Response:

{
  "foodItems": ["grilled chicken", "rice", "vegetables"],
  "calories": 650,
  "protein": 45,
  "carbs": 60,
  "fats": 20,
  "confidence": 0.85
}

POST /api/meals/log

Log a meal to the daily summary.

Request Body:

{
  "name": "Grilled Chicken with Rice",
  "foodItems": ["grilled chicken", "rice"],
  "calories": 650,
  "protein": 45,
  "carbs": 60,
  "fats": 20
}

Response: Updated daily summary

🎯 Usage

1. Start both servers (backend on port 3001, frontend on port 3000)
2. Open the app in your browser at http://localhost:3000
3. View your daily targets - See calories, protein, carbs, and fats goals
4. Take a photo - Click "Take Photo & Analyze Meal" button
5. Review analysis - The AI will analyze your meal and show nutritional breakdown
6. Confirm meal - Review the projected remaining calories and confirm to add the meal
7. Track progress - See your remaining calories and macros update in real-time

🔧 Technology Stack

• Frontend: React 18, TypeScript, Vite
• Backend: NestJS, TypeScript
• AI: Google Gemini Pro Vision API
• State Management: React Hooks
• HTTP Client: Axios

📝 Notes

• Currently uses in-memory storage (meals are lost on server restart)
• Default user is "default-user" (authentication not implemented yet)
• Default targets are 2000 calories, 150g protein, 250g carbs, 65g fats
• For production, replace in-memory storage with a database (Firebase Firestore, PostgreSQL, etc.)

🚧 Future Enhancements

• User authentication (Firebase Auth)
• Database integration (Firebase Firestore)
• User profile and personalized targets
• BMI calculation and goal-based recommendations
• Weight tracking
• Historical data and charts
• Barcode scanning
• Meal plan suggestions

📄 License

MIT