🍽️ Caldish - AI-Powered Food Nutrition Analysis

A modern web application that uses artificial intelligence to analyze food photos and provide detailed nutritional information including calories, macronutrients, vitamins, and minerals.

✨ Features

• 🤖 AI-Powered Analysis: Advanced computer vision with OpenAI GPT-4V
• ⚡ Instant Results: Get nutrition facts in seconds
• 📊 Comprehensive Data: Calories, macros, vitamins, minerals, and portion estimates
• 📱 Responsive Design: Works perfectly on desktop and mobile
• 🎨 Beautiful UI: Modern, clean interface built with Tailwind CSS
• 🔒 Privacy-First: Images are processed temporarily and not stored

🚀 Getting Started

Prerequisites

• Node.js 18+ installed
• OpenAI API key with GPT-4V access

Installation

1. Clone the repository

   git clone https://github.com/yourusername/caldish.git
   cd caldish

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env.local

   Edit .env.local and add your OpenAI API key:

   OPENAI_API_KEY=sk-your-openai-api-key-here
   NEXT_PUBLIC_APP_URL=http://localhost:3000

4. Run the development server

   npm run dev

5. Open your browser Navigate to http://localhost:3000

🛠️ Tech Stack

Frontend

• Next.js 14 - React framework with App Router
• TypeScript - Type safety and better DX
• Tailwind CSS - Utility-first CSS framework
• React Query - Data fetching and state management
• Chart.js - Beautiful nutrition charts
• React Dropzone - Drag-and-drop file uploads

Backend

• Next.js API Routes - Serverless API endpoints
• OpenAI GPT-4V - AI vision for food analysis
• Sharp - High-performance image processing
• Zod - Runtime type validation

Deployment

• Vercel - Recommended hosting platform
• Environment Variables - Secure API key management

📖 Usage

1. Upload Image: Drag and drop or click to select a food photo
2. Configure Options: Choose analysis settings (vitamins, confidence level, etc.)
3. Analyze: Click "Analyze Nutrition" to start AI processing
4. View Results: Get detailed nutrition breakdown with confidence scores
5. Export/Share: Save results or analyze another image

Supported Image Formats

• JPEG (.jpg, .jpeg)
• PNG (.png)
• WebP (.webp)
• Maximum size: 10MB
• Recommended: Well-lit, clear images with full dish visible

🎯 API Usage

Analyze Food Endpoint

POST /api/analyze-food

// Form Data
{
  image: File,
  options?: {
    includeVitamins: boolean,
    includeMinerals: boolean,
    confidenceThreshold: number,
    detailedBreakdown: boolean
  }
}

// Response
{
  success: boolean,
  data: {
    analysis: {
      id: string,
      foods: FoodItem[],
      nutrition: NutritionData,
      overallConfidence: number,
      processingTime: number,
      notes?: string,
      warnings?: string[]
    }
  }
}

Health Check

GET /api/analyze-food

// Response
{
  success: true,
  message: "Food analysis API is running",
  timestamp: string,
  version: string
}

⚙️ Configuration

Environment Variables

Variable	Description	Required
OPENAI_API_KEY	OpenAI API key with GPT-4V access	Yes
NEXT_PUBLIC_APP_URL	Application URL for metadata	No
NEXT_PUBLIC_MAX_FILE_SIZE	Maximum upload size in bytes	No
GEMINI_API_KEY	Google Gemini API key (backup)	No

Image Processing Settings

// lib/constants.ts
export const IMAGE_CONFIG = {
  maxSize: 10 * 1024 * 1024, // 10MB
  allowedTypes: ['image/jpeg', 'image/png', 'image/webp'],
  maxDimensions: { width: 2048, height: 2048 },
  compressionQuality: 0.9,
};

Analysis Options

interface AnalysisOptions {
  includeVitamins?: boolean;        // Include vitamin analysis
  includeMinerals?: boolean;        // Include mineral analysis
  confidenceThreshold?: number;     // Minimum confidence (0.1-1.0)
  detailedBreakdown?: boolean;      // Detailed ingredient analysis
}

🧪 Testing

# Type checking
npm run type-check

# Linting
npm run lint

# Build verification
npm run build

🚀 Deployment

Vercel (Recommended)

1. Connect to Vercel

   npm install -g vercel
   vercel

2. Set environment variables in Vercel dashboard

3. Deploy: Automatic deployment on git push

Manual Deployment

1. Build the application

   npm run build

2. Start production server

   npm run start

💡 Tips for Best Results

Photography Tips

• Good Lighting: Use natural light when possible
• Full Dish: Capture the entire meal in frame
• Clear Focus: Avoid blurry or heavily filtered images
• Minimal Background: Reduce visual distractions

Analysis Accuracy

• Single Dish: Works best with one main dish per photo
• Visible Ingredients: Ensure key ingredients are clearly visible
• Standard Servings: Results are more accurate for typical portion sizes
• Fresh Foods: Better recognition for whole foods vs. heavily processed items

🔧 Troubleshooting

Common Issues

"Failed to analyze image"

• Check OpenAI API key is valid and has GPT-4V access
• Verify image meets size and format requirements
• Try with a clearer, well-lit image

"Network error"

• Check internet connection
• Verify API endpoint is accessible
• Check for any firewall or proxy issues

"Rate limit exceeded"

• OpenAI API rate limits reached
• Wait a few minutes before trying again
• Consider upgrading OpenAI plan for higher limits

Debug Mode

Set NODE_ENV=development to see detailed error messages and logs.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

⚠️ Disclaimer

Nutrition analysis is AI-generated and provided for informational purposes only. Results are estimates and may not be completely accurate. Always consult with healthcare professionals for dietary advice and medical nutrition therapy.

🙏 Acknowledgments

• OpenAI for GPT-4V technology
• Next.js team for the amazing framework
• Tailwind CSS for the beautiful styling system
• All contributors and users of this project

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Made with ❤️ by Raymond

For support or questions, please open an issue or contact your-email@example.com