Food Scan AI

A comprehensive nutrition analysis platform that helps users track their nutritional intake by analyzing food product labels and meals using AI. Built with a Flutter frontend and Spring Boot backend, leveraging Google Cloud's Vertex AI for intelligent food analysis.

Features

• 📸 Scan product labels and food items using your device's camera
• 🔍 AI-powered nutrition analysis using Google Cloud Vertex AI
• 📊 Track daily nutrient intake with detailed breakdowns
• 📅 View historical food consumption data
• 📈 Visual representations of macronutrient distribution
• 👤 User profiles with personalized dietary preferences and health metrics
• ⚡ Real-time nutritional insights and recommendations
• 🔐 Secure Firebase authentication integration
• 🌐 Cross-platform support with synchronized data

Screenshots

FoodScanAI_Recording.mp4

Architecture

Food Scan AI follows a modern, scalable architecture:

• Frontend: Flutter mobile app with MVVM architecture
• Backend: Spring Boot RESTful API service
• Database: PostgreSQL for persistent storage
• Authentication: Firebase Authentication
• AI Analysis: Google Cloud Vertex AI (Gemini 2.0 Flash)
• Cloud Infrastructure: Google Cloud Platform

Project Structure

/
├── flutter-app/              # Flutter frontend application
│   ├── lib/
│   │   ├── core/             # App-wide constants
│   │   ├── config/           # Environment configuration
│   │   ├── models/           # Data models
│   │   ├── repositories/     # Data access layer with interfaces
│   │   ├── services/         # Business logic services
│   │   ├── theme/            # App theme definitions  
│   │   ├── viewmodels/       # ViewModels for business logic
│   │   ├── views/            # UI components
│   │   │   ├── screens/      # Full page views
│   │   │   └── widgets/      # Reusable UI components
│   │   └── main.dart         # Application entry point
│   └── assets/               # App resources (images, fonts, etc.)
│
└── spring-backend/           # Spring Boot backend service
    ├── src/
    │   ├── main/
    │   │   ├── java/com/scanmyfood/backend/
    │   │   │   ├── configurations/  # App configs
    │   │   │   ├── controllers/     # REST API endpoints
    │   │   │   ├── models/          # Data entities
    │   │   │   ├── repositories/    # Database access
    │   │   │   ├── services/        # Business logic
    │   │   │   └── ScanMyFoodBackendApplication.java
    │   │   └── resources/           # Application properties
    │   └── test/                    # Unit and integration tests
    └── pom.xml                      # Maven dependencies

Getting Started

Prerequisites

• Flutter SDK (>=3.4.3)
• Java JDK 17+
• Maven 3.6+
• PostgreSQL 14+
• Firebase project with Authentication enabled
• Google Cloud project with Vertex AI API enabled

Frontend Setup

1. Clone the repository

git clone https://github.com/nikhileshmeher0204/read_the_label.git
cd read_the_label

2. Navigate to the Flutter app directory

cd flutter-app

3. Set up Firebase

   • Create a Firebase project at firebase.google.com
   • Add Android and iOS apps to your project
   • Download and add the configuration files (google-services.json for Android and GoogleService-Info.plist for iOS)
   • Enable Authentication with Email/Password provider

4. Create a .env file in the Flutter app directory:

# Optional local development URL (if not using default)
API_BASE_URL=http://localhost:8080/api

5. Install dependencies

flutter pub get

6. Run the app

flutter run

Backend Setup

1. Navigate to the Spring Boot backend directory

cd spring-backend

2. Configure Google Cloud credentials

   • Create a service account in your Google Cloud project
   • Grant it access to Vertex AI API
   • Download the service account key file as firebase-service-account.json
   • Place it in src/main/resources/

3. Configure PostgreSQL

   • Create a PostgreSQL database named scanmyfood
   • Update database connection details in application.properties if needed:

spring.datasource.url=jdbc:postgresql://localhost:5432/scanmyfood
spring.datasource.username=your_username
spring.datasource.password=your_password

4. Build and run the Spring Boot application

# Using Maven
mvn spring-boot:run

# Or using the Maven wrapper
./mvnw spring-boot:run

State Management

This application uses a combination of state management approaches:

• Frontend: Provider for Flutter app state management

  • ViewModels:
    • UiViewModel: Handles UI state and navigation
    • ProductAnalysisViewModel: Manages product scanning and analysis
    • MealAnalysisViewModel: Handles food image analysis
    • DailyIntakeViewModel: Controls daily consumption tracking
    • OnboardingViewModel: Manages user onboarding flow

• Backend: Spring services for business logic

  • UserService: User profile and preferences management
  • VertexAiServiceImpl: AI analysis implementation
  • Authentication handled via Firebase integration

Technologies Used

Frontend

• Flutter SDK (>=3.4.3)
• Dart SDK
• Firebase Authentication for user management
• Provider for state management
• SharedPreferences for local storage
• Material Design 3 with custom theme
• Key Flutter packages:
  • image_picker for camera integration
  • flutter_dotenv for environment variables
  • fl_chart for data visualization
  • rive for animations
  • http for API communication

Backend

• Spring Boot 3.4.5 for RESTful API development
• Spring Data JPA for database operations
• PostgreSQL for data persistence
• Google Cloud Vertex AI for AI-powered analysis
• Firebase Admin SDK for authentication verification
• Maven for dependency management

Infrastructure

• Google Cloud Platform:
  • Vertex AI (Gemini 2.0 Flash model)
  • Firebase Authentication
  • Cloud Storage (optional for production)

Deployment

Frontend

• Build the Flutter app for production:

flutter build apk --release  # for Android
flutter build ios --release  # for iOS

Backend

• Package the Spring Boot application:

mvn clean package

• Deploy the resulting JAR file to your server or cloud provider

Future Enhancements

• Real-time data synchronization between devices
• Advanced meal planning with AI recommendations
• Barcode scanning for quick product lookup
• Machine learning for personalized nutritional insights
• Dietary goal tracking and achievement rewards
• Social sharing and community features

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some 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.

Acknowledgments

• Google Cloud Vertex AI for powerful AI capabilities
• Spring Boot team for the excellent backend framework
• Flutter team for the amazing cross-platform UI toolkit
• Firebase for seamless authentication
• PostgreSQL for reliable data storage