Firebase AI Friendly Meals - Flutter

An AI-powered meal preparation app built with Flutter, Firebase AI, and modern architecture patterns.

Features

• 📸 Image Analysis: Take photos or select images of food ingredients
• 🤖 AI-Powered Ingredient Recognition: Use Gemini to identify ingredients from images
• 🍳 Recipe Generation: Generate recipes based on identified ingredients and user notes
• 🎨 Recipe Image Creation: Generate professional food photography with Imagen
• 📱 Modern UI: Clean, Material 3 design with responsive layout

Architecture

This Flutter app follows modern architectural patterns:

Data Layer

lib/data/
├── datasource/
│   └── ai_remote_data_source.dart     # AI API calls (Gemini & Imagen)
├── repository/
│   └── ai_repository.dart             # Repository pattern implementation
└── model/
    └── recipe.dart                    # Recipe data model

UI Layer

lib/ui/
└── home/
    ├── home_page.dart                 # Main UI screen
    ├── cubit/
    │   ├── home_cubit.dart            # State management (BLoC/Cubit)
    │   └── home_state.dart            # UI state definitions
    └── widgets/
        ├── home_ingredients_section.dart
        ├── home_recipe_section.dart
        └── widgets.dart

Core Infrastructure

lib/core/
├── di/
│   └── firebase_module.dart           # DI for Firebase AI models
├── exceptions/
│   └── ai_exceptions.dart             # Custom exception classes
├── theme/                             # App theming
└── widgets/                           # Reusable UI components

Tech Stack

Core Framework

• Flutter: Cross-platform UI framework
• Dart: Programming language

State Management & Architecture

• BLoC/Cubit: State management pattern (flutter_bloc)
• Equatable: Value equality for state classes
• GetIt: Service locator for dependency injection
• Injectable: Code generation for DI

Firebase & AI

• Firebase Core: Firebase initialization
• Firebase AI: Gemini and Imagen model access
• Firebase App Check: App verification and security
• Image Picker: Camera and gallery integration

UI & Design

• Material 3: Modern Material Design
• GPT Markdown: Recipe content formatting
• Cupertino Icons: iOS-style icons

Getting Started

Prerequisites

1. Flutter SDK (3.8.0 or later)
2. Firebase Project with AI Logic enabled
3. Firebase AI API access (Gemini Developer API or Vertex AI)

Setup

1. Clone the repository

   git clone <repository-url>
   cd firebase_ai_friendly_meals

2. Install dependencies

   flutter pub get

3. Generate dependency injection code

   dart run build_runner build

4. Configure Firebase

   • Add your google-services.json (Android) and GoogleService-Info.plist (iOS)
   • Enable Firebase AI Logic in your Firebase project
   • Set up API keys for Gemini and Imagen models

5. Run the app

   flutter run

Project Structure Details

Key Components

AIRemoteDataSource

Handles direct API calls to Firebase AI services:

• generateIngredients(): Multimodal Gemini call with image input
• generateRecipe(): Text generation based on ingredients and notes
• generateRecipeImage(): Imagen model for recipe photography

AIRepository

Provides a clean interface for the UI layer, abstracting the data source implementation.

HomeCubit (State Management)

Manages the home screen state using BLoC pattern:

• Image selection and preview
• Ingredient loading states
• Recipe generation workflow
• Error handling and validation

HomeState

Immutable state class containing:

• ingredients: Current ingredient list
• notes: User-provided cooking notes
• selectedImage: Selected image for analysis
• recipe: Generated recipe result
• status: Current view state (initial, loading, success, failure)
• errorMessage: Error messages for user feedback

HomePage (UI)

Responsive UI with sections for:

• Image capture/selection
• Ingredients display
• Notes input
• Recipe generation
• Results display

Firebase AI Integration

The app uses Firebase AI Logic to access:

• Gemini Models: For text generation and multimodal analysis
• Imagen Models: For high-quality recipe image generation

// Example Gemini call
final response = await _generativeModel.generateContent([
  Content.text(prompt)
]);

// Example Imagen call
final imageResponse = await _imagenModel.generateImages(prompt);

Development Workflow

Code Generation

Some features require code generation:

# Generate dependency injection code
dart run build_runner build

# Watch for changes during development
dart run build_runner watch

Next Steps

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 part of the Firebase Extended samples and follows the same licensing terms.

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Built with ❤️ using Flutter and Firebase AI Logic