ImaginAI 🎨

A modern iOS application built with SwiftUI that generates AI-powered images from text descriptions using the Hugging Face API. ImaginAI transforms your imagination into visual reality through advanced machine learning models.

✨ Features

• 🤖 AI-Powered Image Generation: Create unique images from text descriptions using the FLUX.1-dev model
• 📱 Modern SwiftUI Interface: Clean, intuitive user experience with native iOS design patterns
• 🖼️ Image History: Browse and manage previously generated images
• 💾 Save to Photos: One-tap save functionality for generated images
• ⚡ Real-time Generation: Live progress indicators and status updates
• 🎯 Detail View: Full-screen image viewing with tap-to-save functionality
• 🔄 Smart State Management: Efficient handling of loading states and error conditions

🛠️ Technical Stack

• Framework: SwiftUI 3.0+
• Language: Swift 5.9
• Target: iOS 15.0+
• API: Hugging Face Inference API
• Architecture: MVVM with async/await patterns
• Image Processing: UIKit integration for image handling

📱 Screenshots

[Screenshots would be added here]

🚀 Getting Started

Prerequisites

• Xcode 15.0 or later
• iOS 15.0+ device or simulator
• Active internet connection for API calls

Installation

1. Clone the repository

   git clone https://github.com/abrahao-dev/imaginAI.git
   cd imaginAI

2. Open in Xcode

   open ImaginAI.xcodeproj

3. Build and Run

   • Select your target device or simulator
   • Press Cmd + R or click the Run button
   • The app will launch and be ready to generate images

API Configuration

The app uses the Hugging Face Inference API with the FLUX.1-dev model. The API key is currently embedded in the code for demonstration purposes. For production use, consider:

• Moving the API key to a secure configuration file
• Implementing proper key management
• Adding rate limiting and error handling

🎯 Usage

1. Enter Description: Type a detailed description of the image you want to generate

   • Example: "A futuristic cityscape with flying cars and neon lights"
   • Maximum 200 characters for optimal results

2. Generate Image: Tap "Gerar Imagem" to start the generation process

   • Generation typically takes 30 seconds to 2 minutes
   • Progress indicator shows current status

3. View Results: The generated image appears with options to:

   • Save to photo album (tap the image)
   • View in detail view (tap images in history)
   • Generate new images (confirmation dialog prevents accidental overwrites)

4. Browse History: Scroll through previously generated images in the horizontal gallery

🏗️ Project Structure

ImaginAI/
├── ImaginAIApp.swift          # App entry point
├── ContentView.swift          # Main UI and business logic
├── ImageGeneratorService.swift # API service layer
├── Assets.xcassets/          # App icons and colors
└── Info.plist               # App configuration

Key Components

• ContentView: Main SwiftUI view with MVVM architecture
• ImageGeneratorService: Handles API communication and image processing
• ImageSaver: Manages photo album integration
• ImageDetailView: Full-screen image viewing component

🔧 Architecture Highlights

MVVM Pattern

• Model: Image data and API responses
• View: SwiftUI views with reactive UI updates
• ViewModel: State management in ContentView

Async/Await Integration

• Modern concurrency patterns for API calls
• Proper error handling and user feedback
• Background processing with UI updates

SwiftUI Best Practices

• Reactive state management with @State properties
• Proper view composition and reusability
• Native iOS design patterns and accessibility

🚨 Error Handling

The app implements comprehensive error handling:

• Network Errors: Connection issues and API failures
• Input Validation: Empty descriptions and character limits
• Image Processing: Data conversion and format issues
• User Feedback: Clear error messages and recovery options

📊 Performance Considerations

• Image Caching: Generated images stored in memory for quick access
• API Optimization: Efficient request handling with proper timeouts
• Memory Management: Automatic cleanup of large image data
• UI Responsiveness: Background processing with loading indicators

🔒 Security Notes

• API key is currently embedded in source code (demo purposes only)
• No sensitive data is stored locally
• Network requests use HTTPS
• Input sanitization prevents injection attacks

🤝 Contributing

We welcome contributions! Please follow these steps:

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

Development Guidelines

• Follow Swift style guidelines
• Add comments for complex logic
• Test on multiple iOS versions
• Ensure accessibility compliance

📄 License

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

👨‍💻 Developer

Matheus Abrahão

• GitHub: @abrahao-dev
• LinkedIn: linkedin.com/in/matheusabrahao
• Portfolio: matheusabrahao.com.br

🙏 Acknowledgments

• Hugging Face for providing the FLUX.1-dev model
• SwiftUI for the modern UI framework
• Apple Developer Documentation for comprehensive guides

📈 Future Enhancements

• User authentication and cloud sync
• Multiple AI model support
• Image editing and filters
• Social sharing features
• Offline mode with cached images
• Advanced prompt templates
• Batch image generation
• Custom model fine-tuning

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