DiaryX

DiaryX is a privacy-first, AI-powered digital journaling application built with Flutter. It leverages Google's Gemma 3n model through MediaPipe (Google AI Edge) or Ollama for completely offline, on-device AI processing, ensuring your personal thoughts and memories never leave your device.

🌟 Key Features

📝 Multimodal Content Capture

• Text Moments: Rich text journaling with AI-powered enhancement
• Voice Recording: Audio capture with real-time transcription
• Photo Capture: Image journaling with intelligent analysis
• Video Recording: Video moments with contextual understanding

🤖 On-Device AI Processing

• Gemma 3n Integration: Google's latest multimodal AI model via MediaPipe
• Complete Privacy: All AI processing happens locally on your device
• Offline Functionality: Full feature set without internet connectivity
• Intelligent Enhancement: AI-powered content expansion and insights

💬 AI Chat Companion

• Conversational Interface: Chat with your personal AI about your experiences
• Context-Aware: AI understands your journal history for meaningful conversations
• Real-time Streaming: Responsive AI interactions with streaming responses

📊 Personal Analytics

• Mood Tracking: Multi-dimensional emotional state analysis
• Content Insights: Writing patterns and frequency analysis
• Visual Charts: Beautiful data visualizations with FL Chart
• Smart Tagging: Automatic and manual content categorization

🔒 Privacy & Security

• Local Storage: SQLite database with encrypted secure storage
• No Data Collection: Zero telemetry or data sharing
• Offline First: Complete functionality without cloud dependencies

🛠 Technical Stack

Frontend

• Framework: Flutter 3.32.0
• Language: Dart 3.8.1
• State Management: Provider
• UI Components: Material Design with custom themes

Backend & AI

• AI Engine: Gemma 3n via MediaPipe LLM Inference or Ollama
• Database: SQLite with Drift ORM
• Local Storage: Flutter Secure Storage
• Media Processing: Native camera, audio, and video handling

Platform Support

• Android: API 24+ (Android 7.0 Nougat)
• iOS: iOS 12.0+
• Architecture: ARM64, x86_64

🏗 Architecture Overview

DiaryX follows a clean architecture pattern with clear separation of concerns:

lib/
├── app.dart                    # App configuration and providers
├── main.dart                   # Application entry point
├── consts/                     # Constants and configuration
├── databases/                  # Drift database definitions
├── models/                     # Data models with Freezed
├── screens/                    # UI screens and pages
│   ├── capture/               # Content capture screens
│   ├── chat/                  # AI chat interface
│   ├── home/                  # Main dashboard
│   ├── timeline/              # Journal timeline
│   ├── insight/               # Analytics and insights
│   └── profile/               # User settings
├── services/                   # Business logic and services
│   ├── ai/                    # AI service implementation
│   │   ├── implementations/   # MediaPipe/Ollama LLM engine
│   │   └── models/           # AI-related models
│   └── task/                  # Background task processing
├── stores/                     # State management
├── themes/                     # App theming
├── utils/                      # Utility functions
└── widgets/                    # Reusable UI components

Key Components

• AI Service: Manages Gemma 3n model integration and streaming responses
• MediaPipe/Ollma Engine: Native Android/iOS implementation for AI inference
• Drift Database: Type-safe SQLite ORM for local data persistence
• Task Queue: Universal background task processing system
• Provider Stores: Reactive state management for UI components

🚀 Getting Started

Prerequisites

• Flutter SDK: 3.32.0 or higher
• Dart SDK: 3.8.1 or higher
• Android Studio: For Android development
• Xcode: For iOS development (macOS only)
• Android NDK: 27.0.12077973 (for MediaPipe native components)

Installation

1. Clone the repository

   git clone https://github.com/xalanq/DiaryX.git
   cd DiaryX

2. Install dependencies

   flutter pub get

3. Generate code

   dart run build_runner build

4. Setup MediaPipe Models

   # Download Gemma 3n models (instructions in docs/)
   # Place models in platform-specific directories

Running the App

# Development mode
flutter run

# Release mode
flutter run --release

# Specific platform
flutter run -d android
flutter run -d ios

Building

# Android APK
flutter build apk --release

# Android App Bundle
flutter build appbundle --release

# iOS
flutter build ios --release

🧪 Development

Code Generation

The project uses several code generation tools:

# Generate all code
dart run build_runner build

# Watch for changes
dart run build_runner watch

# Clean generated files
dart run build_runner clean

Database Migrations

# Generate migration files
dart run drift_dev schema generate drift_schemas/app_database/

Testing

# Run all tests
flutter test

# Run with coverage
flutter test --coverage

Linting

# Analyze code
flutter analyze

# Fix formatting
dart format lib/ test/

📁 Project Structure

Core Modules

• /lib/services/ai/: AI integration and model management
• /lib/databases/: Local data persistence layer
• /lib/models/: Data transfer objects and entity models
• /lib/stores/: Application state management
• /lib/screens/: User interface screens
• /lib/widgets/: Reusable UI components

Platform-Specific Code

• /android/app/src/main/kotlin/: Android native MediaPipe integration
• /ios/Runner/: iOS native code (MediaPipe integration planned)

Configuration

• pubspec.yaml: Dependencies and asset configuration
• analysis_options.yaml: Dart linting rules
• android/app/build.gradle.kts: Android build configuration

🔧 Configuration

Environment Variables

Create lib/consts/env_config.dart with your configuration:

class EnvConfig {
  static const String appName = 'DiaryX';
  static const bool debugMode = false;
  // Add other configuration as needed
}

AI Model Setup

1. Download Gemma 3n models from the official repository
2. Place models in the appropriate platform directories
3. Update model paths in AIConfigService

🤝 Contributing

We welcome contributions! Please read our contributing guidelines before submitting PRs.

Development Workflow

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

Code Style

• Follow Dart style guide
• Use dart format for consistent formatting
• Ensure flutter analyze passes without warnings
• Add tests for new features

📝 License

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

🙏 Acknowledgments

• Google MediaPipe for AI inference framework
• Ollama for AI inference framework
• Gemma 3n for the multimodal AI model
• Flutter Team for the amazing cross-platform framework
• Drift for type-safe database operations

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: Contact maintainer

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Made with ❤️ for privacy-conscious journaling