A privacy-first, multi-provider AI desktop client for Linux systems that unifies multiple AI providers and local models into a single conversational interface.

• Multi-Provider Support: OpenAI, Ollama (local), Groq, HuggingFace, OpenRouter
• Offline Capability: Full functionality with local Ollama models
• Streaming Responses: Real-time token-by-token response rendering
• Privacy-First: No telemetry, local key storage, optional encryption
• Intelligent Routing: Automatic model selection based on requirements
• Extensible Architecture: Plugin system for custom providers and tools
• Modern UI: Dark theme with PyQt6 interface

1. Clone the repository:

git clone https://github.com/yourusername/chat-linux-client.git
cd chat-linux-client

2. Run the installation script:

./scripts/install.sh

3. Run the application:

./scripts/run.sh

1. Create virtual environment:

python3 -m venv venv
source venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python main.py

Configure API keys through the application settings or by setting environment variables:

• GROQ_API_KEY: Groq API key
• HUGGINGFACE_API_KEY: HuggingFace API key
• OPENROUTER_API_KEY: OpenRouter API key

For offline AI support, install Ollama:

curl -fsSL https://ollama.ai/install.sh | sh

Then pull models:

ollama pull llama2
ollama pull mistral

1. Select Model: Choose your preferred AI provider and model from the dropdown
2. Configure Settings: Set up API keys and preferences in the settings menu
3. Start Chatting: Type your message and press Enter or click Send
4. View History: Chat history is automatically saved and can be exported

chat-linux-client/
|
|--- main.py                 # Application entry point
|--- requirements.txt        # Python dependencies
|--- README.md              # This file
|
|--- core/                  # Core AI provider logic
|    |--- api_client.py     # Base API client interface
|    |--- ollama_client.py  # Ollama local AI client
|    |--- groq_client.py    # Groq API client
|    |--- huggingface_client.py # HuggingFace API client
|    |--- openrouter_client.py  # OpenRouter API client
|    |--- provider_router.py    # Intelligent routing engine
|    |--- settings.py       # Configuration management
|    |--- model_manager.py  # Model information and selection
|
|--- ui/                    # User interface
|    |--- main_window.py    # Main PyQt6 window
|
|--- storage/               # Data persistence
|    |--- history_manager.py # Chat history storage
|    |--- config_manager.py # Application configuration
|
|--- utils/                 # Utility modules
|    |--- markdown_renderer.py # Markdown to HTML rendering
|    |--- key_handler.py    # Secure API key storage
|    |--- system_checks.py  # Environment validation
|
|--- styles/                # UI styling
|    |--- dark.qss         # Dark theme stylesheet
|
|--- assets/                # Static assets
|    |--- icon.png         # Application icon
|
|--- scripts/               # Build and run scripts
|    |--- install.sh       # Installation script
|    |--- run.sh          # Application launcher
|    |--- build_appimage.sh # AppImage build script
|
|--- packaging/             # Distribution packaging
|    |--- chatgpt-client.desktop # Desktop entry
|    |--- AppImageBuilder.yml   # AppImage configuration

The system follows a modular architecture with clear separation of concerns:

1. UI Layer: PyQt6 desktop interface with dark theme
2. Routing Engine: Intelligent model selection and request routing
3. Provider Layer: Multiple AI provider implementations
4. Storage Layer: Persistent chat history and configuration
5. Utility Layer: Helper functions and system integration

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

./scripts/build_appimage.sh

# Format code
black .

# Lint code
flake8 .

# Type checking
mypy .

• Python: 3.8 or higher
• Operating System: Linux (Ubuntu 20.04+, Fedora 35+, Arch Linux)
• Memory: 4GB RAM minimum (8GB recommended)
• Storage: 500MB free space
• Optional: Ollama for local AI models

1. Import Errors: Ensure virtual environment is activated
2. API Connection: Check internet connectivity and API keys
3. Ollama Not Found: Install Ollama for local model support
4. Permission Errors: Check file permissions for config/data directories

Run comprehensive system checks:

python main.py --check-system

Application logs are stored in:

• Linux: ~/.local/share/chat-linux-client/logs/

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

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

For issues and questions:

• Check the troubleshooting section
• Run system checks for diagnostics
• Create an issue on the project repository

Future features planned:

• Voice interface (speech-to-text + TTS)
• Local RAG knowledge system
• Multi-window chat sessions
• Agent-based task automation
• System tray background assistant mode
• Plugin marketplace
• Custom theme support