BOB AI is a comprehensive private AI assistant that runs locally on your machine. It combines multiple AI capabilities including:
- Natural language processing with local LLMs (GPT4All)
- Voice interaction capabilities
- Web interface via Flask
- Self-learning and enhancement capabilities
- Code generation and analysis
- Voice Interface: Interact with BOB AI using voice commands
- Web Dashboard: Access BOB AI through a web interface
- Local LLM Integration: Uses GPT4All to run models locally
- Self-Learning: Can learn and improve from interactions
- Code Generation: Can generate and test Python code
- Task Scheduling: Can manage and execute scheduled tasks
- Extensible Architecture: Easily add new capabilities
- Python 3.8+
- PyTorch
- TensorFlow (optional)
- GPT4All
- Flask and Flask-SocketIO
- SoundDevice and SpeechRecognition
- Transformers library
- Clone the repository:
git clone https://github.com/yourusername/bob_ai.git
cd bob_ai- Install dependencies:
pip install -r requirements.txt- Download a compatible model for GPT4All (e.g., Mistral 7B):
python model_downloader.pyRun BOB AI from the command line:
python gpt4all_cli.pyStart the web server:
python flask_api.pyThen open your browser to http://localhost:5000
Launch the GUI dashboard:
python bob_ai.pyStart the voice interface:
python voice_interface.pybob_ai.py: Main controller for BOB AIvoice_interface.py: Voice command processingflask_api.py: Web API and interfacegpt4all_cli.py: Command-line interfaceenhancer.py: Code generation and enhancementself_learning.py: Self-improvement capabilitiestask_scheduler.py: Task scheduling and managementcode_analyzer.py: Code analysis toolscode_generator.py: Code generation tools
BOB AI is designed to be easily extensible. To add new capabilities:
- Create a new module in the
enhancementsdirectory - Implement your functionality
- Import and integrate it with the main BOB AI controller
This project is licensed under the MIT License - see the LICENSE file for details.
- GPT4All for providing local LLM capabilities
- Hugging Face for transformer models
- PyTorch and TensorFlow communities