feat: enhance LocalAgent configuration and UI components
- Updated .env.example to provide clearer configuration instructions and API key setup. - Removed debug_env.py as it was no longer needed. - Refactored main.py to streamline application initialization and workspace setup. - Introduced a new HistoryManager for managing task execution history. - Enhanced UI components in chat_view.py and task_guide_view.py to improve user interaction and code preview functionality. - Added loading indicators and improved task history display in the UI. - Implemented unit tests for history management and intent classification.
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Mimikko-zeus
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README.md
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README.md
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# LocalAgent - Windows Local AI Execution Assistant
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A Windows-based local AI assistant that can understand natural language commands and execute file processing tasks safely in a sandboxed environment.
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## Features
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- **Intent Recognition**: Automatically distinguishes between chat conversations and execution tasks
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- **Code Generation**: Generates Python code based on user requirements
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- **Safety Checks**: Multi-layer security with static analysis and LLM review
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- **Sandbox Execution**: Runs generated code in an isolated environment
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- **Task History**: Records all executed tasks for review
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- **Streaming Responses**: Real-time display of LLM responses
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## Project Structure
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```
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LocalAgent/
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├── app/ # Main application
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│ └── agent.py # Core application class
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├── llm/ # LLM integration
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│ ├── client.py # API client with retry support
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│ └── prompts.py # Prompt templates
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├── intent/ # Intent classification
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│ ├── classifier.py # Intent classifier
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│ └── labels.py # Intent labels
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├── safety/ # Security checks
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│ ├── rule_checker.py # Static rule checker
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│ └── llm_reviewer.py # LLM-based code review
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├── executor/ # Code execution
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│ └── sandbox_runner.py # Sandbox executor
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├── history/ # Task history
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│ └── manager.py # History manager
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├── ui/ # User interface
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│ ├── chat_view.py # Chat interface
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│ ├── task_guide_view.py # Task confirmation view
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│ └── history_view.py # History view
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├── tests/ # Unit tests
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├── workspace/ # Working directory (auto-created)
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│ ├── input/ # Input files
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│ ├── output/ # Output files
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│ ├── codes/ # Generated code
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│ └── logs/ # Execution logs
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├── main.py # Entry point
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├── requirements.txt # Dependencies
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└── .env.example # Configuration template
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```
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## Installation
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### Prerequisites
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- Python 3.10+
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- Windows OS
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- SiliconFlow API Key ([Get one here](https://siliconflow.cn))
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### Setup
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1. **Clone the repository**
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```bash
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git clone <repository-url>
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cd LocalAgent
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```
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2. **Create virtual environment** (recommended using Anaconda)
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```bash
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conda create -n localagent python=3.10
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conda activate localagent
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```
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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4. **Configure environment**
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```bash
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cp .env.example .env
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# Edit .env and add your API key
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```
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5. **Run the application**
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```bash
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python main.py
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```
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## Configuration
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Edit `.env` file with your settings:
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```env
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# SiliconFlow API Configuration
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LLM_API_URL=https://api.siliconflow.cn/v1/chat/completions
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LLM_API_KEY=your_api_key_here
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# Model Configuration
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INTENT_MODEL_NAME=Qwen/Qwen2.5-7B-Instruct
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GENERATION_MODEL_NAME=Qwen/Qwen2.5-72B-Instruct
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```
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## Usage
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### Chat Mode
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Simply type questions or have conversations:
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- "What is Python?"
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- "Explain machine learning"
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### Execution Mode
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Describe file processing tasks:
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- "Copy all files from input to output"
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- "Convert all PNG images to JPG format"
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- "Rename files with today's date prefix"
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### Workflow
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1. Place input files in `workspace/input/`
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2. Describe your task in the chat
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3. Review the execution plan and generated code
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4. Click "Execute" to run
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5. Find results in `workspace/output/`
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## Security
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LocalAgent implements multiple security layers:
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1. **Hard Rules** - Blocks dangerous operations:
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- Network modules (socket, subprocess)
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- Code execution (eval, exec)
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- System commands (os.system, os.popen)
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2. **Soft Rules** - Warns about sensitive operations:
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- File deletion
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- Network requests (requests, urllib)
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3. **LLM Review** - Semantic analysis of generated code
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4. **Sandbox Execution** - Isolated subprocess with limited permissions
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## Testing
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Run unit tests:
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```bash
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python -m pytest tests/ -v
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```
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## Supported File Operations
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The generated code can use these libraries:
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**Standard Library:**
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- os, sys, pathlib - Path operations
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- shutil - File copy/move
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- json, csv - Data formats
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- zipfile, tarfile - Compression
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- And more...
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**Third-party Libraries:**
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- Pillow - Image processing
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- openpyxl - Excel files
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- python-docx - Word documents
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- PyPDF2 - PDF files
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- chardet - Encoding detection
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## License
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MIT License
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## Contributing
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Contributions are welcome! Please feel free to submit issues and pull requests.
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