coding-assistant

# Coding Assistant Your Local AI Pair Programmer: Point this AI assistant to your project folder and start asking questions! It reads your code locally to provide truly context-aware help, explanations, and suggestions directly related to your files. Code smarter, locally.  ## Prerequisites 📝 - **Python:** Version 3.11 or higher recommended. - **`tree` command:** The `get_tree_folders` tool relies on this. - **Linux (Debian/Ubuntu):** `sudo apt update && sudo apt install tree` - **macOS (using Homebrew):** `brew install tree` ## Installation ⚙️ 1. **Clone the repository:** ```bash git clone https://github.com/Bessouat40/coding-assistant cd coding-assistant ``` 2. **Install Python dependencies:** ```bash pip install -r requirements.txt ``` ## Configuration 🔑 1. **API Keys & Settings:** Sensitive information like LLM API keys should be stored in a `.env` file in the project's root directory. - Copy the example file or create a new file named `.env`: ```bash cp .env.example .env ``` - Edit the `.env` file and add your keys: ```dotenv # .env MISTRAL_API_KEY=your_mistral_api_key_here # GOOGLE_API_KEY=your_google_api_key_here # Add other variables if needed, e.g., MAX_CONTEXT_TOKENS=7000 ``` 2. **LLM Provider:** Change LLM provider if you want : - Go to `api/utils.py` and modify `loadl_llm` function. Uncomment the line with the provider you need. By default, it's set to `Google` : ```python def load_llm(logger): try: model = ChatOllama(model="llama3.1:8b") # model = ChatMistralAI(model="codestral-latest") # model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001") logger.info(f"ChatGoogleGenerativeAI model '{model.model}' initialized successfully.") return model except Exception as e: logger.error(f"Failed to initialize the LLM model: {e}") raise RuntimeError(f"Could not initialize LLM: {e}") from e ``` ## Running the Application ▶️ A convenience script `launch_assistant.sh` is provided to start all components. 1. **Make the script executable:** ```bash chmod +x launch_assistant.sh ``` 2. **Run the script:** ```bash ./launch_assistant.sh ``` This script will: - Load environment variables from `.env`. - Start the MCP Tool Server (`agent_tools.py`) in the background (logs to `mcp_server.log`). - Start the FastAPI Backend (`api.py`) in the background (logs to `fastapi_api.log`). - Start the Streamlit UI (`streamlit_app.py`) in the background (logs to `streamlit_ui.log`). - Print the URLs and PIDs for each component. You can view logs in the specified `.log` files for debugging. Press `Ctrl+C` in the terminal where you ran the script to stop all components gracefully. ## Usage 🧑‍💻 1. **Access the UI:** Open your web browser and navigate to the Streamlit URL (usually `http://localhost:8501`). 2. **Set Project Directory:** In the sidebar, enter the **absolute path** to the local project directory you want the assistant to work with. Click "Set Directory". 3. **Chat:** Use the chat input at the bottom to ask questions about the code in the specified directory. Examples: - "Explain the purpose of the `run_agent` function in `api.py`." - "Show me the file structure of this project." (Uses `tree`) - "What arguments does the `generate_response_api` function take?" - "Read the contents of `prompt.py`." (Uses `cat`) - "Can you suggest improvements to the error handling in `streamlit_app.py`?" 4. **Follow-up:** The assistant remembers the context of your current chat session. Ask follow-up questions naturally. 5. **Clear History:** Use the "Clear Chat History" button in the sidebar to start a fresh conversation.