LLaMa-MCP-Streamlit

# Llama MCP Streamlit This project is an interactive AI assistant built with **Streamlit**, **NVIDIA NIM's API (LLaMa 3.3:70b)/Ollama**, and **Model Control Protocol (MCP)**. It provides a conversational interface where you can interact with an LLM to execute real-time external tools via MCP, retrieve data, and perform actions seamlessly. The assistant supports: - **Custom model selection** (NVIDIA NIM / Ollama) - **API configuration** for different backends - **Tool integration via MCP** to enhance usability and real-time data processing - **A user-friendly chat-based experience** with Streamlit ## 📸 Screenshots     --- ## 📁 Project Structure ``` llama_mcp_streamlit/ │── ui/ │ ├── sidebar.py # UI components for Streamlit sidebar │ ├── chat_ui.py # Chat interface components │── utils/ │ ├── agent.py # Handles interaction with LLM and tools │ ├── mcp_client.py # MCP client for connecting to external tools │ ├── mcp_server.py # Configuration for MCP server selection │── config.py # Configuration settings │── main.py # Entry point for the Streamlit app .env # Environment variables Dockerfile # Docker configuration pyproject.toml # Poetry dependency management ``` --- ## 🔧 Environment Variables Before running the project, configure the `.env` file with your API keys: ``` # Endpoint for the NVIDIA Integrate API API_ENDPOINT=https://integrate.api.nvidia.com/v1 API_KEY=your_api_key_here # Endpoint for the Ollama API API_ENDPOINT=http://localhost:11434/v1/ API_KEY=ollama ``` --- ## 🚀 Running the Project ### Using Poetry 1. Install dependencies: ```bash poetry install ``` 2. Run the Streamlit app: ```bash poetry run streamlit run llama_mcp_streamlit/main.py ``` ### Using Docker 1. Build the Docker image: ```bash docker build -t llama-mcp-assistant . ``` 2. Run the container: ```bash docker compose up ``` --- ## 🔄 Changing MCP Server Configuration To modify which MCP server to use, update the `utils/mcp_server.py` file. You can use either NPX or Docker as the MCP server: ### NPX Server ```python server_params = StdioServerParameters( command="npx", args=[ "-y", "@modelcontextprotocol/server-filesystem", "/Users/username/Desktop", "/path/to/other/allowed/dir" ], env=None, ) ``` ### Docker Server ```python server_params = StdioServerParameters( command="docker", args=[ "run", "-i", "--rm", "--mount", "type=bind,src=/Users/username/Desktop,dst=/projects/Desktop", "--mount", "type=bind,src=/path/to/other/allowed/dir,dst=/projects/other/allowed/dir,ro", "--mount", "type=bind,src=/path/to/file.txt,dst=/projects/path/to/file.txt", "mcp/filesystem", "/projects" ], env=None, ) ``` Modify the `server_params` configuration as needed to fit your setup. --- ## 📌 Features - **Real-time tool execution via MCP** - **LLM-powered chat interface** - **Streamlit UI with interactive chat elements** - **Support for multiple LLM backends (NVIDIA NIM & Ollama)** - **Docker support for easy deployment** --- ## 🛠 Dependencies - Python 3.11+ - Streamlit - OpenAI API (for NVIDIA NIM integration) - MCP (Model Control Protocol) - Poetry (for dependency management) - Docker (optional, for containerized deployment) --- ## 📜 License This project is licensed under the MIT License. --- ## 🤝 Contributing Feel free to submit pull requests or report issues! --- ## 📬 Contact For any questions, reach out via GitHub Issues. ---