This Python application leverages the power of Ollama large language models (LLMs) to create a dynamic and engaging chat experience. Users can interact with various Ollama models directly from the interface, providing a fun and informative way to explore their capabilities.
- Python 3.7 or later
- Streamlit: Install using
pip install streamlit - Ollama: Follow installation instructions from the official Ollama repository: https://github.com/ollama/ollama (https://github.com/facebookresearch/llama)
Optional (for local development):
- Ollama Server: If you prefer running Ollama locally, set up a server instance according to the Ollama documentation. Alternatively, you can provide a remote server URL in the
BACKEND_URLvariable.
- Clone or download the repository.
- Install dependencies: Run
pip install -r requirements.txt(assuming you have arequirements.txtfile listing the necessary packages). - (Optional) Configure Ollama server: If using a local Ollama server, ensure it's running. Otherwise, update the
BACKEND_URLvariable in the code with your remote server's address. - Run the application: Execute
streamlit run chat-app-ollama.py.
- Model Selection: Choose from a variety of Ollama models using the sidebar dropdown.
- Streaming: Select between streaming responses (real-time updates as the model generates text) or non-streaming (displays the complete response after processing).
- Chat Interface: Enter messages in the chat input box and receive responses from the chosen Ollama model.
- You can add more Ollama models to the
modellist in the code. - Consider incorporating error handling for potential API issues or unexpected responses.
- Enhance the visual design using Streamlit's layout and styling options.
- Explore integrating with other Ollama functionalities beyond chat.
- Implement user authentication for personalized interactions.
- Build a more robust server-side component if needed for scalability or security.