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Gradio Chatbot + LiteLLM Tutorial

Simple tutorial for integrating LiteLLM completion calls with streaming Gradio chatbot demos

Install & Import Dependencies​

!pip install gradio litellm
import gradio
import litellm

Define Inference Function​

Remember to set model and api_base as expected by the server hosting your LLM.

def inference(message, history):
    try:
        flattened_history = [item for sublist in history for item in sublist]
        full_message = " ".join(flattened_history + [message])
        messages_litellm = [{"role": "user", "content": full_message}] # litellm message format
        partial_message = ""
        for chunk in litellm.completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf",
                                        api_base="x.x.x.x:xxxx",
                                        messages=messages_litellm,
                                        max_new_tokens=512,
                                        temperature=.7,
                                        top_k=100,
                                        top_p=.9,
                                        repetition_penalty=1.18,
                                        stream=True):
            partial_message += chunk['choices'][0]['delta']['content'] # extract text from streamed litellm chunks
            yield partial_message
    except Exception as e:
        print("Exception encountered:", str(e))
        yield f"An Error occured please 'Clear' the error and try your question again"

Define Chat Interface​

gr.ChatInterface(
    inference,
    chatbot=gr.Chatbot(height=400),
    textbox=gr.Textbox(placeholder="Enter text here...", container=False, scale=5),
    description=f"""
    CURRENT PROMPT TEMPLATE: {model_name}.
    An incorrect prompt template will cause performance to suffer.
    Check the API specifications to ensure this format matches the target LLM.""",
    title="Simple Chatbot Test Application",
    examples=["Define 'deep learning' in once sentence."],
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    theme=theme,
).queue().launch()

Launch Gradio App​

  1. From command line: python app.py or gradio app.py (latter enables live deployment updates)
  2. Visit provided hyperlink in your browser.
  3. Enjoy prompt-agnostic interaction with remote LLM server.
  • Add command line arguments to define target model & inference endpoints

Credits to ZQ, for this tutorial.