app.py

import gradio as gr
from llama_cpp import Llama

llm = Llama(
    model_path="./qwen2-0_5b-instruct-q5_k_m.gguf",
    verbose=True
)

def predict(message, history):
    messages = [{"role": "system", "content": "You are a helpful assistant."}]
    for user_message, bot_message in history:
        if user_message:
            messages.append({"role": "user", "content": user_message})
        if bot_message:
            messages.append({"role": "assistant", "content": bot_message})
    messages.append({"role": "user", "content": message})
    
    response = ""
    for chunk in llm.create_chat_completion(
        stream=True,
        messages=messages,
    ):
        part = chunk["choices"][0]["delta"].get("content", None)
        if part:
            response += part
        yield response

demo = gr.ChatInterface(predict)

if __name__ == "__main__":
    demo.launch()