import spaces
import gradio as gr
from llama_cpp import Llama
import os

# 初始化LLM
llm = Llama.from_pretrained(
    repo_id="matteogeniaccio/phi-4",
    filename="phi-4-Q4_K_M.gguf",
    verbose=True,
    main_gpu=0,
    n_gpu_layers=-1
)

# 响应函数
@spaces.GPU
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # 构造消息内容
    messages = [{"role": "system", "content": system_message}]
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    messages.append({"role": "user", "content": message})

    # 使用llama-cpp-python的方式生成响应
    response = llm.create_chat_completion(
        messages=messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=False
    )

    # 返回流式响应
    for chunk in response:
        if chunk and chunk.get("choices") and chunk["choices"][0].get("delta", {}).get("content"):
            yield chunk["choices"][0]["delta"]["content"]

# Gradio 界面
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
        ),
    ],
)

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