import gradio as gr
from vllm import LLM, SamplingParams

llm = LLM(
    model="stepfun-ai/Step-Audio-2-mini-Think",  # 修改为你需要的模型
    trust_remote_code=True,
    tensor_parallel_size=2,  # 如果有多张GPU，设置并行数量
    # gpu_memory_utilization=0.9,  # GPU显存利用率
    max_model_len=8192, 
)


def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    """
    使用 vllm 在本地进行推理
    """
    # 构建对话消息
    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": message})

    # 设置采样参数
    sampling_params = SamplingParams(
        temperature=temperature,
        top_p=top_p,
        max_tokens=max_tokens,
    )

    # 使用 vllm 的 chat 接口进行推理
    outputs = llm.chat(
        messages=messages,
        sampling_params=sampling_params,
        use_tqdm=False,
    )

    # 获取生成的文本
    response = outputs[0].outputs[0].text

    # 模拟流式输出效果（逐字符yield）
    for i in range(1, len(response) + 1):
        yield response[:i]


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    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)",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


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