import gradio as gr
from transformers import pipeline

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""

# Use a pipeline as a high-level helper
pipe = pipeline("text-generation", model="vc3vc3/qwen3-0.6B-finetune")

messages = [
    {"role": "user", "content": "Who are you? 用中文回答，风格调皮一些。"},
]
pipe(messages)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # 拼接历史消息和当前消息为 prompt
    prompt = system_message + "\n"
    for val in history:
        if val[0]:
            prompt += f"用户: {val[0]}\n"
        if val[1]:
            prompt += f"助手: {val[1]}\n"
    prompt += f"用户: {message}\n助手:"

    # 使用 pipe 生成回复
    response = ""
    for out in pipe(
        prompt,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
        return_full_text=False,
        truncation=True,
        stream=True,
    ):
        token = out["generated_text"] if isinstance(out, dict) and "generated_text" in out else str(out)
        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()