1、效果图

2、实现代码

# import gradio as gr

# def reverse_text(text):
#     return text[::-1]


# demo=gr.Interface(fn=reverse_text,inputs="text",outputs="text")

# demo.launch(share="True")

import gradio as gr
import openai
from typing import List, Any, Iterator

# 配置DeepSeek API
api_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
api_base = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

client = openai.OpenAI(api_key=api_key, base_url=api_base)

def chat_stream(message: str, 
               history: List[List[str]], 
               temperature: float = 0.7,
               top_k: int = 40,
               system_prompt: str = "你是一个有帮助的助手。") -> Iterator[Any]:
    """流式输出DeepSeek响应"""
    messages = [{"role": "system", "content": system_prompt}]
    
    # 添加历史记录
    for human_msg, ai_msg in history:
        messages.append({"role": "user", "content": human_msg})
        messages.append({"role": "assistant", "content": ai_msg})
    
    # 添加当前消息
    messages.append({"role": "user", "content": message})

    # 调用API进行流式输出
    response = client.chat.completions.create(
        model="Qwen/Qwen2.5-VL-72B-Instruct",
        messages=messages,
        temperature=temperature,
        top_p=1-(1.0/top_k) if top_k > 1 else 1.0,
        stream=True
    )

    full_response = ""
    for chunk in response:
        if chunk.choices and len(chunk.choices) > 0:
            content = chunk.choices[0].delta.content
            if content:
                full_response += content
                yield full_response

# 自定义CSS样式
custom_css = """
#chatbot {
    height: 600px !important;
    border-radius: 10px;
    border: 1px solid #e0e0e0;
    font-family: "Microsoft YaHei", "微软雅黑", sans-serif;
}
.message {
    padding: 10px;
    border-radius: 5px;
    margin: 5px 0;
    font-size: 15px;
    line-height: 1.6;
}
.user-message {
    background-color: #e3f2fd;
}
.bot-message {
    background-color: #f5f5f5;
}
.gradio-container {
    font-family: "Microsoft YaHei", "微软雅黑", sans-serif;
}
"""

# 创建Gradio界面
with gr.Blocks(title="DeepSeek 智能助手", css=custom_css, theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🤖 DeepSeek 智能助手
    欢迎使用 DeepSeek 智能助手！您可以通过右侧的设置来调整 AI 的行为。
    """)
    
    with gr.Row():
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(
                height=600,
                bubble_full_width=False,
                show_copy_button=True,
                elem_id="chatbot"
            )
            with gr.Row():
                msg = gr.Textbox(
                    label="输入消息",
                    placeholder="在这里输入您的问题...",
                    scale=8,
                    container=False
                )
                submit_btn = gr.Button("发送", variant="primary", scale=1)
            
            with gr.Row():
                clear = gr.Button("清除历史", variant="secondary")
        
        with gr.Column(scale=1):
            system_prompt = gr.Textbox(
                label="系统提示词",
                value="你是一个有帮助的助手。",
                lines=3
            )
            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.7,
                step=0.1,
                label="温度",
                info="较高的值会使输出更加随机，较低的值会使输出更加确定"
            )
            top_k = gr.Slider(
                minimum=1,
                maximum=100,
                value=40,
                step=1,
                label="Top K",
                info="控制输出词汇的多样性"
            )

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history, temp, top_k_val, sys_prompt):
        history[-1][1] = ""
        for response in chat_stream(history[-1][0], history[:-1], temp, top_k_val, sys_prompt):
            history[-1][1] = response
            yield history

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, [chatbot, temperature, top_k, system_prompt], chatbot
    )
    submit_btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, [chatbot, temperature, top_k, system_prompt], chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)

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

3、温度和TopK的作用对比

温度和TopK的调节可以使用大模型的回答更加精准和多样化。