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deepseek-r1
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import gradio as gr
import os
import requests

# 从环境变量中读取 API 密钥
DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY")
DASHSCOPE_API_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"

# 设置 DeepSeek API 客户端
class DeepSeekClient:
    def __init__(self, api_key, base_url):
        self.api_key = api_key
        self.base_url = base_url

    def chat(self, question):
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
        }

        data = {
            "model": "deepseek-r1",  # 使用 DeepSeek 模型
            "messages": [{"role": "user", "content": question}],
        }

        try:
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=data
            )
            response.raise_for_status()
            result = response.json()

            # 获取思考过程和最终答案
            reasoning_content = result["choices"][0].get("reasoning_content", "没有思考过程")
            bot_response = result["choices"][0]["message"]["content"]

            return bot_response, reasoning_content

        except Exception as e:
            return f"Error: {e}", None

# 创建 DeepSeek 客户端
client = DeepSeekClient(api_key=DASHSCOPE_API_KEY, base_url=DASHSCOPE_API_URL)

# 定义聊天函数
def answer(question, history):
    if history is None:
        history = []

    # 获取模型响应
    bot_response, reasoning_content = client.chat(question)

    # 输出思考过程与最终答案（用于调试）
    print("思考过程：", reasoning_content)
    print("最终答案：", bot_response)

    # 更新历史记录
    history.append({"role": "user", "content": question})
    history.append({"role": "assistant", "content": bot_response})

    return history, history

# 创建 Gradio 界面
with gr.Blocks(css="""
    /* 修改布局适应手机端 */
    .gradio-container {
        background-color: #f0f0f0;
        border-radius: 15px;
        padding: 20px;
        max-width: 500px;
        margin: auto;
        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
    }
    .gr-button {
        background-color: #4CAF50;
        color: white;
        border-radius: 10px;
        padding: 12px 25px;
        border: none;
        font-weight: bold;
    }
    .gr-button:hover {
        background-color: #45a049;
    }
    .gr-chatbot {
        font-family: 'Arial', sans-serif;
        height: 300px;
        max-height: 300px;
        overflow-y: auto;
        padding: 10px;
        margin-bottom: 20px;
        background-color: #ffffff;
        border-radius: 15px;
        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
    }
    .chatbot-message {
        margin: 10px;
        padding: 10px;
        border-radius: 12px;
        background-color: #e1f5fe;
    }
    .user-message {
        background-color: #a5d6a7;
        color: #ffffff;
    }
    .assistant-message {
        background-color: #e3f2fd;
        color: #000000;
    }
    input[type="text"] {
        font-size: 16px;
        border-radius: 10px;
        padding: 12px;
        width: 100%;
        background-color: #f9f9f9;
        margin-top: 10px;
    }
    /* 手机端优化：输入框宽度和响应式设计 */
    @media (max-width: 600px) {
        .gradio-container {
            padding: 15px;
            width: 90%;
        }
        .gr-button {
            padding: 10px 20px;
        }
    }
""") as demo:

    chatbot = gr.Chatbot(elem_id="chatbot", type="messages")  # 使用 Gradio 的聊天组件
    state = gr.State([])

    with gr.Row():
        txt = gr.Textbox(placeholder="请输入您的问题并按回车发送", lines=1)

    txt.submit(answer, [txt, state], [chatbot, state])

demo.launch()