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from fastapi import FastAPI
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from fastapi.responses import StreamingResponse, PlainTextResponse
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import socket
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from openai import OpenAI
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app = FastAPI()
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# 阿里云中用来调用 deepseek v3 的密钥
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MODEL_API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
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MODEL_NAME = "deepseek-v3"
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# 初始化 OpenAI 客户端
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client = OpenAI(
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api_key=MODEL_API_KEY,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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)
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# 获取本机所有 IPv4 地址
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def get_local_ips():
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ips = []
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hostname = socket.gethostname()
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try:
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# 获取所有 IP 地址
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addrs = socket.getaddrinfo(hostname, None, family=socket.AF_INET) # 只获取 IPv4 地址
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for addr in addrs:
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ip = addr[4][0]
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if ip not in ips:
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ips.append(ip)
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except Exception as e:
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print(f"获取 IP 地址失败: {e}")
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return ips
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# 流式生成数据的函数
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async def generate_stream(course_name: str):
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# 调用阿里云 API，启用流式响应
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stream = client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{'role': 'system', 'content': '你是一个教学经验丰富的基础教育教师'},
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{'role': 'user', 'content': '帮我设计一下' + course_name + '的课件提纲，用markdown格式返回。'}
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],
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stream=True, # 启用流式响应
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timeout=6000,
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)
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# 逐字返回数据
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for chunk in stream:
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if chunk.choices[0].delta.content:
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for char in chunk.choices[0].delta.content:
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yield char.encode("utf-8")
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await asyncio.sleep(0.05) # 控制逐字输出的速度
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# 根路由，返回提示信息
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@app.get("/")
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def root():
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return PlainTextResponse("Hello ApiStream")
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# 流式返回数据
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@app.get("/stream")
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async def stream_data(course_name: str):
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if not course_name:
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return PlainTextResponse("请提供课程名称,例：course_name=三角形面积")
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# 返回流式响应
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return StreamingResponse(
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generate_stream(course_name),
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media_type="text/event-stream", # 使用 text/event-stream
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headers={
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"Cache-Control": "no-cache", # 禁用缓存
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"Connection": "keep-alive", # 保持连接
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"Access-Control-Allow-Origin": "*", # 允许跨域
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"X-Accel-Buffering": "no" # 禁用 Nginx 缓冲（如果有代理）
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}
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)
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# 运行应用
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if __name__ == "__main__":
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import uvicorn
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import asyncio
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# 获取本机所有 IPv4 地址
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ips = get_local_ips()
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if not ips:
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print("无法获取本机 IP 地址，使用默认地址 127.0.0.1")
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ips = ["127.0.0.1"]
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# 打印所有 IP 地址
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print("服务将在以下 IP 地址上运行：")
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for ip in ips:
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print(f"http://{ip}:8000")
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# 启动 FastAPI 应用，绑定到所有 IP 地址
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uvicorn.run(app, host="0.0.0.0", port=8000) |
