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from fastapi import FastAPI, Query, Body
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from fastapi.responses import StreamingResponse, PlainTextResponse
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from fastapi.middleware.cors import CORSMiddleware
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import socket
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from openai import OpenAI
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import asyncio
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import json
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app = FastAPI()
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# 添加 CORS 中间件
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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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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# 解析 Markdown 内容并生成 JSON 数据
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def parse_markdown_to_json(content: str):
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sections = []
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current_section = None
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current_subsection = None
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for line in content.split("\n"):
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# 忽略无用的标记
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if line.strip() in ["```markdown", "```"]:
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continue
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if line.startswith("# "): # 一级标题（忽略，作为整体标题）
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continue
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elif line.startswith("## "): # 二级标题
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if current_section:
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sections.append(current_section)
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current_section = {"title": line[3:].strip(), "items": []}
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current_subsection = None
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elif line.startswith("### "): # 三级标题
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if current_section:
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current_subsection = {"title": line[4:].strip(), "text": ""}
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current_section["items"].append(current_subsection)
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elif line.strip() and (line.startswith("- ") or (line[0].isdigit() and line[1] == '.')): # 列表项或数字加点
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if not current_subsection: # 如果 current_subsection 为 None，则创建一个默认子章节
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current_subsection = {"title": "", "text": ""}
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if current_section:
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current_section["items"].append(current_subsection)
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current_subsection["text"] += line.strip() + " "
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elif line.strip(): # 普通文本
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if current_subsection:
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current_subsection["text"] += line.strip() + " "
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if current_section:
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sections.append(current_section)
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return sections
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# 生成流式 JSON 数据（中文输出，逐行返回）
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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（支持 GET 和 POST 方法）
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@app.api_route("/api/tools/aippt_outline", methods=["GET", "POST"])
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async def aippt_outline(
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course_name: str = Query(None, description="课程名称（GET 方法使用）"), # 从查询参数中获取 course_name
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course_name_body: str = Body(None, embed=True, description="课程名称（POST 方法使用）") # 从请求体中获取 course_name
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):
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# 检查 course_name 是否为空
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if not course_name and not course_name_body:
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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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# 优先使用 POST 请求体中的 course_name
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course_name = course_name_body if course_name_body else course_name
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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",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Access-Control-Allow-Origin": "*",
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"X-Accel-Buffering": "no"
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}
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)
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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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# 生成流式 JSON 数据（中文输出，逐行返回）
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async def generate_json_stream(content: str): # 新增 content 参数
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# 解析 Markdown 内容
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sections = parse_markdown_to_json(content)
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# 封面（使用第一个章节的标题作为封面标题）
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first_section_title = sections[0]["title"] if sections else "默认标题"
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yield json.dumps({"type": "cover", "data": {"title": first_section_title, "text": "课程简介"}}, ensure_ascii=False) + "\n"
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await asyncio.sleep(0.5)
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# 目录（使用所有章节的标题）
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yield json.dumps({"type": "contents", "data": {"items": [section["title"] for section in sections]}}, ensure_ascii=False) + "\n"
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await asyncio.sleep(0.5)
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# 逐章节生成内容
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for section in sections:
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# 过渡（使用章节标题动态生成过渡文本）
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yield json.dumps({"type": "transition", "data": {"title": section["title"], "text": section["title"]}}, ensure_ascii=False) + "\n"
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await asyncio.sleep(0.5)
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# 具体内容（直接使用解析后的章节内容）
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yield json.dumps({"type": "content", "data": {"title": section["title"], "items": section["items"]}}, ensure_ascii=False) + "\n"
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await asyncio.sleep(0.5)
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# 结束
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yield json.dumps({"type": "end"}, ensure_ascii=False) + "\n"
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# 从文件中读取 Markdown 并生成流式 JSON（中文输出，逐行返回）
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@app.get("/api/tools/aippt")
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async def aippt(content: str = Query(..., description="Markdown 内容")): # 新增 content 参数
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return StreamingResponse(
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generate_json_stream(content), # 传入 content
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media_type="text/plain", # 使用 text/plain 格式
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Access-Control-Allow-Origin": "*",
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"X-Accel-Buffering": "no",
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"Content-Type": "text/plain; charset=utf-8" # 明确设置 Content-Type
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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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# 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}:5173")
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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=5173) |
