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import json
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import uuid
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import uvicorn # 导入 uvicorn
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from fastapi import FastAPI, Depends, Form, Query
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from openai import OpenAI
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from starlette.responses import StreamingResponse
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from starlette.staticfiles import StaticFiles
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from Config import MODEL_API_KEY, MODEL_API_URL, MODEL_NAME
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from Model.biModel import *
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from Text2Sql.Util.MarkdownToDocxUtil import markdown_to_docx
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from Text2Sql.Util.PostgreSQLUtil import get_db
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from Text2Sql.Util.SaveToExcel import save_to_excel
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from Text2Sql.Util.VannaUtil import VannaUtil
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# 初始化 FastAPI
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app = FastAPI()
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# 配置静态文件目录
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app.mount("/static", StaticFiles(directory="static"), name="static")
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# 初始化一次vanna的类
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vn = VannaUtil()
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@app.get("/")
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def read_root():
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return {"message": "Welcome to AI SQL World!"}
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# 通过语义生成Excel
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# http://10.10.21.20:8000/questions/get_excel
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@app.post("/questions/get_excel")
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def get_excel(question_id: str = Form(...), question_str: str = Form(...), db: PostgreSQLUtil = Depends(get_db)):
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# 只接受guid号
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if len(question_id) != 36:
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return {"success": False, "message": "question_id格式错误"}
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common_prompt = '''
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返回的信息要求：
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1、行政区划为NULL 或者是空字符的不参加统计
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2、目标数据库是Postgresql 16
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'''
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question = question_str + common_prompt
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# 先删除后插入,防止重复插入
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delete_question(db, question_id)
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insert_question(db, question_id, question)
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# 获取完整 SQL
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sql = vn.generate_sql(question)
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print("生成的查询 SQL:\n", sql)
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# 更新question_id
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update_question_by_id(db, question_id=question_id, sql=sql, state_id=1)
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# 执行SQL查询
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_data = db.execute_query(sql)
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# 在static目录下，生成一个guid号的临时文件
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uuid_str = str(uuid.uuid4())
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filename = f"static/{uuid_str}.xlsx"
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save_to_excel(_data, filename)
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# 更新EXCEL文件名称
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update_question_by_id(db, question_id, excel_file_name=filename)
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# 返回静态文件URL
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return {"success": True, "message": "Excel文件生成成功", "download_url": f"/static/{uuid_str}.xlsx"}
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# http://10.10.21.20:8000/questions/get_docx?question_id_get=af15d834-e7f5-46b4-a0f6-15f1f888f443
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@app.api_route("/questions/get_docx", methods=["POST", "GET"])
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async def get_docx(
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question_id: str = Form(None, description="问题ID（POST请求）"), # POST 请求参数
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question_id_get: str = Query(None, description="问题ID（GET请求）"), # GET 请求参数
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db: PostgreSQLUtil = Depends(get_db)
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):
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# 根据请求方式获取 question_id
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if question_id is not None: # POST 请求
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question_id = question_id
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elif question_id_get is not None: # GET 请求
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question_id = question_id_get
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else:
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return {"success": False, "message": "缺少问题ID参数"}
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# 根据问题ID获取查询sql
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sql = get_question_sql_by_id(db, question_id)
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# 4、生成word报告
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prompt = '''
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请根据以下 JSON 数据，整理出2000字左右的话描述当前数据情况。要求：
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1、以Markdown格式返回，我将直接通过markdown格式生成Word。
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2、标题统一为：长春云校数据分析报告
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3、内容中不要提到JSON数据，统一称：数据
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4、尽量以条目列出，这样更清晰
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5、数据：
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'''
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_data = db.execute_query(sql)
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prompt = prompt + json.dumps(_data, ensure_ascii=False)
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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=MODEL_API_URL,
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)
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# 调用 OpenAI API 生成总结（流式输出）
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": "你是一个数据分析助手，擅长从 JSON 数据中提取关键信息并生成详细的总结。"},
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{"role": "user", "content": prompt}
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],
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max_tokens=3000, # 控制生成内容的长度
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temperature=0.7, # 控制生成内容的创造性
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stream=True # 启用流式输出
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)
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# 生成 Word 文档的文件名
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uuid_str = str(uuid.uuid4())
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filename = f"static/{uuid_str}.docx"
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# 定义一个生成器函数，用于逐字返回流式结果
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async def generate_stream():
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summary = ""
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try:
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for chunk in response:
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if chunk.choices[0].delta.content: # 检查是否有内容
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chunk_content = chunk.choices[0].delta.content
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# 逐字拆分并返回
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for char in chunk_content:
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print(char, end="", flush=True) # 逐字输出到控制台
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yield char.encode("utf-8") # 将字符编码为 UTF-8 字节
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summary += char # 将内容拼接到 summary 中
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# 流式传输完成后，生成 Word 文档
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markdown_to_docx(summary, output_file=filename)
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# 返回最终的 JSON 数据
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final_response = json.dumps({
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"success": True,
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"message": "Word文件生成成功",
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"download_url": f"/static/{uuid_str}.docx"
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})
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print(final_response) # 输出最终 JSON 到控制台
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yield final_response.encode("utf-8") # 将 JSON 数据编码为 UTF-8 字节
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except Exception as e:
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# 如果发生异常，返回错误信息
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error_response = json.dumps({
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"success": False,
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"message": f"生成Word文件失败: {str(e)}"
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})
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print(error_response) # 输出错误信息到控制台
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yield error_response.encode("utf-8") # 将错误信息编码为 UTF-8 字节
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finally:
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# 确保资源释放
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if "response" in locals():
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response.close()
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# 使用 StreamingResponse 返回流式结果
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return StreamingResponse(
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generate_stream(),
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media_type="text/plain; charset=utf-8", # 明确指定字符编码为 UTF-8
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headers={
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"Cache-Control": "no-cache", # 禁用缓存
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"Content-Type": "text/plain; charset=utf-8", # 设置内容类型和字符编码
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}
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)
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# 确保直接运行脚本时启动 FastAPI 应用
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if __name__ == "__main__":
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uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
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