278 lines
10 KiB
Python
278 lines
10 KiB
Python
from fastapi import FastAPI, Request, Response, Depends, HTTPException, BackgroundTasks
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from fastapi.responses import JSONResponse, StreamingResponse, FileResponse, RedirectResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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from typing import List, Optional, Generator
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from stock_analyzer import StockAnalyzer
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from us_stock_service import USStockService
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from fund_service import FundService
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import asyncio
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import threading
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import os
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import traceback
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import httpx
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from logger import get_logger
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from utils.api_utils import APIUtils
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# 加载环境变量
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from dotenv import load_dotenv
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import uvicorn
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load_dotenv()
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# 获取日志器
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logger = get_logger()
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app = FastAPI(
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title="Stock Scanner API",
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description="股票分析API",
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version="1.0.0"
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)
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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=["*"], # 开发环境允许所有来源，生产环境应该限制
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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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# 设置静态文件
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frontend_dist = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'frontend', 'dist')
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if os.path.exists(frontend_dist):
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app.mount("/", StaticFiles(directory=frontend_dist, html=True), name="frontend")
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analyzer = StockAnalyzer()
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us_stock_service = USStockService()
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fund_service = FundService()
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# 定义请求和响应模型
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class AnalyzeRequest(BaseModel):
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stock_codes: List[str]
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market_type: str = "A"
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api_url: Optional[str] = None
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api_key: Optional[str] = None
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api_model: Optional[str] = None
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api_timeout: Optional[str] = None
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class TestAPIRequest(BaseModel):
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api_url: str
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api_key: str
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api_model: Optional[str] = None
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api_timeout: Optional[int] = 10
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@app.get("/")
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async def index(request: Request):
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# 检查是否使用前端构建版本
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if os.path.exists(frontend_dist):
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index_file = os.path.join(frontend_dist, 'index.html')
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return FileResponse(index_file)
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else:
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# 不再使用模板渲染，而是重定向到API文档页面
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logger.warning("前端构建目录不存在，重定向到API文档页面")
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return RedirectResponse(url="/docs")
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@app.get("/config")
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async def get_config():
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"""返回系统配置信息"""
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config = {
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'announcement': os.getenv('ANNOUNCEMENT_TEXT') or '',
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'default_api_url': os.getenv('API_URL', ''),
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'default_api_model': os.getenv('API_MODEL', 'gpt-3.5-turbo'),
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'default_api_timeout': os.getenv('API_TIMEOUT', '60')
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}
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return config
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@app.post("/analyze")
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async def analyze(request: AnalyzeRequest):
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try:
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logger.info("开始处理分析请求")
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stock_codes = request.stock_codes
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market_type = request.market_type
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logger.debug(f"接收到分析请求: stock_codes={stock_codes}, market_type={market_type}")
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# 获取自定义API配置
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custom_api_url = request.api_url
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custom_api_key = request.api_key
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custom_api_model = request.api_model
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custom_api_timeout = request.api_timeout
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logger.debug(f"自定义API配置: URL={custom_api_url}, 模型={custom_api_model}, API Key={'已提供' if custom_api_key else '未提供'}, Timeout={custom_api_timeout}")
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# 创建新的分析器实例，使用自定义配置
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custom_analyzer = StockAnalyzer(
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custom_api_url=custom_api_url,
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custom_api_key=custom_api_key,
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custom_api_model=custom_api_model,
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custom_api_timeout=custom_api_timeout
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)
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if not stock_codes:
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logger.warning("未提供股票代码")
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raise HTTPException(status_code=400, detail="请输入代码")
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# 定义流式生成器
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async def generate_stream():
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if len(stock_codes) == 1:
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# 单个股票分析流式处理
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stock_code = stock_codes[0].strip()
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logger.info(f"开始单股流式分析: {stock_code}")
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init_message = f'{{"stream_type": "single", "stock_code": "{stock_code}"}}\n'
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yield init_message
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logger.debug(f"开始处理股票 {stock_code} 的流式响应")
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chunk_count = 0
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# 使用线程池执行同步分析
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def run_analysis():
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return list(custom_analyzer.analyze_stock(stock_code, market_type, stream=True))
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# 在线程中执行同步操作
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loop = asyncio.get_event_loop()
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chunks = await loop.run_in_executor(None, run_analysis)
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for chunk in chunks:
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chunk_count += 1
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yield chunk + '\n'
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logger.info(f"股票 {stock_code} 流式分析完成，共发送 {chunk_count} 个块")
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else:
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# 批量分析流式处理
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logger.info(f"开始批量流式分析: {stock_codes}")
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init_message = f'{{"stream_type": "batch", "stock_codes": {stock_codes}}}\n'
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yield init_message
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logger.debug(f"开始处理批量股票的流式响应")
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chunk_count = 0
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# 使用线程池执行同步分析
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def run_batch_analysis():
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return list(custom_analyzer.scan_stocks(
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[code.strip() for code in stock_codes],
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min_score=0,
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market_type=market_type,
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stream=True
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))
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# 在线程中执行同步操作
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loop = asyncio.get_event_loop()
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chunks = await loop.run_in_executor(None, run_batch_analysis)
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for chunk in chunks:
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chunk_count += 1
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yield chunk + '\n'
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logger.info(f"批量流式分析完成，共发送 {chunk_count} 个块")
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logger.info("成功创建流式响应生成器")
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return StreamingResponse(generate_stream(), media_type='application/json')
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except Exception as e:
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error_msg = f"分析时出错: {str(e)}"
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logger.error(error_msg)
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logger.exception(e)
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raise HTTPException(status_code=500, detail=error_msg)
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@app.get("/search_us_stocks")
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async def search_us_stocks(keyword: str = ""):
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try:
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if not keyword:
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raise HTTPException(status_code=400, detail="请输入搜索关键词")
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# 在异步上下文中运行同步的搜索函数
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loop = asyncio.get_event_loop()
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results = await loop.run_in_executor(None, us_stock_service.search_us_stocks, keyword)
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return {"results": results}
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except Exception as e:
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logger.error(f"搜索美股代码时出错: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/search_funds")
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async def search_funds(keyword: str = "", market_type: str = ""):
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try:
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if not keyword:
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raise HTTPException(status_code=400, detail="请输入搜索关键词")
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# 在异步上下文中运行同步的搜索函数
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loop = asyncio.get_event_loop()
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results = await loop.run_in_executor(None, lambda: fund_service.search_funds(keyword, market_type))
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return {"results": results}
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except Exception as e:
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logger.error(f"搜索基金代码时出错: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/test_api_connection")
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async def test_api_connection(request: TestAPIRequest):
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"""测试API连接"""
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try:
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logger.info("开始测试API连接")
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api_url = request.api_url
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api_key = request.api_key
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api_model = request.api_model
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api_timeout = request.api_timeout
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logger.debug(f"测试API连接: URL={api_url}, 模型={api_model}, API Key={'已提供' if api_key else '未提供'}, Timeout={api_timeout}")
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if not api_url:
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logger.warning("未提供API URL")
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raise HTTPException(status_code=400, detail="请提供API URL")
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if not api_key:
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logger.warning("未提供API Key")
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raise HTTPException(status_code=400, detail="请提供API Key")
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# 构建API URL
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test_url = APIUtils.format_api_url(api_url)
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logger.debug(f"完整API测试URL: {test_url}")
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# 使用异步HTTP客户端发送测试请求
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async with httpx.AsyncClient(timeout=float(api_timeout)) as client:
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response = await client.post(
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test_url,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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},
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json={
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"model": api_model or "gpt-3.5-turbo",
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"messages": [
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{"role": "user", "content": "Hello, this is a test message. Please respond with 'API connection successful'."}
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],
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"max_tokens": 20
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}
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)
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# 检查响应
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if response.status_code == 200:
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logger.info(f"API 连接测试成功: {response.status_code}")
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return {"success": True, "message": "API 连接测试成功"}
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else:
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error_data = response.json()
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error_message = error_data.get('error', {}).get('message', '未知错误')
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logger.warning(f"API连接测试失败: {response.status_code} - {error_message}")
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return JSONResponse(
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status_code=400,
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content={"success": False, "message": f"API 连接测试失败: {error_message}", "status_code": response.status_code}
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)
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except httpx.RequestError as e:
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logger.error(f"API 连接请求错误: {str(e)}")
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return JSONResponse(
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status_code=400,
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content={"success": False, "message": f"请求错误: {str(e)}"}
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)
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except Exception as e:
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logger.error(f"测试 API 连接时出错: {str(e)}")
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logger.exception(e)
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return JSONResponse(
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status_code=500,
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content={"success": False, "message": f"API 测试连接时出错: {str(e)}"}
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)
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if __name__ == '__main__':
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logger.info("股票分析系统启动")
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uvicorn.run("web_server:app", host="127.0.0.1", port=8888, reload=True) |