185 lines
7.4 KiB
Python
185 lines
7.4 KiB
Python
from flask import Flask, render_template, request, jsonify, Response, stream_with_context
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from stock_analyzer import StockAnalyzer
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from us_stock_service import USStockService
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import threading
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import os
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import traceback
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import requests
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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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load_dotenv()
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# 获取日志器
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logger = get_logger()
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app = Flask(__name__)
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analyzer = StockAnalyzer()
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us_stock_service = USStockService()
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@app.route('/')
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def index():
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announcement = os.getenv('ANNOUNCEMENT_TEXT') or None
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# 获取默认API配置信息
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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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# 不传递API_KEY到前端，出于安全考虑
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return render_template('index.html',
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announcement=announcement,
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default_api_url=default_api_url,
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default_api_model=default_api_model,
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default_api_timeout=default_api_timeout)
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@app.route('/analyze', methods=['POST'])
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def analyze():
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try:
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logger.info("开始处理分析请求")
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data = request.json
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stock_codes = data.get('stock_codes', [])
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market_type = data.get('market_type', 'A')
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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 = data.get('api_url')
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custom_api_key = data.get('api_key')
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custom_api_model = data.get('api_model')
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custom_api_timeout = data.get('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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return jsonify({'error': '请输入代码'}), 400
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# 使用流式响应
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def generate():
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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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for chunk in custom_analyzer.analyze_stock(stock_code, market_type, stream=True):
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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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for chunk in 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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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 Response(stream_with_context(generate()), mimetype='application/json')
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except Exception as e:
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error_msg = f"分析股票时出错: {str(e)}"
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logger.error(error_msg)
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logger.exception(e)
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return jsonify({'error': error_msg}), 500
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@app.route('/search_us_stocks', methods=['GET'])
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def search_us_stocks():
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try:
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keyword = request.args.get('keyword', '')
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if not keyword:
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return jsonify({'error': '请输入搜索关键词'}), 400
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results = us_stock_service.search_us_stocks(keyword)
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return jsonify({'results': results})
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except Exception as e:
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print(f"搜索美股代码时出错: {str(e)}")
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return jsonify({'error': str(e)}), 500
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@app.route('/test_api_connection', methods=['POST'])
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def test_api_connection():
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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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data = request.json
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api_url = data.get('api_url')
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api_key = data.get('api_key')
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api_model = data.get('api_model')
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api_timeout = data.get('api_timeout', 10) # 默认测试连接超时为10秒
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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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return jsonify({'error': '请提供API URL'}), 400
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if not api_key:
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logger.warning("未提供API Key")
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return jsonify({'error': '请提供API Key'}), 400
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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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# 发送测试请求
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response = requests.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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timeout=int(api_timeout)
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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 jsonify({'success': True, 'message': 'API 连接测试成功'})
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else:
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error_message = response.json().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 jsonify({'success': False, 'message': f'API 连接测试失败: {error_message}', 'status_code': response.status_code}), 400
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except requests.exceptions.RequestException as e:
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logger.error(f"API 连接请求错误: {str(e)}")
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return jsonify({'success': False, 'message': f'请求错误: {str(e)}'}), 400
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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 jsonify({'success': False, 'message': f'API 测试连接时出错: {str(e)}'}), 500
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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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app.run(host='0.0.0.0', port=8888, debug=True) |