From 6df78314d6e069cf8344b4a2b17e3c381ead13d3 Mon Sep 17 00:00:00 2001 From: Cassianvale Date: Tue, 4 Mar 2025 15:03:08 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=94=AF=E6=8C=81=E6=B5=81=E5=BC=8F?= =?UTF-8?q?=E8=BE=93=E5=87=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- logger.py | 58 ++++++ stock_analyzer.py | 331 ++++++++++++++++++++++++----- templates/index.html | 485 ++++++++++++++++++++++++++++++++++--------- tests/test_stream.py | 137 ++++++++++++ web_server.py | 83 ++++++-- 5 files changed, 928 insertions(+), 166 deletions(-) create mode 100644 logger.py create mode 100644 tests/test_stream.py diff --git a/logger.py b/logger.py new file mode 100644 index 0000000..8313c52 --- /dev/null +++ b/logger.py @@ -0,0 +1,58 @@ +from loguru import logger +import sys +import os +from datetime import datetime + +# 获取当前时间作为日志文件名的一部分 +current_time = datetime.now().strftime("%Y%m%d_%H%M%S") + +# 创建日志目录 +log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs") +os.makedirs(log_dir, exist_ok=True) + +# 配置日志 +logger.remove() # 移除默认的处理器 + +# 添加标准输出处理器（控制台） +logger.add( + sys.stdout, + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{line} - {message}", + level="DEBUG" +) + +# 添加文件处理器（debug级别） +logger.add( + os.path.join(log_dir, f"debug_{current_time}.log"), + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{line} - {message}", + level="DEBUG", + rotation="100 MB", + retention="1 week" +) + +# 添加文件处理器（error级别） +logger.add( + os.path.join(log_dir, f"error_{current_time}.log"), + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{line} - {message}", + level="ERROR", + rotation="100 MB", + retention="1 month" +) + +# 添加流处理器（用于记录流式输出） +logger.add( + os.path.join(log_dir, f"stream_{current_time}.log"), + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {message}", + filter=lambda record: "STREAM" in record["extra"], + level="INFO" +) + +# 创建专用于流式输出的日志器 +stream_logger = logger.bind(STREAM=True) + +def get_logger(): + """获取通用日志器""" + return logger + +def get_stream_logger(): + """获取流式输出专用日志器""" + return stream_logger diff --git a/stock_analyzer.py b/stock_analyzer.py index 105278d..7d040ca 100644 --- a/stock_analyzer.py +++ b/stock_analyzer.py @@ -3,8 +3,14 @@ import numpy as np from datetime import datetime, timedelta import os import requests -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Generator from dotenv import load_dotenv +import json +from logger import get_logger, get_stream_logger + +# 获取日志器 +logger = get_logger() +stream_logger = get_stream_logger() class StockAnalyzer: def __init__(self, initial_cash=1000000, custom_api_url=None, custom_api_key=None, custom_api_model=None): @@ -17,6 +23,8 @@ class StockAnalyzer: self.API_KEY = custom_api_key or os.getenv('API_KEY') self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo') + logger.debug(f"初始化StockAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}") + # 配置参数 self.params = { 'ma_periods': {'short': 5, 'medium': 20, 'long': 60}, @@ -205,9 +213,10 @@ class StockAnalyzer: print(f"计算评分时出错: {str(e)}") raise - def get_ai_analysis(self, df, stock_code): + def get_ai_analysis(self, df, stock_code, stream=False): """使用 OpenAI 进行 AI 分析""" try: + logger.info(f"开始AI分析股票 {stock_code}, 流式模式: {stream}") recent_data = df.tail(14).to_dict('records') technical_summary = { @@ -237,47 +246,202 @@ class StockAnalyzer: 请基于技术指标和市场动态进行分析，给出具体数据支持。 """ - # OpenAI API 调用 - api_urls = [ - f"{self.API_URL}/chat/completions", - f"{self.API_URL}/v1/chat/completions" - ] + logger.debug(f"生成的AI分析提示词: {prompt[:100]}...") - last_error = None - for api_url in api_urls: + # 检查API配置 + if not self.API_URL: + error_msg = "API URL未配置，无法进行AI分析" + logger.error(error_msg) + return error_msg if not stream else (yield json.dumps({"error": error_msg})) + + if not self.API_KEY: + error_msg = "API Key未配置，无法进行AI分析" + logger.error(error_msg) + return error_msg if not stream else (yield json.dumps({"error": error_msg})) + + # 标准化API URL + api_url = self.API_URL + if not (api_url.endswith('/chat/completions') or api_url.endswith('/v1/chat/completions')): + if api_url.endswith('/v1'): + api_url = f"{api_url}/chat/completions" + elif api_url.endswith('/'): + api_url = f"{api_url}v1/chat/completions" + else: + api_url = f"{api_url}/v1/chat/completions" + + logger.debug(f"标准化后的API URL: {api_url}") + + # 构建请求头和请求体 + headers = { + "Authorization": f"Bearer {self.API_KEY}", + "Content-Type": "application/json" + } + + payload = { + "model": self.API_MODEL, + "messages": [{"role": "user", "content": prompt}] + } + + # 流式处理设置 + if stream: + logger.debug(f"配置流式参数，使用API URL: {api_url}") + payload["stream"] = True # 明确设置stream参数为True + + try: + logger.debug(f"发起流式API请求: {api_url}") + logger.debug(f"请求载荷: {json.dumps(payload, indent=2)}") + + response = requests.post( + api_url, + headers=headers, + json=payload, + timeout=60, # 增加超时时间 + stream=True + ) + + logger.debug(f"API流式响应状态码: {response.status_code}") + + if response.status_code == 200: + logger.info(f"成功获取API流式响应，开始处理") + yield from self._process_ai_stream(response, stock_code) + else: + try: + error_response = response.json() + error_text = json.dumps(error_response, indent=2) + except: + error_text = response.text[:500] if response.text else "无响应内容" + + error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}" + logger.error(error_msg) + yield json.dumps({"stock_code": stock_code, "error": error_msg}) + + except Exception as e: + error_msg = f"流式API请求异常: {str(e)}" + logger.error(error_msg) + logger.exception(e) + yield json.dumps({"stock_code": stock_code, "error": error_msg}) + else: + # 非流式处理 + logger.debug(f"发起非流式API请求: {api_url}") + try: response = requests.post( api_url, - headers={ - "Authorization": f"Bearer {self.API_KEY}", - "Content-Type": "application/json" - }, - json={ - "model": self.API_MODEL, - "messages": [{"role": "user", "content": prompt}] - }, - timeout=30 + headers=headers, + json=payload, + timeout=60 ) + logger.debug(f"API非流式响应状态码: {response.status_code}") + if response.status_code == 200: - return response.json()['choices'][0]['message']['content'] + api_response = response.json() + content = api_response['choices'][0]['message']['content'] + logger.info(f"成功获取AI分析结果，长度: {len(content)}") + logger.debug(f"AI分析结果前100字符: {content[:100]}...") + return content else: - last_error = f"API 错误: {response.status_code} - {response.text}" - continue + try: + error_response = response.json() + error_text = json.dumps(error_response, indent=2) + except: + error_text = response.text[:500] if response.text else "无响应内容" + + error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}" + logger.error(error_msg) + return error_msg except Exception as e: - last_error = str(e) - continue - - print(f"AI 分析暂时无法使用: {last_error}") - return f"AI 分析暂时无法使用: {last_error}" + error_msg = f"非流式API请求异常: {str(e)}" + logger.error(error_msg) + logger.exception(e) + return error_msg except Exception as e: - print(f"AI 分析发生错误: {str(e)}") - return f"AI 分析过程中发生错误: {str(e)}" + error_msg = f"AI 分析过程中发生错误: {str(e)}" + logger.error(error_msg) + logger.exception(e) + if stream: + logger.debug("在流式模式下返回异常信息") + error_json = json.dumps({"stock_code": stock_code, "error": error_msg}) + stream_logger.info(f"流式异常输出: {error_json}") + yield error_json + else: + return error_msg + + def _process_ai_stream(self, response, stock_code) -> Generator[str, None, None]: + """处理AI流式响应""" + logger.info(f"开始处理股票 {stock_code} 的AI流式响应") + buffer = "" + chunk_count = 0 + + try: + for line in response.iter_lines(): + if line: + line = line.decode('utf-8') + stream_logger.info(f"原始流式行: {line}") + + # 跳过保持连接的空行 + if line.strip() == '': + logger.debug("跳过空行") + continue + + # 数据行通常以"data: "开头 + if line.startswith('data: '): + data_content = line[6:] # 移除 "data: " 前缀 + stream_logger.info(f"数据内容: {data_content}") + + # 检查是否为流的结束 + if data_content.strip() == '[DONE]': + logger.debug("收到流结束标记 [DONE]") + break + + try: + json_data = json.loads(data_content) + logger.debug(f"解析的JSON数据: {json.dumps(json_data)[:100]}...") + + if 'choices' in json_data: + delta = json_data['choices'][0].get('delta', {}) + content = delta.get('content', '') + + if content: + chunk_count += 1 + buffer += content + logger.debug(f"收到内容片段 #{chunk_count}: {content}") + stream_logger.info(f"发送内容片段: {content}") + + # 创建包含AI分析片段的JSON + chunk_json = json.dumps({ + "stock_code": stock_code, + "ai_analysis_chunk": content + }) + stream_logger.info(f"流式输出JSON: {chunk_json}") + yield chunk_json + except json.JSONDecodeError as e: + logger.error(f"JSON解析错误: {str(e)}, 行内容: {data_content}") + # 忽略无法解析的JSON + pass + else: + logger.warning(f"收到非'data:'开头的行: {line}") + + logger.info(f"AI流式处理完成，共收到 {chunk_count} 个内容片段，总长度: {len(buffer)}") + + # 如果buffer不为空，最后一次发送完整内容 + if buffer and not buffer.endswith('\n'): + logger.debug("发送换行符") + yield json.dumps({"stock_code": stock_code, "ai_analysis_chunk": "\n"}) + + except Exception as e: + error_msg = f"处理AI流式响应时出错: {str(e)}" + logger.error(error_msg) + logger.exception(e) + yield json.dumps({"stock_code": stock_code, "error": error_msg}) + + def get_recommendation(self, score): """根据得分给出建议""" + logger.debug(f"根据评分 {score} 生成投资建议") if score >= 80: return '强烈推荐买入' elif score >= 60: @@ -288,18 +452,24 @@ class StockAnalyzer: return '建议卖出' else: return '强烈建议卖出' - - def analyze_stock(self, stock_code, market_type='A'): + + def analyze_stock(self, stock_code, market_type='A', stream=False): """分析单个股票""" try: + logger.info(f"开始分析股票: {stock_code}, 市场: {market_type}, 流式模式: {stream}") + # 获取股票数据 + logger.debug(f"获取股票 {stock_code} 数据") df = self.get_stock_data(stock_code, market_type) # 计算技术指标 + logger.debug(f"计算股票 {stock_code} 技术指标") df = self.calculate_indicators(df) # 评分系统 + logger.debug(f"计算股票 {stock_code} 评分") score = self.calculate_score(df) + logger.info(f"股票 {stock_code} 评分结果: {score}") # 获取最新数据 latest = df.iloc[-1] @@ -316,29 +486,92 @@ class StockAnalyzer: 'rsi': latest['RSI'], 'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL', 'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL', - 'recommendation': self.get_recommendation(score), - 'ai_analysis': self.get_ai_analysis(df, stock_code) + 'recommendation': self.get_recommendation(score) } + logger.debug(f"生成股票 {stock_code} 基础报告: {json.dumps(report)[:100]}...") - return report + if stream: + logger.info(f"以流式模式返回股票 {stock_code} 分析结果") + # 先返回基本报告结构 + base_report = dict(report) + base_report['ai_analysis'] = '' + base_report_json = json.dumps(base_report) + logger.debug(f"基础报告JSON: {base_report_json[:100]}...") + stream_logger.info(f"发送基础报告: {base_report_json}") + yield base_report_json + + # 然后流式返回AI分析部分 + logger.debug(f"开始获取股票 {stock_code} 的流式AI分析") + ai_chunks_count = 0 + for ai_chunk in self.get_ai_analysis(df, stock_code, stream=True): + ai_chunks_count += 1 + stream_logger.info(f"股票 {stock_code} 流式块 #{ai_chunks_count}: {ai_chunk}") + yield ai_chunk + logger.info(f"股票 {stock_code} 流式AI分析完成，共发送 {ai_chunks_count} 个块") + else: + logger.info(f"以非流式模式返回股票 {stock_code} 分析结果") + logger.debug(f"开始获取股票 {stock_code} 的AI分析") + report['ai_analysis'] = self.get_ai_analysis(df, stock_code) + logger.debug(f"AI分析结果长度: {len(report['ai_analysis'])}") + return report except Exception as e: - print(f"分析股票时出错: {str(e)}") - raise + error_msg = f"分析股票 {stock_code} 时出错: {str(e)}" + logger.error(error_msg) + logger.exception(e) - def scan_market(self, stock_list, min_score=60, market_type='A'): + if stream: + error_json = json.dumps({'stock_code': stock_code, 'error': error_msg}) + stream_logger.info(f"流式错误输出: {error_json}") + yield error_json + else: + raise + + def scan_market(self, stock_list, min_score=60, market_type='A', stream=False): """扫描市场，寻找符合条件的股票""" - recommendations = [] + logger.info(f"开始扫描市场，股票数量: {len(stock_list)}, 最低分数: {min_score}, 市场: {market_type}, 流式模式: {stream}") - for stock_code in stock_list: - try: - report = self.analyze_stock(stock_code, market_type) - if report['score'] >= min_score: - recommendations.append(report) - except Exception as e: - print(f"分析股票 {stock_code} 时出错: {str(e)}") - continue - - # 按得分排序 - recommendations.sort(key=lambda x: x['score'], reverse=True) - return recommendations + if not stream: + recommendations = [] + + for stock_code in stock_list: + try: + logger.debug(f"分析股票: {stock_code}") + report = self.analyze_stock(stock_code, market_type) + if report['score'] >= min_score: + logger.info(f"股票 {stock_code} 评分 {report['score']} >= {min_score}，添加到推荐列表") + recommendations.append(report) + else: + logger.debug(f"股票 {stock_code} 评分 {report['score']} < {min_score}，不添加到推荐列表") + except Exception as e: + logger.error(f"分析股票 {stock_code} 时出错: {str(e)}") + logger.exception(e) + continue + + # 按得分排序 + recommendations.sort(key=lambda x: x['score'], reverse=True) + logger.info(f"扫描完成，找到 {len(recommendations)} 个推荐股票") + return recommendations + else: + # 流式处理每个股票 + logger.info(f"开始流式扫描 {len(stock_list)} 只股票") + stock_count = 0 + for stock_code in stock_list: + stock_count += 1 + logger.debug(f"流式分析股票 {stock_code} ({stock_count}/{len(stock_list)})") + try: + # 分析单只股票并获取流式结果 + chunk_count = 0 + for chunk in self.analyze_stock(stock_code, market_type, stream=True): + chunk_count += 1 + stream_logger.info(f"股票 {stock_code} 流式块 #{chunk_count}: {chunk}") + yield chunk + logger.debug(f"股票 {stock_code} 流式分析完成，共 {chunk_count} 个块") + except Exception as e: + error_msg = f"分析股票 {stock_code} 时出错: {str(e)}" + logger.error(error_msg) + logger.exception(e) + error_json = json.dumps({'stock_code': stock_code, 'error': error_msg}) + stream_logger.info(f"流式错误输出: {error_json}") + yield error_json + logger.info(f"流式扫描完成，处理了 {stock_count} 只股票") diff --git a/templates/index.html b/templates/index.html index 3074210..74f9a6b 100644 --- a/templates/index.html +++ b/templates/index.html @@ -328,6 +328,7 @@ @@ -575,5 +773,94 @@ }); }); + \ No newline at end of file diff --git a/tests/test_stream.py b/tests/test_stream.py new file mode 100644 index 0000000..e501081 --- /dev/null +++ b/tests/test_stream.py @@ -0,0 +1,137 @@ +import os +import requests +import json +from logger import get_logger, get_stream_logger +from dotenv import load_dotenv + +# 获取日志器 +logger = get_logger() +stream_logger = get_stream_logger() + +def test_api_stream(): + """ + 测试API流式响应功能 + """ + # 加载环境变量 + load_dotenv() + + # 获取API配置 + api_url = os.getenv('API_URL') + api_key = os.getenv('API_KEY') + api_model = os.getenv('API_MODEL', 'gpt-3.5-turbo') + + logger.info(f"开始测试API流式响应，API URL: {api_url}, MODEL: {api_model}") + + # 检查API配置 + if not api_url: + logger.error("API URL未配置，无法进行测试") + return + + if not api_key: + logger.error("API Key未配置，无法进行测试") + return + + # 标准化API URL + if not (api_url.endswith('/chat/completions') or api_url.endswith('/v1/chat/completions')): + if api_url.endswith('/v1'): + api_url = f"{api_url}/chat/completions" + elif api_url.endswith('/'): + api_url = f"{api_url}v1/chat/completions" + else: + api_url = f"{api_url}/v1/chat/completions" + + logger.debug(f"标准化后的API URL: {api_url}") + + # 构建简单的测试提示 + prompt = "这是一个API流式响应测试。请给出一个简短的股票分析样例。" + + # 构建请求头和请求体 + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json" + } + + payload = { + "model": api_model, + "messages": [{"role": "user", "content": prompt}], + "stream": True # 明确设置stream参数为True + } + + logger.debug(f"请求载荷: {json.dumps(payload, indent=2)}") + + try: + logger.info(f"发起流式API请求: {api_url}") + + response = requests.post( + api_url, + headers=headers, + json=payload, + timeout=60, + stream=True + ) + + logger.info(f"API流式响应状态码: {response.status_code}") + logger.debug(f"响应头: {response.headers}") + + if response.status_code == 200: + logger.info("成功获取API流式响应，开始处理") + + buffer = "" + chunk_count = 0 + + for line in response.iter_lines(): + if line: + line_str = line.decode('utf-8') + logger.info(f"原始流式行: {line_str}") + + # 跳过保持连接的空行 + if line_str.strip() == '': + logger.debug("跳过空行") + continue + + # 数据行通常以"data: "开头 + if line_str.startswith('data: '): + data_content = line_str[6:].strip() # 移除 "data: " 前缀并去除前后空格 + logger.info(f"数据内容: {data_content}") + + # 检查是否为流的结束 + if data_content == '[DONE]': + logger.info("收到流结束标记 [DONE]") + break + + try: + # 解析JSON数据 + json_data = json.loads(data_content) + logger.debug(f"JSON结构: {json.dumps(json_data, indent=2)}") + + if 'choices' in json_data: + delta = json_data['choices'][0].get('delta', {}) + content = delta.get('content', '') + + if content: + chunk_count += 1 + buffer += content + logger.info(f"内容片段 #{chunk_count}: {content}") + except json.JSONDecodeError as e: + logger.error(f"JSON解析错误: {e}, 内容: {data_content}") + else: + logger.warning(f"收到非'data:'开头的行: {line_str}") + + logger.info(f"流式处理完成，共收到 {chunk_count} 个内容片段") + logger.info(f"完整内容:\n{buffer}") + + else: + try: + error_response = response.json() + error_text = json.dumps(error_response, indent=2) + except: + error_text = response.text[:500] if response.text else "无响应内容" + + logger.error(f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}") + + except Exception as e: + logger.error(f"测试过程中发生异常: {str(e)}") + logger.exception(e) + +if __name__ == "__main__": + test_api_stream() diff --git a/web_server.py b/web_server.py index dc54012..f1c9f89 100644 --- a/web_server.py +++ b/web_server.py @@ -1,10 +1,15 @@ -from flask import Flask, render_template, request, jsonify +from flask import Flask, render_template, request, jsonify, Response, stream_with_context from stock_analyzer import StockAnalyzer from us_stock_service import USStockService import threading import os import traceback import requests +from logger import get_logger, get_stream_logger + +# 获取日志器 +logger = get_logger() +stream_logger = get_stream_logger() app = Flask(__name__) analyzer = StockAnalyzer() @@ -25,15 +30,20 @@ def index(): @app.route('/analyze', methods=['POST']) def analyze(): try: + logger.info("开始处理分析请求") data = request.json stock_codes = data.get('stock_codes', []) market_type = data.get('market_type', 'A') + logger.debug(f"接收到分析请求: stock_codes={stock_codes}, market_type={market_type}") + # 获取自定义API配置 custom_api_url = data.get('api_url') custom_api_key = data.get('api_key') custom_api_model = data.get('api_model') + 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'}") + # 创建新的分析器实例，使用自定义配置 custom_analyzer = StockAnalyzer( custom_api_url=custom_api_url, @@ -42,26 +52,50 @@ def analyze(): ) if not stock_codes: + logger.warning("未提供股票代码") return jsonify({'error': '请输入代码'}), 400 + + # 使用流式响应 + def generate(): + if len(stock_codes) == 1: + # 单个股票分析流式处理 + stock_code = stock_codes[0].strip() + logger.info(f"开始单股流式分析: {stock_code}") + + stream_logger.info(f"初始化单股分析流: {stock_code}") + init_message = f'{{"stream_type": "single", "stock_code": "{stock_code}"}}\n' + stream_logger.info(f"发送初始化消息: {init_message}") + yield init_message + + for chunk in custom_analyzer.analyze_stock(stock_code, market_type, stream=True): + stream_logger.info(f"流式输出块: {chunk}") + yield chunk + '\n' + else: + # 批量分析流式处理 + logger.info(f"开始批量流式分析: {stock_codes}") + + stream_logger.info(f"初始化批量分析流: {stock_codes}") + init_message = f'{{"stream_type": "batch", "stock_codes": {stock_codes}}}\n' + stream_logger.info(f"发送初始化消息: {init_message}") + yield init_message + + for chunk in custom_analyzer.scan_market( + [code.strip() for code in stock_codes], + min_score=0, + market_type=market_type, + stream=True + ): + stream_logger.info(f"流式输出块: {chunk}") + yield chunk + '\n' + + logger.info("成功创建流式响应生成器") + return Response(stream_with_context(generate()), mimetype='application/json') - results = [] - for stock_code in stock_codes: - try: - # 使用自定义配置的分析器 - result = custom_analyzer.analyze_stock(stock_code.strip(), market_type) - results.append(result) - except Exception as e: - print(f"分析股票 {stock_code} 失败: {str(e)}") - print(f"详细错误: {traceback.format_exc()}") - results.append({ - 'code': stock_code, - 'error': f"分析失败: {str(e)}" - }) - - return jsonify({'results': results}) except Exception as e: - print(f"分析股票时出错: {str(e)}") - return jsonify({'error': str(e)}), 500 + error_msg = f"分析股票时出错: {str(e)}" + logger.error(error_msg) + logger.exception(e) + return jsonify({'error': error_msg}), 500 @app.route('/search_us_stocks', methods=['GET']) def search_us_stocks(): @@ -81,15 +115,20 @@ def search_us_stocks(): def test_api_connection(): """测试API连接""" try: + logger.info("开始测试API连接") data = request.json api_url = data.get('api_url') api_key = data.get('api_key') api_model = data.get('api_model') + 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'}") + if not api_url: + logger.warning("未提供API URL") return jsonify({'error': '请提供API URL'}), 400 if not api_key: + logger.warning("未提供API Key") return jsonify({'error': '请提供API Key'}), 400 # 构建API URL @@ -101,6 +140,8 @@ def test_api_connection(): test_url = f"{api_url}chat/completions" else: test_url = f"{api_url}/v1/chat/completions" + + logger.debug(f"完整API测试URL: {test_url}") # 发送测试请求 response = requests.post( @@ -121,15 +162,21 @@ def test_api_connection(): # 检查响应 if response.status_code == 200: + logger.info(f"API连接测试成功: {response.status_code}") return jsonify({'success': True, 'message': '连接成功'}) else: error_message = response.json().get('error', {}).get('message', '未知错误') + logger.warning(f"API连接测试失败: {response.status_code} - {error_message}") return jsonify({'success': False, 'message': f'连接失败: {error_message}', 'status_code': response.status_code}), 400 except requests.exceptions.RequestException as e: + logger.error(f"API连接请求错误: {str(e)}") return jsonify({'success': False, 'message': f'请求错误: {str(e)}'}), 400 except Exception as e: + logger.error(f"测试API连接时出错: {str(e)}") + logger.exception(e) return jsonify({'success': False, 'message': f'测试连接时出错: {str(e)}'}), 500 if __name__ == '__main__': + logger.info("股票分析系统启动") app.run(host='0.0.0.0', port=8888, debug=True) \ No newline at end of file