import os import tempfile import base64 from datetime import datetime import streamlit as st def generate_markdown_report(stock_info, agents_results, discussion_result, final_decision): """生成Markdown格式的分析报告""" # 获取当前时间 current_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S") markdown_content = f""" # AI股票分析报告 **生成时间**: {current_time} --- ## 📊 股票基本信息 | 项目 | 值 | |------|-----| | **股票代码** | {stock_info.get('symbol', 'N/A')} | | **股票名称** | {stock_info.get('name', 'N/A')} | | **当前价格** | {stock_info.get('current_price', 'N/A')} | | **涨跌幅** | {stock_info.get('change_percent', 'N/A')}% | | **市盈率(PE)** | {stock_info.get('pe_ratio', 'N/A')} | | **市净率(PB)** | {stock_info.get('pb_ratio', 'N/A')} | | **市值** | {stock_info.get('market_cap', 'N/A')} | | **市场** | {stock_info.get('market', 'N/A')} | | **交易所** | {stock_info.get('exchange', 'N/A')} | --- ## 🔍 各分析师详细分析 """ # 添加各分析师的分析结果 agent_names = { 'technical_analyst': '📈 技术分析师', 'fundamental_analyst': '📊 基本面分析师', 'fund_analyst': '💰 资金面分析师', 'risk_analyst': '⚠️ 风险管理师', 'sentiment_analyst': '📈 市场情绪分析师' } for agent_key, agent_name in agent_names.items(): if agent_key in agents_results: markdown_content += f""" ### {agent_name} {agents_results[agent_key]} --- """ # 添加团队讨论结果 markdown_content += f""" ## 🤝 团队综合讨论 {discussion_result} --- ## 📋 最终投资决策 {final_decision} --- ## 📝 免责声明 本报告由AI系统生成,仅供参考,不构成投资建议。投资有风险,入市需谨慎。请在做出投资决策前咨询专业的投资顾问。 --- *报告生成时间: {current_time}* *AI股票分析系统 v1.0* """ return markdown_content def create_download_link(content, filename, link_text): """创建下载链接""" b64 = base64.b64encode(content.encode()).decode() href = f'{link_text}' return href def generate_pdf_report(stock_info, agents_results, discussion_result, final_decision): """生成PDF报告并提供下载""" try: # 生成Markdown内容 markdown_content = generate_markdown_report(stock_info, agents_results, discussion_result, final_decision) # 创建临时文件 with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as temp_md: temp_md.write(markdown_content) temp_md_path = temp_md.name # 生成文件名 stock_symbol = stock_info.get('symbol', 'unknown') timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"股票分析报告_{stock_symbol}_{timestamp}" # 提供Markdown下载 st.markdown("### 📄 报告下载") # Markdown下载链接 md_download_link = create_download_link( markdown_content, f"{filename}.md", "📝 下载Markdown报告" ) st.markdown(md_download_link, unsafe_allow_html=True) # 提供HTML预览和下载 html_content = f"""
') html_body = f"
{html_body}
" # 处理表格 lines = html_body.split('\n') in_table = False processed_lines = [] for line in lines: if '|' in line and not in_table: processed_lines.append('| {cell} | ') processed_lines.append('
|---|
| {cell} | ') processed_lines.append('