import os import tempfile import base64 import re 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': '📈 技术分析师', 'fundamental': '📊 基本面分析师', 'fund_flow': '💰 资金面分析师', 'risk_management': '⚠️ 风险管理师', 'market_sentiment': '📈 市场情绪分析师' } for agent_key, agent_name in agent_names.items(): if agent_key in agents_results: agent_result = agents_results[agent_key] if isinstance(agent_result, dict): analysis_text = agent_result.get('analysis', '暂无分析') else: analysis_text = str(agent_result) markdown_content += f""" ### {agent_name} {analysis_text} --- """ # 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 """ # 处理最终决策的显示 if isinstance(final_decision, dict) and "decision_text" not in final_decision: # JSON格式的决策 markdown_content += f""" **投资评级**: {final_decision.get('rating', '未知')} **目标价位**: {final_decision.get('target_price', 'N/A')} **操作建议**: {final_decision.get('operation_advice', '暂无建议')} **进场区间**: {final_decision.get('entry_range', 'N/A')} **止盈位**: {final_decision.get('take_profit', 'N/A')} **止损位**: {final_decision.get('stop_loss', 'N/A')} **持有周期**: {final_decision.get('holding_period', 'N/A')} **仓位建议**: {final_decision.get('position_size', 'N/A')} **信心度**: {final_decision.get('confidence_level', 'N/A')}/10 **风险提示**: {final_decision.get('risk_warning', '无')} """ else: # 文本格式的决策 decision_text = final_decision.get('decision_text', str(final_decision)) markdown_content += decision_text markdown_content += """ --- ## 📝 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 create_html_download_link(content, filename, link_text): """创建HTML下载链接""" b64 = base64.b64encode(content.encode('utf-8')).decode() href = f'{link_text}' return href def generate_html_content(markdown_content): """将Markdown转换为HTML""" html_content = f""" AI股票分析报告
""" # 简单的Markdown到HTML转换 html_body = markdown_content html_body = html_body.replace('\n# ', '\n

').replace('\n## ', '\n

').replace('\n### ', '\n

') html_body = html_body.replace('# ', '

').replace('## ', '

').replace('### ', '

') html_body = html_body.replace('\n---\n', '\n
\n') # 处理粗体文本 html_body = re.sub(r'\*\*(.*?)\*\*', r'\1', 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 and line.strip().startswith('|'): processed_lines.append('') in_table = True cells = [cell.strip() for cell in line.split('|')[1:-1]] processed_lines.append('') for cell in cells: processed_lines.append(f'') processed_lines.append('') elif '|' in line and in_table: if '---' not in line: cells = [cell.strip() for cell in line.split('|')[1:-1]] processed_lines.append('') for cell in cells: processed_lines.append(f'') processed_lines.append('') elif in_table and '|' not in line: processed_lines.append('
{cell}
{cell}
') processed_lines.append(line) in_table = False else: processed_lines.append(line) if in_table: processed_lines.append('') html_body = '\n'.join(processed_lines) # 处理段落 paragraphs = html_body.split('\n\n') processed_paragraphs = [] for para in paragraphs: para = para.strip() if para and not para.startswith('<') and not para.startswith('---'): processed_paragraphs.append(f'

{para}

') else: processed_paragraphs.append(para) html_body = '\n'.join(processed_paragraphs) html_content += html_body + """

""" return html_content def display_pdf_export_section(stock_info, agents_results, discussion_result, final_decision): """显示PDF导出区域 - 修复报告生成问题""" st.markdown("---") st.markdown("## 📄 导出分析报告") col1, col2, col3 = st.columns([1, 2, 1]) with col2: # 生成报告按钮 import uuid import time button_key = f"generate_report_btn_{int(time.time())}_{uuid.uuid4().hex[:8]}" if st.button("📊 生成并下载报告", type="primary", use_container_width=True, key=button_key): with st.spinner("正在生成报告..."): try: # 生成Markdown内容 markdown_content = generate_markdown_report(stock_info, agents_results, discussion_result, final_decision) # 生成HTML内容 html_content = generate_html_content(markdown_content) # 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}" st.success("✅ 报告生成成功！") st.balloons() # 立即显示下载链接 st.markdown("### 📄 报告下载") # 创建下载链接 md_link = create_download_link( markdown_content, f"{filename}.md", "📝 下载Markdown报告" ) html_link = create_html_download_link( html_content, f"{filename}.html", "🌐 下载HTML报告" ) # 显示下载链接 st.markdown(f"""
{md_link} {html_link}
""", unsafe_allow_html=True) st.info("💡 提示：点击上方按钮即可下载对应格式的报告文件") except Exception as e: st.error(f"❌ 生成报告时出错: {str(e)}") import traceback st.error(f"详细错误信息: {traceback.format_exc()}")