戒酒的李白
341 lines
11 KiB
Python
341 lines
11 KiB
Python
"""
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HTML生成节点
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将整合后的内容转换为美观的HTML报告
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"""
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import json
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from datetime import datetime
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from typing import Dict, Any
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from .base_node import StateMutationNode
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from ..llms.base import BaseLLM
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from ..state.state import ReportState
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from ..prompts import SYSTEM_PROMPT_HTML_GENERATION
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# 不再需要text_processing依赖
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class HTMLGenerationNode(StateMutationNode):
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"""HTML生成处理节点"""
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def __init__(self, llm_client: BaseLLM):
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"""
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初始化HTML生成节点
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Args:
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llm_client: LLM客户端
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"""
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super().__init__(llm_client, "HTMLGenerationNode")
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def run(self, input_data: Dict[str, Any], **kwargs) -> str:
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"""
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执行HTML生成
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Args:
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input_data: 包含报告数据的字典
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- query: 原始查询
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- query_engine_report: QueryEngine报告内容
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- media_engine_report: MediaEngine报告内容
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- insight_engine_report: InsightEngine报告内容
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- forum_logs: 论坛日志内容
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- selected_template: 选择的模板内容
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Returns:
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生成的HTML内容
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"""
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self.log_info("开始生成HTML报告...")
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try:
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# 准备LLM输入数据
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llm_input = {
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"query": input_data.get('query', ''),
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"query_engine_report": input_data.get('query_engine_report', ''),
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"media_engine_report": input_data.get('media_engine_report', ''),
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"insight_engine_report": input_data.get('insight_engine_report', ''),
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"forum_logs": input_data.get('forum_logs', ''),
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"selected_template": input_data.get('selected_template', '')
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}
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# 转换为JSON格式
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message = json.dumps(llm_input, ensure_ascii=False, indent=2)
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# 调用LLM生成HTML
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response = self.llm_client.invoke(SYSTEM_PROMPT_HTML_GENERATION, message)
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# 处理响应
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processed_response = self.process_output(response)
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self.log_info("HTML报告生成完成")
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return processed_response
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except Exception as e:
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self.log_error(f"HTML生成失败: {str(e)}")
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# 返回备用HTML
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return self._generate_fallback_html(input_data)
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def mutate_state(self, input_data: Dict[str, Any], state: ReportState, **kwargs) -> ReportState:
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"""
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修改报告状态，添加生成的HTML内容
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Args:
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input_data: 输入数据
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state: 当前报告状态
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**kwargs: 额外参数
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Returns:
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更新后的报告状态
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"""
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# 生成HTML
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html_content = self.run(input_data, **kwargs)
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# 更新状态
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state.html_content = html_content
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state.mark_completed()
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return state
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def process_output(self, output: str) -> str:
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"""
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处理LLM输出，提取HTML内容
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Args:
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output: LLM原始输出
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Returns:
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清理后的HTML内容
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"""
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try:
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self.log_info(f"处理LLM原始输出，长度: {len(output)} 字符")
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html_content = ""
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# 尝试解析JSON响应
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try:
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result = json.loads(output)
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html_content = result.get('html_content', '')
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self.log_info("成功从JSON中提取html_content")
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except json.JSONDecodeError:
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self.log_info("不是JSON格式，直接使用原始输出")
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html_content = output
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# 如果还是没有内容，尝试其他提取方法
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if not html_content.strip():
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# 查找HTML标记
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if '<!DOCTYPE html>' in output:
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start_idx = output.find('<!DOCTYPE html>')
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html_content = output[start_idx:]
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elif '<html' in output:
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start_idx = output.find('<html')
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html_content = output[start_idx:]
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else:
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html_content = output
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# 清理markdown代码块标记
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if html_content.startswith('```html'):
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html_content = html_content.replace('```html', '').replace('```', '').strip()
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elif html_content.startswith('```'):
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html_content = html_content.replace('```', '').strip()
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# 处理转义字符
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html_content = html_content.replace('\\n', '\n')
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html_content = html_content.replace('\\t', '\t')
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html_content = html_content.replace('\\r', '\r')
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html_content = html_content.replace('\\"', '"')
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html_content = html_content.replace("\\'", "'")
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# 验证HTML内容
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if not html_content.strip():
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raise ValueError("生成的HTML内容为空")
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# 确保HTML有基本结构
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if not html_content.strip().startswith('<!DOCTYPE') and not html_content.strip().startswith('<html'):
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self.log_info("HTML缺少基本结构，添加包装")
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html_content = f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>智能舆情分析报告</title>
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</head>
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<body>
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{html_content}
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</body>
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</html>"""
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self.log_info(f"HTML处理完成，最终长度: {len(html_content)} 字符")
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return html_content.strip()
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except Exception as e:
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self.log_error(f"处理HTML输出失败: {str(e)}")
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return self._generate_error_html(str(e))
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def _generate_fallback_html(self, input_data: Dict[str, Any]) -> str:
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"""
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生成备用HTML报告（当LLM失败时使用）
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Args:
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input_data: 输入数据
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Returns:
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备用HTML内容
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"""
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self.log_info("使用备用HTML生成方法")
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query = input_data.get('query', '智能舆情分析报告')
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query_report = input_data.get('query_engine_report', '')
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media_report = input_data.get('media_engine_report', '')
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insight_report = input_data.get('insight_engine_report', '')
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forum_logs = input_data.get('forum_logs', '')
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generation_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
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html_content = f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>{query} - 智能舆情分析报告</title>
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<style>
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body {{
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
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line-height: 1.6;
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color: #333;
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max-width: 1200px;
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margin: 0 auto;
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padding: 20px;
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background: #f5f5f5;
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}}
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.container {{
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background: white;
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padding: 40px;
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border-radius: 8px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}}
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h1 {{
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color: #2c3e50;
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border-bottom: 3px solid #3498db;
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padding-bottom: 10px;
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}}
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h2 {{
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color: #34495e;
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margin-top: 30px;
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margin-bottom: 15px;
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}}
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.section {{
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margin-bottom: 30px;
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padding: 20px;
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border-left: 4px solid #3498db;
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background: #f8f9fa;
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}}
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.meta {{
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background: #e9ecef;
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padding: 15px;
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border-radius: 5px;
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margin-bottom: 20px;
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}}
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.footer {{
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margin-top: 40px;
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padding-top: 20px;
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border-top: 1px solid #eee;
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text-align: center;
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color: #666;
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}}
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pre {{
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background: #f4f4f4;
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padding: 15px;
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border-radius: 5px;
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overflow-x: auto;
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white-space: pre-wrap;
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}}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>{query}</h1>
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<div class="meta">
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<strong>报告生成时间:</strong> {generation_time}<br>
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<strong>数据来源:</strong> QueryEngine、MediaEngine、InsightEngine、ForumEngine<br>
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<strong>报告类型:</strong> 综合舆情分析报告
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</div>
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<h2>执行摘要</h2>
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<div class="section">
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本报告整合了多个分析引擎的研究结果，为您提供全面的舆情分析洞察。
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通过对查询主题"{query}"的深度分析，我们从多个维度展现了当前的舆情态势。
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</div>
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{f'<h2>QueryEngine分析结果</h2><div class="section"><pre>{query_report}</pre></div>' if query_report else ''}
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{f'<h2>MediaEngine分析结果</h2><div class="section"><pre>{media_report}</pre></div>' if media_report else ''}
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{f'<h2>InsightEngine分析结果</h2><div class="section"><pre>{insight_report}</pre></div>' if insight_report else ''}
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{f'<h2>论坛监控数据</h2><div class="section"><pre>{forum_logs}</pre></div>' if forum_logs else ''}
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<h2>综合结论</h2>
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<div class="section">
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基于多个分析引擎的综合研究，我们对"{query}"主题进行了全面分析。
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各引擎从不同角度提供了深入洞察，为决策提供了重要参考。
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</div>
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<div class="footer">
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<p>本报告由智能舆情分析平台自动生成</p>
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<p>ReportEngine v1.0 | 生成时间: {generation_time}</p>
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</div>
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</div>
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</body>
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</html>"""
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return html_content
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def _generate_error_html(self, error_message: str) -> str:
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"""
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生成错误HTML页面
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Args:
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error_message: 错误信息
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Returns:
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错误HTML内容
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"""
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return f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>报告生成失败</title>
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<style>
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body {{
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font-family: Arial, sans-serif;
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text-align: center;
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padding: 50px;
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background: #f8f9fa;
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}}
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.error-container {{
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background: white;
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padding: 40px;
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border-radius: 8px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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max-width: 600px;
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margin: 0 auto;
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}}
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.error-title {{
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color: #e74c3c;
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font-size: 24px;
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margin-bottom: 20px;
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}}
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.error-message {{
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color: #666;
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margin-bottom: 20px;
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}}
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</style>
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</head>
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<body>
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<div class="error-container">
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<div class="error-title">报告生成失败</div>
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<div class="error-message">错误信息: {error_message}</div>
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<p>请检查输入数据或稍后重试。</p>
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</div>
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</body>
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</html>"""
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