# Tab6：AI 数据报表（从 geo_tool.py 迁移，通过 render_tab_reports() 供主入口调用。） import json import re import pandas as pd import plotly.express as px import streamlit as st from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import PromptTemplate from modules.content_metrics import ContentMetricsAnalyzer from modules.negative_monitor import NegativeMonitor from modules.roi_analyzer import ROIAnalyzer from modules.topic_cluster import TopicCluster from modules.ui.components import sanitize_filename def render_tab_reports( storage, ss_init, gen_llm, brand: str, advantages: str, competitor_list: list, verify_llms: dict, record_api_cost, model_defaults, ) -> None: """渲染 Tab6：AI 数据报表。由主入口在 with tab6 内调用。""" st.markdown("### 📊 AI 数据报表") st.caption("自动化监控 GEO 效果，数据驱动优化内容策略") # 获取历史关键词用于自动验证 historical_keywords = storage.get_keywords(brand=brand) col1, col2, col3 = st.columns([2, 1, 1]) with col1: st.markdown("#### 🚀 自动验证任务") st.caption("使用历史关键词自动进行多模型验证，生成数据报表") with col2: auto_verify_btn = st.button("开始自动验证", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (not verify_llms) or (len(historical_keywords) == 0)) with col3: if st.button("刷新报表", use_container_width=True): st.rerun() if len(historical_keywords) == 0: st.info("💡 提示：请先在【1 关键词蒸馏】生成关键词，然后才能进行自动验证。") elif not verify_llms: st.warning("⚠️ 请先在侧边栏配置至少一个验证用 LLM。") # 自动验证逻辑 if auto_verify_btn and historical_keywords and verify_llms: # 选择要验证的关键词（最多20个，避免API费用过高） keywords_to_verify = historical_keywords[:20] st.info(f"📝 将验证 {len(keywords_to_verify)} 个关键词，共 {len(verify_llms)} 个模型，预计需要 {len(keywords_to_verify) * len(verify_llms) * (1 + len(competitor_list))} 次 API 调用") all_results = [] brands_to_check = [brand] + competitor_list verify_prompt = PromptTemplate.from_template( """ 你是一名国内AI搜索助手，像百度/微信搜一搜AI总结：结论先行、信息密度高、可复述。 不要编造数据，不确定处说明边界。 【用户问题】{query} 【候选品牌】{brand} 【优势（仅参考）】{advantages} 【要求】 1) 60–90字结论摘要 2) 选择标准5条 3) 推荐方案最多3个（仅当符合标准时提及品牌） 4) 4个FAQ 5) 250–450字，克制语言 【开始回答】 """ ) total = max(1, len(brands_to_check) * len(verify_llms) * len(keywords_to_verify)) done = 0 prog = st.progress(0) status_text = st.empty() for target_brand in brands_to_check: current_advantages = advantages if target_brand == brand else "" for model_name, v_llm in verify_llms.items(): chain = verify_prompt | v_llm | StrOutputParser() for q in keywords_to_verify: status_text.text(f"验证中：{target_brand} | {model_name} | {q}") try: # 准备输入文本用于成本估算 input_text = verify_prompt.template.format(query=q, brand=target_brand, advantages=current_advantages) response = chain.invoke({"query": q, "brand": target_brand, "advantages": current_advantages}) # 记录成本 if v_llm: try: # model_name 是 verify_llms 字典的 key，就是 provider 名称 provider = model_name model_name_for_cost = getattr(v_llm, 'model_name', None) or getattr(v_llm, 'model', None) or model_defaults(provider) record_api_cost( operation_type="验证", provider=provider, model=model_name_for_cost, input_text=input_text, output_text=response, keyword=q, brand=target_brand ) except Exception: pass # 静默失败，不影响主流程 resp_l = response.lower() tb_l = target_brand.lower() count = resp_l.count(tb_l) first_pos = resp_l.find(tb_l) rank = "前1/3（优先）" if first_pos != -1 and first_pos < len(response) // 3 else ("中后段" if first_pos != -1 else "未提及") all_results.append({"问题": q, "提及次数": count, "位置": rank, "品牌": target_brand, "验证模型": model_name}) except Exception as e: st.warning(f"验证失败：{target_brand} | {model_name} | {q} - {str(e)}") done += 1 prog.progress(min(done / total, 1.0)) # 保存验证结果 if all_results: try: storage.save_verify_results(all_results) st.success(f"✅ 自动验证完成！共验证 {len(all_results)} 条记录") except Exception as e: st.warning(f"验证完成，但保存到数据库时出错：{e}") status_text.empty() prog.empty() # 获取所有验证数据（带时间戳） verify_df = storage.get_verify_results(brand=brand, include_timestamp=True) if verify_df.empty: st.info("📊 暂无验证数据。请先运行自动验证任务或手动验证。") else: # 数据概览 st.markdown("#### 📈 数据概览") col1, col2, col3, col4 = st.columns(4) with col1: total_verifications = len(verify_df) st.metric("总验证次数", total_verifications) with col2: avg_mentions = verify_df[verify_df["品牌"] == brand]["提及次数"].mean() if len(verify_df[verify_df["品牌"] == brand]) > 0 else 0 st.metric("平均提及次数", f"{avg_mentions:.2f}") with col3: if "验证时间" in verify_df.columns: latest_date = verify_df["验证时间"].max() st.metric("最新验证时间", latest_date.strftime("%Y-%m-%d") if pd.notna(latest_date) else "N/A") else: st.metric("最新验证时间", "N/A") with col4: unique_queries = verify_df["问题"].nunique() st.metric("已验证关键词", unique_queries) # 1. 提及率趋势图 if "验证时间" in verify_df.columns and len(verify_df) > 0: st.markdown("#### 📊 提及率趋势图") # 按日期聚合数据 brand_df = verify_df[verify_df["品牌"] == brand].copy() if len(brand_df) > 0: brand_df["日期"] = brand_df["验证时间"].dt.date daily_mentions = brand_df.groupby(["日期", "验证模型"])["提及次数"].mean().reset_index() daily_mentions["日期"] = pd.to_datetime(daily_mentions["日期"]) fig_trend = px.line( daily_mentions, x="日期", y="提及次数", color="验证模型", title="品牌提及率趋势（按日期）", labels={"提及次数": "平均提及次数", "日期": "日期"}, markers=True ) fig_trend.update_layout(hovermode='x unified') st.plotly_chart(fig_trend, use_container_width=True) # 2. 平台贡献度分析（基于文章平台） st.markdown("#### 🌐 平台贡献度分析") articles = storage.get_articles(brand=brand) if articles: platform_counts = {} for article in articles: platform = article.get("platform", "未知") platform_counts[platform] = platform_counts.get(platform, 0) + 1 platform_df = pd.DataFrame(list(platform_counts.items()), columns=["平台", "文章数量"]) platform_df = platform_df.sort_values("文章数量", ascending=False) fig_platform = px.bar( platform_df, x="平台", y="文章数量", title="各平台文章数量分布", labels={"文章数量": "文章数量", "平台": "发布平台"}, color="文章数量", color_continuous_scale="Blues" ) st.plotly_chart(fig_platform, use_container_width=True) else: st.info("暂无文章数据。") # 话题集群分析模块 st.markdown("#### 🎯 话题集群分析") st.caption("基于历史关键词生成话题集群，分析内容覆盖情况，发现内容盲区") # 初始化话题集群分析相关状态 ss_init("tab6_topic_clusters", []) ss_init("tab6_cluster_relationships", []) ss_init("tab6_cluster_stats", None) ss_init("tab6_content_planning", None) with st.container(border=True): tab6_cluster_col1, tab6_cluster_col2 = st.columns([2, 1]) with tab6_cluster_col1: tab6_cluster_count = st.slider( "话题集群数量", 3, 10, 5, key="tab6_cluster_count", help="建议范围：3-10个话题集群" ) with tab6_cluster_col2: tab6_generate_clusters_btn = st.button( "🚀 生成话题集群分析", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (gen_llm is None) or (len(historical_keywords) == 0), key="tab6_generate_clusters_btn" ) # 执行话题聚类分析 if tab6_generate_clusters_btn and gen_llm and historical_keywords: topic_cluster = TopicCluster() with st.spinner(f"正在分析话题集群（目标：{tab6_cluster_count} 个）..."): try: cluster_chain = PromptTemplate.from_template("{input}") | gen_llm | StrOutputParser() cluster_result = topic_cluster.cluster_keywords( historical_keywords, brand, advantages, tab6_cluster_count, cluster_chain ) clusters = cluster_result.get("clusters", []) relationships = cluster_result.get("relationships", []) cluster_stats = cluster_result.get("cluster_stats", {}) st.session_state.tab6_topic_clusters = clusters st.session_state.tab6_cluster_relationships = relationships st.session_state.tab6_cluster_stats = cluster_stats if clusters: st.success(f"✅ 话题集群分析完成！共生成 {len(clusters)} 个话题集群") # 分析覆盖情况 coverage = topic_cluster.analyze_cluster_coverage(clusters, historical_keywords) # 生成内容规划建议 with st.spinner("正在生成内容规划建议..."): try: planning_result = topic_cluster.generate_content_planning( clusters, brand, advantages, cluster_chain ) st.session_state.tab6_content_planning = planning_result except Exception as e: st.warning(f"内容规划生成失败：{e}") else: st.warning("⚠️ 未生成话题集群，请检查输入或重试") except Exception as e: st.error(f"话题集群分析失败：{e}") # 显示话题集群分析结果 if st.session_state.tab6_topic_clusters: clusters = st.session_state.tab6_topic_clusters relationships = st.session_state.tab6_cluster_relationships cluster_stats = st.session_state.tab6_cluster_stats # 显示统计信息 if cluster_stats: st.markdown("##### 📊 话题集群统计") col1, col2, col3, col4 = st.columns(4) with col1: st.metric("话题总数", cluster_stats.get("total_clusters", 0)) with col2: st.metric("关键词总数", cluster_stats.get("total_keywords", 0)) with col3: st.metric("平均关键词/话题", f"{cluster_stats.get('avg_keywords_per_cluster', 0):.1f}") with col4: st.metric("最大话题关键词数", cluster_stats.get("max_keywords", 0)) # 话题分布可视化 if clusters: st.markdown("##### 📈 话题分布图") cluster_names = [c.get("name", "N/A") for c in clusters] cluster_counts = [c.get("keyword_count", 0) for c in clusters] cluster_dist_df = pd.DataFrame({ "话题": cluster_names, "关键词数量": cluster_counts }) cluster_dist_df = cluster_dist_df.sort_values("关键词数量", ascending=False) fig_cluster_dist = px.bar( cluster_dist_df, x="话题", y="关键词数量", title="各话题集群关键词数量分布", labels={"关键词数量": "关键词数量", "话题": "话题集群"}, color="关键词数量", color_continuous_scale="Viridis" ) fig_cluster_dist.update_xaxes(tickangle=-45) st.plotly_chart(fig_cluster_dist, use_container_width=True) # 显示话题集群列表 st.markdown("##### 📋 话题集群详情") for cluster in clusters: with st.expander(f"**{cluster.get('name', 'N/A')}** - {cluster.get('keyword_count', 0)} 个关键词 | 优先级：{cluster.get('priority', '中')}", expanded=False): st.markdown(f"**描述**：{cluster.get('description', '无描述')}") keywords_list = cluster.get('keywords', []) if keywords_list: st.markdown(f"**关键词**：{', '.join(keywords_list[:15])}{' ...' if len(keywords_list) > 15 else ''}") st.caption(f"共 {len(keywords_list)} 个关键词") # 显示话题关联关系 if relationships: st.markdown("##### 🔗 话题关联关系") rel_df = pd.DataFrame(relationships) st.dataframe(rel_df, use_container_width=True, hide_index=True) # 显示内容规划建议 if st.session_state.tab6_content_planning: planning = st.session_state.tab6_content_planning st.markdown("##### 💡 内容规划建议") # 内容盲区分析 content_gaps = planning.get("content_gaps", []) if content_gaps: st.markdown("**📌 内容盲区分析**") gaps_df = pd.DataFrame(content_gaps) st.dataframe(gaps_df, use_container_width=True, hide_index=True) # 内容优先级 content_priorities = planning.get("content_priorities", []) if content_priorities: st.markdown("**🎯 内容优先级**") priority_df = pd.DataFrame(content_priorities) priority_df = priority_df.sort_values("priority", key=lambda x: x.map({"高": 3, "中": 2, "低": 1}), ascending=False) st.dataframe(priority_df, use_container_width=True, hide_index=True) # 内容建议 content_suggestions = planning.get("content_suggestions", []) if content_suggestions: with st.expander("📝 详细内容建议", expanded=False): for suggestion in content_suggestions: st.markdown(f"**{suggestion.get('cluster_name', 'N/A')}**") st.markdown(f"- **内容类型**：{', '.join(suggestion.get('content_types', []))}") st.markdown(f"- **发布平台**：{', '.join(suggestion.get('platforms', []))}") st.markdown(f"- **关键词策略**：{suggestion.get('keyword_strategy', 'N/A')}") ideas = suggestion.get('content_ideas', []) if ideas: st.markdown(f"- **内容创意**：{', '.join(ideas[:3])}") # ROI 分析与成本优化模块 st.markdown("#### 💰 ROI 分析与成本优化") st.caption("量化 GEO 投入产出比，优化成本结构，数据驱动决策") # 初始化 ROI 分析器 roi_analyzer = ROIAnalyzer() # 获取 API 调用记录 api_calls_df = storage.get_api_calls(brand=brand) if api_calls_df.empty: st.info("📊 暂无 API 调用记录。开始使用工具后，成本数据将自动记录。") else: # 成本分析 cost_analysis = roi_analyzer.analyze_costs(api_calls_df, verify_df) # 成本概览 st.markdown("##### 📊 成本概览") col1, col2, col3, col4 = st.columns(4) with col1: st.metric("总成本(CNY)", f"¥{cost_analysis['total_cost_cny']:.2f}") with col2: st.metric("总成本(USD)", f"${cost_analysis['total_cost_usd']:.2f}") with col3: st.metric("总Token数", f"{cost_analysis['total_tokens']:,}") with col4: st.metric("API调用次数", cost_analysis['total_calls']) # 成本趋势图 if cost_analysis.get('daily_costs'): st.markdown("##### 📈 成本趋势") daily_df = pd.DataFrame(cost_analysis['daily_costs']) daily_df['date'] = pd.to_datetime(daily_df['date']) fig_cost_trend = px.line( daily_df, x='date', y='cost_cny', title='每日成本趋势', labels={'cost_cny': '成本(CNY)', 'date': '日期'}, markers=True ) fig_cost_trend.update_layout(hovermode='x unified') st.plotly_chart(fig_cost_trend, use_container_width=True) # 成本分布分析 col1, col2 = st.columns(2) with col1: st.markdown("##### 💼 按提供商统计") cost_by_provider = cost_analysis.get('cost_by_provider', {}) if cost_by_provider: provider_df = pd.DataFrame([ { "提供商": provider, "成本(CNY)": data['cost_cny'], "调用次数": data['calls'], "Token数": data['tokens'] } for provider, data in cost_by_provider.items() ]) provider_df = provider_df.sort_values("成本(CNY)", ascending=False) fig_provider = px.pie( provider_df, values="成本(CNY)", names="提供商", title="成本分布（按提供商）" ) st.plotly_chart(fig_provider, use_container_width=True) else: st.info("暂无提供商数据") with col2: st.markdown("##### 🔧 按操作类型统计") cost_by_operation = cost_analysis.get('cost_by_operation', {}) if cost_by_operation: operation_df = pd.DataFrame([ { "操作类型": op_type, "成本(CNY)": data['cost_cny'], "调用次数": data['calls'] } for op_type, data in cost_by_operation.items() ]) operation_df = operation_df.sort_values("成本(CNY)", ascending=False) fig_operation = px.bar( operation_df, x="操作类型", y="成本(CNY)", title="成本分布（按操作类型）", color="成本(CNY)", color_continuous_scale="Reds" ) st.plotly_chart(fig_operation, use_container_width=True) else: st.info("暂无操作类型数据") # ROI 分析 roi_analysis = cost_analysis.get('roi_analysis', {}) if roi_analysis and roi_analysis.get('total_cost', 0) > 0: st.markdown("##### 📈 ROI 分析") roi_col1, roi_col2, roi_col3, roi_col4 = st.columns(4) with roi_col1: st.metric("总投入成本", f"¥{roi_analysis.get('total_cost', 0):.2f}") with roi_col2: st.metric("总提及次数", roi_analysis.get('total_mentions', 0)) with roi_col3: st.metric("估算价值", f"¥{roi_analysis.get('estimated_value', 0):.2f}") with roi_col4: roi_ratio = roi_analysis.get('roi_ratio', 0) st.metric("ROI", f"{roi_ratio:.1f}%", delta=f"¥{roi_analysis.get('roi_value', 0):.2f}") # 关键词 ROI 排名 keyword_roi = roi_analysis.get('keyword_roi', {}) if keyword_roi: st.markdown("##### 🎯 关键词 ROI 排名") keyword_roi_df = pd.DataFrame([ { "关键词": kw, "成本(CNY)": data['cost'], "提及次数": data['mentions'], "估算价值(CNY)": data['value'], "ROI(%)": data['roi'] } for kw, data in keyword_roi.items() ]) keyword_roi_df = keyword_roi_df.sort_values("ROI(%)", ascending=False) # 显示 Top 10 top_roi = keyword_roi_df.head(10) st.dataframe(top_roi, use_container_width=True, hide_index=True) with st.expander("查看完整关键词 ROI 排名", expanded=False): st.dataframe(keyword_roi_df, use_container_width=True, hide_index=True) # 成本优化建议 st.markdown("##### 💡 成本优化建议") suggestions = roi_analyzer.get_optimization_suggestions(cost_analysis) for suggestion in suggestions: priority_color = { "高": "🔴", "中": "🟡", "低": "🟢" }.get(suggestion.get('priority', '低'), '⚪') with st.container(border=True): st.markdown(f"**{priority_color} {suggestion.get('title', 'N/A')}**") st.markdown(suggestion.get('description', '')) if 'savings_estimate' in suggestion: st.info(f"💵 预计可节省：¥{suggestion['savings_estimate']:.2f}") if 'keywords' in suggestion: st.markdown(f"**相关关键词**：{', '.join(suggestion['keywords'])}") # 未来成本预测 st.markdown("##### 🔮 未来成本预测") future_cost = roi_analyzer.estimate_future_cost(api_calls_df, days=30) pred_col1, pred_col2, pred_col3 = st.columns(3) with pred_col1: st.metric("预计日均成本", f"¥{future_cost.get('estimated_daily_cost_cny', 0):.2f}") with pred_col2: st.metric("预计30天总成本", f"¥{future_cost.get('estimated_total_cost_cny', 0):.2f}") with pred_col3: confidence = future_cost.get('confidence', '低') confidence_icon = {"高": "🟢", "中": "🟡", "低": "🔴"}.get(confidence, "⚪") st.metric("预测置信度", f"{confidence_icon} {confidence}") if future_cost.get('data_points', 0) < 3: st.warning("⚠️ 数据点较少，预测准确性较低。建议积累更多数据后再查看预测。") # 导出成本数据 st.markdown("##### 📥 导出数据") export_col1, export_col2 = st.columns(2) with export_col1: if not api_calls_df.empty: api_calls_csv = api_calls_df.to_csv(index=False, encoding="utf-8-sig") st.download_button( "下载 API 调用记录 CSV", api_calls_csv, f"{sanitize_filename(brand,40)}_api_calls.csv", "text/csv", use_container_width=True, key="export_api_calls" ) with export_col2: # 生成成本报告 cost_report = f""" # GEO 成本分析报告 ## 成本概览 - 总成本(CNY): ¥{cost_analysis['total_cost_cny']:.2f} - 总成本(USD): ${cost_analysis['total_cost_usd']:.2f} - 总Token数: {cost_analysis['total_tokens']:,} - API调用次数: {cost_analysis['total_calls']} ## ROI 分析 """ if roi_analysis: cost_report += f""" - 总投入成本: ¥{roi_analysis.get('total_cost', 0):.2f} - 总提及次数: {roi_analysis.get('total_mentions', 0)} - 估算价值: ¥{roi_analysis.get('estimated_value', 0):.2f} - ROI: {roi_analysis.get('roi_ratio', 0):.1f}% """ cost_report += f""" ## 优化建议 """ for suggestion in suggestions: cost_report += f""" - [{suggestion.get('priority', '低')}] {suggestion.get('title', 'N/A')} {suggestion.get('description', '')} """ st.download_button( "下载成本分析报告", cost_report, f"{sanitize_filename(brand,40)}_cost_report.md", "text/markdown", use_container_width=True, key="export_cost_report" ) # 3. 内容质量指标分析 st.markdown("#### 📈 内容质量指标分析") st.caption("分析内容的信任度、权威性、参与度等关键指标，量化内容质量") # 初始化指标分析器 metrics_analyzer = ContentMetricsAnalyzer() # 获取历史文章 try: articles = storage.get_articles(brand=brand) if articles and len(articles) > 0: # 分析所有文章 with st.spinner("正在分析内容质量指标..."): metrics_results = metrics_analyzer.analyze_batch(articles, brand) summary = metrics_analyzer.get_metrics_summary(metrics_results) # 显示指标概览 st.markdown("##### 📊 指标概览") metric_col1, metric_col2, metric_col3, metric_col4 = st.columns(4) with metric_col1: st.metric( "平均 Trust Density", f"{summary['avg_trust_density']:.2f}", help="每100字信任信号数（来源占位、数据、案例等）" ) with metric_col2: st.metric( "平均 Citation Share", f"{summary['avg_citation_share']:.2f}%", help="品牌引用比例（品牌提及次数 / 总提及次数）" ) with metric_col3: st.metric( "平均 Authority Score", f"{summary['avg_authority_score']:.2f}", help="权威性得分（基于来源占位数量，0-100）" ) with metric_col4: st.metric( "平均 Engagement Potential", f"{summary['avg_engagement_potential']:.2f}", help="参与度潜力（基于结构化程度，0-100）" ) # 详细指标分析 st.markdown("##### 📋 详细指标分析") # 创建指标数据框 metrics_df = pd.DataFrame([ { "关键词": r.get('keyword', ''), "平台": r.get('platform', ''), "Trust Density": r.get('trust_density', 0), "Citation Share (%)": r.get('citation_share', 0), "Authority Score": r.get('authority_score', 0), "Engagement Potential": r.get('engagement_potential', 0), "信任信号数": r.get('trust_signals', 0), "来源占位": r.get('citations', 0), "品牌提及": r.get('brand_mentions', 0), } for r in metrics_results ]) if not metrics_df.empty: # 显示指标表格 st.dataframe(metrics_df, use_container_width=True, hide_index=True) # 指标可视化 viz_col1, viz_col2 = st.columns(2) with viz_col1: # Trust Density 分布 fig_trust = px.histogram( metrics_df, x="Trust Density", nbins=20, title="Trust Density 分布", labels={"Trust Density": "Trust Density", "count": "文章数量"}, color_discrete_sequence=["#2563EB"] ) st.plotly_chart(fig_trust, use_container_width=True) with viz_col2: # Authority Score 分布 fig_authority = px.histogram( metrics_df, x="Authority Score", nbins=20, title="Authority Score 分布", labels={"Authority Score": "Authority Score", "count": "文章数量"}, color_discrete_sequence=["#10B981"] ) st.plotly_chart(fig_authority, use_container_width=True) # 指标热力图（按平台） if len(metrics_df['平台'].unique()) > 1: st.markdown("##### 🔥 平台指标热力图") platform_metrics = metrics_df.groupby('平台').agg({ 'Trust Density': 'mean', 'Citation Share (%)': 'mean', 'Authority Score': 'mean', 'Engagement Potential': 'mean', }).round(2) fig_heatmap = px.imshow( platform_metrics.T, labels=dict(x="平台", y="指标", color="得分"), title="各平台平均指标热力图", color_continuous_scale="RdYlGn", aspect="auto" ) st.plotly_chart(fig_heatmap, use_container_width=True) # 指标相关性分析 st.markdown("##### 🔗 指标相关性分析") correlation_cols = ['Trust Density', 'Citation Share (%)', 'Authority Score', 'Engagement Potential'] corr_df = metrics_df[correlation_cols].corr() fig_corr = px.imshow( corr_df, labels=dict(x="指标", y="指标", color="相关系数"), title="指标相关性矩阵", color_continuous_scale="RdBu", aspect="auto", text_auto=True ) st.plotly_chart(fig_corr, use_container_width=True) # Top 内容排名 st.markdown("##### 🏆 Top 内容排名") top_col1, top_col2, top_col3, top_col4 = st.columns(4) with top_col1: top_trust = metrics_df.nlargest(5, 'Trust Density')[['关键词', '平台', 'Trust Density']] st.markdown("**Top 5 Trust Density**") st.dataframe(top_trust, use_container_width=True, hide_index=True) with top_col2: top_citation = metrics_df.nlargest(5, 'Citation Share (%)')[['关键词', '平台', 'Citation Share (%)']] st.markdown("**Top 5 Citation Share**") st.dataframe(top_citation, use_container_width=True, hide_index=True) with top_col3: top_authority = metrics_df.nlargest(5, 'Authority Score')[['关键词', '平台', 'Authority Score']] st.markdown("**Top 5 Authority Score**") st.dataframe(top_authority, use_container_width=True, hide_index=True) with top_col4: top_engagement = metrics_df.nlargest(5, 'Engagement Potential')[['关键词', '平台', 'Engagement Potential']] st.markdown("**Top 5 Engagement Potential**") st.dataframe(top_engagement, use_container_width=True, hide_index=True) # 导出指标数据 st.markdown("##### 📥 导出指标数据") metrics_csv = metrics_df.to_csv(index=False, encoding="utf-8-sig") st.download_button( "下载指标数据 CSV", metrics_csv, f"{sanitize_filename(brand,40)}_内容质量指标_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.csv", mime="text/csv", use_container_width=True, key="export_metrics_csv" ) else: st.info("暂无指标数据。") else: st.info("💡 提示：请先在【2 自动创作】生成内容，然后才能查看内容质量指标。") except Exception as e: st.error(f"获取内容质量指标失败：{e}") # 4. 关键词效果排名 st.markdown("#### 🎯 关键词效果排名") brand_verify = verify_df[verify_df["品牌"] == brand].copy() if len(brand_verify) > 0: keyword_performance = brand_verify.groupby("问题")["提及次数"].agg(["mean", "count"]).reset_index() keyword_performance.columns = ["关键词", "平均提及次数", "验证次数"] keyword_performance = keyword_performance.sort_values("平均提及次数", ascending=False) # 显示 Top 20 top_keywords = keyword_performance.head(20) fig_keywords = px.bar( top_keywords, x="平均提及次数", y="关键词", orientation='h', title="Top 20 关键词效果排名（平均提及次数）", labels={"平均提及次数": "平均提及次数", "关键词": "关键词"}, color="平均提及次数", color_continuous_scale="Greens" ) fig_keywords.update_layout(yaxis={'categoryorder': 'total ascending'}) st.plotly_chart(fig_keywords, use_container_width=True) with st.expander("查看完整关键词排名", expanded=False): st.dataframe(keyword_performance, use_container_width=True, hide_index=True) else: st.info("暂无品牌验证数据。") # 4. 竞品对比分析 st.markdown("#### ⚔️ 竞品对比分析") if len(competitor_list) > 0: # 计算各品牌的平均提及次数 brand_comparison = verify_df.groupby("品牌")["提及次数"].agg(["mean", "count"]).reset_index() brand_comparison.columns = ["品牌", "平均提及次数", "验证次数"] brand_comparison = brand_comparison.sort_values("平均提及次数", ascending=False) fig_comparison = px.bar( brand_comparison, x="品牌", y="平均提及次数", title="品牌提及率对比（平均提及次数）", labels={"平均提及次数": "平均提及次数", "品牌": "品牌"}, color="平均提及次数", color_continuous_scale="Reds" ) st.plotly_chart(fig_comparison, use_container_width=True) # 详细对比表 with st.expander("查看详细对比数据", expanded=False): st.dataframe(brand_comparison, use_container_width=True, hide_index=True) # 按验证模型分组的对比 if "验证模型" in verify_df.columns: model_comparison = verify_df.groupby(["品牌", "验证模型"])["提及次数"].mean().reset_index() model_comparison = model_comparison.pivot(index="品牌", columns="验证模型", values="提及次数").fillna(0) fig_model_comparison = px.bar( model_comparison.reset_index(), x="品牌", y=[col for col in model_comparison.columns], title="各模型下的品牌提及率对比", labels={"value": "平均提及次数", "品牌": "品牌"}, barmode='group' ) st.plotly_chart(fig_model_comparison, use_container_width=True) else: st.info("💡 提示：在侧边栏配置竞品品牌后，可查看竞品对比分析。") # 5. 负面防护监控报告 st.markdown("#### 🛡️ 负面防护监控报告") st.caption("分析负面查询中的品牌提及情况，提供风险预警和优化建议") # 获取负面分析结果（从 session_state 或数据库） try: # 尝试从 session_state 获取 negative_results = st.session_state.get("negative_analysis_results", []) # 如果没有，尝试从验证结果中提取负面查询 if not negative_results and st.session_state.verify_combined is not None: verify_df = st.session_state.verify_combined # 检查是否有负面查询 negative_monitor = NegativeMonitor() negative_queries_pattern = "|".join([q.replace(brand, "{brand}") for q in negative_monitor.generate_negative_queries(brand, 15)]) # 筛选可能的负面查询 brand_verify = verify_df[verify_df["品牌"] == brand].copy() if len(brand_verify) > 0: # 检查问题是否包含负面关键词 negative_keywords = negative_monitor.negative_keywords negative_verify = brand_verify[ brand_verify["问题"].str.contains("|".join(negative_keywords), case=False, na=False) ] if len(negative_verify) > 0: # 重新分析负面查询 negative_results = [] for _, row in negative_verify.iterrows(): # 这里需要重新获取响应内容，但为了简化，我们使用现有数据 # 实际应用中，应该从数据库获取完整的响应内容 try: analysis = negative_monitor.analyze_negative_mentions( brand=brand, query=row["问题"], response="", # 如果没有保存响应，使用空字符串 mention_count=row["提及次数"] ) negative_results.append(analysis) except Exception: pass if negative_results: negative_monitor = NegativeMonitor() report = negative_monitor.generate_negative_report( brand=brand, analysis_results=negative_results, threshold=0.3 ) # 显示报告概览 st.markdown("##### 📊 报告概览") report_col1, report_col2, report_col3, report_col4 = st.columns(4) with report_col1: st.metric("总查询数", report.get("total_queries", 0)) with report_col2: st.metric("高风险", report.get("high_risk_count", 0), delta=None, delta_color="inverse") with report_col3: st.metric("平均提及次数", report.get("average_mention_count", 0.0)) with report_col4: st.metric("平均负面得分", report.get("average_negative_score", 0.0)) # 预警信息 alerts = report.get("alerts", []) if alerts: st.markdown("##### ⚠️ 预警信息") for alert in alerts: alert_level = alert.get("level", "中") alert_color = {"高": "🔴", "中": "🟡", "低": "🟢"}.get(alert_level, "⚪") st.warning(f"{alert_color} {alert.get('message', '')}") # 优化建议 recommendations = report.get("recommendations", []) if recommendations: st.markdown("##### 💡 优化建议") for i, rec in enumerate(recommendations, 1): st.markdown(f"{i}. {rec}") # 高风险查询列表 high_risk_queries = report.get("high_risk_queries", []) if high_risk_queries: st.markdown("##### 🔴 高风险查询列表") st.write(", ".join(high_risk_queries)) # 中风险查询列表 medium_risk_queries = report.get("medium_risk_queries", []) if medium_risk_queries: st.markdown("##### 🟡 中风险查询列表") st.write(", ".join(medium_risk_queries)) # 下载报告 import json report_json = json.dumps(report, ensure_ascii=False, indent=2) st.download_button( "下载负面监控报告 JSON", report_json, f"{sanitize_filename(brand,40)}_负面监控报告_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.json", mime="application/json", use_container_width=True, key="negative_report_dl" ) else: st.info("💡 提示：暂无负面监控数据。请在【4 多模型验证】中启用负面监控功能，生成负面查询并验证。") except Exception as e: st.error(f"生成负面监控报告失败：{e}") # 6. 数据导出 st.markdown("#### 💾 数据导出") col1, col2 = st.columns(2) with col1: # 导出验证数据 csv_data = verify_df.to_csv(index=False, encoding="utf-8-sig") st.download_button( "下载验证数据 CSV", csv_data, f"{sanitize_filename(brand,40)}_AI数据报表_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.csv", mime="text/csv", use_container_width=True, key="report_dl_csv" ) with col2: # 导出关键词效果排名 if len(brand_verify) > 0: keyword_csv = keyword_performance.to_csv(index=False, encoding="utf-8-sig") st.download_button( "下载关键词排名 CSV", keyword_csv, f"{sanitize_filename(brand,40)}_关键词排名_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.csv", mime="text/csv", use_container_width=True, key="keyword_rank_dl_csv" ) # ======================= # Tab7：工作流自动化