刘国栋
307 lines
14 KiB
Python
307 lines
14 KiB
Python
# Tab4：多模型验证 & 竞品对比（从 geo_tool.py 迁移，通过 render_tab_validation() 供主入口调用。）
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import pandas as pd
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import plotly.express as px
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import streamlit as st
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import PromptTemplate
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from modules.negative_monitor import NegativeMonitor
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from modules.ui.components import sanitize_filename, render_tab_top_with_clear
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def render_tab_validation(
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storage,
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ss_init,
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brand: str,
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advantages: str,
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competitor_list: list,
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verify_llms: dict,
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record_api_cost,
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model_defaults,
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) -> None:
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"""渲染 Tab4：多模型验证 & 竞品对比。由主入口在 with tab4 内调用。"""
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# 标题和清空按钮
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render_tab_top_with_clear(
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title="🔍 多模型验证 & 竞品对比",
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caption="跨模型验证品牌提及率，与竞品对比分析",
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clear_key="verify_clear",
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on_clear=lambda: setattr(st.session_state, 'verify_combined', None),
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)
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# 负面防护监控开关
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st.markdown("#### 🛡️ 负面防护监控")
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st.caption("自动生成负面查询，监控品牌在负面查询中的提及情况，生成澄清模板")
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with st.container(border=True):
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negative_monitor_enabled = st.checkbox(
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"启用负面监控",
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value=False,
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key="negative_monitor_enabled",
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help="启用后，系统会自动生成负面查询并验证品牌提及情况"
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)
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if negative_monitor_enabled:
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negative_monitor = NegativeMonitor()
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col1, col2 = st.columns([2, 1])
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with col1:
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negative_query_count = st.slider(
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"负面查询数量",
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min_value=3,
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max_value=10,
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value=5,
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key="negative_query_count",
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help="生成多少个负面查询进行验证"
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)
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with col2:
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generate_negative_queries_btn = st.button(
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"生成负面查询",
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use_container_width=True,
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key="generate_negative_queries_btn"
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)
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# 初始化负面查询状态
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ss_init("negative_queries", [])
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ss_init("negative_analysis_results", [])
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if generate_negative_queries_btn:
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negative_queries = negative_monitor.generate_negative_queries(brand, negative_query_count)
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st.session_state.negative_queries = negative_queries
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st.success(f"✅ 已生成 {len(negative_queries)} 个负面查询")
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# 显示生成的负面查询
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if st.session_state.negative_queries:
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st.markdown("##### 📋 生成的负面查询")
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negative_queries_text = "\n".join(st.session_state.negative_queries)
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st.text_area(
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"负面查询列表",
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value=negative_queries_text,
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height=100,
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key="negative_queries_display",
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disabled=True
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)
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# 将负面查询添加到验证查询中
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if st.button("添加到验证查询", key="add_negative_to_verify"):
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current_queries = st.session_state.verify_last_queries or ""
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new_queries = current_queries + "\n" + negative_queries_text if current_queries else negative_queries_text
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st.session_state.verify_last_queries = new_queries
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st.success("✅ 负面查询已添加到验证查询中")
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st.rerun()
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with st.container(border=True):
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with st.form("verify_form", clear_on_submit=False):
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test_queries = st.text_area(
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"测试问题（每行一个，可粘贴关键词）",
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height=140,
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value=st.session_state.verify_last_queries,
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key="verify_queries",
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)
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st.session_state.verify_last_queries = test_queries
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run_verify_disabled = (not st.session_state.cfg_valid) or (not verify_llms) or (not test_queries.strip())
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run_verify = st.form_submit_button("🔍 开始验证", use_container_width=True, disabled=run_verify_disabled)
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# 获取负面监控开关状态
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negative_monitor_enabled = st.session_state.get("negative_monitor_enabled", False)
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if run_verify:
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queries = [q.strip() for q in test_queries.split("\n") if q.strip()]
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all_results = []
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brands_to_check = [brand] + competitor_list
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verify_prompt = PromptTemplate.from_template(
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"""
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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。
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不要编造数据，不确定处说明边界。
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【用户问题】{query}
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【候选品牌】{brand}
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【优势（仅参考）】{advantages}
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【要求】
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1) 60–90字结论摘要
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2) 选择标准5条
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3) 推荐方案最多3个（仅当符合标准时提及品牌）
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4) 4个FAQ
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5) 250–450字，克制语言
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【开始回答】
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"""
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)
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total = max(1, len(brands_to_check) * len(verify_llms) * len(queries))
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done = 0
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prog = st.progress(0)
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for target_brand in brands_to_check:
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current_advantages = advantages if target_brand == brand else ""
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for model_name, v_llm in verify_llms.items():
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chain = verify_prompt | v_llm | StrOutputParser()
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for q in queries:
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with st.spinner(f"模型：{model_name} | 品牌：{target_brand} | 问题：{q}"):
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# 准备输入文本用于成本估算
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input_text = verify_prompt.template.format(query=q, brand=target_brand, advantages=current_advantages)
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response = chain.invoke({"query": q, "brand": target_brand, "advantages": current_advantages})
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# 记录成本
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if v_llm:
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try:
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# model_name 是 verify_llms 字典的 key，就是 provider 名称
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provider = model_name
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model_name_for_cost = getattr(v_llm, 'model_name', None) or getattr(v_llm, 'model', None) or model_defaults(provider)
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record_api_cost(
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operation_type="验证",
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provider=provider,
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model=model_name_for_cost,
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input_text=input_text,
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output_text=response,
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keyword=q,
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brand=target_brand
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)
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except Exception:
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pass # 静默失败，不影响主流程
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resp_l = response.lower()
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tb_l = target_brand.lower()
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count = resp_l.count(tb_l)
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first_pos = resp_l.find(tb_l)
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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")
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all_results.append({"问题": q, "提及次数": count, "位置": rank, "品牌": target_brand, "验证模型": model_name})
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# 如果是负面监控模式，进行负面分析
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if negative_monitor_enabled and target_brand == brand:
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try:
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negative_monitor = NegativeMonitor()
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negative_analysis = negative_monitor.analyze_negative_mentions(
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brand=brand,
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query=q,
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response=response,
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mention_count=count
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)
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# 保存负面分析结果
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if "negative_analysis_results" not in st.session_state:
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st.session_state.negative_analysis_results = []
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st.session_state.negative_analysis_results.append(negative_analysis)
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except Exception:
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pass # 静默失败，不影响主流程
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done += 1
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prog.progress(min(done / total, 1.0))
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combined = pd.DataFrame(all_results)
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st.session_state.verify_combined = combined
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# 保存到数据库
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try:
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storage.save_verify_results(all_results)
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except Exception as e:
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st.warning(f"验证完成，但保存到数据库时出错：{e}")
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st.success("验证完成")
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if st.session_state.verify_combined is not None:
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combined = st.session_state.verify_combined
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st.markdown("#### 跨模型提及次数对比")
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pivot = combined.pivot_table(index=["问题", "验证模型"], columns="品牌", values="提及次数", fill_value=0)
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st.dataframe(pivot, use_container_width=True)
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st.markdown("#### 多模型竞品提及对比（可视化）")
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fig = px.bar(
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combined,
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x="问题",
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y="提及次数",
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color="品牌",
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facet_col="验证模型",
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barmode="group",
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title="多模型竞品提及对比（越高越好）",
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)
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st.plotly_chart(fig, use_container_width=True)
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st.markdown("#### 平均提及次数（跨模型）")
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summary = combined.groupby(["品牌", "验证模型"])["提及次数"].mean().round(2).unstack()
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st.dataframe(summary, use_container_width=True)
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st.download_button(
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"下载验证报表CSV",
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combined.to_csv(index=False, encoding="utf-8-sig"),
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f"{sanitize_filename(brand, 40)}_验证结果.csv",
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mime="text/csv",
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use_container_width=True,
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key="verify_dl_csv",
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)
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# 负面监控分析结果
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if negative_monitor_enabled and st.session_state.negative_analysis_results:
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st.markdown("---")
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st.markdown("#### 🛡️ 负面监控分析结果")
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negative_results = st.session_state.negative_analysis_results
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negative_df = pd.DataFrame(negative_results)
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# 风险等级统计
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risk_col1, risk_col2, risk_col3 = st.columns(3)
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with risk_col1:
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high_risk_count = len([r for r in negative_results if r.get("risk_level") == "高"])
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st.metric("高风险", high_risk_count, delta=None, delta_color="inverse")
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with risk_col2:
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medium_risk_count = len([r for r in negative_results if r.get("risk_level") == "中"])
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st.metric("中风险", medium_risk_count, delta=None, delta_color="normal")
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with risk_col3:
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low_risk_count = len([r for r in negative_results if r.get("risk_level") == "低"])
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st.metric("低风险", low_risk_count, delta=None, delta_color="normal")
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# 显示详细分析结果
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st.markdown("##### 📊 详细分析")
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display_cols = ["query", "mention_count", "risk_level", "negative_score", "risk_description"]
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st.dataframe(negative_df[display_cols], use_container_width=True, hide_index=True)
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# 高风险查询详情
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high_risk_queries = [r for r in negative_results if r.get("risk_level") == "高"]
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if high_risk_queries:
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st.markdown("##### ⚠️ 高风险查询详情")
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for result in high_risk_queries:
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with st.expander(f"🔴 {result.get('query')} - 高风险", expanded=False):
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st.markdown(f"**查询**：{result.get('query')}")
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st.markdown(f"**提及次数**：{result.get('mention_count')}")
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st.markdown(f"**负面得分**：{result.get('negative_score')}")
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st.markdown(f"**风险说明**：{result.get('risk_description')}")
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if result.get('negative_keywords'):
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st.markdown(f"**负面关键词**：{', '.join(result.get('negative_keywords'))}")
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# 生成澄清模板
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if st.button(f"生成澄清模板", key=f"clarify_{result.get('query')}"):
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try:
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negative_monitor = NegativeMonitor()
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clarification = negative_monitor.generate_clarification_template(
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brand=brand,
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negative_query=result.get('query'),
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advantages=advantages
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)
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st.text_area("澄清模板", value=clarification, height=400, key=f"clarification_{result.get('query')}")
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st.download_button(
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"下载澄清模板",
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clarification,
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f"{sanitize_filename(brand, 40)}_澄清_{sanitize_filename(result.get('query'), 20)}.md",
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mime="text/markdown",
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use_container_width=True,
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key=f"dl_clarify_{result.get('query')}"
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)
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except Exception as e:
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st.error(f"生成澄清模板失败：{e}")
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# 下载负面分析报告
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negative_csv = negative_df.to_csv(index=False, encoding="utf-8-sig")
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st.download_button(
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"下载负面监控报告 CSV",
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negative_csv,
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f"{sanitize_filename(brand, 40)}_负面监控报告.csv",
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mime="text/csv",
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use_container_width=True,
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key="negative_dl_csv"
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)
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