刘国栋
8f7f082c3d
- 重构项目目录结构，将功能模块移至 modules/ 目录 - 创建平台同步基础架构，包括发布器基类和 GitHub 发布器 - 新增 UI 状态管理模块 (modules/ui/state.py) 统一管理会话状态 - 更新依赖配置，添加平台同步所需依赖 (httpx, pyperclip) - 整理文档结构，将所有文档分类移至 docs/ 目录 - 添加 .cursorrules 文件定义项目开发规范 - 清理根目录重复文件，保持项目结构整洁
1729 lines
71 KiB
Python
1729 lines
71 KiB
Python
import json
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import math
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import streamlit as st
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from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
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from langchain_core.prompts import PromptTemplate
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from modules.keyword_mining import KeywordMining
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from modules.semantic_expander import SemanticExpander
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from modules.topic_cluster import TopicCluster
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INVALID_FS_CHARS = r'<>:"/\\|?*\n\r\t'
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def sanitize_filename(name: str, max_len: int = 80) -> str:
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"""Copy of utility from geo_tool, kept local to avoid circular imports."""
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if not name:
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return "untitled"
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name = name.strip()
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# 延续主应用中的命名清理规则
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import re # 局部导入，避免在模块顶部重复导入
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name = re.sub(rf"[{re.escape(INVALID_FS_CHARS)}]", "_", name)
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name = re.sub(r"_+", "_", name).strip("_")
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return name[:max_len] if len(name) > max_len else name
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def extract_json_array(text: str):
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"""从模型输出中抽取 JSON 数组（JsonOutputParser 失败时兜底）。"""
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if not text:
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return None
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import re
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m = re.search(r"\[[\s\S]*\]", text)
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if not m:
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return None
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try:
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return json.loads(m.group(0))
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except Exception:
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return None
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def render_tab_keywords(storage, ss_init, gen_llm, brand: str, advantages: str) -> None:
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"""
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渲染 Tab1：关键词蒸馏。
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该实现直接从 `geo_tool.py` 中迁移而来，仅进行了最小必要的结构调整：
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- 包装为函数，便于从主入口调用
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- 通过参数接收 `storage` / `ss_init` / `gen_llm` / `brand` / `advantages`
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"""
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# ========== 区域 1：模式选择 ==========
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st.markdown("**🎯 生成模式**")
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generation_mode = st.radio(
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"选择生成模式",
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["AI生成", "托词工具", "混合模式"],
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index=["AI生成", "托词工具", "混合模式"].index(
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st.session_state.kw_generation_mode
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),
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horizontal=True,
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key="kw_mode_radio",
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help="AI生成：使用 LLM 直接生成；托词工具：基于词库组合；混合模式：先组合再润色",
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)
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st.session_state.kw_generation_mode = generation_mode
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st.markdown("---")
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# ========== 区域 2：配置区（条件显示） ==========
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if generation_mode in ["托词工具", "混合模式"]:
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# 初始化词库
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if st.session_state.wordbanks is None:
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st.session_state.wordbanks = st.session_state.keyword_tool.load_wordbanks()
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# 初始化组合模式选择
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ss_init("selected_patterns", list(st.session_state.keyword_tool.combination_patterns))
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wordbanks = st.session_state.wordbanks
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# 组合模式选择
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with st.container(border=True):
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st.markdown("**📐 组合模式选择**")
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pattern_descriptions = st.session_state.keyword_tool.get_pattern_descriptions()
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all_patterns = st.session_state.keyword_tool.combination_patterns
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# 显示所有可用模式
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pattern_options = []
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for pattern in all_patterns:
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pattern_str = "+".join(pattern)
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desc = pattern_descriptions.get(pattern_str, pattern_str)
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pattern_options.append((pattern_str, pattern, desc))
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# 多选组合模式
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selected_pattern_strs = st.multiselect(
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"选择要使用的组合模式（可多选）",
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options=[opt[0] for opt in pattern_options],
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default=[
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opt[0]
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for opt in pattern_options
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if opt[1] in st.session_state.selected_patterns
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],
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key="kw_pattern_select",
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help="选择要使用的组合模式，至少选择一个",
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)
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# 更新选中的模式
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selected_patterns = []
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for pattern_str, pattern, desc in pattern_options:
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if pattern_str in selected_pattern_strs:
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selected_patterns.append(pattern)
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st.session_state.selected_patterns = (
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selected_patterns if selected_patterns else all_patterns
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)
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# 显示模式说明
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with st.expander("📖 组合模式说明", expanded=False):
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for pattern_str, pattern, desc in pattern_options:
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st.markdown(f"**{pattern_str}**: {' + '.join(desc)}")
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# 词库管理
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with st.container(border=True):
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st.markdown("**📚 词库管理**")
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wordbank_tab1, wordbank_tab2 = st.tabs(["编辑词库", "导入/导出"])
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with wordbank_tab1:
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st.markdown("**词库编辑**")
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bank_types = list(wordbanks.keys())
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# 横向展示所有词库类型（6列）
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st.caption(
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"💡 提示：所有词库类型横向展示，可直接编辑，点击各列的「更新」按钮或使用下方的「更新所有词库」按钮保存修改"
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)
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cols = st.columns(6)
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edited_wordbanks = {}
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for idx, bank_type in enumerate(bank_types):
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with cols[idx]:
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# 显示词库类型名称
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st.markdown(f"**{bank_type}**")
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# 显示当前词库内容
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current_words = wordbanks.get(bank_type, [])
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edited_words = st.text_area(
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f"{bank_type} 词汇（每行一个）",
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"\n".join(current_words),
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height=200,
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key=f"kw_bank_edit_{bank_type}",
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label_visibility="collapsed",
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)
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# 保存编辑内容
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edited_wordbanks[bank_type] = edited_words
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# 每个词库单独的更新按钮
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if st.button(
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"更新",
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key=f"kw_update_{bank_type}",
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use_container_width=True,
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):
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new_words = [
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w.strip() for w in edited_words.split("\n") if w.strip()
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]
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wordbanks[bank_type] = new_words
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st.session_state.wordbanks = wordbanks
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st.success(f"✅ {bank_type} 已更新（{len(new_words)} 个词汇）")
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st.info(
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"💡 提示：词库已更新，建议重新生成关键词以应用新词库"
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)
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st.rerun()
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# 统一更新所有词库按钮
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st.markdown("---")
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if st.button(
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"💾 更新所有词库",
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use_container_width=True,
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type="primary",
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key="kw_update_all",
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):
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updated_count = 0
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for bank_type, edited_text in edited_wordbanks.items():
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new_words = [
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w.strip() for w in edited_text.split("\n") if w.strip()
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]
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if new_words != wordbanks.get(bank_type, []):
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wordbanks[bank_type] = new_words
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updated_count += 1
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if updated_count > 0:
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st.session_state.wordbanks = wordbanks
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st.success(f"✅ 已更新 {updated_count} 个词库")
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st.info(
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"💡 提示：词库已更新，建议重新生成关键词以应用新词库"
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)
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st.rerun()
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else:
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st.info("没有词库需要更新")
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with wordbank_tab2:
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st.markdown("**词库导入/导出**")
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# 导出
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wordbanks_json = json.dumps(wordbanks, ensure_ascii=False, indent=2)
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st.download_button(
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"导出词库（JSON）",
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wordbanks_json,
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"wordbanks.json",
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"application/json",
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use_container_width=True,
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key="kw_export_json",
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)
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st.markdown("---")
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# 导入
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uploaded_wordbanks = st.file_uploader(
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"导入词库（JSON）",
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type=["json"],
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key="kw_import_json",
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)
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if uploaded_wordbanks:
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try:
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imported = json.loads(
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uploaded_wordbanks.read().decode("utf-8")
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)
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if isinstance(imported, dict):
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st.session_state.wordbanks = imported
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st.success("词库导入成功！")
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st.rerun()
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except Exception as e:
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st.error(f"导入失败：{e}")
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st.markdown("---")
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# 重置为默认词库
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if st.button(
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"重置为默认词库",
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use_container_width=True,
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key="kw_reset_banks",
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):
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st.session_state.wordbanks = (
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st.session_state.keyword_tool.load_wordbanks()
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)
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st.success("已重置为默认词库")
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st.rerun()
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st.markdown("---")
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# ========== 区域 3：生成控制 ==========
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with st.container(border=True):
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st.markdown("**⚙️ 生成控制**")
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ss_init("kw_last_num", 20) # 确保默认值初始化
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c1, c2, c3 = st.columns([2, 1, 1])
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with c1:
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st.session_state.kw_last_num = st.slider(
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"生成数量",
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5,
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200,
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st.session_state.kw_last_num,
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key="kw_num",
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help="建议范围：10-50 个关键词",
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)
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with c2:
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# 根据模式调整禁用条件
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if generation_mode == "托词工具":
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run_kw_disabled = (
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not st.session_state.get("selected_patterns")
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or len(st.session_state.get("selected_patterns", [])) == 0
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)
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else:
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run_kw_disabled = (not st.session_state.cfg_valid) or (gen_llm is None)
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# 防止并发点击
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if st.session_state.get("kw_generating", False):
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run_kw_disabled = True
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run_kw = st.button(
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"🚀 生成关键词",
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type="primary",
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use_container_width=True,
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disabled=run_kw_disabled,
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key="kw_run",
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)
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with c3:
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if st.button(
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"🗑️ 清空结果", use_container_width=True, key="kw_clear"
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):
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# 清空所有相关状态
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st.session_state.keywords = []
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st.session_state.expanded_keywords = []
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st.session_state.topic_clusters = []
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st.session_state.cluster_relationships = []
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st.session_state.cluster_stats = None
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st.session_state.content_planning = None
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st.session_state.mined_keywords = []
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st.session_state.competition_analysis = {}
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st.session_state.trend_analysis = {}
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st.session_state.value_matrix = {}
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st.session_state.keyword_recommendations = []
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st.toast("已清空所有关键词和相关数据")
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st.rerun()
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if run_kw:
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# ========== 参数验证 ==========
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if generation_mode == "AI生成":
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if not brand or not advantages:
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st.error("❌ **请先在侧边栏配置品牌名称和核心优势**")
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st.stop()
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# ========== 防止并发点击 ==========
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if st.session_state.get("kw_generating", False):
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st.warning("⏳ 正在生成中，请勿重复点击")
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st.stop()
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st.session_state.kw_generating = True
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keywords = []
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if generation_mode == "AI生成":
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# 原有 AI 生成逻辑
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keyword_prompt = PromptTemplate.from_template(
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"""
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你是GEO（Generative Engine Optimization）专家，目标是提升品牌在大模型自然回答中的提及率。
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【输入】
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- 品牌：{brand}
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- 核心优势：{advantages}
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- 数量：{num_keywords}
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【GEO核心要求】
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1) 覆盖用户真实搜索意图：
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- 根据品牌和优势，识别用户可能的搜索场景（对比、评测、使用、购买、问题、教程等）
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- 关键词应反映用户真实需求，而非营销术语
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- 考虑不同用户角色和搜索阶段的需求
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2) 品牌词占比策略：
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- 约30%包含品牌词（建立护城河，提升品牌提及率）
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- 约70%为泛词（扩大覆盖面，获取新流量）
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- 品牌词应自然融入，避免生硬拼接
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3) 表达要求：
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- 口语化、自然、符合用户搜索习惯
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- 长度控制在 12-28 字
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- 避免过于正式或营销化
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4) 多样性要求：
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- 去重：避免生成相同或过于相似的关键词
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- 均衡意图：覆盖不同搜索意图（对比、评测、使用、购买、问题等）
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- 多样化表达：使用不同的表达方式
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【输出格式】
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请严格按照以下 JSON 数组格式输出，不要添加任何其他内容：
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["关键词1", "关键词2", "关键词3", ...]
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如果无法生成 JSON 格式，请每行输出一个关键词（纯文本格式）。
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【开始生成】
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"""
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)
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chain_json = keyword_prompt | gen_llm | JsonOutputParser()
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chain_text = keyword_prompt | gen_llm | StrOutputParser()
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# 改进加载状态
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progress_bar = st.progress(0)
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status_text = st.empty()
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status_text.text("🔄 正在生成关键词...")
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progress_bar.progress(10)
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status_text.text("🤖 调用 AI 模型生成关键词...")
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progress_bar.progress(30)
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try:
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result = chain_json.invoke(
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{
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"brand": brand,
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"advantages": advantages,
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"num_keywords": st.session_state.kw_last_num,
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}
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)
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keywords = result if isinstance(result, list) else []
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progress_bar.progress(80)
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except Exception:
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raw = chain_text.invoke(
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{
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"brand": brand,
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"advantages": advantages,
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"num_keywords": st.session_state.kw_last_num,
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}
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)
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keywords = extract_json_array(raw) or []
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progress_bar.progress(80)
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status_text.text("✨ 处理生成结果...")
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progress_bar.progress(100)
|
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progress_bar.empty()
|
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status_text.empty()
|
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|
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elif generation_mode == "托词工具":
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# 托词工具生成
|
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progress_bar = st.progress(0)
|
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status_text = st.empty()
|
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|
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status_text.text("🔧 加载词库和组合模式...")
|
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progress_bar.progress(20)
|
||
|
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wordbanks = (
|
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st.session_state.wordbanks
|
||
or st.session_state.keyword_tool.load_wordbanks()
|
||
)
|
||
selected_patterns = st.session_state.get(
|
||
"selected_patterns", st.session_state.keyword_tool.combination_patterns
|
||
)
|
||
|
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# 检查词库是否为空（在生成前检查）
|
||
empty_banks = [k for k, v in wordbanks.items() if not v]
|
||
if empty_banks:
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
st.error(
|
||
f"❌ 以下词库为空，请先添加词汇：{', '.join(empty_banks)}"
|
||
)
|
||
st.session_state.kw_generating = False
|
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st.stop()
|
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|
||
status_text.text("🔄 生成关键词组合...")
|
||
progress_bar.progress(60)
|
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|
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keywords = st.session_state.keyword_tool.generate_combinations(
|
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wordbanks=wordbanks,
|
||
patterns=selected_patterns,
|
||
max_results=st.session_state.kw_last_num,
|
||
similarity_threshold=0.8,
|
||
)
|
||
|
||
status_text.text("✨ 去重和筛选...")
|
||
progress_bar.progress(100)
|
||
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
|
||
elif generation_mode == "混合模式":
|
||
# 混合模式：先托词生成，再 LLM 润色
|
||
progress_bar = st.progress(0)
|
||
status_text = st.empty()
|
||
|
||
status_text.text("🔧 加载词库和组合模式...")
|
||
progress_bar.progress(10)
|
||
|
||
wordbanks = (
|
||
st.session_state.wordbanks
|
||
or st.session_state.keyword_tool.load_wordbanks()
|
||
)
|
||
selected_patterns = st.session_state.get(
|
||
"selected_patterns", st.session_state.keyword_tool.combination_patterns
|
||
)
|
||
|
||
# 检查词库是否为空（在生成前检查）
|
||
empty_banks = [k for k, v in wordbanks.items() if not v]
|
||
if empty_banks:
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
st.error(
|
||
f"❌ 以下词库为空，请先添加词汇：{', '.join(empty_banks)}"
|
||
)
|
||
st.session_state.kw_generating = False
|
||
st.stop()
|
||
|
||
status_text.text("🔄 托词生成中...")
|
||
progress_bar.progress(30)
|
||
|
||
raw_keywords = st.session_state.keyword_tool.generate_combinations(
|
||
wordbanks=wordbanks,
|
||
patterns=selected_patterns,
|
||
max_results=st.session_state.kw_last_num * 2, # 生成更多，因为会去重
|
||
similarity_threshold=0.8,
|
||
)
|
||
|
||
if raw_keywords and gen_llm:
|
||
status_text.text("🤖 LLM 润色中...")
|
||
progress_bar.progress(60)
|
||
|
||
# 使用 LLM 润色
|
||
polish_template = PromptTemplate.from_template("{input}")
|
||
polish_chain = polish_template | gen_llm | StrOutputParser()
|
||
keywords = st.session_state.keyword_tool.polish_with_llm(
|
||
keywords=raw_keywords,
|
||
llm_chain=polish_chain,
|
||
brand=brand,
|
||
max_polish=min(
|
||
len(raw_keywords), st.session_state.kw_last_num
|
||
),
|
||
)
|
||
progress_bar.progress(90)
|
||
else:
|
||
keywords = raw_keywords
|
||
progress_bar.progress(90)
|
||
|
||
status_text.text("✨ 处理生成结果...")
|
||
progress_bar.progress(100)
|
||
|
||
progress_bar.empty()
|
||
status_text.empty()
|
||
|
||
# 清理和去重
|
||
cleaned, seen = [], set()
|
||
for k in keywords:
|
||
if not isinstance(k, str):
|
||
continue
|
||
kk = k.strip()
|
||
if not kk:
|
||
continue
|
||
kl = kk.lower()
|
||
if kl in seen:
|
||
continue
|
||
seen.add(kl)
|
||
cleaned.append(kk)
|
||
|
||
# 限制数量
|
||
cleaned = cleaned[: st.session_state.kw_last_num]
|
||
|
||
# 清理生成状态
|
||
st.session_state.kw_generating = False
|
||
|
||
if cleaned:
|
||
# 清空扩展和集群相关状态（避免数据混乱）
|
||
st.session_state.expanded_keywords = []
|
||
st.session_state.topic_clusters = []
|
||
st.session_state.cluster_relationships = []
|
||
st.session_state.cluster_stats = None
|
||
st.session_state.content_planning = None
|
||
|
||
st.session_state.keywords = cleaned
|
||
# 保存到数据库
|
||
try:
|
||
storage.save_keywords(cleaned, brand)
|
||
except Exception as e:
|
||
st.warning(f"关键词已生成，但保存到数据库时出错：{e}")
|
||
st.success(f"✅ 生成完成！共生成 {len(cleaned)} 个关键词")
|
||
else:
|
||
# 分场景错误提示
|
||
if generation_mode == "AI生成":
|
||
st.error(
|
||
"""
|
||
❌ **AI 生成失败**
|
||
|
||
**可能原因：**
|
||
- API Key 配置错误或余额不足
|
||
- 网络连接问题
|
||
- 品牌名称或核心优势为空
|
||
|
||
**解决建议：**
|
||
1. 检查侧边栏的 API Key 配置
|
||
2. 确认品牌名称和核心优势已填写
|
||
3. 稍后重试或联系技术支持
|
||
"""
|
||
)
|
||
elif generation_mode == "托词工具":
|
||
wordbanks = (
|
||
st.session_state.wordbanks
|
||
or st.session_state.keyword_tool.load_wordbanks()
|
||
)
|
||
empty_banks = [k for k, v in wordbanks.items() if not v]
|
||
if empty_banks:
|
||
st.error(
|
||
f"""
|
||
❌ **词库为空**
|
||
|
||
以下词库为空，请先添加词汇：
|
||
- {', '.join(empty_banks)}
|
||
|
||
**操作步骤：**
|
||
1. 点击"词库管理"
|
||
2. 选择空的词库类型
|
||
3. 添加至少 3-5 个词汇
|
||
4. 点击"更新词库"
|
||
5. 重新生成关键词
|
||
"""
|
||
)
|
||
elif not st.session_state.get("selected_patterns") or len(
|
||
st.session_state.get("selected_patterns", [])
|
||
) == 0:
|
||
st.error(
|
||
"""
|
||
❌ **未选择组合模式**
|
||
|
||
请至少选择一个组合模式：
|
||
1. 在"组合模式选择"区域
|
||
2. 勾选至少一个模式
|
||
3. 重新生成关键词
|
||
"""
|
||
)
|
||
else:
|
||
st.error(
|
||
"""
|
||
❌ **生成失败**
|
||
|
||
请检查词库配置或选择更多组合模式后重试。
|
||
"""
|
||
)
|
||
elif generation_mode == "混合模式":
|
||
wordbanks = (
|
||
st.session_state.wordbanks
|
||
or st.session_state.keyword_tool.load_wordbanks()
|
||
)
|
||
empty_banks = [k for k, v in wordbanks.items() if not v]
|
||
if empty_banks:
|
||
st.error(
|
||
f"""
|
||
❌ **词库为空**
|
||
|
||
以下词库为空，请先添加词汇：
|
||
- {', '.join(empty_banks)}
|
||
|
||
**操作步骤：**
|
||
1. 点击"词库管理"
|
||
2. 选择空的词库类型
|
||
3. 添加至少 3-5 个词汇
|
||
4. 点击"更新词库"
|
||
5. 重新生成关键词
|
||
"""
|
||
)
|
||
elif not st.session_state.get("selected_patterns") or len(
|
||
st.session_state.get("selected_patterns", [])
|
||
) == 0:
|
||
st.error(
|
||
"""
|
||
❌ **未选择组合模式**
|
||
|
||
请至少选择一个组合模式后重试。
|
||
"""
|
||
)
|
||
elif not gen_llm:
|
||
st.error(
|
||
"""
|
||
❌ **LLM 配置缺失**
|
||
|
||
混合模式需要 LLM 进行润色，请检查侧边栏的 API Key 配置。
|
||
"""
|
||
)
|
||
else:
|
||
st.error(
|
||
"""
|
||
❌ **生成失败**
|
||
|
||
请检查配置后重试。
|
||
"""
|
||
)
|
||
|
||
if st.session_state.keywords:
|
||
# 语义足迹扩展功能
|
||
st.markdown("---")
|
||
st.markdown("**🌐 语义足迹扩展**")
|
||
st.caption(
|
||
"基于现有关键词，通过语义相似度扩展出更多相关关键词，提升关键词覆盖面"
|
||
)
|
||
|
||
# 使用容器包装，使布局更清晰
|
||
with st.container(border=True):
|
||
# 第一行：扩展数量滑块（单独一行，更清晰）
|
||
current_keyword_count = len(st.session_state.keywords)
|
||
max_expansion = max(
|
||
11, min(100, current_keyword_count * 3)
|
||
) # 最多扩展到当前数量的3倍，但确保至少为11（因为最小值是10）
|
||
default_expansion = min(
|
||
30, max(10, current_keyword_count)
|
||
) # 默认值不超过当前数量
|
||
|
||
expansion_count = st.slider(
|
||
"扩展数量",
|
||
10,
|
||
max_expansion,
|
||
default_expansion,
|
||
key="semantic_expansion_count",
|
||
help=f"期望扩展的关键词数量（当前有 {current_keyword_count} 个关键词，建议扩展 10-{max_expansion} 个）",
|
||
)
|
||
|
||
# 第二行：按钮和合并策略并排
|
||
expand_col1, expand_col2 = st.columns([2, 1])
|
||
|
||
with expand_col1:
|
||
expand_keywords_btn = st.button(
|
||
"🚀 开始语义扩展",
|
||
use_container_width=True,
|
||
disabled=(
|
||
(not st.session_state.cfg_valid)
|
||
or (gen_llm is None)
|
||
or (len(st.session_state.keywords) == 0)
|
||
),
|
||
key="semantic_expand_btn",
|
||
)
|
||
|
||
with expand_col2:
|
||
merge_strategy = st.selectbox(
|
||
"合并策略",
|
||
["追加", "替换", "交替"],
|
||
index=0,
|
||
key="merge_strategy",
|
||
help="追加：在现有关键词后添加扩展词；替换：用扩展词替换现有关键词；交替：交替插入",
|
||
)
|
||
|
||
# 初始化语义扩展相关状态
|
||
ss_init("expanded_keywords", [])
|
||
ss_init("expansion_stats", None)
|
||
ss_init("expansion_details", [])
|
||
ss_init("original_keywords_before_expansion", []) # 保存扩展前的原始关键词
|
||
|
||
# 执行语义扩展
|
||
if expand_keywords_btn and gen_llm and st.session_state.keywords:
|
||
# 保存扩展前的原始关键词列表（用于撤销功能）
|
||
if not st.session_state.original_keywords_before_expansion:
|
||
st.session_state.original_keywords_before_expansion = (
|
||
st.session_state.keywords.copy()
|
||
)
|
||
|
||
semantic_expander = SemanticExpander()
|
||
with st.spinner(f"正在扩展关键词（目标：{expansion_count} 个）..."):
|
||
try:
|
||
expand_chain = (
|
||
PromptTemplate.from_template("{input}")
|
||
| gen_llm
|
||
| StrOutputParser()
|
||
)
|
||
expansion_result = semantic_expander.expand_keywords(
|
||
st.session_state.keywords,
|
||
brand,
|
||
advantages,
|
||
expansion_count,
|
||
expand_chain,
|
||
)
|
||
|
||
expanded_keywords = expansion_result.get("expanded_keywords", [])
|
||
st.session_state.expanded_keywords = expanded_keywords
|
||
st.session_state.expansion_stats = expansion_result.get(
|
||
"expansion_stats", {}
|
||
)
|
||
st.session_state.expansion_details = expansion_result.get(
|
||
"expansion_details", []
|
||
)
|
||
|
||
if expanded_keywords:
|
||
# 合并关键词
|
||
strategy_map = {"追加": "append", "替换": "replace", "交替": "interleave"}
|
||
merged = semantic_expander.merge_keywords(
|
||
st.session_state.keywords,
|
||
expanded_keywords,
|
||
strategy_map.get(merge_strategy, "append"),
|
||
)
|
||
st.session_state.keywords = merged
|
||
|
||
# 保存到数据库
|
||
try:
|
||
storage.save_keywords(merged, brand)
|
||
except Exception as e:
|
||
st.warning(f"关键词已扩展，但保存到数据库时出错：{e}")
|
||
|
||
st.success(
|
||
f"✅ 语义扩展完成！新增 {len(expanded_keywords)} 个关键词，总计 {len(merged)} 个"
|
||
)
|
||
|
||
# 添加撤销功能提示
|
||
if st.session_state.original_keywords_before_expansion:
|
||
if st.button(
|
||
"↩️ 撤销扩展",
|
||
key="undo_expansion",
|
||
use_container_width=False,
|
||
):
|
||
st.session_state.keywords = (
|
||
st.session_state.original_keywords_before_expansion.copy()
|
||
)
|
||
st.session_state.expanded_keywords = []
|
||
st.session_state.original_keywords_before_expansion = []
|
||
st.success("✅ 已撤销扩展，恢复为原始关键词列表")
|
||
st.rerun()
|
||
else:
|
||
st.warning("⚠️ 未生成扩展关键词，请检查输入或重试")
|
||
except Exception as e:
|
||
# 区分不同类型的错误
|
||
error_msg = str(e)
|
||
if "timeout" in error_msg.lower() or "connection" in error_msg.lower():
|
||
st.error(
|
||
f"""
|
||
❌ **网络连接错误**
|
||
|
||
语义扩展失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查网络连接
|
||
2. 检查 API Key 配置
|
||
3. 稍后重试
|
||
"""
|
||
)
|
||
elif (
|
||
"api" in error_msg.lower()
|
||
or "key" in error_msg.lower()
|
||
or "auth" in error_msg.lower()
|
||
):
|
||
st.error(
|
||
f"""
|
||
❌ **API 配置错误**
|
||
|
||
语义扩展失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查侧边栏的 API Key 配置
|
||
2. 确认 API Key 有效且有足够余额
|
||
3. 检查 API 服务是否正常
|
||
"""
|
||
)
|
||
elif "json" in error_msg.lower() or "parse" in error_msg.lower():
|
||
st.error(
|
||
f"""
|
||
❌ **数据解析错误**
|
||
|
||
语义扩展失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 重试扩展操作
|
||
2. 如果问题持续，请联系技术支持
|
||
"""
|
||
)
|
||
else:
|
||
st.error(
|
||
f"""
|
||
❌ **语义扩展失败**
|
||
|
||
错误信息：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查输入的关键词是否有效
|
||
2. 重试扩展操作
|
||
3. 如果问题持续，请联系技术支持
|
||
"""
|
||
)
|
||
|
||
# 显示扩展统计信息
|
||
if st.session_state.expansion_stats:
|
||
stats = st.session_state.expansion_stats
|
||
st.markdown("##### 📊 扩展统计")
|
||
col1, col2, col3, col4, col5, col6 = st.columns(6)
|
||
with col1:
|
||
st.metric("扩展总数", stats.get("total_expanded", 0))
|
||
with col2:
|
||
st.metric("同义扩展", stats.get("synonym_count", 0))
|
||
with col3:
|
||
st.metric("场景扩展", stats.get("scenario_count", 0))
|
||
with col4:
|
||
st.metric("问题扩展", stats.get("question_count", 0))
|
||
with col5:
|
||
st.metric("功能扩展", stats.get("feature_count", 0))
|
||
with col6:
|
||
st.metric("长尾扩展", stats.get("longtail_count", 0))
|
||
|
||
# 显示扩展详情
|
||
if st.session_state.expansion_details:
|
||
with st.expander("📝 扩展详情", expanded=False):
|
||
for detail in st.session_state.expansion_details[:10]: # 只显示前10个
|
||
st.markdown(f"**原关键词**：{detail.get('original', 'N/A')}")
|
||
st.markdown(f"**扩展类型**：{detail.get('type', 'N/A')}")
|
||
expanded_list = detail.get("expanded", [])
|
||
if expanded_list:
|
||
st.markdown(
|
||
f"**扩展词**：{', '.join(expanded_list[:5])}"
|
||
) # 只显示前5个
|
||
st.markdown("---")
|
||
|
||
# 显示覆盖面分析
|
||
if st.session_state.expanded_keywords and st.session_state.keywords:
|
||
semantic_expander = SemanticExpander()
|
||
# 计算原始关键词数量（扩展前的）
|
||
original_count = len(st.session_state.keywords) - len(
|
||
st.session_state.expanded_keywords
|
||
)
|
||
original_keywords = (
|
||
st.session_state.keywords[:original_count] if original_count > 0 else []
|
||
)
|
||
|
||
coverage = semantic_expander.analyze_expansion_coverage(
|
||
original_keywords,
|
||
st.session_state.expanded_keywords,
|
||
)
|
||
|
||
if coverage.get("coverage_ratio", 0) > 0:
|
||
with st.expander("📈 覆盖面分析", expanded=False):
|
||
st.metric(
|
||
"扩展比例",
|
||
f"{coverage.get('expansion_ratio', 0):.2f}x",
|
||
)
|
||
st.metric("唯一关键词", coverage.get("unique_keywords", 0))
|
||
|
||
categories = coverage.get("categories", {})
|
||
if categories:
|
||
st.markdown("**关键词类别分布：**")
|
||
for cat, count in categories.items():
|
||
if count > 0:
|
||
cat_name = {
|
||
"question": "问题类",
|
||
"scenario": "场景类",
|
||
"comparison": "对比类",
|
||
"feature": "功能类",
|
||
"other": "其他",
|
||
}.get(cat, cat)
|
||
st.markdown(f"- {cat_name}：{count} 个")
|
||
|
||
# 话题集群生成功能
|
||
st.markdown("---")
|
||
st.markdown("**🎯 话题集群生成**")
|
||
st.caption("将关键词聚类为话题集群，系统化规划内容策略，发现内容盲区")
|
||
|
||
# 初始化话题集群相关状态
|
||
ss_init("topic_clusters", [])
|
||
ss_init("cluster_relationships", [])
|
||
ss_init("cluster_stats", None)
|
||
ss_init("content_planning", None)
|
||
|
||
with st.container(border=True):
|
||
cluster_col1, cluster_col2 = st.columns([2, 1])
|
||
|
||
with cluster_col1:
|
||
current_keyword_count = len(st.session_state.keywords)
|
||
# 集群数量不能超过关键词数量，也不能少于3个
|
||
# 每个集群至少3个关键词，但确保 max_clusters >= 4（因为最小值是3）
|
||
max_clusters = max(
|
||
4, min(10, max(4, current_keyword_count // 3))
|
||
) # 确保至少为4
|
||
default_clusters = min(5, max_clusters)
|
||
|
||
cluster_count = st.slider(
|
||
"话题集群数量",
|
||
3,
|
||
max_clusters,
|
||
default_clusters,
|
||
key="cluster_count",
|
||
help=f"建议范围：3-{max_clusters}个话题集群（当前有 {current_keyword_count} 个关键词）",
|
||
)
|
||
|
||
with cluster_col2:
|
||
generate_clusters_btn = st.button(
|
||
"🚀 生成话题集群",
|
||
use_container_width=True,
|
||
disabled=(
|
||
(not st.session_state.cfg_valid)
|
||
or (gen_llm is None)
|
||
or (len(st.session_state.keywords) == 0)
|
||
),
|
||
key="generate_clusters_btn",
|
||
)
|
||
|
||
# 执行话题聚类
|
||
if generate_clusters_btn and gen_llm and st.session_state.keywords:
|
||
topic_cluster = TopicCluster()
|
||
with st.spinner(f"正在生成话题集群（目标：{cluster_count} 个）..."):
|
||
try:
|
||
cluster_chain = (
|
||
PromptTemplate.from_template("{input}")
|
||
| gen_llm
|
||
| StrOutputParser()
|
||
)
|
||
cluster_result = topic_cluster.cluster_keywords(
|
||
st.session_state.keywords,
|
||
brand,
|
||
advantages,
|
||
cluster_count,
|
||
cluster_chain,
|
||
)
|
||
|
||
clusters = cluster_result.get("clusters", [])
|
||
relationships = cluster_result.get("relationships", [])
|
||
cluster_stats = cluster_result.get("cluster_stats", {})
|
||
|
||
st.session_state.topic_clusters = clusters
|
||
st.session_state.cluster_relationships = relationships
|
||
st.session_state.cluster_stats = cluster_stats
|
||
|
||
if clusters:
|
||
st.success(
|
||
f"✅ 话题集群生成完成！共生成 {len(clusters)} 个话题集群"
|
||
)
|
||
|
||
# 自动生成内容规划建议
|
||
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.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:
|
||
# 区分不同类型的错误
|
||
error_msg = str(e)
|
||
if "timeout" in error_msg.lower() or "connection" in error_msg.lower():
|
||
st.error(
|
||
f"""
|
||
❌ **网络连接错误**
|
||
|
||
话题集群生成失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查网络连接
|
||
2. 检查 API Key 配置
|
||
3. 稍后重试
|
||
"""
|
||
)
|
||
elif (
|
||
"api" in error_msg.lower()
|
||
or "key" in error_msg.lower()
|
||
or "auth" in error_msg.lower()
|
||
):
|
||
st.error(
|
||
f"""
|
||
❌ **API 配置错误**
|
||
|
||
话题集群生成失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查侧边栏的 API Key 配置
|
||
2. 确认 API Key 有效且有足够余额
|
||
3. 检查 API 服务是否正常
|
||
"""
|
||
)
|
||
elif "json" in error_msg.lower() or "parse" in error_msg.lower():
|
||
st.error(
|
||
f"""
|
||
❌ **数据解析错误**
|
||
|
||
话题集群生成失败：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 重试生成操作
|
||
2. 如果问题持续，请联系技术支持
|
||
"""
|
||
)
|
||
else:
|
||
st.error(
|
||
f"""
|
||
❌ **话题集群生成失败**
|
||
|
||
错误信息：{error_msg}
|
||
|
||
**解决建议：**
|
||
1. 检查输入的关键词是否有效
|
||
2. 尝试调整话题集群数量
|
||
3. 重试生成操作
|
||
4. 如果问题持续，请联系技术支持
|
||
"""
|
||
)
|
||
|
||
# 显示话题集群结果
|
||
if st.session_state.topic_clusters:
|
||
clusters = st.session_state.topic_clusters
|
||
relationships = st.session_state.cluster_relationships
|
||
cluster_stats = st.session_state.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)
|
||
)
|
||
|
||
# 显示话题集群列表
|
||
st.markdown("##### 📋 话题集群列表")
|
||
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[:10])}{' ...' if len(keywords_list) > 10 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)
|
||
|
||
# 显示可视化（网络图）
|
||
if len(clusters) > 1:
|
||
st.markdown("##### 📈 话题网络图")
|
||
try:
|
||
viz_data = topic_cluster.get_visualization_data(
|
||
clusters, relationships
|
||
)
|
||
|
||
# 准备节点数据
|
||
nodes = viz_data.get("nodes", [])
|
||
edges = viz_data.get("edges", [])
|
||
|
||
if nodes:
|
||
# 创建节点位置（简单的圆形布局）
|
||
n = len(nodes)
|
||
node_x = []
|
||
node_y = []
|
||
node_text = []
|
||
node_sizes = []
|
||
|
||
for i, node in enumerate(nodes):
|
||
angle = 2 * math.pi * i / n
|
||
radius = 1.0
|
||
node_x.append(radius * math.cos(angle))
|
||
node_y.append(radius * math.sin(angle))
|
||
node_text.append(
|
||
f"{node['name']}<br>({node['size']}个关键词)"
|
||
)
|
||
node_sizes.append(node["size"] * 3 + 10)
|
||
|
||
# 创建边
|
||
edge_x = []
|
||
edge_y = []
|
||
for edge in edges:
|
||
source_idx = next(
|
||
(
|
||
i
|
||
for i, n in enumerate(nodes)
|
||
if n["id"] == edge["source"]
|
||
),
|
||
None,
|
||
)
|
||
target_idx = next(
|
||
(
|
||
i
|
||
for i, n in enumerate(nodes)
|
||
if n["id"] == edge["target"]
|
||
),
|
||
None,
|
||
)
|
||
if source_idx is not None and target_idx is not None:
|
||
edge_x.extend(
|
||
[node_x[source_idx], node_x[target_idx], None]
|
||
)
|
||
edge_y.extend(
|
||
[node_y[source_idx], node_y[target_idx], None]
|
||
)
|
||
|
||
# 创建图形
|
||
fig = go.Figure()
|
||
|
||
# 添加边
|
||
fig.add_trace(
|
||
go.Scatter(
|
||
x=edge_x,
|
||
y=edge_y,
|
||
line=dict(width=1, color="#888"),
|
||
hoverinfo="none",
|
||
mode="lines",
|
||
)
|
||
)
|
||
|
||
# 添加节点
|
||
fig.add_trace(
|
||
go.Scatter(
|
||
x=node_x,
|
||
y=node_y,
|
||
mode="markers+text",
|
||
marker=dict(
|
||
size=node_sizes,
|
||
color="#2563EB",
|
||
line=dict(width=2, color="white"),
|
||
),
|
||
text=[node["name"] for node in nodes],
|
||
textposition="middle center",
|
||
textfont=dict(size=10, color="white"),
|
||
hovertext=node_text,
|
||
hoverinfo="text",
|
||
name="话题集群",
|
||
)
|
||
)
|
||
|
||
fig.update_layout(
|
||
title="话题集群网络图",
|
||
showlegend=False,
|
||
hovermode="closest",
|
||
margin=dict(b=20, l=5, r=5, t=40),
|
||
annotations=[
|
||
dict(
|
||
text="节点大小表示关键词数量，连线表示话题关联",
|
||
showarrow=False,
|
||
xref="paper",
|
||
yref="paper",
|
||
x=0.005,
|
||
y=-0.002,
|
||
xanchor="left",
|
||
yanchor="bottom",
|
||
font=dict(size=10, color="#888"),
|
||
)
|
||
],
|
||
xaxis=dict(
|
||
showgrid=False,
|
||
zeroline=False,
|
||
showticklabels=False,
|
||
),
|
||
yaxis=dict(
|
||
showgrid=False,
|
||
zeroline=False,
|
||
showticklabels=False,
|
||
),
|
||
height=500,
|
||
)
|
||
|
||
st.plotly_chart(fig, use_container_width=True)
|
||
except Exception as e:
|
||
st.warning(f"可视化生成失败：{e}")
|
||
|
||
# 显示内容规划建议
|
||
if st.session_state.content_planning:
|
||
planning = st.session_state.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**")
|
||
for gap in content_gaps[:5]: # 只显示前5个
|
||
st.markdown(
|
||
f"- **{gap.get('cluster_name', 'N/A')}**：{gap.get('description', 'N/A')}（优先级：{gap.get('priority', '中')}）"
|
||
)
|
||
|
||
# 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}),
|
||
)
|
||
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])}"
|
||
)
|
||
st.markdown("---")
|
||
|
||
# ========== 区域 5：关键词列表（条件显示） ==========
|
||
st.markdown("---")
|
||
st.markdown("**📋 关键词列表**")
|
||
|
||
# 添加搜索和筛选
|
||
search_col, filter_col = st.columns([3, 1])
|
||
with search_col:
|
||
search_term = st.text_input(
|
||
"🔍 搜索关键词", key="kw_search", placeholder="输入关键词搜索..."
|
||
)
|
||
with filter_col:
|
||
show_original = st.checkbox(
|
||
"仅显示原始关键词", key="kw_filter_original", value=False
|
||
)
|
||
|
||
# 过滤关键词
|
||
display_keywords = st.session_state.keywords
|
||
if search_term and search_term.strip(): # 检查非空字符串
|
||
search_term_lower = search_term.strip().lower()
|
||
display_keywords = [
|
||
kw for kw in display_keywords if search_term_lower in kw.lower()
|
||
]
|
||
if show_original and st.session_state.expanded_keywords:
|
||
original_count = len(st.session_state.keywords) - len(
|
||
st.session_state.expanded_keywords
|
||
)
|
||
display_keywords = (
|
||
display_keywords[:original_count] if original_count > 0 else []
|
||
)
|
||
|
||
# 显示列表（分页）
|
||
if display_keywords:
|
||
page_size = 20
|
||
total_pages = max(1, (len(display_keywords) - 1) // page_size + 1)
|
||
page = st.session_state.get("kw_page", 1)
|
||
|
||
if total_pages > 1:
|
||
page_col1, page_col2, page_col3 = st.columns([1, 2, 1])
|
||
with page_col2:
|
||
page = st.selectbox(
|
||
"页码",
|
||
range(1, total_pages + 1),
|
||
index=min(page - 1, total_pages - 1),
|
||
key="kw_page_select",
|
||
)
|
||
st.session_state.kw_page = page
|
||
else:
|
||
page = 1
|
||
|
||
start_idx = (page - 1) * page_size
|
||
end_idx = start_idx + page_size
|
||
page_keywords = display_keywords[start_idx:end_idx]
|
||
|
||
df = pd.DataFrame(page_keywords, columns=["长尾关键词/问题"])
|
||
st.dataframe(df, use_container_width=True, hide_index=True)
|
||
|
||
st.caption(
|
||
f"显示第 {start_idx + 1}-{min(end_idx, len(display_keywords))} 条，共 {len(display_keywords)} 条关键词"
|
||
)
|
||
|
||
# 区分原始和扩展关键词
|
||
if st.session_state.expanded_keywords:
|
||
original_count = len(st.session_state.keywords) - len(
|
||
st.session_state.expanded_keywords
|
||
)
|
||
st.info(
|
||
f"📌 原始关键词：{original_count} 个 | 🆕 扩展关键词：{len(st.session_state.expanded_keywords)} 个"
|
||
)
|
||
else:
|
||
if search_term or show_original:
|
||
st.info("未找到匹配的关键词")
|
||
else:
|
||
st.info("暂无关键词")
|
||
|
||
# 下载按钮
|
||
st.download_button(
|
||
"📥 下载关键词 CSV",
|
||
pd.DataFrame(
|
||
st.session_state.keywords, columns=["长尾关键词/问题"]
|
||
).to_csv(index=False, encoding="utf-8-sig"),
|
||
f"{sanitize_filename(brand,40)}_keywords.csv",
|
||
mime="text/csv",
|
||
use_container_width=True,
|
||
key="kw_dl_csv",
|
||
)
|
||
|
||
# ========== 区域 6：智能挖掘（条件显示，默认折叠） ==========
|
||
st.markdown("---")
|
||
with st.expander("🔍 智能关键词挖掘与趋势分析", expanded=False):
|
||
st.caption(
|
||
"发现高价值关键词，分析竞争度，预测趋势，优化关键词策略"
|
||
)
|
||
|
||
# 初始化关键词挖掘器
|
||
keyword_miner = KeywordMining(storage)
|
||
|
||
# 创建子标签页
|
||
mining_tab1, mining_tab2, mining_tab3, mining_tab4 = st.tabs(
|
||
[
|
||
"🌐 行业热点挖掘",
|
||
"📊 竞争度分析",
|
||
"📈 趋势预测",
|
||
"💎 价值矩阵",
|
||
]
|
||
)
|
||
|
||
with mining_tab1:
|
||
st.caption("基于行业趋势自动挖掘高价值关键词")
|
||
|
||
with st.container(border=True):
|
||
# 默认使用 brand，允许覆盖
|
||
default_industry = brand if brand else "外贸ERP"
|
||
industry = st.text_input(
|
||
"行业领域",
|
||
value=default_industry,
|
||
key="mining_industry",
|
||
help="输入您的行业领域，如：外贸ERP、AI工具、SaaS产品等",
|
||
)
|
||
num_mine = st.slider("挖掘数量", 10, 50, 20, key="mining_num")
|
||
|
||
mine_btn = st.button(
|
||
"🚀 开始挖掘",
|
||
use_container_width=True,
|
||
disabled=(not st.session_state.cfg_valid) or (gen_llm is None),
|
||
)
|
||
|
||
ss_init("mined_keywords", [])
|
||
|
||
if mine_btn and gen_llm and industry:
|
||
with st.spinner(f"正在挖掘行业关键词（目标：{num_mine} 个）..."):
|
||
try:
|
||
mine_chain = (
|
||
PromptTemplate.from_template("{input}")
|
||
| gen_llm
|
||
| StrOutputParser()
|
||
)
|
||
mined_keywords = keyword_miner.mine_industry_keywords(
|
||
brand=brand,
|
||
industry=industry,
|
||
advantages=advantages,
|
||
num_keywords=num_mine,
|
||
llm_chain=mine_chain,
|
||
)
|
||
|
||
if mined_keywords:
|
||
st.session_state.mined_keywords = mined_keywords
|
||
st.success(
|
||
f"✅ 挖掘完成！发现 {len(mined_keywords)} 个关键词"
|
||
)
|
||
else:
|
||
st.warning(
|
||
"⚠️ 未挖掘到关键词，请检查输入或重试"
|
||
)
|
||
except Exception as e:
|
||
st.error(f"挖掘失败：{e}")
|
||
|
||
# 显示挖掘结果
|
||
if st.session_state.mined_keywords:
|
||
mined_kw_list = st.session_state.mined_keywords
|
||
st.markdown("##### 📋 挖掘结果")
|
||
|
||
for i, kw_data in enumerate(mined_kw_list):
|
||
with st.container(border=True):
|
||
col1, col2, col3 = st.columns([3, 1, 1])
|
||
with col1:
|
||
st.markdown(f"**{kw_data.get('keyword', 'N/A')}**")
|
||
st.caption(
|
||
f"类别：{kw_data.get('category', 'N/A')} | 意图：{kw_data.get('intent', 'N/A')}"
|
||
)
|
||
with col2:
|
||
st.metric(
|
||
"预估价值",
|
||
f"{kw_data.get('estimated_value', 0)}/10",
|
||
)
|
||
with col3:
|
||
if st.button(
|
||
"添加",
|
||
key=f"add_mined_{i}",
|
||
use_container_width=True,
|
||
):
|
||
if kw_data.get("keyword") not in st.session_state.keywords:
|
||
st.session_state.keywords.append(
|
||
kw_data.get("keyword")
|
||
)
|
||
storage.save_keywords(
|
||
[kw_data.get("keyword")], brand
|
||
)
|
||
st.success("已添加")
|
||
st.rerun()
|
||
|
||
with mining_tab2:
|
||
st.caption("分析关键词在 AI 中的提及频率和竞争程度")
|
||
|
||
keywords_to_analyze = st.multiselect(
|
||
"选择要分析的关键词",
|
||
options=st.session_state.keywords
|
||
if st.session_state.keywords
|
||
else [],
|
||
key="comp_keywords_select",
|
||
help="选择要分析竞争度的关键词",
|
||
)
|
||
|
||
analyze_comp_btn = st.button(
|
||
"📊 开始分析",
|
||
use_container_width=True,
|
||
disabled=len(keywords_to_analyze) == 0,
|
||
)
|
||
|
||
ss_init("competition_analysis", {})
|
||
|
||
if analyze_comp_btn and keywords_to_analyze:
|
||
with st.spinner("正在分析竞争度..."):
|
||
try:
|
||
competition_data = keyword_miner.analyze_competition(
|
||
keywords=keywords_to_analyze,
|
||
brand=brand,
|
||
)
|
||
st.session_state.competition_analysis = competition_data
|
||
st.success("✅ 分析完成！")
|
||
except Exception as e:
|
||
st.error(f"分析失败：{e}")
|
||
|
||
if st.session_state.competition_analysis:
|
||
comp_data = st.session_state.competition_analysis
|
||
st.markdown("##### 📊 竞争度分析结果")
|
||
|
||
comp_df_data = []
|
||
for keyword, data in comp_data.items():
|
||
comp_df_data.append(
|
||
{
|
||
"关键词": keyword,
|
||
"提及率": f"{data.get('mention_rate', 0):.2%}",
|
||
"竞争级别": data.get("competition_level", "未知"),
|
||
"竞品提及": data.get("competitor_mentions", 0),
|
||
"总提及": data.get("total_mentions", 0),
|
||
"数据点": data.get("data_points", 0),
|
||
}
|
||
)
|
||
|
||
if comp_df_data:
|
||
comp_df = pd.DataFrame(comp_df_data)
|
||
st.dataframe(comp_df, use_container_width=True, hide_index=True)
|
||
|
||
if len(comp_df_data) > 0:
|
||
fig = px.bar(
|
||
comp_df,
|
||
x="关键词",
|
||
y="提及率",
|
||
color="竞争级别",
|
||
title="关键词竞争度分析",
|
||
labels={"提及率": "提及率 (%)"},
|
||
)
|
||
fig.update_xaxes(tickangle=-45)
|
||
st.plotly_chart(fig, use_container_width=True)
|
||
|
||
with mining_tab3:
|
||
st.caption("基于历史数据预测关键词热度变化趋势")
|
||
|
||
keywords_to_predict = st.multiselect(
|
||
"选择要预测的关键词",
|
||
options=st.session_state.keywords
|
||
if st.session_state.keywords
|
||
else [],
|
||
key="trend_keywords_select",
|
||
help="选择要预测趋势的关键词",
|
||
)
|
||
|
||
predict_days = st.slider(
|
||
"预测未来天数", 7, 90, 30, key="predict_days"
|
||
)
|
||
predict_btn = st.button(
|
||
"🔮 开始预测",
|
||
use_container_width=True,
|
||
disabled=len(keywords_to_predict) == 0,
|
||
)
|
||
|
||
ss_init("trend_analysis", {})
|
||
|
||
if predict_btn and keywords_to_predict:
|
||
with st.spinner("正在预测趋势..."):
|
||
try:
|
||
trend_data = keyword_miner.predict_trend(
|
||
keywords=keywords_to_predict,
|
||
brand=brand,
|
||
days=predict_days,
|
||
)
|
||
st.session_state.trend_analysis = trend_data
|
||
st.success("✅ 预测完成！")
|
||
except Exception as e:
|
||
st.error(f"预测失败：{e}")
|
||
|
||
if st.session_state.trend_analysis:
|
||
trend_data = st.session_state.trend_analysis
|
||
st.markdown("##### 📈 趋势预测结果")
|
||
|
||
trend_df_data = []
|
||
for keyword, data in trend_data.items():
|
||
trend_df_data.append(
|
||
{
|
||
"关键词": keyword,
|
||
"当前提及率": f"{data.get('current_rate', 0):.2%}",
|
||
"预测提及率": f"{data.get('predicted_mention_rate', 0):.2%}",
|
||
"趋势": data.get("trend", "未知"),
|
||
"趋势强度": f"{data.get('trend_strength', 0):.2%}",
|
||
"置信度": f"{data.get('confidence', 0):.2%}",
|
||
"数据点": data.get("data_points", 0),
|
||
}
|
||
)
|
||
|
||
if trend_df_data:
|
||
trend_df = pd.DataFrame(trend_df_data)
|
||
st.dataframe(trend_df, use_container_width=True, hide_index=True)
|
||
|
||
with mining_tab4:
|
||
st.caption("分析关键词的价值和竞争度，找到最优投入策略")
|
||
|
||
keywords_for_matrix = st.multiselect(
|
||
"选择要分析的关键词",
|
||
options=st.session_state.keywords
|
||
if st.session_state.keywords
|
||
else [],
|
||
key="matrix_keywords_select",
|
||
help="选择要分析价值矩阵的关键词",
|
||
)
|
||
|
||
estimated_values = {}
|
||
if st.session_state.mined_keywords:
|
||
for kw_data in st.session_state.mined_keywords:
|
||
if kw_data.get("keyword") in keywords_for_matrix:
|
||
estimated_values[kw_data.get("keyword")] = kw_data.get(
|
||
"estimated_value", 5
|
||
)
|
||
|
||
analyze_matrix_btn = st.button(
|
||
"💎 开始分析",
|
||
use_container_width=True,
|
||
disabled=len(keywords_for_matrix) == 0,
|
||
)
|
||
|
||
ss_init("value_matrix", {})
|
||
ss_init("keyword_recommendations", [])
|
||
|
||
if analyze_matrix_btn and keywords_for_matrix:
|
||
with st.spinner("正在分析价值矩阵..."):
|
||
try:
|
||
if not st.session_state.competition_analysis:
|
||
competition_data = keyword_miner.analyze_competition(
|
||
keywords=keywords_for_matrix,
|
||
brand=brand,
|
||
)
|
||
else:
|
||
competition_data = (
|
||
st.session_state.competition_analysis
|
||
)
|
||
|
||
value_matrix = keyword_miner.calculate_value_matrix(
|
||
keywords=keywords_for_matrix,
|
||
competition_data=competition_data,
|
||
estimated_values=estimated_values
|
||
if estimated_values
|
||
else None,
|
||
)
|
||
st.session_state.value_matrix = value_matrix
|
||
|
||
trend_data = (
|
||
st.session_state.trend_analysis
|
||
if st.session_state.trend_analysis
|
||
else None
|
||
)
|
||
|
||
recommendations = keyword_miner.recommend_keywords(
|
||
keywords=keywords_for_matrix,
|
||
value_matrix=value_matrix,
|
||
competition_data=competition_data,
|
||
trend_data=trend_data,
|
||
top_n=len(keywords_for_matrix),
|
||
)
|
||
st.session_state.keyword_recommendations = recommendations
|
||
|
||
st.success("✅ 分析完成！")
|
||
except Exception as e:
|
||
st.error(f"分析失败：{e}")
|
||
|
||
if st.session_state.value_matrix:
|
||
matrix_data = st.session_state.value_matrix
|
||
st.markdown("##### 💎 价值矩阵结果")
|
||
|
||
matrix_df_data = []
|
||
for keyword, data in matrix_data.items():
|
||
matrix_df_data.append(
|
||
{
|
||
"关键词": keyword,
|
||
"价值分数": data.get("value_score", 0),
|
||
"竞争分数": data.get("competition_score", 0),
|
||
"矩阵位置": data.get("matrix_position", "未知"),
|
||
"推荐建议": data.get("recommendation", ""),
|
||
}
|
||
)
|
||
|
||
if matrix_df_data:
|
||
matrix_df = pd.DataFrame(matrix_df_data)
|
||
st.dataframe(matrix_df, use_container_width=True, hide_index=True)
|
||
|
||
if len(matrix_df_data) > 0:
|
||
fig = px.scatter(
|
||
matrix_df,
|
||
x="竞争分数",
|
||
y="价值分数",
|
||
color="矩阵位置",
|
||
size=[10] * len(matrix_df),
|
||
hover_data=["关键词", "推荐建议"],
|
||
title="关键词价值矩阵",
|
||
labels={
|
||
"竞争分数": "竞争度（越高越激烈）",
|
||
"价值分数": "价值（0-10分）",
|
||
},
|
||
)
|
||
st.plotly_chart(fig, use_container_width=True)
|
||
|
||
if st.session_state.keyword_recommendations:
|
||
recommendations = st.session_state.keyword_recommendations
|
||
st.markdown("##### ⭐ 智能推荐（按推荐度排序）")
|
||
|
||
for i, rec in enumerate(recommendations[:10], 1):
|
||
with st.container(border=True):
|
||
col1, col2, col3, col4 = st.columns([3, 1, 1, 1])
|
||
with col1:
|
||
st.markdown(
|
||
f"**{i}. {rec.get('keyword', 'N/A')}**"
|
||
)
|
||
st.caption(rec.get("recommendation", ""))
|
||
with col2:
|
||
st.metric(
|
||
"推荐分",
|
||
f"{rec.get('recommendation_score', 0):.1f}",
|
||
)
|
||
with col3:
|
||
st.metric(
|
||
"价值", f"{rec.get('value_score', 0):.1f}"
|
||
)
|
||
with col4:
|
||
trend_emoji = {
|
||
"上升": "📈",
|
||
"下降": "📉",
|
||
"稳定": "➡️",
|
||
}.get(rec.get("trend", "稳定"), "➡️")
|
||
st.metric(
|
||
"趋势",
|
||
f"{trend_emoji} {rec.get('trend', '稳定')}",
|
||
)
|
||
else:
|
||
st.info("在左侧完成配置后，点击“生成关键词”。")
|
||
|