# Tab3：文章优化（从 geo_tool.py 迁移，通过 render_tab_optimize() 供主入口调用。） import re import streamlit as st from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import PromptTemplate from modules.eeat_enhancer import EEATEnhancer from modules.fact_density_enhancer import FactDensityEnhancer from modules.optimization_techniques import OptimizationTechniqueManager from modules.schema_generator import SchemaGenerator from modules.technical_config_generator import TechnicalConfigGenerator from modules.ui.components import sanitize_filename, safe_decode_uploaded, render_tab_top_with_clear def render_tab_optimize( storage, ss_init, gen_llm, brand: str, advantages: str, cfg: dict, record_api_cost, model_defaults, ) -> None: """渲染 Tab3：文章优化。由主入口在 with tab3 内调用。""" # 标题和清空按钮 def _clear_optimize_state(): st.session_state.optimized_article = "" st.session_state.opt_changes = "" render_tab_top_with_clear( title="🔧 文章优化", caption="优化已有文章，生成结构化数据和技术配置，提升 GEO 效果", clear_key="opt_clear", on_clear=_clear_optimize_state, ) # === 文章优化功能（主流程） === st.markdown("---") st.markdown("**✏️ 文章内容优化**") with st.container(border=True): st.markdown("粘贴或上传已写文章，一键提升 GEO 效果（结构化、可引用、自然植入品牌）") # 输入方式与文章内容放在表单外，以便粘贴/上传后能触发重跑，从而正确更新「开始优化」按钮的可用状态 input_mode = st.radio( "输入方式", ["粘贴文本", "上传文件（TXT/MD）"], horizontal=True, key="opt_input_mode", ) if input_mode == "粘贴文本": original_article = st.text_area( "粘贴文章内容", height=360, key="opt_text" ) else: uploaded = st.file_uploader( "上传 TXT 或 MD 文件", type=["txt", "md"], key="opt_uploader", ) original_article = "" if uploaded: try: original_article = safe_decode_uploaded(uploaded) or "" except Exception as e: st.error(f"上传文件解析失败：{e}") original_article = "" if original_article: st.text_area( "上传内容预览", original_article, height=200, disabled=True, key="opt_upload_preview", ) with st.form("opt_form", clear_on_submit=False): target_platform = st.selectbox( "目标平台（影响文风，可选）", [ "通用优化", "知乎（专业问答）", "CSDN（技术博客）", "GitHub（README/文档）", "B站（视频脚本）", "头条号（资讯软文）", "微信公众号（长文）", "抖音图文（短内容）", "百家号（资讯）", "网易号（资讯）", "企鹅号（资讯）", "简书（文艺）", ], index=[ "通用优化", "知乎（专业问答）", "CSDN（技术博客）", "GitHub（README/文档）", "B站（视频脚本）", "头条号（资讯软文）", "微信公众号（长文）", "抖音图文（短内容）", "百家号（资讯）", "网易号（资讯）", "企鹅号（资讯）", "简书（文艺）", ].index( st.session_state.opt_platform if st.session_state.opt_platform in [ "通用优化", "知乎（专业问答）", "CSDN（技术博客）", "GitHub（README/文档）", "B站（视频脚本）", "头条号（资讯软文）", "微信公众号（长文）", "抖音图文（短内容）", "百家号（资讯）", "网易号（资讯）", "企鹅号（资讯）", "简书（文艺）", ] else 0 ), key="opt_platform_sel", ) # 高级优化技巧选择器（可选） with st.expander("🎨 高级优化技巧（可选）", expanded=False): opt_technique_manager = OptimizationTechniqueManager() opt_all_techniques = opt_technique_manager.list_techniques() opt_technique_options = [ f"{tech['icon']} {tech['name']}" for tech in opt_all_techniques ] opt_selected_technique_names = st.multiselect( "选择要应用的优化技巧（可多选）", options=opt_technique_options, default=[], key="opt_techniques", help="可选，提高 GEO 效果。技巧会动态调整文章优化策略。", ) # 显示选择的技巧描述 if opt_selected_technique_names: st.caption("已选择：" + "、".join(opt_selected_technique_names)) with st.expander("查看技巧说明", expanded=False): for tech_name in opt_selected_technique_names: tech_icon_name = ( tech_name.split(" ", 1)[1] if " " in tech_name else tech_name ) for tech in opt_all_techniques: if tech["name"] == tech_icon_name: st.markdown(f"**{tech['icon']} {tech['name']}**") st.caption(tech["description"]) break run_opt_disabled = ( (not st.session_state.cfg_valid) or (gen_llm is None) or (not original_article.strip()) ) run_opt = st.form_submit_button( "开始优化", use_container_width=True, disabled=run_opt_disabled ) if run_opt_disabled: if not original_article.strip(): st.caption("请先在上方粘贴文章内容，或上传 TXT/MD 文件。") elif not st.session_state.cfg_valid or gen_llm is None: st.caption("当前未检测到可用的生成模型，请先在【全局设置】中完成模型/API 配置。") if run_opt: st.session_state.opt_platform = target_platform opt_selected_technique_names = st.session_state.get("opt_techniques", []) optimize_prompt_template = """ 你是GEO优化专家，目标是提升文章在大模型中的引用率和品牌自然提及。 【原文章】 {original_article} 【品牌】{brand} 【优势】{advantages} 【目标平台】{platform} 【优化要求（严格GEO原则）】 1) 保留原意和核心信息，不改变事实 2) 增强结构化：标题、清单、FAQ、代码块（适用时） 3) 自然植入品牌2-4次（先通用标准，再品牌适用） 4) 提升权威感：评估维度、匿名案例、来源占位建议（不得编造） 5) 结论先行、信息密度高 6) 长度控制在原长度的1.0-1.3倍 7) 输出两部分：【优化后文章】 + 【变更说明】（列出主要改动点） 【输出格式要求】 请严格按照以下结构输出一次，不要在前后添加其他说明或重复输出： 【优化后文章】 （在此输出完整优化后的文章） 【变更说明】 （在此列出主要变更点，使用条目形式） 【E-E-A-T 强化要求】 - 专业性：增强专业术语使用，展示专业知识深度 - 经验性：添加实际使用经验表述（如"实际应用中"、"使用中发现"），至少1处经验性表述 - 权威性：添加来源占位（数据来源、案例来源、标准来源），至少2处来源占位 - 可信度：明确标注不确定信息，避免编造数据，使用占位建议 【开始优化】 """ # 根据选择的优化技巧增强 Prompt if opt_selected_technique_names: opt_technique_manager = OptimizationTechniqueManager() opt_technique_ids = opt_technique_manager.get_technique_ids_by_names( [ name.split(" ", 1)[1] if " " in name else name for name in opt_selected_technique_names ] ) optimize_prompt_template = opt_technique_manager.enhance_prompt( optimize_prompt_template, opt_technique_ids ) # 对超长文章给出提醒，避免模型上下文溢出 if len(original_article) > 8000: st.warning( "当前文章长度较长（超过 8000 字符），可能导致大模型上下文溢出或响应失败。" " 建议适当拆分文章后分别优化。" ) optimize_prompt = PromptTemplate.from_template(optimize_prompt_template) try: with st.spinner("优化中..."): chain = optimize_prompt | gen_llm | StrOutputParser() # 准备输入文本用于成本估算 input_text = optimize_prompt.template.format( original_article=original_article[ :500 ], # 只取前500字符用于估算 brand=brand, advantages=advantages, platform=target_platform, ) result = chain.invoke( { "original_article": original_article, "brand": brand, "advantages": advantages, "platform": target_platform, } ) # 记录成本 if gen_llm: try: model_name = ( getattr(gen_llm, "model_name", None) or getattr(gen_llm, "model", None) or model_defaults(cfg["gen_provider"]) ) provider = cfg["gen_provider"] record_api_cost( operation_type="优化", provider=provider, model=model_name, input_text=original_article[ :1000 ], # 使用实际输入文本的前1000字符 output_text=result, platform=target_platform, brand=brand, ) except Exception: # 记录成本失败不影响主流程 pass if "【优化后文章】" in result and "【变更说明】" in result: optimized_article = ( result.split("【优化后文章】", 1)[1] .split("【变更说明】", 1)[0] .strip() ) changes = result.split("【变更说明】", 1)[1].strip() else: optimized_article = result.strip() changes = "无详细变更说明（模型未按模板输出）。" st.session_state.optimized_article = optimized_article st.session_state.opt_changes = changes # 保存到数据库 try: storage.save_optimization( original_article, optimized_article, changes, target_platform, brand, ) except Exception as e: st.warning(f"优化完成，但保存到数据库时出错：{e}") except Exception as e: st.error(f"文章优化失败：{e}") # === 优化结果 & 质量评估 === if st.session_state.optimized_article: st.markdown("---") st.markdown("#### 📝 优化结果") # 结果 Tabs：优化后文章 / 变更说明 result_tab1, result_tab2 = st.tabs(["📝 优化后文章", "🧾 变更说明"]) with result_tab1: markdown_platforms = ["GitHub", "微信公众号", "百家号", "网易号", "企鹅号", "简书"] if any(p in st.session_state.opt_platform for p in markdown_platforms): st.code(st.session_state.optimized_article, language="markdown") else: st.markdown(st.session_state.optimized_article) # 确定文件扩展名 ext = ( "md" if any(p in st.session_state.opt_platform for p in markdown_platforms) else "txt" ) st.download_button( "下载优化版", st.session_state.optimized_article, f"{sanitize_filename(brand,40)}_优化文章.{ext}", use_container_width=True, key="opt_dl", ) with result_tab2: st.markdown("#### 变更说明") st.markdown(st.session_state.opt_changes) # 提供简单的版本回退能力 if ( st.session_state.get("optimized_article_backup") and st.session_state.optimized_article_backup != st.session_state.optimized_article ): if st.button("恢复至强化前版本", key="opt_restore_backup"): st.session_state.optimized_article = ( st.session_state.optimized_article_backup ) st.toast("已恢复至强化前版本。") st.markdown( "可选步骤：对优化后的文章进行质量评估与再强化（会调用额外模型）。" ) # E-E-A-T 评估和强化区域 st.markdown("#### 🎯 E-E-A-T 强化 + 来源占位") st.caption("评估和强化内容的专业性、经验性、权威性、可信度") eeat_col1, eeat_col2 = st.columns(2) with eeat_col1: assess_eeat_btn = st.button( "📊 评估 E-E-A-T", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (gen_llm is None), ) with eeat_col2: enhance_eeat_btn = st.button( "✨ 强化 E-E-A-T", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (gen_llm is None), ) st.caption("强化会覆盖当前优化结果，建议先下载备份。") # 初始化 E-E-A-T 相关状态 ss_init("eeat_assessment", None) ss_init("eeat_enhanced_content", "") ss_init("eeat_source_placeholders", []) ss_init("optimized_article_backup", "") # E-E-A-T 评估 if assess_eeat_btn and gen_llm: eeat_enhancer = EEATEnhancer() with st.spinner("正在评估 E-E-A-T..."): try: score_chain = ( PromptTemplate.from_template("{input}") | gen_llm | StrOutputParser() ) assessment = eeat_enhancer.assess_eeat( st.session_state.optimized_article, brand, advantages, st.session_state.opt_platform, score_chain, ) st.session_state.eeat_assessment = assessment except Exception as e: st.error(f"E-E-A-T 评估失败：{e}") # E-E-A-T 强化（带备份与安全校验） if enhance_eeat_btn and gen_llm: eeat_enhancer = EEATEnhancer() st.session_state.optimized_article_backup = ( st.session_state.optimized_article ) with st.spinner("正在强化 E-E-A-T..."): try: enhance_chain = ( PromptTemplate.from_template("{input}") | gen_llm | StrOutputParser() ) enhanced = eeat_enhancer.enhance_eeat( st.session_state.optimized_article, brand, advantages, st.session_state.opt_platform, enhance_chain, ) new_content = enhanced.get("enhanced_content", "") or "" if not new_content.strip() or len(new_content.strip()) < 100: st.error( "E-E-A-T 强化失败：模型返回内容异常，已保留强化前版本。" ) else: st.session_state.eeat_enhanced_content = new_content st.session_state.eeat_source_placeholders = enhanced.get( "source_placeholders", [] ) st.session_state.optimized_article = new_content st.success( f"✅ E-E-A-T 强化完成！已添加 {len(st.session_state.eeat_source_placeholders)} 个来源占位" ) except Exception as e: st.error(f"E-E-A-T 强化失败：{e}") # 显示 E-E-A-T 评估结果 if st.session_state.eeat_assessment: assessment = st.session_state.eeat_assessment scores = assessment.get("eeat_scores", {}) total_score = scores.get("total", 0) eeat_enhancer = EEATEnhancer() level, color = eeat_enhancer.get_eeat_level(total_score) st.markdown("##### 📊 E-E-A-T 评估结果") col1, col2, col3, col4, col5 = st.columns(5) with col1: st.metric("总分", f"{total_score}/100", delta=level, delta_color="off") with col2: st.metric("专业性", f"{scores.get('expertise', 0)}/25") with col3: st.metric("经验性", f"{scores.get('experience', 0)}/25") with col4: st.metric("权威性", f"{scores.get('authoritativeness', 0)}/25") with col5: st.metric("可信度", f"{scores.get('trustworthiness', 0)}/25") # 详细评估和改进建议 with st.container(border=True): st.markdown("##### 📝 详细评估与改进建议") details = assessment.get("details", {}) improvements = assessment.get("improvements", []) source_suggestions = assessment.get("source_suggestions", []) st.markdown("**详细评估：**") st.markdown(f"- **专业性**：{details.get('expertise', '无')}") st.markdown(f"- **经验性**：{details.get('experience', '无')}") st.markdown(f"- **权威性**：{details.get('authoritativeness', '无')}") st.markdown(f"- **可信度**：{details.get('trustworthiness', '无')}") if improvements: st.markdown("**💡 改进建议：**") for improvement in improvements: st.markdown(f"- {improvement}") if source_suggestions: st.markdown("**📚 来源占位建议：**") for suggestion in source_suggestions: st.markdown(f"- {suggestion}") # 来源占位检查 placeholders = assessment.get("source_placeholders", {}) if placeholders: st.markdown("**✅ 已检测到的来源占位：**") if placeholders.get("data_sources"): st.markdown( f"- 数据来源：{len(placeholders['data_sources'])} 处" ) if placeholders.get("case_sources"): st.markdown( f"- 案例来源：{len(placeholders['case_sources'])} 处" ) if placeholders.get("standard_sources"): st.markdown( f"- 标准来源：{len(placeholders['standard_sources'])} 处" ) if placeholders.get("expert_opinions"): st.markdown( f"- 专家观点：{len(placeholders['expert_opinions'])} 处" ) # 显示 E-E-A-T 强化后的来源占位清单 if st.session_state.eeat_source_placeholders: with st.container(border=True): st.markdown("##### 📚 来源占位清单") for placeholder in st.session_state.eeat_source_placeholders: st.markdown(f"- {placeholder}") # 事实密度 + 结构化块评估和强化 st.markdown("---") st.markdown("#### 📊 事实密度 + 结构化块") st.caption("评估和强化内容的事实信息密度和结构化程度") fact_col1, fact_col2 = st.columns(2) with fact_col1: assess_opt_fact = st.button( "📊 评估事实密度", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (gen_llm is None), ) with fact_col2: enhance_opt_fact = st.button( "✨ 强化事实密度", use_container_width=True, disabled=(not st.session_state.cfg_valid) or (gen_llm is None), ) st.caption("强化会覆盖当前优化结果，建议先下载备份。") # 初始化事实密度状态 ss_init("opt_fact_assessment", None) ss_init("opt_fact_enhanced", "") ss_init("opt_fact_details", []) # 事实密度评估 if assess_opt_fact and gen_llm: fact_enhancer = FactDensityEnhancer() with st.spinner("正在评估事实密度和结构化块..."): try: score_chain = ( PromptTemplate.from_template("{input}") | gen_llm | StrOutputParser() ) assessment = fact_enhancer.assess_fact_density( st.session_state.optimized_article, brand, advantages, st.session_state.opt_platform, score_chain, ) st.session_state.opt_fact_assessment = assessment except Exception as e: st.error(f"事实密度评估失败：{e}") # 事实密度强化（带备份与安全校验） if enhance_opt_fact and gen_llm: fact_enhancer = FactDensityEnhancer() st.session_state.optimized_article_backup = ( st.session_state.optimized_article ) with st.spinner("正在强化事实密度和结构化块..."): try: enhance_chain = ( PromptTemplate.from_template("{input}") | gen_llm | StrOutputParser() ) enhanced = fact_enhancer.enhance_fact_density( st.session_state.optimized_article, brand, advantages, st.session_state.opt_platform, enhance_chain, ) new_content = enhanced.get("enhanced_content", "") or "" if not new_content.strip() or len(new_content.strip()) < 100: st.error( "事实密度强化失败：模型返回内容异常，已保留强化前版本。" ) else: st.session_state.opt_fact_enhanced = new_content st.session_state.opt_fact_details = enhanced.get( "enhancement_details", [] ) st.session_state.optimized_article = new_content st.success( f"✅ 事实密度强化完成！已添加 {len(st.session_state.opt_fact_details)} 处事实信息和结构化块" ) except Exception as e: st.error(f"事实密度强化失败：{e}") # 显示事实密度评估结果 if st.session_state.opt_fact_assessment: assessment = st.session_state.opt_fact_assessment scores = assessment.get("scores", {}) total_score = scores.get("total", 0) fact_enhancer = FactDensityEnhancer() level, color = fact_enhancer.get_score_level(total_score) st.markdown("##### 📊 事实密度 + 结构化评估结果") col1, col2, col3 = st.columns(3) with col1: st.metric("总分", f"{total_score}/100", delta=level, delta_color="off") with col2: st.metric("事实密度", f"{scores.get('fact_density', 0)}/50") with col3: st.metric("结构化", f"{scores.get('structure', 0)}/50") # 使用 tabs 组织分析结果 fact_analysis = assessment.get("fact_analysis", {}) structure_analysis = assessment.get("structure_analysis", {}) has_details = bool(st.session_state.get("opt_fact_details")) # 构建可用的 tabs tab_labels = [] if fact_analysis: tab_labels.append("📈 事实密度") if structure_analysis: tab_labels.append("🏗️ 结构化块") if has_details: tab_labels.append("📝 强化详情") if tab_labels: analysis_tabs = st.tabs(tab_labels) tab_idx = 0 # 事实密度分析 if fact_analysis: with analysis_tabs[tab_idx]: with st.container(border=True): col1, col2, col3, col4, col5, col6 = st.columns(6) with col1: st.metric("数据", fact_analysis.get("data_count", 0)) with col2: st.metric("案例", fact_analysis.get("case_count", 0)) with col3: st.metric("标准", fact_analysis.get("standard_count", 0)) with col4: st.metric( "对比", fact_analysis.get("comparison_count", 0) ) with col5: st.metric("时间", fact_analysis.get("time_count", 0)) with col6: st.metric("来源", fact_analysis.get("source_count", 0)) missing_facts = fact_analysis.get("missing_facts", []) if missing_facts: st.markdown("**缺失的事实类型：**") for fact in missing_facts: st.markdown(f"- {fact}") tab_idx += 1 # 结构化分析 if structure_analysis: with analysis_tabs[tab_idx]: with st.container(border=True): col1, col2, col3, col4 = st.columns(4) with col1: st.markdown( f"**标题层级**：{'✅' if structure_analysis.get('has_title') else '❌'}" ) st.markdown( f"**结论摘要**：{'✅' if structure_analysis.get('has_summary') else '❌'}" ) with col2: st.markdown( f"**清单列表**：{'✅' if structure_analysis.get('has_list') else '❌'}" ) st.markdown( f"**FAQ部分**：{'✅' if structure_analysis.get('has_faq') else '❌'}" ) with col3: st.markdown( f"**代码块**：{'✅' if structure_analysis.get('has_code') else '❌'}" ) st.markdown( f"**对比表格**：{'✅' if structure_analysis.get('has_table') else '❌'}" ) with col4: st.markdown( f"**步骤说明**：{'✅' if structure_analysis.get('has_steps') else '❌'}" ) st.markdown( f"**总结部分**：{'✅' if structure_analysis.get('has_conclusion') else '❌'}" ) missing_blocks = structure_analysis.get("missing_blocks", []) if missing_blocks: st.markdown("**缺失的结构化块：**") for block in missing_blocks: st.markdown(f"- {block}") tab_idx += 1 # 强化详情 if has_details: with analysis_tabs[tab_idx]: with st.container(border=True): for detail in st.session_state.opt_fact_details: st.markdown(f"- {detail}") # === 高级：结构化 Schema & 技术配置（折叠区） === with st.expander( "高级：结构化 Schema & 技术 SEO 配置（可选）", expanded=False ): # 结构化数据生成 st.markdown("**📋 结构化数据生成**") st.caption( "生成符合 Schema.org 规范的 JSON-LD 代码，提升品牌在 AI 模型中的实体识别和权威性" ) with st.container(border=True): schema_col1, schema_col2 = st.columns([2, 1]) with schema_col1: schema_type = st.selectbox( "Schema 类型", [ "Organization（组织/公司）", "SoftwareApplication（软件应用）", "Product（产品）", "Service（服务）", "组合（Organization + SoftwareApplication）", ], index=1, key="schema_type_sel", help="选择适合您品牌的 Schema 类型", ) with schema_col2: generate_schema_btn = st.button( "🚀 生成 JSON-LD", use_container_width=True, key="generate_schema_btn", ) # 初始化 JSON-LD 相关状态 ss_init("generated_json_ld", None) ss_init("generated_html_script", None) # 生成 JSON-LD（带基础信息校验） if generate_schema_btn: if not brand or not advantages or len(brand.strip()) < 2: st.warning( "请先在基础信息中填写品牌名称和优势，再生成 Schema。" ) else: try: schema_gen = SchemaGenerator() if schema_type == "Organization（组织/公司）": schema_dict = schema_gen.generate_organization_schema( brand_name=brand, description=advantages, url="", # 用户可以在生成后手动添加 logo="", founding_date="", ) elif schema_type == "SoftwareApplication（软件应用）": schema_dict = schema_gen.generate_software_application_schema( brand_name=brand, application_name=brand, description=advantages, url="", application_category="BusinessApplication", operating_system="Web", ) elif schema_type == "Product（产品）": schema_dict = schema_gen.generate_product_schema( brand_name=brand, product_name=brand, description=advantages, url="", ) elif schema_type == "Service（服务）": schema_dict = schema_gen.generate_service_schema( brand_name=brand, service_name=brand, description=advantages, url="", ) else: # 组合 schema_dict = schema_gen.generate_combined_schema( brand_name=brand, advantages=advantages, schema_types=[ "Organization", "SoftwareApplication", ], ) # 格式化输出 json_ld_code = schema_gen.format_json_ld(schema_dict) html_script = schema_gen.generate_html_script_tag( schema_dict ) st.session_state.generated_json_ld = json_ld_code st.session_state.generated_html_script = html_script st.success("✅ JSON-LD Schema 生成成功！") except Exception as e: st.error(f"JSON-LD 生成失败：{e}") # 显示生成的 JSON-LD if st.session_state.generated_json_ld: st.markdown("##### 📄 JSON-LD 代码") st.code(st.session_state.generated_json_ld, language="json") st.markdown("##### 📄 HTML Script 标签（可直接嵌入网页）") st.code(st.session_state.generated_html_script, language="html") # 下载按钮 col1, col2 = st.columns(2) with col1: st.download_button( "下载 JSON-LD", st.session_state.generated_json_ld, f"{sanitize_filename(brand,40)}_schema.json", mime="application/json", use_container_width=True, key="schema_dl_json", ) with col2: st.download_button( "下载 HTML Script", st.session_state.generated_html_script, f"{sanitize_filename(brand,40)}_schema.html", mime="text/html", use_container_width=True, key="schema_dl_html", ) st.info( "💡 **使用说明**：将 HTML Script 标签复制到您的官网 `` 部分，或将 JSON-LD 代码添加到 GitHub README 中。" ) # 技术配置生成 st.markdown("---") st.markdown("**⚙️ 技术配置生成**") st.caption("生成 robots.txt、sitemap.xml 等技术配置文件，提升内容收录效果（提升 20-30%）") with st.container(border=True): config_tab1, config_tab2 = st.tabs(["🤖 robots.txt", "🗺️ sitemap.xml"]) # robots.txt 生成 with config_tab1: st.markdown("##### 🤖 robots.txt 生成") st.caption("控制搜索引擎爬虫的访问权限，提升内容收录效果") robots_col1, robots_col2 = st.columns([2, 1]) with robots_col1: robots_base_url = st.text_input( "网站基础 URL", value="", key="robots_base_url", placeholder="https://example.com", help="您的网站基础 URL（如 https://example.com）", ) with robots_col2: generate_robots_btn = st.button( "🚀 生成 robots.txt", use_container_width=True, key="generate_robots_btn", ) # 允许/禁止路径配置 robots_config_col1, robots_config_col2 = st.columns(2) with robots_config_col1: allow_paths_input = st.text_area( "允许爬取的路径（每行一个）", value="/\n/blog\n/docs", key="robots_allow_paths", help="每行一个路径，如 /、/blog、/docs", height=100, ) with robots_config_col2: disallow_paths_input = st.text_area( "禁止爬取的路径（每行一个）", value="/admin\n/private\n/api", key="robots_disallow_paths", help="每行一个路径，如 /admin、/private、/api", height=100, ) # 初始化状态 ss_init("generated_robots_txt", None) # 生成 robots.txt（带 URL 校验） if generate_robots_btn: if not robots_base_url.strip(): st.error("请填写网站基础 URL（如 https://example.com）。") else: if not robots_base_url.startswith("http"): st.warning( "建议使用完整 URL（含 http/https），避免 robots.txt 中出现无效链接。" ) try: config_gen = TechnicalConfigGenerator() # 解析允许路径 allow_paths = ( [ p.strip() for p in allow_paths_input.split("\n") if p.strip() ] if allow_paths_input else None ) # 解析禁止路径 disallow_paths = ( [ p.strip() for p in disallow_paths_input.split("\n") if p.strip() ] if disallow_paths_input else None ) robots_txt = config_gen.generate_robots_txt( base_url=robots_base_url, allow_paths=allow_paths, disallow_paths=disallow_paths, sitemap_url="", # 自动生成 user_agent="*", crawl_delay=None, ) st.session_state.generated_robots_txt = robots_txt st.success("✅ robots.txt 生成成功！") except Exception as e: st.error(f"robots.txt 生成失败：{e}") # 显示生成的 robots.txt if st.session_state.generated_robots_txt: st.markdown("##### 📄 robots.txt 内容") st.code(st.session_state.generated_robots_txt, language="text") st.download_button( "下载 robots.txt", st.session_state.generated_robots_txt, "robots.txt", mime="text/plain", use_container_width=True, key="robots_dl", ) st.info( "💡 **使用说明**：将 robots.txt 文件上传到您网站的根目录（如 https://example.com/robots.txt）" ) # sitemap.xml 生成 with config_tab2: st.markdown("##### 🗺️ sitemap.xml 生成") st.caption("帮助搜索引擎发现和索引您的所有页面，提升内容收录效果") sitemap_col1, sitemap_col2 = st.columns([2, 1]) with sitemap_col1: sitemap_base_url = st.text_input( "网站基础 URL", value="", key="sitemap_base_url", placeholder="https://example.com", help="您的网站基础 URL（如 https://example.com）", ) with sitemap_col2: generate_sitemap_btn = st.button( "🚀 生成 sitemap.xml", use_container_width=True, key="generate_sitemap_btn", ) # 选择数据源 sitemap_source = st.radio( "数据源", ["基于关键词生成", "基于历史文章生成"], key="sitemap_source", horizontal=True, ) # 初始化状态 ss_init("generated_sitemap_xml", None) # 生成 sitemap.xml（带 URL 校验） if generate_sitemap_btn: if not sitemap_base_url.strip(): st.error("请填写网站基础 URL（如 https://example.com）。") else: if not sitemap_base_url.startswith("http"): st.warning( "建议使用完整 URL（含 http/https），避免 sitemap.xml 中出现无效链接。" ) try: config_gen = TechnicalConfigGenerator() if sitemap_source == "基于关键词生成": # 基于关键词生成 keywords_for_sitemap = ( st.session_state.keywords if st.session_state.keywords else [] ) if not keywords_for_sitemap: st.warning( "⚠️ 请先在【1 关键词蒸馏】生成关键词，或选择【基于历史文章生成】" ) else: sitemap_xml = ( config_gen.generate_sitemap_xml( base_url=sitemap_base_url, keywords=keywords_for_sitemap, lastmod=None, # 使用当前日期 changefreq="weekly", priority=0.8, ) ) st.session_state.generated_sitemap_xml = ( sitemap_xml ) st.success( f"✅ sitemap.xml 生成成功！包含 {len(keywords_for_sitemap)} 个 URL" ) else: # 基于历史文章生成 try: articles = storage.get_articles(brand=brand) if not articles: st.warning( "⚠️ 暂无历史文章，请先生成内容，或选择【基于关键词生成】" ) else: sitemap_xml = ( config_gen.generate_sitemap_from_articles( base_url=sitemap_base_url, articles=articles, lastmod=None, changefreq="weekly", priority=0.8, ) ) st.session_state.generated_sitemap_xml = ( sitemap_xml ) st.success( f"✅ sitemap.xml 生成成功！包含 {len(articles)} 个 URL" ) except Exception as e: st.error(f"获取历史文章失败：{e}") except Exception as e: st.error(f"sitemap.xml 生成失败：{e}") # 显示生成的 sitemap.xml if st.session_state.generated_sitemap_xml: st.markdown("##### 📄 sitemap.xml 内容") st.code(st.session_state.generated_sitemap_xml, language="xml") st.download_button( "下载 sitemap.xml", st.session_state.generated_sitemap_xml, "sitemap.xml", mime="application/xml", use_container_width=True, key="sitemap_dl", ) st.info( "💡 **使用说明**：将 sitemap.xml 文件上传到您网站的根目录（如 https://example.com/sitemap.xml），并在 Google Search Console 中提交" )