""" services/hotspot.py 热点探测、热点生成、笔记列表缓存（供评论管家主动评论使用） """ import copy import threading import logging import gradio as gr from .llm_service import LLMService from .mcp_client import get_mcp_client from .connection import _get_llm_config from .persona import _resolve_persona logger = logging.getLogger("autobot") # ---- 共用: 线程安全缓存 ---- # 缓存互斥锁，防止并发回调产生竞态（所有缓存共用） _cache_lock = threading.RLock() # 主动评论缓存 _cached_proactive_entries: list[dict] = [] # 我的笔记评论缓存 _cached_my_note_entries: list[dict] = [] # ================================================== # Tab 2: 热点探测 # ================================================== # 最近一次 LLM 热点分析的结构化结果（线程安全，复用 _cache_lock） _last_analysis: dict | None = None def get_last_analysis() -> dict | None: """线程安全地获取最近一次热点分析结果的深拷贝""" with _cache_lock: if _last_analysis is None: return None return copy.deepcopy(_last_analysis) def set_last_analysis(data: dict) -> None: """线程安全地更新热点分析结果（合并 hot_topics / suggestions 并去重）""" global _last_analysis with _cache_lock: if _last_analysis is None: _last_analysis = copy.deepcopy(data) else: # 合并 hot_topics existing_topics = _last_analysis.get("hot_topics", []) new_topics = data.get("hot_topics", []) seen = set(existing_topics) for t in new_topics: if t not in seen: existing_topics.append(t) seen.add(t) _last_analysis["hot_topics"] = existing_topics # 合并 suggestions（按 topic 去重） existing_sug = _last_analysis.get("suggestions", []) existing_sug_topics = {s.get("topic", "") for s in existing_sug} for s in data.get("suggestions", []): if s.get("topic", "") not in existing_sug_topics: existing_sug.append(s) existing_sug_topics.add(s.get("topic", "")) _last_analysis["suggestions"] = existing_sug # 其他字段以最新为准 for key in data: if key not in ("hot_topics", "suggestions"): _last_analysis[key] = data[key] def search_hotspots(keyword, sort_by, mcp_url): """搜索小红书热门内容""" if not keyword: return "❌ 请输入搜索关键词", "" try: client = get_mcp_client(mcp_url) result = client.search_feeds(keyword, sort_by=sort_by) if "error" in result: return f"❌ 搜索失败: {result['error']}", "" text = result.get("text", "无结果") return "✅ 搜索完成", text except Exception as e: logger.error("热点搜索失败: %s", e) return f"❌ 搜索失败: {e}", "" def analyze_and_suggest(model, keyword, search_result): """AI 分析热点并给出建议，同时缓存结构化结果""" if not search_result: return "❌ 请先搜索", "", "", gr.update(choices=[], value=None) api_key, base_url, _ = _get_llm_config() if not api_key: return "❌ 请先配置 LLM 提供商", "", "", gr.update(choices=[], value=None) try: svc = LLMService(api_key, base_url, model) analysis = svc.analyze_hotspots(search_result) # 缓存结构化分析结果（在渲染 Markdown 之前） set_last_analysis(analysis) topics = "\n".join(f"• {t}" for t in analysis.get("hot_topics", [])) patterns = "\n".join(f"• {p}" for p in analysis.get("title_patterns", [])) suggestions_list = analysis.get("suggestions", []) suggestions = "\n".join( f"**{s['topic']}** - {s['reason']}" for s in suggestions_list ) structure = analysis.get("content_structure", "") summary = ( f"## 🔥 热门选题\n{topics}\n\n" f"## 📝 标题套路\n{patterns}\n\n" f"## 📐 内容结构\n{structure}\n\n" f"## 💡 推荐选题\n{suggestions}" ) # 构建选题下拉选项 topic_choices = [s["topic"] for s in suggestions_list if s.get("topic")] dropdown_update = gr.update( choices=topic_choices, value=topic_choices[0] if topic_choices else None, ) return "✅ 分析完成", summary, keyword, dropdown_update except Exception as e: logger.error("热点分析失败: %s", e) return f"❌ 分析失败: {e}", "", "", gr.update(choices=[], value=None) def generate_from_hotspot(model, topic_from_hotspot, style, search_result, sd_model_name, persona_text): """基于热点分析生成文案（自动适配 SD 模型，支持人设，增强分析上下文）""" if not topic_from_hotspot: return "", "", "", "", "❌ 请先选择或输入选题" api_key, base_url, _ = _get_llm_config() if not api_key: return "", "", "", "", "❌ 请先配置 LLM 提供商" try: svc = LLMService(api_key, base_url, model) persona = _resolve_persona(persona_text) if persona_text else None # 构建增强参考上下文：结构化分析摘要 + 原始搜索片段 analysis = get_last_analysis() reference_parts = [] if analysis: topics_str = ", ".join(analysis.get("hot_topics", [])[:5]) sug_str = "; ".join( s.get("topic", "") for s in analysis.get("suggestions", [])[:5] ) structure = analysis.get("content_structure", "") analysis_summary = ( f"[热点分析摘要] 热门选题: {topics_str}\n" f"推荐方向: {sug_str}\n" f"内容结构建议: {structure}\n\n" ) reference_parts.append(analysis_summary) if search_result: reference_parts.append(search_result) combined_reference = "".join(reference_parts)[:3000] data = svc.generate_copy_with_reference( topic=topic_from_hotspot, style=style, reference_notes=combined_reference, sd_model_name=sd_model_name, persona=persona, ) tags = data.get("tags", []) return ( data.get("title", ""), data.get("content", ""), data.get("sd_prompt", ""), ", ".join(tags), "✅ 基于热点的文案已生成", ) except Exception as e: return "", "", "", "", f"❌ 生成失败: {e}" def feed_hotspot_to_engine(topic_engine) -> list[dict]: """将缓存的热点分析结果注入 TopicEngine，返回热点加权推荐列表""" data = get_last_analysis() return topic_engine.recommend_topics(hotspot_data=data) # ================================================== # Tab 3: 评论管家 # ================================================== # ---- 共用: 笔记列表缓存（线程安全）---- def _set_cache(name: str, entries: list): """线程安全地更新笔记列表缓存""" global _cached_proactive_entries, _cached_my_note_entries with _cache_lock: if name == "proactive": _cached_proactive_entries = list(entries) else: _cached_my_note_entries = list(entries) def _get_cache(name: str) -> list: """线程安全地获取笔记列表缓存快照（返回副本）""" with _cache_lock: if name == "proactive": return list(_cached_proactive_entries) return list(_cached_my_note_entries) def _fetch_and_cache(keyword, mcp_url, cache_name="proactive"): """通用: 获取笔记列表并线程安全地缓存""" try: client = get_mcp_client(mcp_url) if keyword and keyword.strip(): entries = client.search_feeds_parsed(keyword.strip()) src = f"搜索「{keyword.strip()}」" else: entries = client.list_feeds_parsed() src = "首页推荐" _set_cache(cache_name, entries) if not entries: return gr.update(choices=[], value=None), f"⚠️ 从{src}未找到笔记" choices = [] for i, e in enumerate(entries): title_short = (e["title"] or "无标题")[:28] label = f"[{i+1}] {title_short} | @{e['author'] or '未知'} | ❤ {e['likes']}" choices.append(label) return ( gr.update(choices=choices, value=choices[0]), f"✅ 从{src}获取 {len(entries)} 条笔记", ) except Exception as e: _set_cache(cache_name, []) return gr.update(choices=[], value=None), f"❌ {e}" def _pick_from_cache(selected, cache_name="proactive"): """通用: 从缓存中提取选中条目的 feed_id / xsec_token / title（线程安全快照）""" cache = _get_cache(cache_name) if not selected or not cache: return "", "", "" try: # 尝试从 [N] 前缀提取序号 idx = int(selected.split("]")[0].replace("[", "")) - 1 if 0 <= idx < len(cache): e = cache[idx] return e["feed_id"], e["xsec_token"], e.get("title", "") except (ValueError, IndexError): pass # 回退: 模糊匹配标题 for e in cache: if e.get("title", "")[:15] in selected: return e["feed_id"], e["xsec_token"], e.get("title", "") return "", "", "" # ---- 模块 A: 主动评论他人 ---- def fetch_proactive_notes(keyword, mcp_url): return _fetch_and_cache(keyword, mcp_url, "proactive") def on_proactive_note_selected(selected): return _pick_from_cache(selected, "proactive")