From b635108b89cbd9da5cf1db2271412455ce6a280e Mon Sep 17 00:00:00 2001 From: zhoujie <929834232@qq.com> Date: Tue, 24 Feb 2026 22:50:56 +0800 Subject: [PATCH] refactor: split monolithic main.py into services/ + ui/ modules (improve-maintainability) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - main.py: 4360 → 146 lines (96.6% reduction), entry layer only - services/: rate_limiter, autostart, persona, connection, profile, hotspot, content, engagement, scheduler, queue_ops (10 business modules) - ui/app.py: all Gradio UI code extracted into build_app(cfg, analytics) - Fix: with gr.Blocks() indented inside build_app function - Fix: cfg.all property (not get_all method) - Fix: STATUS_LABELS, get_persona_keywords, fetch_proactive_notes imports - Fix: queue_ops module-level set_publish_callback moved into configure() - Fix: pub_queue.format_*() wrapped as queue_format_table/calendar helpers - All 14 files syntax-verified, build_app() runtime-verified - 58/58 tasks complete" --- main.py | 4328 +---------------- .../improve-maintainability/.openspec.yaml | 2 + .../changes/improve-maintainability/design.md | 119 + .../improve-maintainability/proposal.md | 38 + .../specs/services-autostart/spec.md | 16 + .../specs/services-connection/spec.md | 16 + .../specs/services-content/spec.md | 16 + .../specs/services-engagement/spec.md | 16 + .../specs/services-hotspot/spec.md | 12 + .../specs/services-persona/spec.md | 16 + .../specs/services-profile/spec.md | 12 + .../specs/services-queue/spec.md | 16 + .../specs/services-rate-limiter/spec.md | 16 + .../specs/services-scheduler/spec.md | 16 + .../specs/ui-tabs-split/spec.md | 30 + .../changes/improve-maintainability/tasks.md | 96 + services/__init__.py | 1 + services/autostart.py | 121 + services/connection.py | 254 + services/content.py | 208 + services/engagement.py | 197 + services/hotspot.py | 190 + services/persona.py | 519 ++ services/profile.py | 176 + services/queue_ops.py | 352 ++ services/rate_limiter.py | 110 + services/scheduler.py | 1086 +++++ ui/app.py | 1368 ++++++ 28 files changed, 5076 insertions(+), 4271 deletions(-) create mode 100644 openspec/changes/improve-maintainability/.openspec.yaml create mode 100644 openspec/changes/improve-maintainability/design.md create mode 100644 openspec/changes/improve-maintainability/proposal.md create mode 100644 openspec/changes/improve-maintainability/specs/services-autostart/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-connection/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-content/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-engagement/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-hotspot/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-persona/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-profile/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-queue/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-rate-limiter/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/services-scheduler/spec.md create mode 100644 openspec/changes/improve-maintainability/specs/ui-tabs-split/spec.md create mode 100644 openspec/changes/improve-maintainability/tasks.md create mode 100644 services/__init__.py create mode 100644 services/autostart.py create mode 100644 services/connection.py create mode 100644 services/content.py create mode 100644 services/engagement.py create mode 100644 services/hotspot.py create mode 100644 services/persona.py create mode 100644 services/profile.py create mode 100644 services/queue_ops.py create mode 100644 services/rate_limiter.py create mode 100644 services/scheduler.py create mode 100644 ui/app.py diff --git a/main.py b/main.py index 4b787e4..65f6c15 100644 --- a/main.py +++ b/main.py @@ -68,4278 +68,64 @@ from publish_queue import ( pub_queue = PublishQueue(OUTPUT_DIR) queue_publisher = QueuePublisher(pub_queue) -# ================================================== -# LLM 多提供商管理 -# ================================================== +from services.connection import ( + _get_llm_config, connect_llm, add_llm_provider, remove_llm_provider, + on_provider_selected, connect_sd, on_sd_model_change, check_mcp_status, + get_login_qrcode, logout_xhs, _auto_fetch_xsec_token, check_login, + save_my_user_id, upload_face_image, load_saved_face_image, +) + +from services.content import ( + generate_copy, generate_images, one_click_export, publish_to_xhs, +) + +from services.hotspot import ( + _cache_lock, _set_cache, _get_cache, _fetch_and_cache, _pick_from_cache, + search_hotspots, analyze_and_suggest, generate_from_hotspot, + fetch_proactive_notes, on_proactive_note_selected, +) + +from services.engagement import ( + load_note_for_comment, ai_generate_comment, send_comment, + fetch_my_notes, on_my_note_selected, fetch_my_note_comments, + ai_reply_comment, send_reply, +) + + + +from services.profile import _parse_profile_json, _parse_count, fetch_my_profile + +from services.scheduler import ( + _auto_log_append, _auto_log, _auto_running, _auto_thread, + _auto_comment_with_log, auto_comment_once, + _auto_like_with_log, auto_like_once, + _auto_favorite_with_log, auto_favorite_once, + _auto_publish_with_log, auto_publish_once, + _auto_reply_with_log, auto_reply_once, + _scheduler_next_times, _scheduler_loop, + start_scheduler, stop_scheduler, get_auto_log, get_scheduler_status, + _learn_running, _learn_scheduler_loop, start_learn_scheduler, stop_learn_scheduler, + analytics_collect_data, analytics_calculate_weights, + analytics_llm_deep_analysis, analytics_get_report, analytics_get_weighted_topics, + configure_analytics, +) +from services.queue_ops import ( + generate_to_queue, queue_refresh_table, + queue_refresh_calendar, queue_preview_item, queue_approve_item, + queue_reject_item, queue_delete_item, queue_retry_item, queue_publish_now, + queue_start_processor, queue_stop_processor, queue_get_status, + queue_batch_approve, queue_generate_and_refresh, + configure as configure_queue_ops, +) + +# 注入依赖 +configure_analytics(analytics) +configure_queue_ops(pub_queue, queue_publisher, analytics, _auto_log_append) + +from ui.app import build_app, _GRADIO_CSS + +app = build_app(cfg, analytics) - -def _get_llm_config() -> tuple[str, str, str]: - """获取当前激活 LLM 的 (api_key, base_url, model)""" - p = cfg.get_active_llm() - if p: - return p["api_key"], p["base_url"], cfg.get("model", "") - return "", "", "" - - -def connect_llm(provider_name): - """连接选中的 LLM 提供商并获取模型列表""" - if not provider_name: - return gr.update(choices=[], value=None), "⚠️ 请先选择或添加 LLM 提供商" - cfg.set_active_llm(provider_name) - p = cfg.get_active_llm() - if not p: - return gr.update(choices=[], value=None), "❌ 未找到该提供商配置" - try: - svc = LLMService(p["api_key"], p["base_url"]) - models = svc.get_models() - if models: - return ( - gr.update(choices=models, value=models[0]), - f"✅ 已连接「{provider_name}」，加载 {len(models)} 个模型", - ) - else: - # API 无法获取模型列表，保留手动输入 - current_model = cfg.get("model", "") - return ( - gr.update(choices=[current_model] if current_model else [], value=current_model or None), - f"⚠️ 已连接「{provider_name}」，但未获取到模型列表，请手动输入模型名", - ) - except Exception as e: - logger.error("LLM 连接失败: %s", e) - current_model = cfg.get("model", "") - return ( - gr.update(choices=[current_model] if current_model else [], value=current_model or None), - f"❌ 连接「{provider_name}」失败: {e}", - ) - - -def add_llm_provider(name, api_key, base_url): - """添加新的 LLM 提供商""" - msg = cfg.add_llm_provider(name, api_key, base_url) - names = cfg.get_llm_provider_names() - active = cfg.get("active_llm", "") - return ( - gr.update(choices=names, value=active), - msg, - ) - - -def remove_llm_provider(provider_name): - """删除 LLM 提供商""" - if not provider_name: - return gr.update(choices=cfg.get_llm_provider_names(), value=cfg.get("active_llm", "")), "⚠️ 请先选择要删除的提供商" - msg = cfg.remove_llm_provider(provider_name) - names = cfg.get_llm_provider_names() - active = cfg.get("active_llm", "") - return ( - gr.update(choices=names, value=active), - msg, - ) - - -def on_provider_selected(provider_name): - """切换 LLM 提供商时更新显示信息""" - if not provider_name: - return "未选择提供商" - for p in cfg.get_llm_providers(): - if p["name"] == provider_name: - cfg.set_active_llm(provider_name) - masked_key = p["api_key"][:8] + "***" if len(p["api_key"]) > 8 else "***" - return f"**{provider_name}** \nAPI Key: `{masked_key}` \nBase URL: `{p['base_url']}`" - return "未找到该提供商" - - -# ================================================== -# Tab 1: 内容创作 -# ================================================== - - -def connect_sd(sd_url): - """连接 SD 并获取模型列表""" - try: - svc = SDService(sd_url) - ok, msg = svc.check_connection() - if ok: - models = svc.get_models() - cfg.set("sd_url", sd_url) - first = models[0] if models else None - info = get_model_profile_info(first) if first else "未检测到模型" - return gr.update(choices=models, value=first), f"✅ {msg}", info - return gr.update(choices=[]), f"❌ {msg}", "" - except Exception as e: - logger.error("SD 连接失败: %s", e) - return gr.update(choices=[]), f"❌ SD 连接失败: {e}", "" - - -def on_sd_model_change(model_name): - """SD 模型切换时显示模型档案信息""" - if not model_name: - return "未选择模型" - return get_model_profile_info(model_name) - - -def check_mcp_status(mcp_url): - """检查 MCP 连接状态""" - try: - client = get_mcp_client(mcp_url) - ok, msg = client.check_connection() - if ok: - cfg.set("mcp_url", mcp_url) - return f"✅ MCP 服务正常 - {msg}" - return f"❌ {msg}" - except Exception as e: - return f"❌ MCP 连接失败: {e}" - - -# ================================================== -# 小红书账号登录 -# ================================================== - - -def get_login_qrcode(mcp_url): - """获取小红书登录二维码""" - try: - client = get_mcp_client(mcp_url) - result = client.get_login_qrcode() - if "error" in result: - return None, f"❌ 获取二维码失败: {result['error']}" - qr_image = result.get("qr_image") - msg = result.get("text", "") - if qr_image: - return qr_image, f"✅ 二维码已生成，请用小红书 App 扫码\n{msg}" - return None, f"⚠️ 未获取到二维码图片，MCP 返回:\n{msg}" - except Exception as e: - logger.error("获取登录二维码失败: %s", e) - return None, f"❌ 获取二维码失败: {e}" - - -def logout_xhs(mcp_url): - """退出登录：清除 cookies 并重置本地 token""" - try: - client = get_mcp_client(mcp_url) - result = client.delete_cookies() - if "error" in result: - return f"❌ 退出失败: {result['error']}" - cfg.set("xsec_token", "") - client._reset() - return "✅ 已退出登录，可以重新扫码登录" - except Exception as e: - logger.error("退出登录失败: %s", e) - return f"❌ 退出失败: {e}" - - -def _auto_fetch_xsec_token(mcp_url) -> str: - """从推荐列表自动获取一个有效的 xsec_token""" - try: - client = get_mcp_client(mcp_url) - entries = client.list_feeds_parsed() - for e in entries: - token = e.get("xsec_token", "") - if token: - return token - except Exception as e: - logger.warning("自动获取 xsec_token 失败: %s", e) - return "" - - -def check_login(mcp_url): - """检查登录状态，登录成功后自动获取 xsec_token 并保存""" - try: - client = get_mcp_client(mcp_url) - result = client.check_login_status() - if "error" in result: - return f"❌ {result['error']}", gr.update(), gr.update() - text = result.get("text", "") - if "未登录" in text: - return f"🔴 {text}", gr.update(), gr.update() - - # 登录成功 → 自动获取 xsec_token - token = _auto_fetch_xsec_token(mcp_url) - if token: - cfg.set("xsec_token", token) - logger.info("自动获取 xsec_token 成功") - return ( - f"🟢 {text}\n\n✅ xsec_token 已自动获取并保存", - gr.update(value=cfg.get("my_user_id", "")), - gr.update(value=token), - ) - return f"🟢 {text}\n\n⚠️ 自动获取 xsec_token 失败，请手动刷新", gr.update(), gr.update() - except Exception as e: - return f"❌ 检查登录状态失败: {e}", gr.update(), gr.update() - - -def save_my_user_id(user_id_input): - """保存用户 ID (验证 24 位十六进制格式)""" - uid = (user_id_input or "").strip() - if not uid: - cfg.set("my_user_id", "") - return "⚠️ 已清除用户 ID" - if not re.match(r'^[0-9a-fA-F]{24}$', uid): - return ( - "❌ 格式错误！用户 ID 应为 24 位十六进制字符串\n" - f"你输入的: `{uid}` ({len(uid)} 位)\n\n" - "💡 如果你输入的是小红书号 (纯数字如 18688457507)，那不是 userId。" - ) - cfg.set("my_user_id", uid) - return f"✅ 用户 ID 已保存: `{uid}`" - - -# ================= 头像/换脸管理 ================= - -def upload_face_image(img): - """上传并保存头像图片""" - if img is None: - return None, "❌ 请上传头像图片" - try: - if isinstance(img, str) and os.path.isfile(img): - img = Image.open(img).convert("RGB") - elif not isinstance(img, Image.Image): - return None, "❌ 无法识别图片格式" - path = SDService.save_face_image(img) - return img, f"✅ 头像已保存至 {os.path.basename(path)}" - except Exception as e: - return None, f"❌ 保存失败: {e}" - - -def load_saved_face_image(): - """加载已保存的头像""" - img = SDService.load_face_image() - if img: - return img, "✅ 已加载保存的头像" - return None, "ℹ️ 尚未设置头像" - - -def generate_copy(model, topic, style, sd_model_name, persona_text): - """生成文案（自动适配 SD 模型的 prompt 风格，支持人设）""" - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "", "", "", "", "❌ 请先配置并连接 LLM 提供商" - try: - svc = LLMService(api_key, base_url, model) - persona = _resolve_persona(persona_text) if persona_text else None - data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) - cfg.set("model", model) - tags = data.get("tags", []) - return ( - data.get("title", ""), - data.get("content", ""), - data.get("sd_prompt", ""), - ", ".join(tags) if tags else "", - "✅ 文案生成完毕", - ) - except Exception as e: - logger.error("文案生成失败: %s", e) - return "", "", "", "", f"❌ 生成失败: {e}" - - -def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img, quality_mode, persona_text=None): - """生成图片（可选 ReActor 换脸，支持质量模式预设，支持人设视觉优化）""" - if not model: - return None, [], "❌ 未选择 SD 模型" - try: - svc = SDService(sd_url) - # 判断是否启用换脸 - face_image = None - if face_swap_on: - # Gradio 可能传 PIL.Image / numpy.ndarray / 文件路径 / None - if face_img is not None: - if isinstance(face_img, Image.Image): - face_image = face_img - elif isinstance(face_img, str) and os.path.isfile(face_img): - face_image = Image.open(face_img).convert("RGB") - else: - # numpy array 等其他格式 - try: - import numpy as np - if isinstance(face_img, np.ndarray): - face_image = Image.fromarray(face_img).convert("RGB") - logger.info("头像从 numpy array 转换为 PIL Image") - except Exception as e: - logger.warning("头像格式转换失败 (%s): %s", type(face_img).__name__, e) - # 如果 UI 没传有效头像，从本地文件加载 - if face_image is None: - face_image = SDService.load_face_image() - if face_image is not None: - logger.info("换脸头像已就绪: %dx%d", face_image.width, face_image.height) - else: - logger.warning("换脸已启用但未找到有效头像") - - persona = _resolve_persona(persona_text) if persona_text else None - images = svc.txt2img( - prompt=prompt, - negative_prompt=neg_prompt, - model=model, - steps=int(steps), - cfg_scale=float(cfg_scale), - face_image=face_image, - quality_mode=quality_mode, - persona=persona, - ) - preset = get_sd_preset(quality_mode) - swap_hint = " (已换脸)" if face_image else "" - return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint} [{quality_mode}]" - except Exception as e: - logger.error("图片生成失败: %s", e) - return None, [], f"❌ 绘图失败: {e}" - - -def one_click_export(title, content, images): - """导出文案和图片到本地""" - if not title: - return "❌ 无法导出：没有标题" - - safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] - folder_name = f"{int(time.time())}_{safe_title}" - folder_path = os.path.join(OUTPUT_DIR, folder_name) - os.makedirs(folder_path, exist_ok=True) - - with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f: - f.write(f"{title}\n\n{content}") - - saved_paths = [] - if images: - for idx, img in enumerate(images): - path = os.path.join(folder_path, f"图{idx+1}.jpg") - if isinstance(img, Image.Image): - if img.mode != "RGB": - img = img.convert("RGB") - img.save(path, format="JPEG", quality=95) - saved_paths.append(os.path.abspath(path)) - - # 尝试打开文件夹 - try: - abs_path = os.path.abspath(folder_path) - if platform.system() == "Windows": - os.startfile(abs_path) - elif platform.system() == "Darwin": - subprocess.call(["open", abs_path]) - else: - subprocess.call(["xdg-open", abs_path]) - except Exception: - pass - - return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)" - - -def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time): - """通过 MCP 发布到小红书（含输入校验和临时文件自动清理）""" - # === 发布前校验 === - if not title: - return "❌ 缺少标题" - if len(title) > 20: - return f"❌ 标题超长：当前 {len(title)} 字，小红书限制 ≤20 字，请精简后再发布" - - client = get_mcp_client(mcp_url) - ai_temp_files: list = [] # 追踪本次写入的临时文件，用于 finally 清理 - - try: - # 收集图片路径 - image_paths = [] - - # 先保存 AI 生成的图片到临时目录 - if images: - temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish") - os.makedirs(temp_dir, exist_ok=True) - for idx, img in enumerate(images): - if isinstance(img, Image.Image): - path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.jpg")) - if img.mode != "RGB": - img = img.convert("RGB") - img.save(path, format="JPEG", quality=95) - image_paths.append(path) - ai_temp_files.append(path) # 登记临时文件 - - # 添加本地上传的图片 - if local_images: - for img_file in local_images: - img_path = img_file.name if hasattr(img_file, 'name') else str(img_file) - if os.path.exists(img_path): - image_paths.append(os.path.abspath(img_path)) - - # === 图片校验 === - if not image_paths: - return "❌ 至少需要 1 张图片才能发布" - if len(image_paths) > 18: - return f"❌ 图片数量超限：当前 {len(image_paths)} 张，小红书限制 ≤18 张，请减少图片" - for p in image_paths: - if not os.path.exists(p): - return f"❌ 图片文件不存在：{p}" - - # 解析标签 - tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None - - # 定时发布 - schedule = schedule_time if schedule_time and schedule_time.strip() else None - - result = client.publish_content( - title=title, - content=content, - images=image_paths, - tags=tags, - schedule_at=schedule, - ) - if "error" in result: - return f"❌ 发布失败: {result['error']}" - return f"✅ 发布成功！\n{result.get('text', '')}" - except Exception as e: - logger.error("发布失败: %s", e) - return f"❌ 发布异常: {e}" - finally: - # 清理本次写入的 AI 临时图片（无论成功/失败） - for tmp_path in ai_temp_files: - try: - if os.path.exists(tmp_path): - os.remove(tmp_path) - except OSError as cleanup_err: - logger.warning("临时文件清理失败 %s: %s", tmp_path, cleanup_err) - - -# ================================================== -# Tab 2: 热点探测 -# ================================================== - - -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""" - if not search_result: - return "❌ 请先搜索", "", "" - 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) - analysis = svc.analyze_hotspots(search_result) - - 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 = "\n".join( - f"**{s['topic']}** - {s['reason']}" - for s in analysis.get("suggestions", []) - ) - 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}" - ) - return "✅ 分析完成", summary, keyword - except Exception as e: - logger.error("热点分析失败: %s", e) - return f"❌ 分析失败: {e}", "", "" - - -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）""" - 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 - data = svc.generate_copy_with_reference( - topic=topic_from_hotspot, - style=style, - reference_notes=search_result[:2000], - 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}" - - -# ================================================== -# Tab 3: 评论管家 -# ================================================== - -# ---- 共用: 笔记列表缓存（线程安全）---- - -# 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] = [] -# 缓存互斥锁，防止并发回调产生竞态 -_cache_lock = threading.RLock() - - -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") - - -def load_note_for_comment(feed_id, xsec_token, mcp_url): - """加载目标笔记详情 (标题+正文+已有评论), 用于 AI 分析""" - if not feed_id or not xsec_token: - return "❌ 请先选择笔记", "", "", "" - try: - client = get_mcp_client(mcp_url) - result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) - if "error" in result: - return f"❌ {result['error']}", "", "", "" - full_text = result.get("text", "") - # 尝试分离正文和评论 - if "评论" in full_text: - parts = full_text.split("评论", 1) - content_part = parts[0].strip() - comments_part = "评论" + parts[1] if len(parts) > 1 else "" - else: - content_part = full_text[:500] - comments_part = "" - return "✅ 笔记内容已加载", content_part[:800], comments_part[:1500], full_text - except Exception as e: - return f"❌ {e}", "", "", "" - - -def ai_generate_comment(model, persona, - post_title, post_content, existing_comments): - """AI 生成主动评论""" - persona = _resolve_persona(persona) - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置" - if not model: - return "⚠️ 请先连接 LLM", "❌ 未选模型" - if not post_title and not post_content: - return "⚠️ 请先加载笔记内容", "❌ 无笔记内容" - try: - svc = LLMService(api_key, base_url, model) - comment = svc.generate_proactive_comment( - persona, post_title, post_content[:600], existing_comments[:800] - ) - return comment, "✅ 评论已生成" - except Exception as e: - logger.error(f"AI 评论生成失败: {e}") - return f"生成失败: {e}", f"❌ {e}" - - -def send_comment(feed_id, xsec_token, comment_content, mcp_url): - """发送评论到别人的笔记""" - if not all([feed_id, xsec_token, comment_content]): - return "❌ 缺少必要参数 (笔记ID / token / 评论内容)" - try: - client = get_mcp_client(mcp_url) - result = client.post_comment(feed_id, xsec_token, comment_content) - if "error" in result: - return f"❌ {result['error']}" - return "✅ 评论已发送！" - except Exception as e: - return f"❌ {e}" - - -# ---- 模块 B: 回复我的笔记评论 ---- - -def fetch_my_notes(mcp_url): - """通过已保存的 userId 获取我的笔记列表""" - my_uid = cfg.get("my_user_id", "") - xsec = cfg.get("xsec_token", "") - if not my_uid: - return ( - gr.update(choices=[], value=None), - "❌ 未配置用户 ID，请先到「账号登录」页填写并保存", - ) - if not xsec: - return ( - gr.update(choices=[], value=None), - "❌ 未获取 xsec_token，请先登录", - ) - try: - client = get_mcp_client(mcp_url) - result = client.get_user_profile(my_uid, xsec) - if "error" in result: - return gr.update(choices=[], value=None), f"❌ {result['error']}" - - # 从 raw 中解析 feeds - raw = result.get("raw", {}) - text = result.get("text", "") - data = None - if raw and isinstance(raw, dict): - for item in raw.get("content", []): - if item.get("type") == "text": - try: - data = json.loads(item["text"]) - except (json.JSONDecodeError, KeyError): - pass - if not data: - try: - data = json.loads(text) - except (json.JSONDecodeError, TypeError): - pass - - feeds = (data or {}).get("feeds") or [] - if not feeds: - return ( - gr.update(choices=[], value=None), - "⚠️ 未找到你的笔记，可能账号还没有发布内容", - ) - - entries = [] - for f in feeds: - nc = f.get("noteCard") or {} - user = nc.get("user") or {} - interact = nc.get("interactInfo") or {} - entries.append({ - "feed_id": f.get("id", ""), - "xsec_token": f.get("xsecToken", ""), - "title": nc.get("displayTitle", "未知标题"), - "author": user.get("nickname", user.get("nickName", "")), - "user_id": user.get("userId", ""), - "likes": interact.get("likedCount", "0"), - "type": nc.get("type", ""), - }) - - _set_cache("my_notes", entries) - choices = [ - f"[{i+1}] {e['title'][:20]} | {e['type']} | ❤{e['likes']}" - for i, e in enumerate(entries) - ] - return ( - gr.update(choices=choices, value=choices[0] if choices else None), - f"✅ 找到 {len(entries)} 篇笔记", - ) - except Exception as e: - return gr.update(choices=[], value=None), f"❌ {e}" - - -def on_my_note_selected(selected): - return _pick_from_cache(selected, "my_notes") - - -def fetch_my_note_comments(feed_id, xsec_token, mcp_url): - """获取我的笔记的评论列表""" - if not feed_id or not xsec_token: - return "❌ 请先选择笔记", "" - try: - client = get_mcp_client(mcp_url) - result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) - if "error" in result: - return f"❌ {result['error']}", "" - return "✅ 评论加载完成", result.get("text", "暂无评论") - except Exception as e: - return f"❌ {e}", "" - - -def ai_reply_comment(model, persona, post_title, comment_text): - """AI 生成评论回复""" - persona = _resolve_persona(persona) - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置" - if not model: - return "⚠️ 请先连接 LLM 并选择模型", "❌ 未选择模型" - if not comment_text: - return "请输入需要回复的评论内容", "⚠️ 请输入评论" - try: - svc = LLMService(api_key, base_url, model) - reply = svc.generate_reply(persona, post_title, comment_text) - return reply, "✅ 回复已生成" - except Exception as e: - logger.error(f"AI 回复生成失败: {e}") - return f"生成失败: {e}", f"❌ {e}" - - -def send_reply(feed_id, xsec_token, reply_content, mcp_url): - """发送评论回复""" - if not all([feed_id, xsec_token, reply_content]): - return "❌ 缺少必要参数" - try: - client = get_mcp_client(mcp_url) - result = client.post_comment(feed_id, xsec_token, reply_content) - if "error" in result: - return f"❌ 回复失败: {result['error']}" - return "✅ 回复已发送" - except Exception as e: - return f"❌ 发送失败: {e}" - - -# ================================================== -# Tab 4: 数据看板 (我的账号) -# ================================================== - - -def _parse_profile_json(text: str): - """尝试从文本中解析用户 profile JSON""" - if not text: - return None - # 直接 JSON - try: - return json.loads(text) - except (json.JSONDecodeError, TypeError): - pass - # 可能包含 Markdown 代码块 - m = re.search(r'```(?:json)?\s*\n([\s\S]+?)\n```', text) - if m: - try: - return json.loads(m.group(1)) - except (json.JSONDecodeError, TypeError): - pass - return None - - -def _parse_count(val) -> float: - """解析数字字符串, 支持 '1.2万' 格式""" - if isinstance(val, (int, float)): - return float(val) - s = str(val).strip() - if "万" in s: - try: - return float(s.replace("万", "")) * 10000 - except ValueError: - pass - try: - return float(s) - except ValueError: - return 0.0 - - -def fetch_my_profile(user_id, xsec_token, mcp_url): - """获取我的账号数据, 返回结构化信息 + 可视化图表""" - if not user_id or not xsec_token: - return "❌ 请填写你的用户 ID 和 xsec_token", "", None, None, None - try: - client = get_mcp_client(mcp_url) - result = client.get_user_profile(user_id, xsec_token) - if "error" in result: - return f"❌ {result['error']}", "", None, None, None - - raw = result.get("raw", {}) - text = result.get("text", "") - - # 尝试从 raw 或 text 解析 JSON - data = None - if raw and isinstance(raw, dict): - content_list = raw.get("content", []) - for item in content_list: - if item.get("type") == "text": - data = _parse_profile_json(item.get("text", "")) - if data: - break - if not data: - data = _parse_profile_json(text) - if not data: - return "✅ 数据加载完成 (纯文本)", text, None, None, None - - # ---- 提取基本信息 (注意 MCP 对新号可能返回 null) ---- - basic = data.get("userBasicInfo") or {} - interactions = data.get("interactions") or [] - feeds = data.get("feeds") or [] - - gender_map = {0: "未知", 1: "男", 2: "女"} - info_lines = [ - f"## 👤 {basic.get('nickname', '未知')}", - f"- **小红书号**: {basic.get('redId', '-')}", - f"- **性别**: {gender_map.get(basic.get('gender', 0), '未知')}", - f"- **IP 属地**: {basic.get('ipLocation', '-')}", - f"- **简介**: {basic.get('desc', '-')}", - "", - "### 📊 核心数据", - ] - for inter in interactions: - info_lines.append(f"- **{inter.get('name', '')}**: {inter.get('count', '0')}") - - info_lines.append(f"\n### 📝 展示笔记: {len(feeds)} 篇") - profile_md = "\n".join(info_lines) - - # ---- 互动数据柱状图 ---- - fig_interact = None - if interactions: - inter_data = {i["name"]: _parse_count(i["count"]) for i in interactions} - fig_interact, ax = plt.subplots(figsize=(4, 3), dpi=100) - labels = list(inter_data.keys()) - values = list(inter_data.values()) - colors = ["#FF6B6B", "#4ECDC4", "#45B7D1"][:len(labels)] - ax.bar(labels, values, color=colors, edgecolor="white", linewidth=0.5) - ax.set_title("账号核心指标", fontsize=12, fontweight="bold") - for i, v in enumerate(values): - display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v)) - ax.text(i, v + max(values) * 0.02, display, ha="center", fontsize=9) - ax.set_ylabel("") - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - fig_interact.tight_layout() - - # ---- 笔记点赞分布图 ---- - fig_notes = None - if feeds: - titles, likes = [], [] - for f in feeds[:15]: - nc = f.get("noteCard") or {} - t = (nc.get("displayTitle", "") or "无标题")[:12] - lk = _parse_count((nc.get("interactInfo") or {}).get("likedCount", "0")) - titles.append(t) - likes.append(lk) - - fig_notes, ax2 = plt.subplots(figsize=(7, 3.5), dpi=100) - ax2.barh(range(len(titles)), likes, color="#FF6B6B", edgecolor="white") - ax2.set_yticks(range(len(titles))) - ax2.set_yticklabels(titles, fontsize=8) - ax2.set_title(f"笔记点赞排行 (Top {len(titles)})", fontsize=12, fontweight="bold") - ax2.invert_yaxis() - for i, v in enumerate(likes): - display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v)) - ax2.text(v + max(likes) * 0.01 if max(likes) > 0 else 0, i, display, va="center", fontsize=8) - ax2.spines["top"].set_visible(False) - ax2.spines["right"].set_visible(False) - fig_notes.tight_layout() - - # ---- 笔记详情表格 (Markdown) ---- - table_lines = [ - "### 📋 笔记数据明细", - "| # | 标题 | 类型 | ❤ 点赞 |", - "|---|------|------|--------|", - ] - for i, f in enumerate(feeds): - nc = f.get("noteCard") or {} - t = (nc.get("displayTitle", "") or "无标题")[:25] - tp = "📹 视频" if nc.get("type") == "video" else "📷 图文" - lk = (nc.get("interactInfo") or {}).get("likedCount", "0") - table_lines.append(f"| {i+1} | {t} | {tp} | {lk} |") - notes_table = "\n".join(table_lines) - - return "✅ 数据加载完成", profile_md, fig_interact, fig_notes, notes_table - - except Exception as e: - logger.error(f"获取我的数据失败: {e}") - return f"❌ {e}", "", None, None, None - - -# ================================================== -# 自动化运营模块 -# ================================================== - -# 自动化状态 -_auto_running = threading.Event() -_auto_thread: threading.Thread | None = None -_auto_log: list[str] = [] - -# ---- 操作记录：防重复 & 每日统计 ---- -_op_history = { - "commented_feeds": set(), # 已评论的 feed_id - "replied_comments": set(), # 已回复的 comment_id - "liked_feeds": set(), # 已点赞的 feed_id - "favorited_feeds": set(), # 已收藏的 feed_id -} -_daily_stats = { - "date": "", - "comments": 0, - "likes": 0, - "favorites": 0, - "publishes": 0, - "replies": 0, - "errors": 0, -} -# 每日操作上限 -DAILY_LIMITS = { - "comments": 30, - "likes": 80, - "favorites": 50, - "publishes": 8, - "replies": 40, -} -# 连续错误计数 → 冷却 -_consecutive_errors = 0 -_error_cooldown_until = 0.0 - - -def _reset_daily_stats_if_needed(): - """每天自动重置统计""" - today = datetime.now().strftime("%Y-%m-%d") - if _daily_stats["date"] != today: - _daily_stats.update({ - "date": today, "comments": 0, "likes": 0, - "favorites": 0, "publishes": 0, "replies": 0, "errors": 0, - }) - # 每日重置历史记录（允许隔天重复互动） - for k in _op_history: - _op_history[k].clear() - - -def _check_daily_limit(op_type: str) -> bool: - """检查是否超出每日限额""" - _reset_daily_stats_if_needed() - limit = DAILY_LIMITS.get(op_type, 999) - current = _daily_stats.get(op_type, 0) - return current < limit - - -def _increment_stat(op_type: str): - """增加操作计数""" - _reset_daily_stats_if_needed() - _daily_stats[op_type] = _daily_stats.get(op_type, 0) + 1 - - -def _record_error(): - """记录错误，连续错误触发冷却""" - global _consecutive_errors, _error_cooldown_until - _consecutive_errors += 1 - _daily_stats["errors"] = _daily_stats.get("errors", 0) + 1 - if _consecutive_errors >= 3: - cooldown = min(60 * _consecutive_errors, 600) # 最多冷却10分钟 - _error_cooldown_until = time.time() + cooldown - _auto_log_append(f"⚠️ 连续 {_consecutive_errors} 次错误，冷却 {cooldown}s") - - -def _clear_error_streak(): - """操作成功后清除连续错误记录""" - global _consecutive_errors - _consecutive_errors = 0 - - -def _is_in_cooldown() -> bool: - """检查是否在错误冷却期""" - return time.time() < _error_cooldown_until - - -def _is_in_operating_hours(start_hour: int = 7, end_hour: int = 23) -> bool: - """检查是否在运营时间段""" - now_hour = datetime.now().hour - return start_hour <= now_hour < end_hour - - -def _get_stats_summary() -> str: - """获取今日运营统计摘要""" - _reset_daily_stats_if_needed() - s = _daily_stats - lines = [ - f"📊 **今日运营统计** ({s['date']})", - f"- 💬 评论: {s['comments']}/{DAILY_LIMITS['comments']}", - f"- ❤️ 点赞: {s['likes']}/{DAILY_LIMITS['likes']}", - f"- ⭐ 收藏: {s['favorites']}/{DAILY_LIMITS['favorites']}", - f"- 🚀 发布: {s['publishes']}/{DAILY_LIMITS['publishes']}", - f"- 💌 回复: {s['replies']}/{DAILY_LIMITS['replies']}", - f"- ❌ 错误: {s['errors']}", - ] - return "\n".join(lines) - -# ================= 人设池 ================= -DEFAULT_PERSONAS = [ - "赛博AI虚拟博主，住在2077年的数码女孩，用AI生成高颜值写真和全球场景打卡，与粉丝超高频互动", - "性感福利主播，身材火辣衣着大胆，专注分享穿衣显身材和私房写真风穿搭", - "身材管理健身美女，热爱分享好身材秘诀和穿搭显身材技巧", - "温柔知性的时尚博主，喜欢分享日常穿搭和生活美学", - "元气满满的大学生，热爱探店和平价好物分享", - "30岁都市白领丽人，专注通勤穿搭和职场干货", - "精致妈妈，分享育儿经验和家居收纳技巧", - "文艺青年摄影师，喜欢记录旅行和城市角落", - "健身达人营养师，专注减脂餐和运动分享", - "资深美妆博主，擅长化妆教程和护肤测评", - "独居女孩，分享租房改造和独居生活仪式感", - "甜品烘焙爱好者，热衷分享自制甜点和下午茶", - "数码科技女生，专注好用App和电子产品测评", - "小镇姑娘在大城市打拼，分享省钱攻略和成长日记", - "中医养生爱好者，分享节气养生和食疗方子", - "二次元coser，喜欢分享cos日常和动漫周边", - "北漂程序媛，分享高效工作法和解压生活", - "复古穿搭博主，热爱vintage风和中古饰品", - "考研上岸学姐，分享学习方法和备考经验", - "新手养猫人，记录和毛孩子的日常生活", - "咖啡重度爱好者，探遍城市独立咖啡馆", - "极简主义生活家，倡导断舍离和高质量生活", - "汉服爱好者，分享传统文化和国风穿搭", - "插画师小姐姐，分享手绘过程和创作灵感", - "海归女孩，分享中西文化差异和海外生活见闻", - "瑜伽老师，分享身心灵修行和自律生活", - "美甲设计师，分享流行甲型和美甲教程", - "家居软装设计师，分享小户型改造和氛围感布置", -] - -RANDOM_PERSONA_LABEL = "🎲 随机人设（每次自动切换）" - -# ================= 人设 → 分类关键词/主题池映射 ================= -# 每个人设对应一组相符的评论关键词和主题，切换人设时自动同步 - -PERSONA_POOL_MAP = { - # ---- 性感福利类 ---- - "性感福利主播": { - "topics": [ - "辣妹穿搭", "内衣测评", "比基尼穿搭", "私房写真风穿搭", "吊带裙穿搭", - "低胸穿搭", "紧身连衣裙", "蕾丝穿搭", "泳衣测评", "居家睡衣穿搭", - "露背装穿搭", "热裤穿搭", "性感御姐穿搭", "渔网袜穿搭", "包臀裙穿搭", - "锁骨链饰品", "身材展示", "好身材日常", "氛围感私房照", "海边度假穿搭", - ], - "keywords": [ - "辣妹", "性感穿搭", "内衣", "比基尼", "吊带", "低胸", - "紧身", "蕾丝", "泳衣", "睡衣", "露背", "热裤", - "御姐", "好身材", "包臀裙", "身材展示", "私房", "氛围感", - ], - }, - # ---- 身材管理类 ---- - "身材管理健身美女": { - "topics": [ - "好身材穿搭", "显身材穿搭", "马甲线养成", "翘臀训练", "直角肩养成", - "天鹅颈锻炼", "小蛮腰秘诀", "腿型矫正", "体态管理", "维密身材", - "居家塑形", "健身穿搭", "运动内衣测评", "蜜桃臀训练", "锁骨养成", - "紧身穿搭", "比基尼身材", "纤腰丰臀", "身材对比照", "自律打卡", - ], - "keywords": [ - "身材", "好身材", "马甲线", "翘臀", "直角肩", "天鹅颈", - "小蛮腰", "健身女孩", "塑形", "体态", "蜜桃臀", "腰臀比", - "紧身", "显身材", "维密", "锁骨", "A4腰", "漫画腿", - ], - }, - # ---- 时尚穿搭类 ---- - "温柔知性的时尚博主": { - "topics": [ - "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "法式穿搭", - "极简穿搭", "氛围感穿搭", "一衣多穿", "秋冬叠穿", "夏日清凉穿搭", - "生活美学", "衣橱整理", "配色技巧", "基础款穿搭", "轻熟风穿搭", - ], - "keywords": [ - "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "法式穿搭", - "极简风", "氛围感", "轻熟风", "高级感穿搭", "配色", - ], - }, - "元气满满的大学生": { - "topics": [ - "学生党穿搭", "宿舍美食", "平价好物", "校园生活", "学生党护肤", - "期末复习", "社团活动", "寝室改造", "奶茶测评", "拍照打卡地", - "一人食食谱", "考研经验", "实习经验", "省钱攻略", - ], - "keywords": [ - "学生党", "平价好物", "宿舍", "校园", "奶茶", "探店", - "拍照", "省钱", "大学生活", "期末", "开学", "室友", - ], - }, - "30岁都市白领丽人": { - "topics": [ - "通勤穿搭", "职场干货", "面试技巧", "简历优化", "时间管理", - "理财入门", "轻熟风穿搭", "职场妆容", "咖啡探店", "高效工作法", - "副业分享", "自律生活", "下班后充电", "职场人际关系", - ], - "keywords": [ - "通勤穿搭", "职场", "面试", "理财", "自律", "高效", - "咖啡", "轻熟", "白领", "上班族", "时间管理", "副业", - ], - }, - "精致妈妈": { - "topics": [ - "育儿经验", "家居收纳", "辅食制作", "亲子游", "母婴好物", - "宝宝穿搭", "早教启蒙", "产后恢复", "家常菜做法", "小户型收纳", - "家庭教育", "孕期护理", "宝宝辅食", "妈妈穿搭", - ], - "keywords": [ - "育儿", "收纳", "辅食", "母婴", "亲子", "早教", - "宝宝", "家居", "待产", "产后", "妈妈", "家常菜", - ], - }, - "文艺青年摄影师": { - "topics": [ - "旅行攻略", "小众旅行地", "拍照打卡地", "城市citywalk", "古镇旅行", - "手机摄影技巧", "胶片摄影", "人像摄影", "风光摄影", "街拍", - "咖啡探店", "文艺书店", "展览打卡", "独立书店", - ], - "keywords": [ - "旅行", "摄影", "打卡", "citywalk", "胶片", "拍照", - "小众", "展览", "文艺", "街拍", "风光", "人像", - ], - }, - "健身达人营养师": { - "topics": [ - "减脂餐分享", "居家健身", "帕梅拉跟练", "跑步入门", "体态矫正", - "增肌餐", "蛋白质补充", "运动穿搭", "健身房攻略", "马甲线养成", - "热量计算", "健康早餐", "运动恢复", "减脂食谱", - ], - "keywords": [ - "减脂", "健身", "减脂餐", "蛋白质", "体态", "马甲线", - "帕梅拉", "跑步", "热量", "增肌", "运动", "健康餐", - ], - }, - "资深美妆博主": { - "topics": [ - "妆容教程", "眼妆教程", "唇妆合集", "底妆测评", "护肤心得", - "防晒测评", "学生党平价护肤", "敏感肌护肤", "美白攻略", - "成分党护肤", "换季护肤", "早C晚A护肤", "抗老护肤", - ], - "keywords": [ - "护肤", "化妆教程", "眼影", "口红", "底妆", "防晒", - "美白", "敏感肌", "成分", "平价", "测评", "粉底", - ], - }, - "独居女孩": { - "topics": [ - "独居生活", "租房改造", "氛围感房间", "一人食食谱", "好物分享", - "香薰推荐", "居家好物", "断舍离", "仪式感生活", "独居安全", - "解压方式", "emo急救指南", "桌面布置", "小户型装修", - ], - "keywords": [ - "独居", "租房改造", "好物", "氛围感", "一人食", "仪式感", - "解压", "居家", "香薰", "ins风", "房间", "断舍离", - ], - }, - "甜品烘焙爱好者": { - "topics": [ - "烘焙教程", "0失败甜品", "下午茶推荐", "蛋糕教程", "面包制作", - "饼干烘焙", "奶油裱花", "巧克力甜品", "网红甜品", "便当制作", - "早餐食谱", "咖啡配甜品", "节日甜品", "低卡甜品", - ], - "keywords": [ - "烘焙", "甜品", "蛋糕", "面包", "下午茶", "曲奇", - "裱花", "抹茶", "巧克力", "奶油", "食谱", "烤箱", - ], - }, - "数码科技女生": { - "topics": [ - "iPad生产力", "手机摄影技巧", "好用App推荐", "电子产品测评", - "桌面布置", "数码好物", "耳机测评", "平板学习", "生产力工具", - "手机壳推荐", "充电设备", "智能家居", - ], - "keywords": [ - "iPad", "App推荐", "数码", "测评", "手机", "耳机", - "桌面", "科技", "电子产品", "平板", "生产力", "充电", - ], - }, - "小镇姑娘在大城市打拼": { - "topics": [ - "省钱攻略", "成长日记", "平价好物", "租房改造", "副业分享", - "理财入门", "独居生活", "面试技巧", "通勤穿搭", "自律生活", - "城市生存指南", "女性成长", "攒钱计划", - ], - "keywords": [ - "省钱", "平价", "租房", "副业", "理财", "成长", - "自律", "打工", "攒钱", "面试", "独居", "北漂", - ], - }, - "中医养生爱好者": { - "topics": [ - "节气养生", "食疗方子", "泡脚养生", "体质调理", "艾灸", - "中药茶饮", "作息调整", "经络按摩", "养胃食谱", "祛湿方法", - "睡眠改善", "女性调理", "养生汤", "二十四节气", - ], - "keywords": [ - "养生", "食疗", "泡脚", "中医", "艾灸", "祛湿", - "节气", "体质", "养胃", "经络", "调理", "药膳", - ], - }, - "二次元coser": { - "topics": [ - "cos日常", "动漫周边", "漫展攻略", "cos化妆教程", "假发造型", - "lolita穿搭", "二次元好物", "手办收藏", "动漫推荐", "cos道具制作", - "jk穿搭", "谷子收藏", "二次元摄影", - ], - "keywords": [ - "cos", "动漫", "二次元", "漫展", "lolita", "手办", - "jk", "假发", "谷子", "周边", "番剧", "coser", - ], - }, - "北漂程序媛": { - "topics": [ - "高效工作法", "程序员日常", "好用App推荐", "副业分享", "自律生活", - "时间管理", "iPad生产力", "解压方式", "通勤穿搭", "理财入门", - "独居生活", "技术学习", "面试经验", "桌面布置", - ], - "keywords": [ - "程序员", "高效", "App推荐", "自律", "副业", "iPad", - "技术", "工作", "北漂", "面试", "代码", "桌面", - ], - }, - "复古穿搭博主": { - "topics": [ - "vintage风穿搭", "中古饰品", "复古妆容", "二手vintage", "古着穿搭", - "法式穿搭", "复古包包", "跳蚤市场", "旧物改造", "港风穿搭", - "文艺穿搭", "配饰搭配", "vintage探店", - ], - "keywords": [ - "vintage", "复古", "中古", "古着", "港风", "法式", - "饰品", "二手", "旧物", "跳蚤市场", "复古穿搭", "文艺", - ], - }, - "考研上岸学姐": { - "topics": [ - "考研经验", "英语学习方法", "书单推荐", "时间管理", "自律生活", - "考研择校", "政治复习", "数学刷题", "考研英语", "复试经验", - "专业课复习", "考研心态", "背诵技巧", "刷题方法", - ], - "keywords": [ - "考研", "英语学习", "书单", "自律", "学习方法", "上岸", - "刷题", "备考", "复习", "笔记", "时间管理", "择校", - ], - }, - "新手养猫人": { - "topics": [ - "养猫日常", "猫粮测评", "猫咪用品", "新手养宠指南", "猫咪健康", - "猫咪行为", "驱虫攻略", "猫砂测评", "猫玩具推荐", "猫咪拍照", - "多猫家庭", "领养代替购买", "猫咪绝育", - ], - "keywords": [ - "养猫", "猫粮", "猫咪", "宠物", "猫砂", "驱虫", - "铲屎官", "喵喵", "猫玩具", "猫零食", "新手养猫", "猫咪日常", - ], - }, - "咖啡重度爱好者": { - "topics": [ - "咖啡探店", "手冲咖啡", "咖啡豆推荐", "咖啡器具", "拿铁艺术", - "家庭咖啡", "咖啡配甜品", "独立咖啡馆", "冷萃咖啡", "咖啡知识", - "意式咖啡", "探店打卡", "咖啡拉花", - ], - "keywords": [ - "咖啡", "手冲", "拿铁", "探店", "咖啡豆", "美式", - "咖啡馆", "意式", "冷萃", "拉花", "咖啡器具", "独立咖啡馆", - ], - }, - "极简主义生活家": { - "topics": [ - "断舍离", "极简生活", "收纳技巧", "高质量生活", "减法生活", - "胶囊衣橱", "极简护肤", "环保生活", "数字断舍离", "极简穿搭", - "极简房间", "消费降级", "物欲管理", - ], - "keywords": [ - "断舍离", "极简", "收纳", "高质量", "减法", "胶囊衣橱", - "简约", "环保", "整理", "少即是多", "极简风", "质感", - ], - }, - "汉服爱好者": { - "topics": [ - "汉服穿搭", "国风穿搭", "传统文化", "汉服发型", "汉服配饰", - "汉服拍照", "古风妆容", "汉服日常", "汉服科普", "形制科普", - "古风摄影", "新中式穿搭", "汉服探店", - ], - "keywords": [ - "汉服", "国风", "传统文化", "古风", "新中式", "形制", - "发簪", "明制", "宋制", "唐制", "汉服日常", "古风摄影", - ], - }, - "插画师小姐姐": { - "topics": [ - "手绘教程", "创作灵感", "iPad绘画", "插画分享", "水彩教程", - "Procreate技巧", "配色方案", "角色设计", "头像绘制", "手账素材", - "接稿经验", "画师日常", "绘画工具推荐", - ], - "keywords": [ - "插画", "手绘", "Procreate", "画画", "iPad绘画", "水彩", - "配色", "创作", "画师", "手账", "教程", "素材", - ], - }, - "海归女孩": { - "topics": [ - "中西文化差异", "海外生活", "留学经验", "英语学习方法", "海归求职", - "旅行攻略", "异国美食", "海外好物", "文化冲击", "语言学习", - "签证攻略", "海归适应", "国外探店", - ], - "keywords": [ - "留学", "海归", "英语", "海外", "文化差异", "旅行", - "异国", "签证", "语言", "出国", "求职", "国外", - ], - }, - "瑜伽老师": { - "topics": [ - "瑜伽入门", "冥想练习", "体态矫正", "呼吸法", "居家瑜伽", - "拉伸教程", "肩颈放松", "瑜伽体式", "自律生活", "身心灵", - "瑜伽穿搭", "晨练瑜伽", "睡前瑜伽", - ], - "keywords": [ - "瑜伽", "冥想", "体态", "拉伸", "放松", "呼吸", - "柔韧", "健康", "自律", "晨练", "入门", "体式", - ], - }, - "美甲设计师": { - "topics": [ - "美甲教程", "流行甲型", "美甲合集", "简约美甲", "法式美甲", - "手绘美甲", "季节美甲", "显白美甲", "美甲配色", "短甲美甲", - "新娘美甲", "美甲工具推荐", "日式美甲", - ], - "keywords": [ - "美甲", "甲型", "法式美甲", "手绘", "显白", "短甲", - "指甲", "美甲教程", "配色", "日式美甲", "腮红甲", "猫眼甲", - ], - }, - "家居软装设计师": { - "topics": [ - "小户型改造", "氛围感布置", "软装搭配", "家居好物", "收纳技巧", - "客厅布置", "卧室改造", "灯光设计", "绿植布置", "装修避坑", - "北欧风格", "ins风家居", "墙面装饰", - ], - "keywords": [ - "家居", "软装", "改造", "收纳", "氛围感", "小户型", - "装修", "灯光", "绿植", "北欧", "ins风", "布置", - ], - }, - # ---- 赛博/AI 虚拟博主类 ---- - "赛博AI虚拟博主": { - "topics": [ - "AI女孩日常", "虚拟人物写真", "AI生成美女", "赛博朴克穿搭", "未来风穿搭", - "全球场景打卡", "巴黎打卡写真", "东京街头拍照", "外太空写真", "古风仙侠写真", - "AI换装挑战", "粉丝许愿穿搭", "二次元风格写真", "女仆装写真", "护士制服写真", - "校园制服写真", "婚纱写真", "水下写真", "AI绘画教程", "虚拟人物背后故事", - ], - "keywords": [ - "AI女孩", "AI美女", "虚拟人物", "赛博朴克", "AI绘画", "AI写真", - "数码女孩", "2077", "未来风", "场景切换", "换装挑战", "粉丝许愿", - "高颜值", "特写", "全球打卡", "制服写真", "AI创作", "互动", - ], - }, -} - -# 为"随机人设"使用的全量池（兼容旧逻辑） -DEFAULT_TOPICS = [ - # 穿搭类 - "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "小个子穿搭", - "学生党穿搭", "韩系穿搭", "日系穿搭", "法式穿搭", "极简穿搭", - "国风穿搭", "运动穿搭", "闺蜜穿搭", "梨形身材穿搭", "微胖穿搭", - "氛围感穿搭", "一衣多穿", "秋冬叠穿", "夏日清凉穿搭", - # 美妆护肤类 - "护肤心得", "妆容教程", "学生党平价护肤", "敏感肌护肤", - "抗老护肤", "美白攻略", "眼妆教程", "唇妆合集", "底妆测评", - "防晒测评", "早C晚A护肤", "成分党护肤", "换季护肤", - # 美食类 - "减脂餐分享", "一人食食谱", "宿舍美食", "烘焙教程", "家常菜做法", - "探店打卡", "咖啡探店", "早餐食谱", "下午茶推荐", "火锅推荐", - "奶茶测评", "便当制作", "0失败甜品", - # 生活家居类 - "好物分享", "平价好物", "居家好物", "收纳技巧", "租房改造", - "小户型装修", "氛围感房间", "香薰推荐", "桌面布置", "断舍离", - # 旅行出行类 - "旅行攻略", "周末去哪玩", "小众旅行地", "拍照打卡地", "露营攻略", - "自驾游攻略", "古镇旅行", "海岛度假", "城市citywalk", - # 学习成长类 - "书单推荐", "自律生活", "时间管理", "考研经验", "英语学习方法", - "理财入门", "副业分享", "简历优化", "面试技巧", - # 数码科技类 - "iPad生产力", "手机摄影技巧", "好用App推荐", "电子产品测评", - # 健身运动类 - "居家健身", "帕梅拉跟练", "跑步入门", "瑜伽入门", "体态矫正", - # 宠物类 - "养猫日常", "养狗经验", "宠物好物", "新手养宠指南", - # 情感心理类 - "独居生活", "emo急救指南", "社恐自救", "女性成长", "情绪管理", -] - -DEFAULT_STYLES = [ - "好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷", - "知识科普", "经验分享", "清单合集", "对比测评", "沉浸式体验", -] - -# 全量评论关键词池（兼容旧逻辑 / 随机人设） -DEFAULT_COMMENT_KEYWORDS = [ - # 穿搭时尚 - "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "小个子穿搭", - # 美妆护肤 - "护肤", "化妆教程", "平价护肤", "防晒", "美白", "眼影", - # 美食 - "美食", "减脂餐", "探店", "咖啡", "烘焙", "食谱", - # 生活好物 - "好物推荐", "平价好物", "居家好物", "收纳", "租房改造", - # 旅行 - "旅行", "攻略", "打卡", "周末去哪玩", "露营", - # 学习成长 - "自律", "书单", "考研", "英语学习", "副业", - # 生活日常 - "生活日常", "独居", "vlog", "仪式感", "解压", - # 健身 - "减脂", "健身", "瑜伽", "体态", - # 宠物 - "养猫", "养狗", "宠物", -] - - -def _match_persona_pools(persona_text: str) -> dict | None: - """根据人设文本模糊匹配对应的关键词池和主题池 - 返回 {"topics": [...], "keywords": [...]} 或 None（未匹配） - """ - if not persona_text or persona_text == RANDOM_PERSONA_LABEL: - return None - # 精确匹配 - for key, pools in PERSONA_POOL_MAP.items(): - if key in persona_text or persona_text in key: - return pools - # 关键词模糊匹配 - _CATEGORY_HINTS = { - "时尚|穿搭|搭配|衣服": "温柔知性的时尚博主", - "大学|学生|校园": "元气满满的大学生", - "白领|职场|通勤|上班": "30岁都市白领丽人", - "妈妈|育儿|宝宝|母婴": "精致妈妈", - "摄影|旅行|旅游|文艺": "文艺青年摄影师", - "健身|运动|减脂|增肌|营养": "健身达人营养师", - "美妆|化妆|护肤|美白": "资深美妆博主", - "独居|租房|一人": "独居女孩", - "烘焙|甜品|蛋糕|面包": "甜品烘焙爱好者", - "数码|科技|App|电子": "数码科技女生", - "小镇|打拼|省钱|攒钱": "小镇姑娘在大城市打拼", - "中医|养生|食疗|节气": "中医养生爱好者", - "二次元|cos|动漫|漫展": "二次元coser", - "程序|代码|开发|码农": "北漂程序媛", - "复古|vintage|中古|古着": "复古穿搭博主", - "考研|备考|上岸|学习方法": "考研上岸学姐", - "猫|铲屎|喵": "新手养猫人", - "咖啡|手冲|拿铁": "咖啡重度爱好者", - "极简|断舍离|简约": "极简主义生活家", - "汉服|国风|传统文化": "汉服爱好者", - "插画|手绘|画画|绘画": "插画师小姐姐", - "海归|留学|海外": "海归女孩", - "瑜伽|冥想|身心灵": "瑜伽老师", - "美甲|甲型|指甲": "美甲设计师", - "家居|软装|装修|改造": "家居软装设计师", - } - for hints, persona_key in _CATEGORY_HINTS.items(): - if any(h in persona_text for h in hints.split("|")): - return PERSONA_POOL_MAP.get(persona_key) - return None - - -def get_persona_topics(persona_text: str) -> list[str]: - """获取人设对应的主题池，未匹配则返回全量池""" - pools = _match_persona_pools(persona_text) - return pools["topics"] if pools else DEFAULT_TOPICS - - -def get_persona_keywords(persona_text: str) -> list[str]: - """获取人设对应的评论关键词池，未匹配则返回全量池""" - pools = _match_persona_pools(persona_text) - return pools["keywords"] if pools else DEFAULT_COMMENT_KEYWORDS - - -def on_persona_changed(persona_text: str): - """人设切换时联动更新评论关键词池、主题池、队列主题池，并保存到配置""" - # 保存人设到配置 - cfg.set("persona", persona_text) - - # 更新关键词和主题池 - keywords = get_persona_keywords(persona_text) - topics = get_persona_topics(persona_text) - keywords_str = ", ".join(keywords) - topics_str = ", ".join(topics) - matched = _match_persona_pools(persona_text) - if matched: - label = persona_text[:15] if len(persona_text) > 15 else persona_text - hint = f"✅ 已切换至「{label}」专属关键词/主题池" - else: - hint = "ℹ️ 使用通用全量关键词/主题池" - - # 返回：自动运营的关键词池、主题池、提示信息、队列主题池 - return keywords_str, topics_str, hint, topics_str - - -def _auto_log_append(msg: str): - """记录自动化日志""" - ts = datetime.now().strftime("%H:%M:%S") - entry = f"[{ts}] {msg}" - _auto_log.append(entry) - if len(_auto_log) > 500: - _auto_log[:] = _auto_log[-300:] - logger.info("[自动化] %s", msg) - - -def _resolve_persona(persona_text: str) -> str: - """解析人设：如果是随机人设则从池中随机选一个，否则原样返回""" - if not persona_text or persona_text == RANDOM_PERSONA_LABEL: - chosen = random.choice(DEFAULT_PERSONAS) - _auto_log_append(f"🎭 本次人设: {chosen[:20]}...") - return chosen - # 检查是否选的是池中某个人设（Dropdown选中） - return persona_text - - -def _auto_comment_with_log(keywords_str, mcp_url, model, persona_text): - """一键评论 + 同步刷新日志""" - msg = auto_comment_once(keywords_str, mcp_url, model, persona_text) - return msg, get_auto_log() - - -def auto_comment_once(keywords_str, mcp_url, model, persona_text): - """一键评论：自动搜索高赞笔记 → AI生成评论 → 发送（含防重复/限额/冷却）""" - try: - if _is_in_cooldown(): - return "⏳ 错误冷却中，请稍后再试" - if not _check_daily_limit("comments"): - return f"🚫 今日评论已达上限 ({DAILY_LIMITS['comments']})" - - persona_text = _resolve_persona(persona_text) - # 如果用户未手动修改关键词池，则使用人设匹配的专属关键词池 - persona_keywords = get_persona_keywords(persona_text) - keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else persona_keywords - keyword = random.choice(keywords) - _auto_log_append(f"🔍 搜索关键词: {keyword}") - - client = get_mcp_client(mcp_url) - - # 随机切换搜索排序，丰富互动对象 - sort_options = ["最多点赞", "综合", "最新"] - sort_by = random.choice(sort_options) - - # 搜索高赞笔记 - entries = client.search_feeds_parsed(keyword, sort_by=sort_by) - if not entries: - _auto_log_append("⚠️ 搜索无结果，尝试推荐列表") - entries = client.list_feeds_parsed() - if not entries: - _record_error() - return "❌ 未找到任何笔记" - - # 过滤掉自己的笔记 & 已评论过的笔记 - my_uid = cfg.get("my_user_id", "") - entries = [ - e for e in entries - if e.get("user_id") != my_uid - and e.get("feed_id") not in _op_history["commented_feeds"] - ] - if not entries: - return "ℹ️ 搜索结果中所有笔记都已评论过，换个关键词试试" - - # 从前10个中随机选择 - target = random.choice(entries[:min(10, len(entries))]) - feed_id = target["feed_id"] - xsec_token = target["xsec_token"] - title = target.get("title", "未知") - _auto_log_append(f"🎯 选中: {title[:30]} (@{target.get('author', '未知')}) [排序:{sort_by}]") - - if not feed_id or not xsec_token: - return "❌ 笔记缺少必要参数 (feed_id/xsec_token)" - - # 模拟浏览延迟 - time.sleep(random.uniform(3, 8)) - - # 加载笔记详情 - result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) - if "error" in result: - _record_error() - return f"❌ 加载笔记失败: {result['error']}" - - full_text = result.get("text", "") - if "评论" in full_text: - parts = full_text.split("评论", 1) - content_part = parts[0].strip()[:600] - comments_part = ("评论" + parts[1])[:800] if len(parts) > 1 else "" - else: - content_part = full_text[:500] - comments_part = "" - - # AI 生成评论 - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置，请先在全局设置中配置提供商" - - svc = LLMService(api_key, base_url, model) - comment = svc.generate_proactive_comment( - persona_text, title, content_part, comments_part - ) - _auto_log_append(f"💬 生成评论: {comment[:60]}...") - - # 随机等待后发送 - time.sleep(random.uniform(3, 10)) - result = client.post_comment(feed_id, xsec_token, comment) - resp_text = result.get("text", "") - _auto_log_append(f"📡 MCP 响应: {resp_text[:200]}") - - if "error" in result: - _record_error() - _auto_log_append(f"❌ 评论发送失败: {result['error']}") - return f"❌ 评论发送失败: {result['error']}" - - # 记录成功操作 - _op_history["commented_feeds"].add(feed_id) - _increment_stat("comments") - _clear_error_streak() - - _auto_log_append(f"✅ 评论已发送到「{title[:20]}」 (今日第{_daily_stats['comments']}条)") - return f"✅ 已评论「{title[:25]}」\n📝 评论: {comment}\n📊 今日评论: {_daily_stats['comments']}/{DAILY_LIMITS['comments']}" - - except Exception as e: - _record_error() - _auto_log_append(f"❌ 一键评论异常: {e}") - return f"❌ 评论失败: {e}" - - -def _auto_like_with_log(keywords_str, like_count, mcp_url): - """一键点赞 + 同步刷新日志""" - msg = auto_like_once(keywords_str, like_count, mcp_url) - return msg, get_auto_log() - - -def auto_like_once(keywords_str, like_count, mcp_url): - """一键点赞：搜索/推荐笔记 → 随机选择 → 批量点赞（含防重复/限额）""" - try: - if _is_in_cooldown(): - return "⏳ 错误冷却中，请稍后再试" - if not _check_daily_limit("likes"): - return f"🚫 今日点赞已达上限 ({DAILY_LIMITS['likes']})" - - keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS - keyword = random.choice(keywords) - like_count = int(like_count) if like_count else 5 - # 不超过当日剩余额度 - remaining = DAILY_LIMITS["likes"] - _daily_stats.get("likes", 0) - like_count = min(like_count, remaining) - _auto_log_append(f"👍 点赞关键词: {keyword} | 目标: {like_count} 个") - - client = get_mcp_client(mcp_url) - - # 搜索笔记 - entries = client.search_feeds_parsed(keyword, sort_by="综合") - if not entries: - _auto_log_append("⚠️ 搜索无结果，尝试推荐列表") - entries = client.list_feeds_parsed() - if not entries: - _record_error() - return "❌ 未找到任何笔记" - - # 过滤自己的笔记 & 已点赞过的 - my_uid = cfg.get("my_user_id", "") - entries = [ - e for e in entries - if e.get("user_id") != my_uid - and e.get("feed_id") not in _op_history["liked_feeds"] - ] - if not entries: - return "ℹ️ 搜索结果中所有笔记都已点赞过" - - # 随机打乱，取前 N 个 - random.shuffle(entries) - targets = entries[:min(like_count, len(entries))] - - liked = 0 - for target in targets: - feed_id = target.get("feed_id", "") - xsec_token = target.get("xsec_token", "") - title = target.get("title", "未知")[:25] - - if not feed_id or not xsec_token: - continue - - # 模拟浏览延迟 - time.sleep(random.uniform(2, 6)) - - result = client.like_feed(feed_id, xsec_token) - if "error" in result: - _auto_log_append(f" ❌ 点赞失败「{title}」: {result['error']}") - else: - liked += 1 - _op_history["liked_feeds"].add(feed_id) - _increment_stat("likes") - _auto_log_append(f" ❤️ 已点赞「{title}」@{target.get('author', '未知')}") - - if liked > 0: - _clear_error_streak() - _auto_log_append(f"👍 点赞完成: 成功 {liked}/{len(targets)} (今日累计{_daily_stats.get('likes', 0)})") - return f"✅ 点赞完成！成功 {liked}/{len(targets)} 个\n📊 今日点赞: {_daily_stats.get('likes', 0)}/{DAILY_LIMITS['likes']}" - - except Exception as e: - _record_error() - _auto_log_append(f"❌ 一键点赞异常: {e}") - return f"❌ 点赞失败: {e}" - - -def _auto_favorite_with_log(keywords_str, fav_count, mcp_url): - """一键收藏 + 同步刷新日志""" - msg = auto_favorite_once(keywords_str, fav_count, mcp_url) - return msg, get_auto_log() - - -def auto_favorite_once(keywords_str, fav_count, mcp_url): - """一键收藏：搜索优质笔记 → 随机选择 → 批量收藏（含防重复/限额）""" - try: - if _is_in_cooldown(): - return "⏳ 错误冷却中，请稍后再试" - if not _check_daily_limit("favorites"): - return f"🚫 今日收藏已达上限 ({DAILY_LIMITS['favorites']})" - - keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS - keyword = random.choice(keywords) - fav_count = int(fav_count) if fav_count else 3 - remaining = DAILY_LIMITS["favorites"] - _daily_stats.get("favorites", 0) - fav_count = min(fav_count, remaining) - _auto_log_append(f"⭐ 收藏关键词: {keyword} | 目标: {fav_count} 个") - - client = get_mcp_client(mcp_url) - - entries = client.search_feeds_parsed(keyword, sort_by="最多收藏") - if not entries: - entries = client.list_feeds_parsed() - if not entries: - _record_error() - return "❌ 未找到任何笔记" - - my_uid = cfg.get("my_user_id", "") - entries = [ - e for e in entries - if e.get("user_id") != my_uid - and e.get("feed_id") not in _op_history["favorited_feeds"] - ] - if not entries: - return "ℹ️ 搜索结果中所有笔记都已收藏过" - - random.shuffle(entries) - targets = entries[:min(fav_count, len(entries))] - - saved = 0 - for target in targets: - feed_id = target.get("feed_id", "") - xsec_token = target.get("xsec_token", "") - title = target.get("title", "未知")[:25] - - if not feed_id or not xsec_token: - continue - - time.sleep(random.uniform(2, 6)) - - result = client.favorite_feed(feed_id, xsec_token) - if "error" in result: - _auto_log_append(f" ❌ 收藏失败「{title}」: {result['error']}") - else: - saved += 1 - _op_history["favorited_feeds"].add(feed_id) - _increment_stat("favorites") - _auto_log_append(f" ⭐ 已收藏「{title}」@{target.get('author', '未知')}") - - if saved > 0: - _clear_error_streak() - _auto_log_append(f"⭐ 收藏完成: 成功 {saved}/{len(targets)} (今日累计{_daily_stats.get('favorites', 0)})") - return f"✅ 收藏完成！成功 {saved}/{len(targets)} 个\n📊 今日收藏: {_daily_stats.get('favorites', 0)}/{DAILY_LIMITS['favorites']}" - - except Exception as e: - _record_error() - _auto_log_append(f"❌ 一键收藏异常: {e}") - return f"❌ 收藏失败: {e}" - - -def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model, persona_text, quality_mode_val, face_swap_on): - """一键发布 + 同步刷新日志""" - msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, - persona_text=persona_text, quality_mode_val=quality_mode_val, - face_swap_on=face_swap_on) - return msg, get_auto_log() - - -def _auto_reply_with_log(max_replies, mcp_url, model, persona_text): - """一键回复 + 同步刷新日志""" - msg = auto_reply_once(max_replies, mcp_url, model, persona_text) - return msg, get_auto_log() - - -def auto_reply_once(max_replies, mcp_url, model, persona_text): - """一键回复：获取我的笔记 → 加载评论 → AI 生成回复 → 发送（含防重复/限额）""" - try: - if _is_in_cooldown(): - return "⏳ 错误冷却中，请稍后再试" - if not _check_daily_limit("replies"): - return f"🚫 今日回复已达上限 ({DAILY_LIMITS['replies']})" - - persona_text = _resolve_persona(persona_text) - my_uid = cfg.get("my_user_id", "") - xsec = cfg.get("xsec_token", "") - if not my_uid: - return "❌ 未配置用户 ID，请到「账号登录」页填写" - if not xsec: - return "❌ 未获取 xsec_token，请先登录" - - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置" - - max_replies = int(max_replies) if max_replies else 3 - remaining = DAILY_LIMITS["replies"] - _daily_stats.get("replies", 0) - max_replies = min(max_replies, remaining) - client = get_mcp_client(mcp_url) - _auto_log_append("💌 开始自动回复评论...") - - # Step 1: 获取我的笔记列表 - result = client.get_user_profile(my_uid, xsec) - if "error" in result: - _auto_log_append(f"❌ 获取我的笔记失败: {result['error']}") - return f"❌ 获取我的笔记失败: {result['error']}" - - # 解析笔记列表 - raw = result.get("raw", {}) - text = result.get("text", "") - data = None - if raw and isinstance(raw, dict): - for item in raw.get("content", []): - if item.get("type") == "text": - try: - data = json.loads(item["text"]) - except (json.JSONDecodeError, KeyError): - pass - if not data: - try: - data = json.loads(text) - except (json.JSONDecodeError, TypeError): - pass - - feeds = (data or {}).get("feeds") or [] - if not feeds: - _auto_log_append("⚠️ 未找到任何笔记") - return "⚠️ 未找到你的笔记" - - # 构建笔记条目 - my_entries = [] - for f in feeds: - nc = f.get("noteCard") or {} - my_entries.append({ - "feed_id": f.get("id", ""), - "xsec_token": f.get("xsecToken", ""), - "title": nc.get("displayTitle", "未知标题"), - }) - - _auto_log_append(f"📝 找到 {len(my_entries)} 篇笔记，开始扫描评论...") - - # Step 2: 遍历笔记，找到未回复的评论 - total_replied = 0 - svc = LLMService(api_key, base_url, model) - - for entry in my_entries: - if total_replied >= max_replies: - break - - feed_id = entry["feed_id"] - xsec_token = entry["xsec_token"] - title = entry["title"] - - if not feed_id or not xsec_token: - continue - - time.sleep(random.uniform(1, 3)) - - # 加载笔记评论（使用结构化接口） - comments = client.get_feed_comments(feed_id, xsec_token, load_all=True) - if not comments: - continue - - # 过滤掉自己的评论 & 已回复过的评论 - other_comments = [ - c for c in comments - if c.get("user_id") and c["user_id"] != my_uid and c.get("content") - and c.get("comment_id", "") not in _op_history["replied_comments"] - ] - - if not other_comments: - continue - - _auto_log_append(f"📖「{title[:20]}」有 {len(other_comments)} 条他人评论") - - for comment in other_comments: - if total_replied >= max_replies: - break - - comment_id = comment.get("comment_id", "") - comment_uid = comment.get("user_id", "") - comment_text = comment.get("content", "") - nickname = comment.get("nickname", "网友") - - if not comment_text.strip(): - continue - - _auto_log_append(f" 💬 @{nickname}: {comment_text[:40]}...") - - # AI 生成回复 - try: - reply = svc.generate_reply(persona_text, title, comment_text) - except Exception as e: - _auto_log_append(f" ❌ AI 回复生成失败: {e}") - continue - - _auto_log_append(f" 🤖 回复: {reply[:50]}...") - - # 发送回复 - time.sleep(random.uniform(2, 6)) - - if comment_id and comment_uid: - # 使用 reply_comment 精确回复 - resp = client.reply_comment( - feed_id, xsec_token, comment_id, comment_uid, reply - ) - else: - # 没有 comment_id 就用 post_comment 发到笔记下 - resp = client.post_comment(feed_id, xsec_token, f"@{nickname} {reply}") - - resp_text = resp.get("text", "") - if "error" in resp: - _auto_log_append(f" ❌ 回复发送失败: {resp['error']}") - else: - _auto_log_append(f" ✅ 已回复 @{nickname}") - total_replied += 1 - if comment_id: - _op_history["replied_comments"].add(comment_id) - _increment_stat("replies") - - if total_replied > 0: - _clear_error_streak() - - if total_replied == 0: - _auto_log_append("ℹ️ 没有找到需要回复的新评论") - return "ℹ️ 没有找到需要回复的新评论\n\n💡 可能所有评论都已回复过" - else: - _auto_log_append(f"✅ 自动回复完成，共回复 {total_replied} 条 (今日累计{_daily_stats.get('replies', 0)})") - return f"✅ 自动回复完成！共回复 {total_replied} 条评论\n📊 今日回复: {_daily_stats.get('replies', 0)}/{DAILY_LIMITS['replies']}" - - except Exception as e: - _record_error() - _auto_log_append(f"❌ 自动回复异常: {e}") - return f"❌ 自动回复失败: {e}" - - -def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, persona_text=None, quality_mode_val=None, face_swap_on=False): - """一键发布：自动生成文案 → 生成图片 → 本地备份 → 发布到小红书（含限额 + 智能权重 + 人设 + 画质）""" - try: - if _is_in_cooldown(): - return "⏳ 错误冷却中，请稍后再试" - if not _check_daily_limit("publishes"): - return f"🚫 今日发布已达上限 ({DAILY_LIMITS['publishes']})" - - topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS - use_weights = cfg.get("use_smart_weights", True) and analytics.has_weights - - if use_weights: - # 智能加权选题 - topic = analytics.get_weighted_topic(topics) - style = analytics.get_weighted_style(DEFAULT_STYLES) - _auto_log_append(f"🧠 [智能] 主题: {topic} | 风格: {style} (加权选择)") - else: - topic = random.choice(topics) - style = random.choice(DEFAULT_STYLES) - _auto_log_append(f"📝 主题: {topic} | 风格: {style} (主题池: {len(topics)} 个)") - - # 生成文案 - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置，请先在全局设置中配置提供商" - - svc = LLMService(api_key, base_url, model) - # 解析人设（随机/指定） - persona = _resolve_persona(persona_text) if persona_text else None - if persona: - _auto_log_append(f"🎭 人设: {persona[:20]}...") - - if use_weights: - # 使用加权文案生成 (携带权重洞察) - weight_insights = f"高权重主题: {', '.join(list(analytics._weights.get('topic_weights', {}).keys())[:5])}\n" - weight_insights += f"权重摘要: {analytics.weights_summary}" - title_advice = analytics.get_title_advice() - hot_tags = ", ".join(analytics.get_top_tags(8)) - try: - data = svc.generate_weighted_copy(topic, style, weight_insights, title_advice, hot_tags, sd_model_name=sd_model_name, persona=persona) - _auto_log_append("🧠 使用智能加权文案模板") - except Exception as e: - logger.warning("加权文案生成失败, 退回普通模式: %s", e) - data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) - _auto_log_append("⚠️ 加权模板异常, 使用普通模板") - else: - data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) - - title = (data.get("title", "") or "")[:20] - content = data.get("content", "") - sd_prompt = data.get("sd_prompt", "") - tags = data.get("tags", []) - - # 如果有高权重标签，补充到 tags 中 - if use_weights: - top_tags = analytics.get_top_tags(5) - for t in top_tags: - if t not in tags: - tags.append(t) - tags = tags[:10] # 限制最多10个标签 - - if not title: - _record_error() - return "❌ 文案生成失败：无标题" - _auto_log_append(f"📄 文案: {title}") - - # 生成图片 - if not sd_url_val or not sd_model_name: - return "❌ SD WebUI 未连接或未选择模型，请先在全局设置中连接" - - sd_svc = SDService(sd_url_val) - # 自动发布也支持换脸 - face_image = None - if face_swap_on: - face_image = SDService.load_face_image() - if face_image: - _auto_log_append("🎭 换脸已启用") - else: - _auto_log_append("⚠️ 换脸已启用但未找到头像，跳过换脸") - images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, - face_image=face_image, - quality_mode=quality_mode_val or "快速 (约30秒)", - persona=persona) - if not images: - _record_error() - return "❌ 图片生成失败：没有返回图片" - _auto_log_append(f"🎨 已生成 {len(images)} 张图片") - - # 本地备份（同时用于发布） - ts = int(time.time()) - safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] - backup_dir = os.path.join(OUTPUT_DIR, f"{ts}_{safe_title}") - os.makedirs(backup_dir, exist_ok=True) - - # 保存文案 - with open(os.path.join(backup_dir, "文案.txt"), "w", encoding="utf-8") as f: - f.write(f"标题: {title}\n风格: {style}\n主题: {topic}\n\n{content}\n\n标签: {', '.join(tags)}\n\nSD Prompt: {sd_prompt}") - - image_paths = [] - for idx, img in enumerate(images): - if isinstance(img, Image.Image): - path = os.path.abspath(os.path.join(backup_dir, f"图{idx+1}.jpg")) - if img.mode != "RGB": - img = img.convert("RGB") - img.save(path, format="JPEG", quality=95) - image_paths.append(path) - - if not image_paths: - return "❌ 图片保存失败" - - _auto_log_append(f"💾 本地已备份至: {backup_dir}") - - # 发布到小红书 - client = get_mcp_client(mcp_url) - result = client.publish_content( - title=title, content=content, images=image_paths, tags=tags - ) - if "error" in result: - _record_error() - _auto_log_append(f"❌ 发布失败: {result['error']} (文案已本地保存)") - return f"❌ 发布失败: {result['error']}\n💾 文案和图片已备份至: {backup_dir}" - - _increment_stat("publishes") - _clear_error_streak() - - # 清理 _temp_publish 中的旧临时文件 - temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish") - try: - if os.path.exists(temp_dir): - for f in os.listdir(temp_dir): - fp = os.path.join(temp_dir, f) - if os.path.isfile(fp) and time.time() - os.path.getmtime(fp) > 3600: - os.remove(fp) - except Exception: - pass - - _auto_log_append(f"🚀 发布成功: {title} (今日第{_daily_stats['publishes']}篇)") - return f"✅ 发布成功！\n📌 标题: {title}\n💾 备份: {backup_dir}\n📊 今日发布: {_daily_stats['publishes']}/{DAILY_LIMITS['publishes']}\n{result.get('text', '')}" - - except Exception as e: - _record_error() - _auto_log_append(f"❌ 一键发布异常: {e}") - return f"❌ 发布失败: {e}" - - -# ================================================== -# 发布队列相关函数 -# ================================================== - -def generate_to_queue(topics_str, sd_url_val, sd_model_name, model, persona_text=None, - quality_mode_val=None, face_swap_on=False, count=1, - scheduled_time=None): - """批量生成内容 → 加入发布队列（不直接发布）""" - try: - topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS - use_weights = cfg.get("use_smart_weights", True) and analytics.has_weights - - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置" - if not sd_url_val or not sd_model_name: - return "❌ SD WebUI 未连接或未选择模型" - - count = max(1, min(int(count), 10)) - results = [] - - for i in range(count): - try: - _auto_log_append(f"📋 [队列生成] 正在生成第 {i+1}/{count} 篇...") - - if use_weights: - topic = analytics.get_weighted_topic(topics) - style = analytics.get_weighted_style(DEFAULT_STYLES) - else: - topic = random.choice(topics) - style = random.choice(DEFAULT_STYLES) - - svc = LLMService(api_key, base_url, model) - persona = _resolve_persona(persona_text) if persona_text else None - - if use_weights: - weight_insights = f"高权重主题: {', '.join(list(analytics._weights.get('topic_weights', {}).keys())[:5])}\n" - weight_insights += f"权重摘要: {analytics.weights_summary}" - title_advice = analytics.get_title_advice() - hot_tags = ", ".join(analytics.get_top_tags(8)) - try: - data = svc.generate_weighted_copy(topic, style, weight_insights, title_advice, hot_tags, sd_model_name=sd_model_name, persona=persona) - except Exception: - data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) - else: - data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) - - title = (data.get("title", "") or "")[:20] - content = data.get("content", "") - sd_prompt = data.get("sd_prompt", "") - tags = data.get("tags", []) - - if use_weights: - top_tags = analytics.get_top_tags(5) - for t in top_tags: - if t not in tags: - tags.append(t) - tags = tags[:10] - - if not title: - _auto_log_append(f"⚠️ 第 {i+1} 篇文案生成失败，跳过") - continue - - # 生成图片 - sd_svc = SDService(sd_url_val) - face_image = None - if face_swap_on: - face_image = SDService.load_face_image() - images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, - face_image=face_image, - quality_mode=quality_mode_val or "快速 (约30秒)", - persona=persona) - if not images: - _auto_log_append(f"⚠️ 第 {i+1} 篇图片生成失败，跳过") - continue - - # 保存备份 - ts = int(time.time()) - safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] - backup_dir = os.path.join(OUTPUT_DIR, f"{ts}_{safe_title}") - os.makedirs(backup_dir, exist_ok=True) - - with open(os.path.join(backup_dir, "文案.txt"), "w", encoding="utf-8") as f: - f.write(f"标题: {title}\n风格: {style}\n主题: {topic}\n\n{content}\n\n标签: {', '.join(tags)}\n\nSD Prompt: {sd_prompt}") - - image_paths = [] - for idx, img in enumerate(images): - if isinstance(img, Image.Image): - path = os.path.abspath(os.path.join(backup_dir, f"图{idx+1}.jpg")) - if img.mode != "RGB": - img = img.convert("RGB") - img.save(path, format="JPEG", quality=95) - image_paths.append(path) - - if not image_paths: - continue - - # 加入队列 - item_id = pub_queue.add( - title=title, content=content, sd_prompt=sd_prompt, - tags=tags, image_paths=image_paths, backup_dir=backup_dir, - topic=topic, style=style, persona=persona or "", - status=STATUS_DRAFT, scheduled_time=scheduled_time, - ) - results.append(f"#{item_id} {title}") - _auto_log_append(f"📋 已加入队列 #{item_id}: {title}") - - # 多篇间隔 - if i < count - 1: - time.sleep(2) - - except Exception as e: - _auto_log_append(f"⚠️ 第 {i+1} 篇生成异常: {e}") - continue - - if not results: - return "❌ 所有内容生成失败，请检查配置" - - return f"✅ 已生成 {len(results)} 篇内容加入队列:\n" + "\n".join(f" - {r}" for r in results) - - except Exception as e: - return f"❌ 批量生成异常: {e}" - - -def _queue_publish_callback(item: dict) -> tuple[bool, str]: - """队列发布回调: 从队列项数据发布到小红书""" - try: - mcp_url = cfg.get("mcp_url", "http://localhost:18060/mcp") - client = get_mcp_client(mcp_url) - title = item.get("title", "") - content = item.get("content", "") - image_paths = item.get("image_paths", []) - tags = item.get("tags", []) - - if not title or not image_paths: - return False, "标题或图片缺失" - - # 验证图片文件存在 - valid_paths = [p for p in image_paths if os.path.isfile(p)] - if not valid_paths: - return False, "所有图片文件不存在" - - result = client.publish_content( - title=title, content=content, images=valid_paths, tags=tags, - ) - if "error" in result: - return False, result["error"] - - _increment_stat("publishes") - _clear_error_streak() - return True, result.get("text", "发布成功") - - except Exception as e: - return False, str(e) - - -# 注册发布回调 -queue_publisher.set_publish_callback(_queue_publish_callback) -queue_publisher.set_log_callback(_auto_log_append) - - -def queue_refresh_table(status_filter): - """刷新队列表格""" - statuses = None - if status_filter and status_filter != "全部": - status_map = {v: k for k, v in STATUS_LABELS.items()} - if status_filter in status_map: - statuses = [status_map[status_filter]] - return pub_queue.format_queue_table(statuses) - - -def queue_refresh_calendar(): - """刷新日历视图""" - return pub_queue.format_calendar(14) - - -def queue_preview_item(item_id_str): - """预览队列项""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - return pub_queue.format_preview(item_id) - except (ValueError, TypeError): - return "❌ 请输入有效的队列项 ID（数字）" - - -def queue_approve_item(item_id_str, scheduled_time_str): - """审核通过""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - sched = scheduled_time_str.strip() if scheduled_time_str else None - ok = pub_queue.approve(item_id, scheduled_time=sched) - if ok: - status = "已排期" if sched else "待发布" - return f"✅ #{item_id} 已审核通过 → {status}" - return f"❌ #{item_id} 无法审核（可能不是草稿/失败状态）" - except (ValueError, TypeError): - return "❌ 请输入有效的 ID" - - -def queue_reject_item(item_id_str): - """拒绝队列项""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - ok = pub_queue.reject(item_id) - return f"✅ #{item_id} 已拒绝" if ok else f"❌ #{item_id} 无法拒绝" - except (ValueError, TypeError): - return "❌ 请输入有效的 ID" - - -def queue_delete_item(item_id_str): - """删除队列项""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - ok = pub_queue.delete(item_id) - return f"✅ #{item_id} 已删除" if ok else f"❌ #{item_id} 无法删除（可能正在发布中）" - except (ValueError, TypeError): - return "❌ 请输入有效的 ID" - - -def queue_retry_item(item_id_str): - """重试失败项""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - ok = pub_queue.retry(item_id) - return f"✅ #{item_id} 已重新加入待发布" if ok else f"❌ #{item_id} 无法重试（不是失败状态）" - except (ValueError, TypeError): - return "❌ 请输入有效的 ID" - - -def queue_publish_now(item_id_str): - """立即发布队列项""" - try: - item_id = int(str(item_id_str).strip().replace("#", "")) - return queue_publisher.publish_now(item_id) - except (ValueError, TypeError): - return "❌ 请输入有效的 ID" - - -def queue_start_processor(): - """启动队列后台处理器""" - if queue_publisher.is_running: - return "⚠️ 队列处理器已在运行中" - queue_publisher.start(check_interval=60) - return "✅ 队列处理器已启动，每分钟检查待发布项" - - -def queue_stop_processor(): - """停止队列后台处理器""" - if not queue_publisher.is_running: - return "⚠️ 队列处理器未在运行" - queue_publisher.stop() - return "🛑 队列处理器已停止" - - -def queue_get_status(): - """获取队列状态摘要""" - counts = pub_queue.count_by_status() - running = "🟢 运行中" if queue_publisher.is_running else "⚪ 未启动" - parts = [f"**队列处理器**: {running}"] - for s, label in STATUS_LABELS.items(): - cnt = counts.get(s, 0) - if cnt > 0: - parts.append(f"{label}: {cnt}") - total = sum(counts.values()) - parts.append(f"**合计**: {total} 项") - return " · ".join(parts) - - -def queue_batch_approve(status_filter): - """批量审核通过所有草稿""" - items = pub_queue.list_by_status([STATUS_DRAFT]) - if not items: - return "📭 没有待审核的草稿" - approved = 0 - for item in items: - if pub_queue.approve(item["id"]): - approved += 1 - return f"✅ 已批量审核通过 {approved} 项" - - -def queue_generate_and_refresh(topics_str, sd_url_val, sd_model_name, model, - persona_text, quality_mode_val, face_swap_on, - gen_count, gen_schedule_time): - """生成内容到队列 + 刷新表格""" - msg = generate_to_queue( - topics_str, sd_url_val, sd_model_name, model, - persona_text=persona_text, quality_mode_val=quality_mode_val, - face_swap_on=face_swap_on, count=gen_count, - scheduled_time=gen_schedule_time.strip() if gen_schedule_time else None, - ) - table = pub_queue.format_queue_table() - calendar = pub_queue.format_calendar(14) - status = queue_get_status() - return msg, table, calendar, status - - -# 调度器下次执行时间追踪 -_scheduler_next_times = {} - - -def _scheduler_loop(comment_enabled, publish_enabled, reply_enabled, like_enabled, - favorite_enabled, - comment_min, comment_max, publish_min, publish_max, - reply_min, reply_max, max_replies_per_run, - like_min, like_max, like_count_per_run, - fav_min, fav_max, fav_count_per_run, - op_start_hour, op_end_hour, - keywords, topics, mcp_url, sd_url_val, sd_model_name, - model, persona_text, quality_mode_val=None, face_swap_on=False): - """后台定时调度循环（含运营时段、冷却、收藏、统计）""" - _auto_log_append("🤖 自动化调度器已启动") - _auto_log_append(f"⏰ 运营时段: {int(op_start_hour)}:00 - {int(op_end_hour)}:00") - - # 首次执行的随机延迟 - next_comment = time.time() + random.randint(10, 60) - next_publish = time.time() + random.randint(30, 120) - next_reply = time.time() + random.randint(15, 90) - next_like = time.time() + random.randint(5, 40) - next_favorite = time.time() + random.randint(10, 50) - - def _update_next_display(): - """更新下次执行时间显示""" - times = {} - if comment_enabled: - times["评论"] = datetime.fromtimestamp(next_comment).strftime("%H:%M:%S") - if like_enabled: - times["点赞"] = datetime.fromtimestamp(next_like).strftime("%H:%M:%S") - if favorite_enabled: - times["收藏"] = datetime.fromtimestamp(next_favorite).strftime("%H:%M:%S") - if reply_enabled: - times["回复"] = datetime.fromtimestamp(next_reply).strftime("%H:%M:%S") - if publish_enabled: - times["发布"] = datetime.fromtimestamp(next_publish).strftime("%H:%M:%S") - _scheduler_next_times.update(times) - - _update_next_display() - - while _auto_running.is_set(): - now = time.time() - - # 检查运营时段 - if not _is_in_operating_hours(int(op_start_hour), int(op_end_hour)): - now_hour = datetime.now().hour - _auto_log_append(f"😴 当前{now_hour}时，不在运营时段({int(op_start_hour)}-{int(op_end_hour)})，休眠中...") - # 休眠到运营时间开始 - for _ in range(300): # 5分钟检查一次 - if not _auto_running.is_set(): - break - time.sleep(1) - continue - - # 检查错误冷却 - if _is_in_cooldown(): - remain = int(_error_cooldown_until - time.time()) - if remain > 0 and remain % 30 == 0: - _auto_log_append(f"⏳ 错误冷却中，剩余 {remain}s") - time.sleep(5) - continue - - # 自动评论 - if comment_enabled and now >= next_comment: - try: - _auto_log_append("--- 🔄 执行自动评论 ---") - msg = auto_comment_once(keywords, mcp_url, model, persona_text) - _auto_log_append(msg) - except Exception as e: - _auto_log_append(f"❌ 自动评论异常: {e}") - interval = random.randint(int(comment_min) * 60, int(comment_max) * 60) - next_comment = time.time() + interval - _auto_log_append(f"⏰ 下次评论: {interval // 60} 分钟后") - _update_next_display() - - # 自动点赞 - if like_enabled and now >= next_like: - try: - _auto_log_append("--- 🔄 执行自动点赞 ---") - msg = auto_like_once(keywords, like_count_per_run, mcp_url) - _auto_log_append(msg) - except Exception as e: - _auto_log_append(f"❌ 自动点赞异常: {e}") - interval = random.randint(int(like_min) * 60, int(like_max) * 60) - next_like = time.time() + interval - _auto_log_append(f"⏰ 下次点赞: {interval // 60} 分钟后") - _update_next_display() - - # 自动收藏 - if favorite_enabled and now >= next_favorite: - try: - _auto_log_append("--- 🔄 执行自动收藏 ---") - msg = auto_favorite_once(keywords, fav_count_per_run, mcp_url) - _auto_log_append(msg) - except Exception as e: - _auto_log_append(f"❌ 自动收藏异常: {e}") - interval = random.randint(int(fav_min) * 60, int(fav_max) * 60) - next_favorite = time.time() + interval - _auto_log_append(f"⏰ 下次收藏: {interval // 60} 分钟后") - _update_next_display() - - # 自动发布 - if publish_enabled and now >= next_publish: - try: - _auto_log_append("--- 🔄 执行自动发布 ---") - msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model, - persona_text=persona_text, quality_mode_val=quality_mode_val, - face_swap_on=face_swap_on) - _auto_log_append(msg) - except Exception as e: - _auto_log_append(f"❌ 自动发布异常: {e}") - interval = random.randint(int(publish_min) * 60, int(publish_max) * 60) - next_publish = time.time() + interval - _auto_log_append(f"⏰ 下次发布: {interval // 60} 分钟后") - _update_next_display() - - # 自动回复评论 - if reply_enabled and now >= next_reply: - try: - _auto_log_append("--- 🔄 执行自动回复评论 ---") - msg = auto_reply_once(max_replies_per_run, mcp_url, model, persona_text) - _auto_log_append(msg) - except Exception as e: - _auto_log_append(f"❌ 自动回复异常: {e}") - interval = random.randint(int(reply_min) * 60, int(reply_max) * 60) - next_reply = time.time() + interval - _auto_log_append(f"⏰ 下次回复: {interval // 60} 分钟后") - _update_next_display() - - # 每5秒检查一次停止信号 - for _ in range(5): - if not _auto_running.is_set(): - break - time.sleep(1) - - _scheduler_next_times.clear() - _auto_log_append("🛑 自动化调度器已停止") - - -def start_scheduler(comment_on, publish_on, reply_on, like_on, favorite_on, - c_min, c_max, p_min, p_max, r_min, r_max, - max_replies_per_run, - l_min, l_max, like_count_per_run, - fav_min, fav_max, fav_count_per_run, - op_start_hour, op_end_hour, - keywords, topics, mcp_url, sd_url_val, sd_model_name, - model, persona_text, quality_mode_val, face_swap_on): - """启动定时自动化""" - global _auto_thread - if _auto_running.is_set(): - return "⚠️ 调度器已在运行中，请先停止" - - if not comment_on and not publish_on and not reply_on and not like_on and not favorite_on: - return "❌ 请至少启用一项自动化功能" - - # 评论/回复需要 LLM，点赞/收藏不需要 - if (comment_on or reply_on): - api_key, _, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置，请先在全局设置中配置提供商" - - _auto_running.set() - _auto_thread = threading.Thread( - target=_scheduler_loop, - args=(comment_on, publish_on, reply_on, like_on, favorite_on, - c_min, c_max, p_min, p_max, r_min, r_max, - max_replies_per_run, - l_min, l_max, like_count_per_run, - fav_min, fav_max, fav_count_per_run, - op_start_hour, op_end_hour, - keywords, topics, mcp_url, sd_url_val, sd_model_name, - model, persona_text), - kwargs={"quality_mode_val": quality_mode_val, "face_swap_on": face_swap_on}, - daemon=True, - ) - _auto_thread.start() - - parts = [] - if comment_on: - parts.append(f"评论({int(c_min)}-{int(c_max)}分)") - if like_on: - parts.append(f"点赞({int(l_min)}-{int(l_max)}分, {int(like_count_per_run)}个/轮)") - if favorite_on: - parts.append(f"收藏({int(fav_min)}-{int(fav_max)}分, {int(fav_count_per_run)}个/轮)") - if publish_on: - parts.append(f"发布({int(p_min)}-{int(p_max)}分)") - if reply_on: - parts.append(f"回复({int(r_min)}-{int(r_max)}分, ≤{int(max_replies_per_run)}条/轮)") - - _auto_log_append(f"调度器已启动: {' + '.join(parts)}") - return f"✅ 自动化已启动 🟢\n⏰ 运营时段: {int(op_start_hour)}:00-{int(op_end_hour)}:00\n任务: {' | '.join(parts)}\n\n💡 点击「刷新日志」查看实时进度" - - -def stop_scheduler(): - """停止定时自动化""" - if not _auto_running.is_set(): - return "⚠️ 调度器未在运行" - _auto_running.clear() - _auto_log_append("⏹️ 收到停止信号，等待当前任务完成...") - return "🛑 调度器停止中...当前任务完成后将完全停止" - - -def get_auto_log(): - """获取自动化运行日志""" - if not _auto_log: - return "📋 暂无日志\n\n💡 点击「一键评论」「一键发布」或启动定时后日志将在此显示" - return "\n".join(_auto_log[-80:]) - - -def get_scheduler_status(): - """获取调度器运行状态 + 下次执行时间 + 今日统计""" - _reset_daily_stats_if_needed() - if _auto_running.is_set(): - lines = ["🟢 **调度器运行中**"] - if _scheduler_next_times: - next_info = " | ".join(f"{k}@{v}" for k, v in _scheduler_next_times.items()) - lines.append(f"⏰ 下次: {next_info}") - s = _daily_stats - lines.append( - f"📊 今日: 💬{s.get('comments',0)} ❤️{s.get('likes',0)} " - f"⭐{s.get('favorites',0)} 🚀{s.get('publishes',0)} " - f"💌{s.get('replies',0)} ❌{s.get('errors',0)}" - ) - if _is_in_cooldown(): - lines.append(f"⏳ 冷却中，{int(_error_cooldown_until - time.time())}s 后恢复") - return "\n".join(lines) - return "⚪ **调度器未运行**" - - -# ================================================== -# 智能学习 & 笔记分析模块 -# ================================================== - -# 定时学习状态 -_learn_running = threading.Event() -_learn_thread: threading.Thread | None = None - - -def analytics_collect_data(mcp_url, user_id, xsec_token): - """采集笔记表现数据""" - if not user_id or not xsec_token: - return "❌ 请先填写用户 ID 和 xsec_token (在「账号登录」Tab 获取)" - try: - client = get_mcp_client(mcp_url) - result = analytics.collect_note_performance(client, user_id, xsec_token) - if "error" in result: - return f"❌ 数据采集失败: {result['error']}" - return ( - f"✅ 数据采集完成！\n" - f"📝 总笔记数: {result['total']}\n" - f"🔄 更新: {result['updated']} 篇\n\n" - f"💡 点击「计算权重」进行智能学习" - ) - except Exception as e: - logger.error("数据采集失败: %s", e) - return f"❌ 采集失败: {e}" - - -def analytics_calculate_weights(): - """计算内容权重""" - try: - result = analytics.calculate_weights() - if "error" in result: - return "❌ " + result["error"], analytics.generate_report() - - top = result.get("top_note") - top_str = f" | 🏆 最佳: {top['title']} (❤️ {top.get('likes', 0)})" if top else "" - msg = ( - f"✅ 权重计算完成！\n" - f"📊 分析了 {result['total_notes']} 篇笔记{top_str}\n\n" - f"💡 权重已自动保存，启用「智能加权发布」后自动生效" - ) - return msg, analytics.generate_report() - except Exception as e: - logger.error("权重计算失败: %s", e) - return f"❌ 计算失败: {e}", "" - - -def analytics_llm_deep_analysis(model): - """LLM 深度分析笔记表现""" - note_data = analytics.generate_llm_analysis_prompt() - if not note_data: - return "❌ 暂无笔记数据，请先采集" - try: - api_key, base_url, _ = _get_llm_config() - if not api_key: - return "❌ LLM 未配置" - svc = LLMService(api_key, base_url, model) - result = svc.analyze_note_performance(note_data) - - lines = ["## 🧠 AI 深度分析报告\n"] - - if result.get("high_perform_features"): - lines.append(f"### ✅ 高表现内容特征\n{result['high_perform_features']}\n") - if result.get("low_perform_issues"): - lines.append(f"### ⚠️ 低表现内容反思\n{result['low_perform_issues']}\n") - if result.get("user_preference"): - lines.append(f"### 👤 用户偏好画像\n{result['user_preference']}\n") - - suggestions = result.get("content_suggestions", []) - if suggestions: - lines.append("### 📌 内容方向建议") - for s in suggestions: - priority = "🔴" if s.get("priority", 3) <= 2 else "🟡" if s.get("priority", 3) <= 3 else "🟢" - lines.append(f"- {priority} **{s.get('topic', '')}**: {s.get('reason', '')}") - lines.append("") - - templates = result.get("title_templates", []) - if templates: - lines.append("### ✏️ 标题模板") - for t in templates: - lines.append(f"- 📝 {t}") - lines.append("") - - tags = result.get("recommended_tags", []) - if tags: - lines.append(f"### 🏷️ 推荐标签\n{' '.join(f'`#{t}`' for t in tags)}\n") - - return "\n".join(lines) - - except Exception as e: - logger.error("LLM 分析失败: %s", e) - return f"❌ AI 分析失败: {e}" - - -def analytics_get_report(): - """获取分析报告""" - return analytics.generate_report() - - -def analytics_get_weighted_topics(): - """获取加权主题列表""" - weighted = analytics.get_weighted_topics_display() - if weighted: - return weighted - return "暂无权重数据，请先执行「采集数据 → 计算权重」" - - -def _learn_scheduler_loop(mcp_url, user_id, xsec_token, model, interval_hours): - """定时学习后台循环""" - logger.info("定时学习已启动, 间隔 %s 小时", interval_hours) - _auto_log_append(f"🧠 定时学习已启动, 每 {interval_hours} 小时自动分析一次") - while _learn_running.is_set(): - try: - # 采集数据 - client = get_mcp_client(mcp_url) - result = analytics.collect_note_performance(client, user_id, xsec_token) - if "error" not in result: - _auto_log_append(f"🧠 自动采集完成: {result['total']} 篇笔记, 更新 {result['updated']} 篇") - # 计算权重 - weight_result = analytics.calculate_weights() - if "error" not in weight_result: - _auto_log_append(f"🧠 权重更新完成: 分析 {weight_result['total_notes']} 篇") - # LLM 深度分析 (如果有配置) - api_key, base_url, _ = _get_llm_config() - if api_key and model: - try: - note_data = analytics.generate_llm_analysis_prompt() - if note_data: - svc = LLMService(api_key, base_url, model) - svc.analyze_note_performance(note_data) - _auto_log_append("🧠 AI 深度分析完成") - except Exception as e: - _auto_log_append(f"⚠️ AI 分析失败 (非致命): {e}") - else: - _auto_log_append(f"⚠️ 自动采集失败: {result.get('error', '未知')}") - except Exception as e: - _auto_log_append(f"⚠️ 定时学习异常: {e}") - - # 等待下一次执行 - wait_seconds = interval_hours * 3600 - for _ in range(int(wait_seconds / 5)): - if not _learn_running.is_set(): - break - time.sleep(5) - - logger.info("定时学习已停止") - _auto_log_append("🧠 定时学习已停止") - - -def start_learn_scheduler(mcp_url, user_id, xsec_token, model, interval_hours): - """启动定时学习""" - global _learn_thread - if _learn_running.is_set(): - return "⚠️ 定时学习已在运行中" - if not user_id or not xsec_token: - return "❌ 请先在「账号登录」获取用户 ID 和 Token" - - _learn_running.set() - _learn_thread = threading.Thread( - target=_learn_scheduler_loop, - args=(mcp_url, user_id, xsec_token, model, interval_hours), - daemon=True, - ) - _learn_thread.start() - return f"✅ 定时学习已启动 🧠 每 {int(interval_hours)} 小时自动分析" - - -def stop_learn_scheduler(): - """停止定时学习""" - if not _learn_running.is_set(): - return "⚠️ 定时学习未在运行" - _learn_running.clear() - return "🛑 定时学习已停止" - - -# ================================================== -# Windows 开机自启管理 -# ================================================== - -_APP_NAME = "XHS_AI_AutoBot" -_STARTUP_REG_KEY = r"Software\Microsoft\Windows\CurrentVersion\Run" - - -def _get_startup_script_path() -> str: - """获取启动脚本路径（.vbs 静默启动，不弹黑窗）""" - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "_autostart.vbs") - - -def _get_startup_bat_path() -> str: - """获取启动 bat 路径""" - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "_autostart.bat") - - -def _create_startup_scripts(): - """创建静默启动脚本（bat + vbs）""" - app_dir = os.path.dirname(os.path.abspath(__file__)) - venv_python = os.path.join(app_dir, ".venv", "Scripts", "pythonw.exe") - # 如果没有 pythonw，退回 python.exe - if not os.path.exists(venv_python): - venv_python = os.path.join(app_dir, ".venv", "Scripts", "python.exe") - main_script = os.path.join(app_dir, "main.py") - - # 创建 bat - bat_path = _get_startup_bat_path() - bat_content = f"""@echo off -cd /d "{app_dir}" -"{venv_python}" "{main_script}" -""" - with open(bat_path, "w", encoding="utf-8") as f: - f.write(bat_content) - - # 创建 vbs（静默运行 bat，不弹出命令行窗口） - vbs_path = _get_startup_script_path() - vbs_content = f"""Set WshShell = CreateObject("WScript.Shell") -WshShell.Run chr(34) & "{bat_path}" & chr(34), 0 -Set WshShell = Nothing -""" - with open(vbs_path, "w", encoding="utf-8") as f: - f.write(vbs_content) - - return vbs_path - - -def is_autostart_enabled() -> bool: - """检查是否已设置开机自启""" - if platform.system() != "Windows": - return False - try: - import winreg - key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_READ) - try: - val, _ = winreg.QueryValueEx(key, _APP_NAME) - winreg.CloseKey(key) - return bool(val) - except FileNotFoundError: - winreg.CloseKey(key) - return False - except Exception: - return False - - -def enable_autostart() -> str: - """启用 Windows 开机自启""" - if platform.system() != "Windows": - return "❌ 此功能仅支持 Windows 系统" - try: - import winreg - vbs_path = _create_startup_scripts() - key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_SET_VALUE) - # 用 wscript 运行 vbs 以实现静默启动 - winreg.SetValueEx(key, _APP_NAME, 0, winreg.REG_SZ, f'wscript.exe "{vbs_path}"') - winreg.CloseKey(key) - logger.info(f"开机自启已启用: {vbs_path}") - return "✅ 开机自启已启用\n下次开机时将自动后台运行本程序" - except Exception as e: - logger.error(f"设置开机自启失败: {e}") - return f"❌ 设置失败: {e}" - - -def disable_autostart() -> str: - """禁用 Windows 开机自启""" - if platform.system() != "Windows": - return "❌ 此功能仅支持 Windows 系统" - try: - import winreg - key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_SET_VALUE) - try: - winreg.DeleteValue(key, _APP_NAME) - except FileNotFoundError: - pass - winreg.CloseKey(key) - # 清理启动脚本 - for f in [_get_startup_script_path(), _get_startup_bat_path()]: - if os.path.exists(f): - os.remove(f) - logger.info("开机自启已禁用") - return "✅ 开机自启已禁用" - except Exception as e: - logger.error(f"禁用开机自启失败: {e}") - return f"❌ 禁用失败: {e}" - - -def toggle_autostart(enabled: bool) -> str: - """切换开机自启状态（供 UI 调用）""" - if enabled: - return enable_autostart() - else: - return disable_autostart() - - -# ================================================== -# UI 构建 -# ================================================== - -config = cfg.all - -_GRADIO_CSS = """ - .status-ok { color: #16a34a; font-weight: bold; } - .status-err { color: #dc2626; font-weight: bold; } - footer { display: none !important; } -""" - -with gr.Blocks( - title="小红书 AI 爆文工坊 V2.0", -) as app: - gr.Markdown( - "# 🍒 小红书 AI 爆文生产工坊 V2.0\n" - "> 灵感 → 文案 → 绘图 → 发布 → 运营，一站式全闭环" - ) - - # 全局状态 - state_search_result = gr.State("") - - # ============ 全局设置栏 ============ - with gr.Accordion("⚙️ 全局设置 (自动保存)", open=False): - gr.Markdown("#### 🤖 LLM 提供商 (支持所有 OpenAI 兼容接口)") - with gr.Row(): - llm_provider = gr.Dropdown( - label="选择 LLM 提供商", - choices=cfg.get_llm_provider_names(), - value=cfg.get("active_llm", ""), - interactive=True, scale=2, - ) - btn_connect_llm = gr.Button("🔗 连接 LLM", size="sm", scale=1) - with gr.Row(): - llm_model = gr.Dropdown( - label="LLM 模型", value=config["model"], - allow_custom_value=True, interactive=True, scale=2, - ) - llm_provider_info = gr.Markdown( - value="*选择提供商后显示详情*", - ) - with gr.Accordion("➕ 添加 / 管理 LLM 提供商", open=False): - with gr.Row(): - new_provider_name = gr.Textbox( - label="名称", placeholder="如: DeepSeek / GPT-4o / 通义千问", - scale=1, - ) - new_provider_key = gr.Textbox( - label="API Key", type="password", scale=2, - ) - new_provider_url = gr.Textbox( - label="Base URL", placeholder="https://api.openai.com/v1", - value="https://api.openai.com/v1", scale=2, - ) - with gr.Row(): - btn_add_provider = gr.Button("✅ 添加提供商", variant="primary", size="sm") - btn_del_provider = gr.Button("🗑️ 删除当前提供商", variant="stop", size="sm") - provider_mgmt_status = gr.Markdown("") - - gr.Markdown("---") - with gr.Row(): - mcp_url = gr.Textbox( - label="MCP Server URL", value=config["mcp_url"], scale=2, - ) - sd_url = gr.Textbox( - label="SD WebUI URL", value=config["sd_url"], scale=2, - ) - with gr.Row(): - persona = gr.Dropdown( - label="博主人设（评论/回复/自动运营通用）", - choices=[RANDOM_PERSONA_LABEL] + DEFAULT_PERSONAS, - value=config.get("persona", RANDOM_PERSONA_LABEL), - allow_custom_value=True, - interactive=True, - scale=5, - ) - with gr.Row(): - btn_connect_sd = gr.Button("🎨 连接 SD", size="sm") - btn_check_mcp = gr.Button("📡 检查 MCP", size="sm") - with gr.Row(): - sd_model = gr.Dropdown( - label="SD 模型", allow_custom_value=True, - interactive=True, scale=2, - ) - sd_model_info = gr.Markdown("选择模型后显示适配信息", elem_id="sd_model_info") - status_bar = gr.Markdown("🔄 等待连接...") - - gr.Markdown("---") - gr.Markdown("#### 🎭 AI 换脸 (ReActor)") - gr.Markdown( - "> 上传你的头像，生成含人物的图片时自动替换为你的脸\n" - "> 需要 SD WebUI 已安装 [ReActor](https://github.com/Gourieff/sd-webui-reactor) 扩展" - ) - with gr.Row(): - face_image_input = gr.Image( - label="上传头像 (正面清晰照片效果最佳)", - type="pil", - height=180, - scale=1, - ) - face_image_preview = gr.Image( - label="当前头像", - type="pil", - height=180, - interactive=False, - value=SDService.load_face_image(), - scale=1, - ) - with gr.Row(): - btn_save_face = gr.Button("💾 保存头像", variant="primary", size="sm") - face_swap_toggle = gr.Checkbox( - label="🎭 生成图片时启用 AI 换脸", - value=os.path.isfile(FACE_IMAGE_PATH), - interactive=True, - ) - face_status = gr.Markdown( - "✅ 头像已就绪" if os.path.isfile(FACE_IMAGE_PATH) else "ℹ️ 尚未设置头像" - ) - - gr.Markdown("---") - gr.Markdown("#### 🖥️ 系统设置") - with gr.Row(): - autostart_toggle = gr.Checkbox( - label="🚀 Windows 开机自启（静默后台运行）", - value=is_autostart_enabled(), - interactive=(platform.system() == "Windows"), - ) - autostart_status = gr.Markdown( - value="✅ 已启用" if is_autostart_enabled() else "⚪ 未启用", - ) - - # ============ Tab 页面 ============ - with gr.Tabs(): - # -------- Tab 1: 内容创作（迁移至 ui/tab_create.py）-------- - _tab1 = build_tab( - config=config, - styles=DEFAULT_STYLES, - sd_preset_names=SD_PRESET_NAMES, - default_negative=DEFAULT_NEGATIVE, - llm_model=llm_model, - sd_model=sd_model, - sd_url=sd_url, - persona=persona, - status_bar=status_bar, - face_swap_toggle=face_swap_toggle, - face_image_preview=face_image_preview, - mcp_url=mcp_url, - fn_gen_copy=generate_copy, - fn_gen_img=generate_images, - fn_export=one_click_export, - fn_publish=publish_to_xhs, - fn_get_sd_preset=get_sd_preset, - fn_cfg_set=cfg.set, - fn_cfg_update=cfg.update, - ) - res_title = _tab1["res_title"] - res_content = _tab1["res_content"] - res_prompt = _tab1["res_prompt"] - res_tags = _tab1["res_tags"] - quality_mode = _tab1["quality_mode"] - steps = _tab1["steps"] - cfg_scale = _tab1["cfg_scale"] - neg_prompt = _tab1["neg_prompt"] - - # -------- Tab 2: 热点探测 -------- - with gr.Tab("🔥 热点探测"): - gr.Markdown("### 搜索热门内容 → AI 分析趋势 → 一键借鉴创作") - with gr.Row(): - with gr.Column(scale=1): - hot_keyword = gr.Textbox( - label="搜索关键词", placeholder="如：春季穿搭", - ) - hot_sort = gr.Dropdown( - ["综合", "最新", "最多点赞", "最多评论", "最多收藏"], - label="排序", value="综合", - ) - btn_search = gr.Button("🔍 搜索", variant="primary") - search_status = gr.Markdown("") - - with gr.Column(scale=2): - search_output = gr.TextArea( - label="搜索结果", lines=12, interactive=False, - ) - - with gr.Row(): - btn_analyze = gr.Button("🧠 AI 分析热点趋势", variant="primary") - analysis_status = gr.Markdown("") - analysis_output = gr.Markdown(label="分析报告") - topic_from_hot = gr.Textbox( - label="选择/输入创作选题", placeholder="基于分析选一个方向", - ) - - with gr.Row(): - hot_style = gr.Dropdown( - ["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷"], - label="风格", value="好物种草", - ) - btn_gen_from_hot = gr.Button("✨ 基于热点生成文案", variant="primary") - - with gr.Row(): - hot_title = gr.Textbox(label="生成的标题", interactive=True) - hot_content = gr.TextArea(label="生成的正文", lines=8, interactive=True) - with gr.Row(): - hot_prompt = gr.TextArea(label="绘图提示词", lines=3, interactive=True) - hot_tags = gr.Textbox(label="标签", interactive=True) - hot_gen_status = gr.Markdown("") - btn_sync_to_create = gr.Button( - "📋 同步到「内容创作」Tab → 绘图 & 发布", - variant="primary", - ) - - # -------- Tab 3: 评论管家 -------- - with gr.Tab("💬 评论管家"): - gr.Markdown("### 智能评论管理：主动评论引流 & 自动回复粉丝") - - with gr.Tabs(): - # ======== 子 Tab A: 主动评论他人 ======== - with gr.Tab("✍️ 主动评论引流"): - gr.Markdown( - "> **流程**：搜索/浏览笔记 → 选择目标 → 加载内容 → " - "AI 分析笔记+已有评论自动生成高质量评论 → 一键发送" - ) - - # 笔记选择器 - with gr.Row(): - pro_keyword = gr.Textbox( - label="🔍 搜索关键词 (留空则获取推荐)", - placeholder="穿搭、美食、旅行…", - ) - btn_pro_fetch = gr.Button("🔍 获取笔记", variant="primary") - with gr.Row(): - pro_selector = gr.Dropdown( - label="📋 选择目标笔记", - choices=[], interactive=True, - ) - pro_fetch_status = gr.Markdown("") - - # 隐藏字段 - with gr.Row(): - pro_feed_id = gr.Textbox(label="笔记 ID", interactive=False) - pro_xsec_token = gr.Textbox(label="xsec_token", interactive=False) - pro_title = gr.Textbox(label="标题", interactive=False) - - # 加载内容 & AI 分析 - btn_pro_load = gr.Button("📖 加载笔记内容", variant="secondary") - pro_load_status = gr.Markdown("") - - with gr.Row(): - with gr.Column(scale=1): - pro_content = gr.TextArea( - label="📄 笔记正文摘要", lines=8, interactive=False, - ) - with gr.Column(scale=1): - pro_comments = gr.TextArea( - label="💬 已有评论", lines=8, interactive=False, - ) - # 隐藏: 完整文本 - pro_full_text = gr.Textbox(visible=False) - - gr.Markdown("---") - with gr.Row(): - with gr.Column(scale=1): - btn_pro_ai = gr.Button( - "🤖 AI 智能生成评论", variant="primary", size="lg", - ) - pro_ai_status = gr.Markdown("") - with gr.Column(scale=2): - pro_comment_text = gr.TextArea( - label="✏️ 评论内容 (可手动修改)", lines=3, - interactive=True, - placeholder="点击左侧按钮自动生成，也可手动编写", - ) - with gr.Row(): - btn_pro_send = gr.Button("📩 发送评论", variant="primary") - pro_send_status = gr.Markdown("") - - # ======== 子 Tab B: 回复我的评论 ======== - with gr.Tab("💌 回复粉丝评论"): - gr.Markdown( - "> **流程**：选择我的笔记 → 加载评论 → " - "粘贴要回复的评论 → AI 生成回复 → 一键发送" - ) - - # 笔记选择器 (自动用已保存的 userId 获取) - with gr.Row(): - btn_my_fetch = gr.Button("🔍 获取我的笔记", variant="primary") - with gr.Row(): - my_selector = gr.Dropdown( - label="📋 选择我的笔记", - choices=[], interactive=True, - ) - my_fetch_status = gr.Markdown("") - - with gr.Row(): - my_feed_id = gr.Textbox(label="笔记 ID", interactive=False) - my_xsec_token = gr.Textbox(label="xsec_token", interactive=False) - my_title = gr.Textbox(label="笔记标题", interactive=False) - - btn_my_load_comments = gr.Button("📥 加载评论", variant="primary") - my_comment_status = gr.Markdown("") - - my_comments_display = gr.TextArea( - label="📋 粉丝评论列表", lines=12, interactive=False, - ) - - gr.Markdown("---") - gr.Markdown("#### 📝 回复评论") - with gr.Row(): - with gr.Column(scale=1): - my_target_comment = gr.TextArea( - label="要回复的评论内容", lines=3, - placeholder="从上方评论列表中复制粘贴要回复的评论", - ) - btn_my_ai_reply = gr.Button( - "🤖 AI 生成回复", variant="secondary", - ) - my_reply_gen_status = gr.Markdown("") - with gr.Column(scale=1): - my_reply_content = gr.TextArea( - label="回复内容 (可修改)", lines=3, - interactive=True, - ) - btn_my_send_reply = gr.Button( - "📩 发送回复", variant="primary", - ) - my_reply_status = gr.Markdown("") - - # -------- Tab 4: 账号登录 -------- - with gr.Tab("🔐 账号登录"): - gr.Markdown( - "### 小红书账号登录\n" - "> 扫码登录后自动获取 xsec_token，配合用户 ID 即可使用所有功能" - ) - with gr.Row(): - with gr.Column(scale=1): - gr.Markdown( - "**操作步骤：**\n" - "1. 确保 MCP 服务已启动\n" - "2. 点击「获取登录二维码」→ 用小红书 App 扫码\n" - "3. 点击「检查登录状态」→ 自动获取并保存 xsec_token\n" - "4. 首次使用请填写你的用户 ID 并点击保存\n\n" - "⚠️ 登录后不要在其他网页端登录同一账号，否则会被踢出" - ) - btn_get_qrcode = gr.Button( - "📱 获取登录二维码", variant="primary", size="lg", - ) - btn_check_login = gr.Button( - "🔍 检查登录状态 (自动获取 Token)", - variant="secondary", size="lg", - ) - btn_logout = gr.Button( - "🚪 退出登录 (重新扫码)", - variant="stop", size="lg", - ) - login_status = gr.Markdown("🔄 等待操作...") - - gr.Markdown("---") - gr.Markdown( - "#### 📌 我的账号信息\n" - "> **注意**: 小红书号 ≠ 用户 ID\n" - "> - **小红书号 (redId)**: 如 `18688457507`，是你在 App 个人页看到的\n" - "> - **用户 ID (userId)**: 如 `5a695db6e8ac2b72e8af2a53`，24位十六进制字符串\n\n" - "💡 **如何获取 userId?**\n" - "1. 用浏览器打开你的小红书主页\n" - "2. 网址格式为: `xiaohongshu.com/user/profile/xxxxxxxx`\n" - "3. `profile/` 后面的就是你的 userId" - ) - login_user_id = gr.Textbox( - label="我的用户 ID (24位 userId, 非小红书号)", - value=config.get("my_user_id", ""), - placeholder="如: 5a695db6e8ac2b72e8af2a53", - ) - login_xsec_token = gr.Textbox( - label="xsec_token (登录后自动获取)", - value=config.get("xsec_token", ""), - interactive=False, - ) - btn_save_uid = gr.Button( - "💾 保存用户 ID", variant="secondary", - ) - save_uid_status = gr.Markdown("") - - with gr.Column(scale=1): - qr_image = gr.Image( - label="扫码登录", height=350, width=350, - ) - - # -------- Tab 5: 数据看板 -------- - with gr.Tab("📊 数据看板"): - gr.Markdown( - "### 我的账号数据看板\n" - "> 用户 ID 和 xsec_token 从「账号登录」自动获取，直接点击加载即可" - ) - - with gr.Row(): - with gr.Column(scale=1): - data_user_id = gr.Textbox( - label="我的用户 ID (自动填充)", - value=config.get("my_user_id", ""), - interactive=True, - ) - data_xsec_token = gr.Textbox( - label="xsec_token (自动填充)", - value=config.get("xsec_token", ""), - interactive=True, - ) - btn_refresh_token = gr.Button( - "🔄 刷新 Token", variant="secondary", - ) - btn_load_my_data = gr.Button( - "📊 加载我的数据", variant="primary", size="lg", - ) - data_status = gr.Markdown("") - - with gr.Column(scale=2): - profile_card = gr.Markdown( - value="*等待加载...*", - label="账号概览", - ) - - gr.Markdown("---") - gr.Markdown("### 📈 数据可视化") - with gr.Row(): - with gr.Column(scale=1): - chart_interact = gr.Plot(label="📊 核心指标") - with gr.Column(scale=2): - chart_notes = gr.Plot(label="❤ 笔记点赞排行") - - gr.Markdown("---") - notes_detail = gr.Markdown( - value="*加载数据后显示笔记明细表格*", - label="笔记数据明细", - ) - - # -------- Tab 6: 智能学习 -------- - with gr.Tab("🧠 智能学习"): - gr.Markdown( - "### 🧠 智能内容学习引擎\n" - "> 自动分析已发布笔记的表现，学习哪些内容受欢迎，用权重指导未来创作\n\n" - "**工作流程**: 采集数据 → 计算权重 → AI 深度分析 → 自动优化创作\n\n" - "💡 启用后，自动发布将优先生成高权重主题的内容" - ) - - with gr.Row(): - # 左栏: 数据采集 & 权重计算 - with gr.Column(scale=1): - gr.Markdown("#### 📊 数据采集") - learn_user_id = gr.Textbox( - label="用户 ID", value=config.get("my_user_id", ""), - interactive=True, - ) - learn_xsec_token = gr.Textbox( - label="xsec_token", value=config.get("xsec_token", ""), - interactive=True, - ) - btn_learn_collect = gr.Button( - "📊 采集笔记数据", variant="primary", size="lg", - ) - learn_collect_status = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### ⚖️ 权重计算") - btn_learn_calc = gr.Button( - "⚖️ 计算内容权重", variant="primary", size="lg", - ) - learn_calc_status = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### 🤖 AI 深度分析") - gr.Markdown("> 用 LLM 分析笔记数据，找出内容规律，生成策略建议") - btn_learn_ai = gr.Button( - "🧠 AI 深度分析", variant="primary", size="lg", - ) - - gr.Markdown("---") - gr.Markdown("#### ⏰ 定时自动学习") - gr.Markdown("> 每隔 N 小时自动采集数据 + 计算权重 + AI 分析") - learn_interval = gr.Number( - label="学习间隔 (小时)", value=config.get("learn_interval", 6), minimum=1, maximum=48, - ) - with gr.Row(): - btn_learn_start = gr.Button( - "▶ 启动定时学习", variant="primary", size="sm", - ) - btn_learn_stop = gr.Button( - "⏹ 停止", variant="stop", size="sm", - ) - learn_sched_status = gr.Markdown("⚪ 定时学习未启动") - - gr.Markdown("---") - gr.Markdown("#### 🎯 加权主题预览") - gr.Markdown("> 当前权重最高的主题 (自动发布会优先选择)") - btn_show_topics = gr.Button("🔄 刷新加权主题", size="sm") - learn_weighted_topics = gr.Textbox( - label="加权主题池 (权重从高到低)", - value=analytics.get_weighted_topics_display() or "暂无权重数据", - interactive=False, - lines=2, - ) - learn_use_weights = gr.Checkbox( - label="🧠 自动发布时使用智能权重 (推荐)", - value=cfg.get("use_smart_weights", True), - interactive=True, - ) - - # 右栏: 分析报告 - with gr.Column(scale=2): - gr.Markdown("#### 📋 智能学习报告") - learn_report = gr.Markdown( - value=analytics.generate_report(), - label="分析报告", - ) - gr.Markdown("---") - learn_ai_report = gr.Markdown( - value="*点击「AI 深度分析」生成*", - label="AI 深度分析报告", - ) - - # -------- Tab 7: 自动运营 -------- - with gr.Tab("🤖 自动运营"): - gr.Markdown( - "### 🤖 无人值守自动化运营\n" - "> 一键评论引流 + 一键点赞 + 一键收藏 + 一键回复 + 一键发布 + 随机定时全自动\n\n" - "⚠️ **注意**: 请确保已连接 LLM、SD WebUI 和 MCP 服务" - ) - persona_pool_hint = gr.Markdown( - value=f"🎭 当前人设池: **{config.get('persona', '随机')[:20]}** → 关键词/主题池已匹配", - ) - - with gr.Row(): - # 左栏: 一键操作 - with gr.Column(scale=1): - gr.Markdown("#### 💬 一键智能评论") - gr.Markdown( - "> 自动搜索高赞笔记 → AI 分析内容 → 生成评论 → 发送\n" - "每次随机选关键词搜索，从结果中随机选笔记" - ) - auto_comment_keywords = gr.Textbox( - label="评论关键词池 (逗号分隔，随人设自动切换)", - value=", ".join(get_persona_keywords(config.get("persona", ""))), - placeholder="关键词1, 关键词2, ... (切换人设自动更新)", - ) - btn_auto_comment = gr.Button( - "💬 一键评论 (单次)", variant="primary", size="lg", - ) - auto_comment_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### 👍 一键自动点赞") - gr.Markdown( - "> 搜索笔记 → 随机选择多篇 → 依次点赞\n" - "提升账号活跃度，无需 LLM" - ) - auto_like_count = gr.Number( - label="单次点赞数量", value=config.get("auto_like_count", 5), minimum=1, maximum=20, - ) - btn_auto_like = gr.Button( - "👍 一键点赞 (单次)", variant="primary", size="lg", - ) - auto_like_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### ⭐ 一键自动收藏") - gr.Markdown( - "> 搜索笔记 → 随机选择多篇 → 依次收藏\n" - "提升账号活跃度，与点赞互补" - ) - auto_fav_count = gr.Number( - label="单次收藏数量", value=config.get("auto_fav_count", 3), minimum=1, maximum=15, - ) - btn_auto_favorite = gr.Button( - "⭐ 一键收藏 (单次)", variant="primary", size="lg", - ) - auto_favorite_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### 💌 一键自动回复") - gr.Markdown( - "> 扫描我的所有笔记 → 找到粉丝评论 → AI 生成回复 → 逐条发送\n" - "自动跳过自己的评论，模拟真人间隔回复" - ) - auto_reply_max = gr.Number( - label="单次最多回复条数", value=config.get("auto_reply_max", 5), minimum=1, maximum=20, - ) - btn_auto_reply = gr.Button( - "💌 一键回复 (单次)", variant="primary", size="lg", - ) - auto_reply_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### 🚀 一键智能发布") - gr.Markdown( - "> 随机选主题+风格 → AI 生成文案 → SD 生成图片 → 自动发布" - ) - auto_publish_topics = gr.Textbox( - label="主题池 (逗号分隔，随人设自动切换)", - value=", ".join(get_persona_topics(config.get("persona", ""))), - placeholder="主题会从池中随机选取，切换人设自动更新", - ) - btn_auto_publish = gr.Button( - "🚀 一键发布 (单次)", variant="primary", size="lg", - ) - auto_publish_result = gr.Markdown("") - - # 右栏: 定时自动化 - with gr.Column(scale=1): - gr.Markdown("#### ⏰ 随机定时自动化") - gr.Markdown( - "> 设置时间间隔后启动，系统将在随机时间自动执行\n" - "> 模拟真人操作节奏，降低被检测风险" - ) - sched_status = gr.Markdown("⚪ **调度器未运行**") - - # 运营时段设置 - with gr.Group(): - gr.Markdown("##### ⏰ 运营时段") - with gr.Row(): - sched_start_hour = gr.Number( - label="开始时间(整点)", value=config.get("sched_start_hour", 8), minimum=0, maximum=23, - ) - sched_end_hour = gr.Number( - label="结束时间(整点)", value=config.get("sched_end_hour", 23), minimum=1, maximum=24, - ) - - with gr.Group(): - sched_comment_on = gr.Checkbox( - label="✅ 启用自动评论", value=config.get("sched_comment_on", True), - ) - with gr.Row(): - sched_c_min = gr.Number( - label="评论最小间隔(分钟)", value=config.get("sched_c_min", 15), minimum=5, - ) - sched_c_max = gr.Number( - label="评论最大间隔(分钟)", value=config.get("sched_c_max", 45), minimum=10, - ) - - with gr.Group(): - sched_like_on = gr.Checkbox( - label="✅ 启用自动点赞", value=config.get("sched_like_on", True), - ) - with gr.Row(): - sched_l_min = gr.Number( - label="点赞最小间隔(分钟)", value=config.get("sched_l_min", 10), minimum=3, - ) - sched_l_max = gr.Number( - label="点赞最大间隔(分钟)", value=config.get("sched_l_max", 30), minimum=5, - ) - sched_like_count = gr.Number( - label="每轮点赞数量", value=config.get("sched_like_count", 5), minimum=1, maximum=15, - ) - - with gr.Group(): - sched_fav_on = gr.Checkbox( - label="✅ 启用自动收藏", value=config.get("sched_fav_on", True), - ) - with gr.Row(): - sched_fav_min = gr.Number( - label="收藏最小间隔(分钟)", value=config.get("sched_fav_min", 12), minimum=3, - ) - sched_fav_max = gr.Number( - label="收藏最大间隔(分钟)", value=config.get("sched_fav_max", 35), minimum=5, - ) - sched_fav_count = gr.Number( - label="每轮收藏数量", value=config.get("sched_fav_count", 3), minimum=1, maximum=10, - ) - - with gr.Group(): - sched_reply_on = gr.Checkbox( - label="✅ 启用自动回复评论", value=config.get("sched_reply_on", True), - ) - with gr.Row(): - sched_r_min = gr.Number( - label="回复最小间隔(分钟)", value=config.get("sched_r_min", 20), minimum=5, - ) - sched_r_max = gr.Number( - label="回复最大间隔(分钟)", value=config.get("sched_r_max", 60), minimum=10, - ) - sched_reply_max = gr.Number( - label="每轮最多回复条数", value=config.get("sched_reply_max", 3), minimum=1, maximum=10, - ) - - with gr.Group(): - sched_publish_on = gr.Checkbox( - label="✅ 启用自动发布", value=config.get("sched_publish_on", True), - ) - with gr.Row(): - sched_p_min = gr.Number( - label="发布最小间隔(分钟)", value=config.get("sched_p_min", 60), minimum=30, - ) - sched_p_max = gr.Number( - label="发布最大间隔(分钟)", value=config.get("sched_p_max", 180), minimum=60, - ) - - with gr.Row(): - btn_start_sched = gr.Button( - "▶️ 启动定时", variant="primary", size="lg", - ) - btn_stop_sched = gr.Button( - "⏹️ 停止定时", variant="stop", size="lg", - ) - sched_result = gr.Markdown("") - - gr.Markdown("---") - with gr.Row(): - with gr.Column(scale=2): - gr.Markdown("#### 📋 运行日志") - with gr.Row(): - btn_refresh_log = gr.Button("🔄 刷新日志", size="sm") - btn_clear_log = gr.Button("🗑️ 清空日志", size="sm") - btn_refresh_stats = gr.Button("📊 刷新统计", size="sm") - auto_log_display = gr.TextArea( - label="自动化运行日志", - value="📋 暂无日志\n\n💡 执行操作后日志将在此显示", - lines=15, - interactive=False, - ) - with gr.Column(scale=1): - gr.Markdown("#### 📊 今日运营统计") - auto_stats_display = gr.Markdown( - value=_get_stats_summary(), - ) - - # -------- Tab 8: 内容排期 📅 -------- - with gr.Tab("📅 内容排期"): - gr.Markdown( - "### 📅 内容排期日历 + 发布队列\n" - "> 批量生成内容 → 预览审核 → 排期定时 → 自动发布，内容创作全流程管控\n\n" - "**工作流**: 生成内容 → 📝草稿 → ✅审核通过 → 🕐排期/立即发布 → 🚀自动发布" - ) - - with gr.Row(): - # ===== 左栏: 生成 & 队列控制 ===== - with gr.Column(scale=1): - gr.Markdown("#### 🔧 批量生成到队列") - queue_gen_topics = gr.Textbox( - label="主题池 (逗号分隔，随人设自动切换)", - value=", ".join(get_persona_topics(config.get("persona", ""))), - placeholder="会从池中随机选取，切换人设自动更新", - ) - with gr.Row(): - queue_gen_count = gr.Number( - label="生成数量", value=config.get("queue_gen_count", 3), minimum=1, maximum=10, - ) - queue_gen_schedule = gr.Textbox( - label="排期时间 (可选)", - placeholder="如 2026-02-10 18:00:00，留空=仅草稿", - ) - btn_queue_generate = gr.Button( - "📝 批量生成 → 加入队列", variant="primary", size="lg", - ) - queue_gen_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### ⚙️ 队列处理器") - queue_processor_status = gr.Markdown( - value=queue_get_status(), - ) - with gr.Row(): - btn_queue_start = gr.Button( - "▶️ 启动队列处理", variant="primary", - ) - btn_queue_stop = gr.Button( - "⏹️ 停止队列处理", variant="stop", - ) - queue_processor_result = gr.Markdown("") - - gr.Markdown("---") - gr.Markdown("#### 🔍 操作单个队列项") - queue_item_id = gr.Textbox( - label="队列项 ID", placeholder="输入 # 号，如 1", - ) - with gr.Row(): - btn_queue_preview = gr.Button("👁️ 预览", size="sm") - btn_queue_approve = gr.Button("✅ 通过", size="sm", variant="primary") - btn_queue_reject = gr.Button("🚫 拒绝", size="sm") - with gr.Row(): - btn_queue_publish_now = gr.Button("🚀 立即发布", size="sm", variant="primary") - btn_queue_retry = gr.Button("🔄 重试", size="sm") - btn_queue_delete = gr.Button("🗑️ 删除", size="sm", variant="stop") - queue_schedule_time = gr.Textbox( - label="排期时间 (审核通过时可指定)", - placeholder="如 2026-02-10 20:00:00，留空=立即待发布", - ) - btn_queue_batch_approve = gr.Button( - "✅ 批量通过所有草稿", variant="secondary", - ) - queue_op_result = gr.Markdown("") - - # ===== 右栏: 队列列表 & 日历 ===== - with gr.Column(scale=2): - gr.Markdown("#### 📋 发布队列") - with gr.Row(): - queue_filter = gr.Dropdown( - label="状态筛选", - choices=["全部"] + list(STATUS_LABELS.values()), - value="全部", - ) - btn_queue_refresh = gr.Button("🔄 刷新", size="sm") - queue_table = gr.Markdown( - value=pub_queue.format_queue_table(), - label="队列列表", - ) - - gr.Markdown("---") - gr.Markdown("#### 📅 排期日历") - queue_calendar = gr.Markdown( - value=pub_queue.format_calendar(14), - label="日历视图", - ) - - gr.Markdown("---") - gr.Markdown("#### 👁️ 内容预览") - queue_preview_display = gr.Markdown( - value="*选择队列项 ID 后点击预览*", - ) - - # ================================================== - # 事件绑定 - # ================================================== - - # ---- 全局设置: LLM 提供商管理 ---- - btn_connect_llm.click( - fn=connect_llm, inputs=[llm_provider], - outputs=[llm_model, status_bar], - ) - llm_provider.change( - fn=on_provider_selected, - inputs=[llm_provider], - outputs=[llm_provider_info], - ) - btn_add_provider.click( - fn=add_llm_provider, - inputs=[new_provider_name, new_provider_key, new_provider_url], - outputs=[llm_provider, provider_mgmt_status], - ) - btn_del_provider.click( - fn=remove_llm_provider, - inputs=[llm_provider], - outputs=[llm_provider, provider_mgmt_status], - ) - btn_connect_sd.click( - fn=connect_sd, inputs=[sd_url], - outputs=[sd_model, status_bar, sd_model_info], - ) - sd_model.change( - fn=on_sd_model_change, inputs=[sd_model], - outputs=[sd_model_info], - ) - btn_check_mcp.click( - fn=check_mcp_status, inputs=[mcp_url], - outputs=[status_bar], - ) - - # ---- 头像/换脸管理 ---- - btn_save_face.click( - fn=upload_face_image, - inputs=[face_image_input], - outputs=[face_image_preview, face_status], - ) - - # ---- Tab 2: 热点探测 ---- - btn_search.click( - fn=search_hotspots, - inputs=[hot_keyword, hot_sort, mcp_url], - outputs=[search_status, search_output], - ) - # 搜索结果同步到 state - search_output.change( - fn=lambda x: x, inputs=[search_output], outputs=[state_search_result], - ) - - btn_analyze.click( - fn=analyze_and_suggest, - inputs=[llm_model, hot_keyword, search_output], - outputs=[analysis_status, analysis_output, topic_from_hot], - ) - - btn_gen_from_hot.click( - fn=generate_from_hotspot, - inputs=[llm_model, topic_from_hot, hot_style, search_output, sd_model, persona], - outputs=[hot_title, hot_content, hot_prompt, hot_tags, hot_gen_status], - ) - - # 同步热点文案到内容创作 Tab - btn_sync_to_create.click( - fn=lambda t, c, p, tg: (t, c, p, tg, "✅ 已同步到「内容创作」，可切换 Tab 继续绘图和发布"), - inputs=[hot_title, hot_content, hot_prompt, hot_tags], - outputs=[res_title, res_content, res_prompt, res_tags, status_bar], - ) - - # ---- Tab 3: 评论管家 ---- - - # == 子 Tab A: 主动评论引流 == - btn_pro_fetch.click( - fn=fetch_proactive_notes, - inputs=[pro_keyword, mcp_url], - outputs=[pro_selector, pro_fetch_status], - ) - pro_selector.change( - fn=on_proactive_note_selected, - inputs=[pro_selector], - outputs=[pro_feed_id, pro_xsec_token, pro_title], - ) - btn_pro_load.click( - fn=load_note_for_comment, - inputs=[pro_feed_id, pro_xsec_token, mcp_url], - outputs=[pro_load_status, pro_content, pro_comments, pro_full_text], - ) - btn_pro_ai.click( - fn=ai_generate_comment, - inputs=[llm_model, persona, - pro_title, pro_content, pro_comments], - outputs=[pro_comment_text, pro_ai_status], - ) - btn_pro_send.click( - fn=send_comment, - inputs=[pro_feed_id, pro_xsec_token, pro_comment_text, mcp_url], - outputs=[pro_send_status], - ) - - # == 子 Tab B: 回复粉丝评论 == - btn_my_fetch.click( - fn=fetch_my_notes, - inputs=[mcp_url], - outputs=[my_selector, my_fetch_status], - ) - my_selector.change( - fn=on_my_note_selected, - inputs=[my_selector], - outputs=[my_feed_id, my_xsec_token, my_title], - ) - btn_my_load_comments.click( - fn=fetch_my_note_comments, - inputs=[my_feed_id, my_xsec_token, mcp_url], - outputs=[my_comment_status, my_comments_display], - ) - btn_my_ai_reply.click( - fn=ai_reply_comment, - inputs=[llm_model, persona, - my_title, my_target_comment], - outputs=[my_reply_content, my_reply_gen_status], - ) - btn_my_send_reply.click( - fn=send_reply, - inputs=[my_feed_id, my_xsec_token, my_reply_content, mcp_url], - outputs=[my_reply_status], - ) - - # ---- Tab 4: 账号登录 ---- - btn_get_qrcode.click( - fn=get_login_qrcode, - inputs=[mcp_url], - outputs=[qr_image, login_status], - ) - btn_check_login.click( - fn=check_login, - inputs=[mcp_url], - outputs=[login_status, login_user_id, login_xsec_token], - ) - btn_logout.click( - fn=logout_xhs, - inputs=[mcp_url], - outputs=[login_status], - ) - btn_save_uid.click( - fn=save_my_user_id, - inputs=[login_user_id], - outputs=[save_uid_status], - ) - - # ---- Tab 5: 数据看板 ---- - def refresh_xsec_token(mcp_url): - token = _auto_fetch_xsec_token(mcp_url) - if token: - cfg.set("xsec_token", token) - return gr.update(value=token), "✅ Token 已刷新" - return gr.update(value=cfg.get("xsec_token", "")), "❌ 刷新失败，请确认已登录" - - btn_refresh_token.click( - fn=refresh_xsec_token, - inputs=[mcp_url], - outputs=[data_xsec_token, data_status], - ) - btn_load_my_data.click( - fn=fetch_my_profile, - inputs=[data_user_id, data_xsec_token, mcp_url], - outputs=[data_status, profile_card, chart_interact, chart_notes, notes_detail], - ) - - # ---- Tab 6: 智能学习 ---- - btn_learn_collect.click( - fn=analytics_collect_data, - inputs=[mcp_url, learn_user_id, learn_xsec_token], - outputs=[learn_collect_status], - ) - btn_learn_calc.click( - fn=analytics_calculate_weights, - inputs=[], - outputs=[learn_calc_status, learn_report], - ) - btn_learn_ai.click( - fn=analytics_llm_deep_analysis, - inputs=[llm_model], - outputs=[learn_ai_report], - ) - btn_learn_start.click( - fn=start_learn_scheduler, - inputs=[mcp_url, learn_user_id, learn_xsec_token, llm_model, learn_interval], - outputs=[learn_sched_status], - ) - btn_learn_stop.click( - fn=stop_learn_scheduler, - inputs=[], - outputs=[learn_sched_status], - ) - btn_show_topics.click( - fn=analytics_get_weighted_topics, - inputs=[], - outputs=[learn_weighted_topics], - ) - learn_use_weights.change( - fn=lambda v: cfg.set("use_smart_weights", v) or ("✅ 智能权重已启用" if v else "⚪ 智能权重已关闭"), - inputs=[learn_use_weights], - outputs=[learn_sched_status], - ) - - # ---- Tab 7: 自动运营 ---- - # 人设切换 → 联动更新评论关键词池、主题池和队列主题池（同时保存到配置） - persona.change( - fn=on_persona_changed, - inputs=[persona], - outputs=[auto_comment_keywords, auto_publish_topics, persona_pool_hint, queue_gen_topics], - ) - - btn_auto_comment.click( - fn=_auto_comment_with_log, - inputs=[auto_comment_keywords, mcp_url, llm_model, persona], - outputs=[auto_comment_result, auto_log_display], - ) - btn_auto_like.click( - fn=_auto_like_with_log, - inputs=[auto_comment_keywords, auto_like_count, mcp_url], - outputs=[auto_like_result, auto_log_display], - ) - btn_auto_favorite.click( - fn=_auto_favorite_with_log, - inputs=[auto_comment_keywords, auto_fav_count, mcp_url], - outputs=[auto_favorite_result, auto_log_display], - ) - btn_auto_reply.click( - fn=_auto_reply_with_log, - inputs=[auto_reply_max, mcp_url, llm_model, persona], - outputs=[auto_reply_result, auto_log_display], - ) - btn_auto_publish.click( - fn=_auto_publish_with_log, - inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model, persona, quality_mode, face_swap_toggle], - outputs=[auto_publish_result, auto_log_display], - ) - btn_start_sched.click( - fn=start_scheduler, - inputs=[sched_comment_on, sched_publish_on, sched_reply_on, sched_like_on, - sched_fav_on, - sched_c_min, sched_c_max, sched_p_min, sched_p_max, - sched_r_min, sched_r_max, sched_reply_max, - sched_l_min, sched_l_max, sched_like_count, - sched_fav_min, sched_fav_max, sched_fav_count, - sched_start_hour, sched_end_hour, - auto_comment_keywords, auto_publish_topics, - mcp_url, sd_url, sd_model, llm_model, persona, - quality_mode, face_swap_toggle], - outputs=[sched_result], - ) - btn_stop_sched.click( - fn=stop_scheduler, - inputs=[], - outputs=[sched_result], - ) - btn_refresh_log.click( - fn=lambda: (get_auto_log(), get_scheduler_status()), - inputs=[], - outputs=[auto_log_display, sched_status], - ) - btn_clear_log.click( - fn=lambda: (_auto_log.clear() or "📋 日志已清空"), - inputs=[], - outputs=[auto_log_display], - ) - btn_refresh_stats.click( - fn=lambda: (get_scheduler_status(), _get_stats_summary()), - inputs=[], - outputs=[sched_status, auto_stats_display], - ) - - # ---- 全局设置参数自动保存 ---- - # persona的保存已整合到on_persona_changed函数中 - mcp_url.change(fn=lambda v: cfg.set("mcp_url", v), inputs=[mcp_url], outputs=[]) - sd_url.change(fn=lambda v: cfg.set("sd_url", v), inputs=[sd_url], outputs=[]) - llm_model.change(fn=lambda v: cfg.set("model", v), inputs=[llm_model], outputs=[]) - - # ---- 自动运营参数自动保存 ---- - sched_comment_on.change(fn=lambda v: cfg.set("sched_comment_on", v), inputs=[sched_comment_on], outputs=[]) - sched_like_on.change(fn=lambda v: cfg.set("sched_like_on", v), inputs=[sched_like_on], outputs=[]) - sched_fav_on.change(fn=lambda v: cfg.set("sched_fav_on", v), inputs=[sched_fav_on], outputs=[]) - sched_reply_on.change(fn=lambda v: cfg.set("sched_reply_on", v), inputs=[sched_reply_on], outputs=[]) - sched_publish_on.change(fn=lambda v: cfg.set("sched_publish_on", v), inputs=[sched_publish_on], outputs=[]) - sched_c_min.change(fn=lambda v: cfg.set("sched_c_min", v), inputs=[sched_c_min], outputs=[]) - sched_c_max.change(fn=lambda v: cfg.set("sched_c_max", v), inputs=[sched_c_max], outputs=[]) - sched_l_min.change(fn=lambda v: cfg.set("sched_l_min", v), inputs=[sched_l_min], outputs=[]) - sched_l_max.change(fn=lambda v: cfg.set("sched_l_max", v), inputs=[sched_l_max], outputs=[]) - sched_like_count.change(fn=lambda v: cfg.set("sched_like_count", v), inputs=[sched_like_count], outputs=[]) - sched_fav_min.change(fn=lambda v: cfg.set("sched_fav_min", v), inputs=[sched_fav_min], outputs=[]) - sched_fav_max.change(fn=lambda v: cfg.set("sched_fav_max", v), inputs=[sched_fav_max], outputs=[]) - sched_fav_count.change(fn=lambda v: cfg.set("sched_fav_count", v), inputs=[sched_fav_count], outputs=[]) - sched_r_min.change(fn=lambda v: cfg.set("sched_r_min", v), inputs=[sched_r_min], outputs=[]) - sched_r_max.change(fn=lambda v: cfg.set("sched_r_max", v), inputs=[sched_r_max], outputs=[]) - sched_reply_max.change(fn=lambda v: cfg.set("sched_reply_max", v), inputs=[sched_reply_max], outputs=[]) - sched_p_min.change(fn=lambda v: cfg.set("sched_p_min", v), inputs=[sched_p_min], outputs=[]) - sched_p_max.change(fn=lambda v: cfg.set("sched_p_max", v), inputs=[sched_p_max], outputs=[]) - sched_start_hour.change(fn=lambda v: cfg.set("sched_start_hour", v), inputs=[sched_start_hour], outputs=[]) - sched_end_hour.change(fn=lambda v: cfg.set("sched_end_hour", v), inputs=[sched_end_hour], outputs=[]) - auto_like_count.change(fn=lambda v: cfg.set("auto_like_count", v), inputs=[auto_like_count], outputs=[]) - auto_fav_count.change(fn=lambda v: cfg.set("auto_fav_count", v), inputs=[auto_fav_count], outputs=[]) - auto_reply_max.change(fn=lambda v: cfg.set("auto_reply_max", v), inputs=[auto_reply_max], outputs=[]) - - # ---- 智能学习参数自动保存 ---- - learn_interval.change(fn=lambda v: cfg.set("learn_interval", v), inputs=[learn_interval], outputs=[]) - - # ---- 内容排期参数自动保存 ---- - queue_gen_count.change(fn=lambda v: cfg.set("queue_gen_count", v), inputs=[queue_gen_count], outputs=[]) - - # ---- 开机自启 ---- - autostart_toggle.change( - fn=toggle_autostart, - inputs=[autostart_toggle], - outputs=[autostart_status], - ) - - # ---- Tab 8: 内容排期 ---- - # 队列主题池的更新已整合到 Tab 7 的 persona.change() 事件中 - - # 批量生成到队列 - btn_queue_generate.click( - fn=queue_generate_and_refresh, - inputs=[queue_gen_topics, sd_url, sd_model, llm_model, - persona, quality_mode, face_swap_toggle, - queue_gen_count, queue_gen_schedule], - outputs=[queue_gen_result, queue_table, queue_calendar, queue_processor_status], - ) - - # 刷新队列 - btn_queue_refresh.click( - fn=lambda sf: (queue_refresh_table(sf), queue_refresh_calendar(), queue_get_status()), - inputs=[queue_filter], - outputs=[queue_table, queue_calendar, queue_processor_status], - ) - queue_filter.change( - fn=lambda sf: queue_refresh_table(sf), - inputs=[queue_filter], - outputs=[queue_table], - ) - - # 单项操作 - btn_queue_preview.click( - fn=queue_preview_item, - inputs=[queue_item_id], - outputs=[queue_preview_display], - ) - btn_queue_approve.click( - fn=lambda iid, st: (queue_approve_item(iid, st), pub_queue.format_queue_table(), pub_queue.format_calendar(14)), - inputs=[queue_item_id, queue_schedule_time], - outputs=[queue_op_result, queue_table, queue_calendar], - ) - btn_queue_reject.click( - fn=lambda iid: (queue_reject_item(iid), pub_queue.format_queue_table()), - inputs=[queue_item_id], - outputs=[queue_op_result, queue_table], - ) - btn_queue_delete.click( - fn=lambda iid: (queue_delete_item(iid), pub_queue.format_queue_table(), pub_queue.format_calendar(14)), - inputs=[queue_item_id], - outputs=[queue_op_result, queue_table, queue_calendar], - ) - btn_queue_retry.click( - fn=lambda iid: (queue_retry_item(iid), pub_queue.format_queue_table()), - inputs=[queue_item_id], - outputs=[queue_op_result, queue_table], - ) - btn_queue_publish_now.click( - fn=lambda iid: (queue_publish_now(iid), pub_queue.format_queue_table(), pub_queue.format_calendar(14), queue_get_status()), - inputs=[queue_item_id], - outputs=[queue_op_result, queue_table, queue_calendar, queue_processor_status], - ) - btn_queue_batch_approve.click( - fn=lambda sf: (queue_batch_approve(sf), pub_queue.format_queue_table(), pub_queue.format_calendar(14)), - inputs=[queue_filter], - outputs=[queue_op_result, queue_table, queue_calendar], - ) - - # 队列处理器 - btn_queue_start.click( - fn=lambda: (queue_start_processor(), queue_get_status()), - inputs=[], - outputs=[queue_processor_result, queue_processor_status], - ) - btn_queue_stop.click( - fn=lambda: (queue_stop_processor(), queue_get_status()), - inputs=[], - outputs=[queue_processor_result, queue_processor_status], - ) - - # ---- 启动时自动加载全局设置 ---- - def load_global_settings(): - """页面加载时恢复全局设置""" - config = cfg.all - providers = cfg.get_llm_provider_names() - active_llm = cfg.get("active_llm", "") - persona_val = config.get("persona", RANDOM_PERSONA_LABEL) - - # 初始化LLM提供商信息显示 - provider_info = on_provider_selected(active_llm) if active_llm else "*选择提供商后显示详情*" - - # 获取人设对应的关键词和主题池 - keywords = get_persona_keywords(persona_val) - topics = get_persona_topics(persona_val) - keywords_str = ", ".join(keywords) - topics_str = ", ".join(topics) - - # 尝试连接 SD 并获取模型列表 - sd_models = [] - sd_status = "🔄 等待连接..." - sd_model_val = None - try: - svc = SDService(config["sd_url"]) - ok, msg = svc.check_connection() - if ok: - sd_models = svc.get_models() - sd_model_val = sd_models[0] if sd_models else None - sd_status = f"✅ {msg}" - except Exception: - pass - - # 图片生成参数 - quality_mode_val = config.get("quality_mode", "标准 (约1分钟)") - steps_val = config.get("sd_steps", 20) - cfg_scale_val = config.get("sd_cfg_scale", 5.5) - neg_prompt_val = config.get("sd_negative_prompt", DEFAULT_NEGATIVE) - - # 自动运营参数 - return ( - gr.update(choices=providers, value=active_llm), # llm_provider - provider_info, # llm_provider_info - config["mcp_url"], # mcp_url - config["sd_url"], # sd_url - persona_val, # persona - gr.update(choices=sd_models, value=sd_model_val), # sd_model - sd_status, # status_bar - keywords_str, # auto_comment_keywords - topics_str, # auto_publish_topics - topics_str, # queue_gen_topics - quality_mode_val, # quality_mode - steps_val, # steps - cfg_scale_val, # cfg_scale - neg_prompt_val, # neg_prompt - config.get("auto_like_count", 5), # auto_like_count - config.get("auto_fav_count", 3), # auto_fav_count - config.get("auto_reply_max", 5), # auto_reply_max - config.get("sched_comment_on", True), # sched_comment_on - config.get("sched_like_on", True), # sched_like_on - config.get("sched_fav_on", True), # sched_fav_on - config.get("sched_reply_on", True), # sched_reply_on - config.get("sched_publish_on", True), # sched_publish_on - config.get("sched_c_min", 15), # sched_c_min - config.get("sched_c_max", 45), # sched_c_max - config.get("sched_l_min", 10), # sched_l_min - config.get("sched_l_max", 30), # sched_l_max - config.get("sched_like_count", 5), # sched_like_count - config.get("sched_fav_min", 12), # sched_fav_min - config.get("sched_fav_max", 35), # sched_fav_max - config.get("sched_fav_count", 3), # sched_fav_count - config.get("sched_r_min", 20), # sched_r_min - config.get("sched_r_max", 60), # sched_r_max - config.get("sched_reply_max", 3), # sched_reply_max - config.get("sched_p_min", 60), # sched_p_min - config.get("sched_p_max", 180), # sched_p_max - config.get("sched_start_hour", 8), # sched_start_hour - config.get("sched_end_hour", 23), # sched_end_hour - config.get("learn_interval", 6), # learn_interval - config.get("queue_gen_count", 3), # queue_gen_count - config.get("use_smart_weights", True), # learn_use_weights - ) - - app.load( - fn=load_global_settings, - inputs=[], - outputs=[ - llm_provider, llm_provider_info, mcp_url, sd_url, persona, sd_model, status_bar, - auto_comment_keywords, auto_publish_topics, queue_gen_topics, - quality_mode, steps, cfg_scale, neg_prompt, - auto_like_count, auto_fav_count, auto_reply_max, - sched_comment_on, sched_like_on, sched_fav_on, sched_reply_on, sched_publish_on, - sched_c_min, sched_c_max, sched_l_min, sched_l_max, sched_like_count, - sched_fav_min, sched_fav_max, sched_fav_count, - sched_r_min, sched_r_max, sched_reply_max, - sched_p_min, sched_p_max, sched_start_hour, sched_end_hour, - learn_interval, queue_gen_count, learn_use_weights, - ], - ) - - -# ================================================== if __name__ == "__main__": logger.info("🍒 小红书 AI 爆文工坊 V2.0 启动中...") diff --git a/openspec/changes/improve-maintainability/.openspec.yaml b/openspec/changes/improve-maintainability/.openspec.yaml new file mode 100644 index 0000000..69e221f --- /dev/null +++ b/openspec/changes/improve-maintainability/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-02-24 diff --git a/openspec/changes/improve-maintainability/design.md b/openspec/changes/improve-maintainability/design.md new file mode 100644 index 0000000..9f7c60e --- /dev/null +++ b/openspec/changes/improve-maintainability/design.md @@ -0,0 +1,119 @@ +## Context + +`main.py` 是整个项目的单一入口，目前 4359 行，包含 10+ 个业务域的业务逻辑、全局状态、UI 组件和事件绑定。上一轮重构已将 Tab 1（内容创作）提取为 `ui/tab_create.py`，建立了 Tab 模块化的模式。本次设计延续该模式，将业务逻辑提取为 `services/` 目录，并完成剩余 Tab 的 UI 拆分。 + +已有外部服务层：`config_manager.py`、`llm_service.py`、`sd_service.py`、`mcp_client.py`、`analytics_service.py`、`publish_queue.py`。 +`main.py` 中剩余的函数是对这些服务的**编排层**，应该独立成模块而非继续膨胀在入口文件里。 + +约束： +- Python 3.10+（当前环境） +- 不引入新的外部依赖 +- 重构不改变任何业务行为 + +## Goals / Non-Goals + +**Goals:** +- 将 `main.py` 瘦身至 ~300 行纯入口层（导入 + UI 组装 + `app.launch()`） +- 建立清晰的分层：`services/`（业务编排）→ `ui/`（Gradio 组件）→ `main.py`（入口） +- 消除模块间隐式依赖，所有依赖通过函数参数显式传递 +- 保持所有函数签名、Gradio 回调绑定不变 + +**Non-Goals:** +- 不重构或重写任何现有业务逻辑（本次是纯搬迁） +- 不改变 `config_manager.py`、`llm_service.py` 等已有服务层 +- 不引入类/面向对象重构（保持现有函数式风格） +- 不添加单元测试（独立变更） + +## Decisions + +### D1：分层架构 —— `services/` 不依赖 `ui/`，`ui/` 不依赖 `services/` + +``` +main.py (入口层：组装 + 启动) +├── services/ (业务编排层：纯 Python，无 Gradio) +│ ├── connection.py +│ ├── content.py +│ ├── hotspot.py +│ ├── engagement.py +│ ├── rate_limiter.py +│ ├── profile.py +│ ├── persona.py +│ ├── scheduler.py +│ ├── queue_ops.py +│ └── autostart.py +├── ui/ (UI 层：Gradio 组件 + 事件绑定) +│ ├── tab_create.py ← 已存在 +│ ├── tab_hotspot.py +│ ├── tab_engage.py +│ ├── tab_profile.py +│ ├── tab_auto.py +│ ├── tab_queue.py +│ ├── tab_analytics.py +│ └── tab_settings.py +``` + +**为何不让 `ui/` 依赖 `services/`：** 每个 `build_tab()` 接收回调函数作为参数（已有 `tab_create.py` 的模式），而非直接 import service。这样 UI 层完全解耦，可独立测试或替换。 + +### D2：共享单例通过 `main.py` 初始化，作为参数传入 + +`cfg`、`mcp`、`analytics`、`pub_queue`、`queue_publisher` 仍在 `main.py` 顶层初始化。Service 函数需要它们时通过**函数参数**接收，不在 service 模块顶层 import。 + +**为何不在各 service 模块初始化单例：** 防止循环依赖、防止多次初始化、保持测试时可替换。 + +### D3：有状态模块使用模块级变量（不封装成类） + +`rate_limiter`、`scheduler`、`engagement` 内部有 `threading.Event`、`_daily_stats` 等状态。保持现有模块级变量风格（不改成类），仅将变量和函数整体搬迁到对应模块。 + +**为何不改成类：** 本次目标是结构拆分，不是重构设计模式，避免引入额外变更风险。 + +### D4：迁移策略 —— 先提取后删除，不做重定向 + +每个域的迁移步骤： +1. 在 service 模块中写入函数（复制粘贴 + 调整 import） +2. 在 `main.py` 中删除对应函数，改为 `from services.xxx import ...` +3. 运行 `ast.parse()` 验证语法 +4. 运行应用验证启动不报错 + +**为何不做 `main.py` 中的临时 re-export：** 简单场景直接删+导入更清晰，且 Gradio 回调绑定在 `main.py` 中通过变量名引用，只需保证同名变量在作用域内即可。 + +### D5：UI Tab 模块统一使用 `build_tab(fn_*, ...)` 签名 + +复用 `tab_create.py` 已建立的模式： +- 每个 `build_tab()` 接收所需的回调函数和共享 Gradio 组件作为参数 +- 函数内部创建本 Tab 的所有 Gradio 组件及事件绑定 +- 返回 `dict`，包含需要被其他 Tab 或 `app.load()` 引用的组件 + +### D6：`services/` 和 `ui/` 均需 `__init__.py` + +使用空文件标记为 Python 包，与 `ui/__init__.py` 已有做法一致。 + +## Risks / Trade-offs + +- **[风险] 大量 import 调整可能遗漏** → 每个模块完成后执行 `ast.parse()` + 应用启动验证，逐域推进 +- **[风险] 全局状态的隐式共享** → 调度器、限流器的模块级变量在模块首次 import 时初始化，Python 模块单例语义保证只初始化一次，行为与当前一致 +- **[权衡] `build_tab()` 参数列表长** → 与现有 `tab_create.py` 的做法一致，接受这种显式依赖的冗长性，可在后续变更中引入 dataclass 参数包 + +## Migration Plan + +按以下顺序逐域提取，每步验证后再继续： + +1. `services/rate_limiter.py` —— 无外部依赖，最安全的起点 +2. `services/autostart.py` —— 独立，平台相关逻辑隔离 +3. `services/persona.py` —— 仅依赖 `cfg` +4. `services/connection.py` —— 依赖 `cfg`、`llm_service`、`sd_service`、`mcp_client` +5. `services/profile.py` —— 依赖 `mcp_client` +6. `services/hotspot.py` —— 依赖 `llm_service`、`mcp_client` +7. `services/content.py` —— 依赖多个服务，最复杂 +8. `services/engagement.py` —— 依赖 `rate_limiter`、`mcp_client` +9. `services/scheduler.py` —— 依赖 `engagement`、`content` +10. `services/queue_ops.py` —— 依赖 `content`、`pub_queue` +11. `ui/tab_hotspot.py` ~ `ui/tab_settings.py` —— 7 个 Tab UI 拆分 + +**回滚策略：** 所有修改通过 git 追踪；每个 service 提取为一个独立 commit，任意步骤可 `git revert`。 + +## Open Questions + +- `_auto_log` 列表（被 `engagement` 和 `scheduler` 共同写入）归属哪个模块？ + → 暂定置于 `services/scheduler.py`，`engagement` 接收 `log_fn` 回调参数 +- `queue_publisher` 的 callback 注册（`set_publish_callback`）在哪里调用？ + → 保留在 `main.py` 初始化段，callback 函数迁移到 `services/queue_ops.py` diff --git a/openspec/changes/improve-maintainability/proposal.md b/openspec/changes/improve-maintainability/proposal.md new file mode 100644 index 0000000..713c96c --- /dev/null +++ b/openspec/changes/improve-maintainability/proposal.md @@ -0,0 +1,38 @@ +## Why + +`main.py` 目前共 4359 行，将连接管理、内容生成、自动化运营、调度、队列、UI 等 10+ 个业务域全部混入单一文件，导致阅读困难、修改风险高、模块间依赖不清晰。随着功能继续增长，维护成本将持续上升。现在是在文件进一步膨胀前完成结构化拆分的最佳时机。 + +## What Changes + +- 按业务域将 `main.py` 中的函数提取为独立的 `services/` 模块 +- 将剩余 UI Tab 提取为独立的 `ui/tab_*.py` 模块（`tab_create.py` 已完成，需继续完成其余 Tab） +- `main.py` 保留为**入口层**：仅负责组装 Gradio UI、注册事件、启动应用 +- 所有模块保持向后兼容，不改变对外行为 + +## Capabilities + +### New Capabilities + +- `services-connection`: LLM / SD / MCP 连接管理（`connect_llm`、`connect_sd`、`check_mcp_status`、登录相关） +- `services-content`: 内容生成（`generate_copy`、`generate_images`、`publish_to_xhs`、`one_click_export`、face image 上传） +- `services-hotspot`: 热点探测（`search_hotspots`、`analyze_and_suggest`、`generate_from_hotspot`） +- `services-engagement`: 互动自动化（`auto_comment_once`、`auto_like_once`、`auto_favorite_once`、`auto_reply_once` 及对应 `_with_log` 包装） +- `services-rate-limiter`: 频率控制与每日限额（`_reset_daily_stats_if_needed`、`_check_daily_limit`、`_is_in_cooldown` 等） +- `services-profile`: 用户主页解析（`fetch_my_profile`、`_parse_profile_json`、`_parse_count`） +- `services-persona`: 人设管理（`_match_persona_pools`、`get_persona_topics`、`get_persona_keywords`、`on_persona_changed`） +- `services-scheduler`: 自动调度器（`_scheduler_loop`、`start_scheduler`、`stop_scheduler`、`get_scheduler_status`） +- `services-queue`: 内容排期队列（`generate_to_queue`、`queue_*` 系列函数、`_queue_publish_callback`） +- `services-autostart`: 开机自启管理（`enable_autostart`、`disable_autostart`、`toggle_autostart` 等） +- `ui-tabs-split`: 将其余 Gradio Tab（热点、互动、我的主页、自动运营、队列、数据分析、设置）提取为 `ui/tab_*.py` + +### Modified Capabilities + +（无需求层面变更，仅为实现重构） + +## Impact + +- **主要受影响文件**：`main.py`（从 4359 行缩减至 ~300 行入口层） +- **新增目录**：`services/`（10 个模块）、`ui/`（8 个 Tab 模块，`tab_create.py` 已存在） +- **依赖关系**：`services/` 模块之间通过函数参数传递依赖，避免循环导入；`main.py` 统一导入并组装 +- **无 API 变更**：所有函数签名保持不变，Gradio 回调绑定不受影响 +- **运行时影响**：零，重构不改变业务逻辑 diff --git a/openspec/changes/improve-maintainability/specs/services-autostart/spec.md b/openspec/changes/improve-maintainability/specs/services-autostart/spec.md new file mode 100644 index 0000000..ab4983d --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-autostart/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 开机自启管理函数迁移至独立模块 +系统 SHALL 将开机自启相关常量和函数从 `main.py` 提取至 `services/autostart.py`，包括：`_APP_NAME`、`_STARTUP_REG_KEY`、`_get_startup_script_path`、`_get_startup_bat_path`、`_create_startup_scripts`、`is_autostart_enabled`、`enable_autostart`、`disable_autostart`、`toggle_autostart`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.autostart import toggle_autostart, is_autostart_enabled` +- **THEN** 函数可正常调用 + +#### Scenario: Windows 注册表操作行为不变 +- **WHEN** `enable_autostart()` 在 Windows 系统上被调用 +- **THEN** SHALL 向注册表 `HKCU\Software\Microsoft\Windows\CurrentVersion\Run` 写入启动项，行为与迁移前完全一致 + +#### Scenario: 非 Windows 平台处理不变 +- **WHEN** `enable_autostart()` 在非 Windows 系统上被调用 +- **THEN** SHALL 返回与迁移前相同的平台不支持提示信息 diff --git a/openspec/changes/improve-maintainability/specs/services-connection/spec.md b/openspec/changes/improve-maintainability/specs/services-connection/spec.md new file mode 100644 index 0000000..94ae5dc --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-connection/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 连接管理函数迁移至独立模块 +系统 SHALL 将所有 LLM / SD / MCP 连接管理及认证相关函数从 `main.py` 提取至 `services/connection.py`，包括：`_get_llm_config`、`connect_llm`、`add_llm_provider`、`remove_llm_provider`、`on_provider_selected`、`connect_sd`、`on_sd_model_change`、`check_mcp_status`、`get_login_qrcode`、`logout_xhs`、`_auto_fetch_xsec_token`、`check_login`、`save_my_user_id`、`upload_face_image`、`load_saved_face_image`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.connection import connect_llm, connect_sd` 等导入 +- **THEN** 所有函数可正常调用，行为与迁移前完全一致 + +#### Scenario: 外部依赖通过参数传入 +- **WHEN** `services/connection.py` 中的函数需要访问 `cfg`、`llm`（`LLMService`）、`sd`（`SDService`）、`mcp`（`MCPClient`） +- **THEN** 这些依赖 SHALL 通过函数参数接收，`services/connection.py` 模块顶层不创建单例实例 + +#### Scenario: 无循环导入 +- **WHEN** Python 解释器加载 `services/connection.py` +- **THEN** 不产生 `ImportError` 或循环导入错误 diff --git a/openspec/changes/improve-maintainability/specs/services-content/spec.md b/openspec/changes/improve-maintainability/specs/services-content/spec.md new file mode 100644 index 0000000..d570fb8 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-content/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 内容生成函数迁移至独立模块 +系统 SHALL 将内容生成、图片生成、发布及导出相关函数从 `main.py` 提取至 `services/content.py`，包括：`generate_copy`、`generate_images`、`one_click_export`、`publish_to_xhs`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.content import generate_copy, generate_images, publish_to_xhs, one_click_export` +- **THEN** 所有函数可正常调用，行为与迁移前完全一致 + +#### Scenario: 内容生成保留现有验证逻辑 +- **WHEN** 调用 `publish_to_xhs` 时标题超过 20 字或图片数量不合法 +- **THEN** 函数 SHALL 返回与迁移前相同的错误提示，不改变验证行为 + +#### Scenario: 临时文件清理逻辑保留 +- **WHEN** `publish_to_xhs` 执行完毕（成功或失败） +- **THEN** `finally` 块中的 AI 临时文件清理逻辑 SHALL 正常执行 diff --git a/openspec/changes/improve-maintainability/specs/services-engagement/spec.md b/openspec/changes/improve-maintainability/specs/services-engagement/spec.md new file mode 100644 index 0000000..40c5236 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-engagement/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 互动自动化函数迁移至独立模块 +系统 SHALL 将评论、点赞、收藏、回复等互动自动化函数从 `main.py` 提取至 `services/engagement.py`，包括：`load_note_for_comment`、`ai_generate_comment`、`send_comment`、`fetch_my_notes`、`on_my_note_selected`、`fetch_my_note_comments`、`ai_reply_comment`、`send_reply`、`auto_comment_once`、`_auto_comment_with_log`、`auto_like_once`、`_auto_like_with_log`、`auto_favorite_once`、`_auto_favorite_with_log`、`auto_reply_once`、`_auto_reply_with_log`、`_auto_publish_with_log`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.engagement import auto_comment_once, auto_like_once` 等导入 +- **THEN** 所有函数可正常调用 + +#### Scenario: 日志回调参数化 +- **WHEN** `engagement.py` 中的 `_with_log` 函数需要追加日志时 +- **THEN** 函数 SHALL 接收 `log_fn` 参数（callable）用于写入日志，不直接依赖外部 `_auto_log` 列表 + +#### Scenario: 频率限制集成 +- **WHEN** `auto_comment_once` 等函数执行前需要检查每日限额和冷却状态 +- **THEN** 通过调用 `rate_limiter` 模块中的函数实现，不在 `engagement.py` 内复制限流逻辑 diff --git a/openspec/changes/improve-maintainability/specs/services-hotspot/spec.md b/openspec/changes/improve-maintainability/specs/services-hotspot/spec.md new file mode 100644 index 0000000..9707691 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-hotspot/spec.md @@ -0,0 +1,12 @@ +## ADDED Requirements + +### Requirement: 热点探测函数迁移至独立模块 +系统 SHALL 将热点搜索与分析相关函数从 `main.py` 提取至 `services/hotspot.py`，包括：`search_hotspots`、`analyze_and_suggest`、`generate_from_hotspot`、`_set_cache`、`_get_cache`、`_fetch_and_cache`、`_pick_from_cache`、`fetch_proactive_notes`、`on_proactive_note_selected`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.hotspot import search_hotspots, analyze_and_suggest` 等导入 +- **THEN** 所有函数可正常调用 + +#### Scenario: 线程安全缓存随模块迁移 +- **WHEN** `_cache_lock`（`threading.RLock`）随函数一起迁移至 `services/hotspot.py` +- **THEN** `_set_cache` / `_get_cache` 的线程安全行为保持不变 diff --git a/openspec/changes/improve-maintainability/specs/services-persona/spec.md b/openspec/changes/improve-maintainability/specs/services-persona/spec.md new file mode 100644 index 0000000..904769e --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-persona/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 人设管理函数及常量迁移至独立模块 +系统 SHALL 将人设相关的常量和函数从 `main.py` 提取至 `services/persona.py`，包括：`DEFAULT_PERSONAS`、`RANDOM_PERSONA_LABEL`、`PERSONA_POOL_MAP`、`DEFAULT_TOPICS`、`DEFAULT_STYLES`、`DEFAULT_COMMENT_KEYWORDS`、`_match_persona_pools`、`get_persona_topics`、`get_persona_keywords`、`on_persona_changed`、`_resolve_persona`。 + +#### Scenario: 常量可从模块导入 +- **WHEN** `main.py` 执行 `from services.persona import DEFAULT_PERSONAS, PERSONA_POOL_MAP` +- **THEN** 常量值 SHALL 与迁移前完全一致 + +#### Scenario: 人设解析正确处理随机人设标签 +- **WHEN** `_resolve_persona(RANDOM_PERSONA_LABEL)` 被调用 +- **THEN** SHALL 返回从人设池中随机选取的人设文本，行为与迁移前一致 + +#### Scenario: 人设变更回调正常触发 +- **WHEN** `on_persona_changed(persona_text)` 被调用 +- **THEN** SHALL 返回更新后的话题列表和关键词列表，供 Gradio UI 使用 diff --git a/openspec/changes/improve-maintainability/specs/services-profile/spec.md b/openspec/changes/improve-maintainability/specs/services-profile/spec.md new file mode 100644 index 0000000..ab39ab8 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-profile/spec.md @@ -0,0 +1,12 @@ +## ADDED Requirements + +### Requirement: 用户主页解析函数迁移至独立模块 +系统 SHALL 将用户主页数据获取与解析相关函数从 `main.py` 提取至 `services/profile.py`，包括：`_parse_profile_json`、`_parse_count`、`fetch_my_profile`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.profile import fetch_my_profile` +- **THEN** 函数可正常调用，行为与迁移前一致 + +#### Scenario: 解析容错性保留 +- **WHEN** `_parse_count` 接收到格式异常的数值字符串（如 "1.2万"、"--"） +- **THEN** SHALL 返回与迁移前相同的浮点数或 0，不抛出异常 diff --git a/openspec/changes/improve-maintainability/specs/services-queue/spec.md b/openspec/changes/improve-maintainability/specs/services-queue/spec.md new file mode 100644 index 0000000..54e3376 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-queue/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 排期队列操作函数迁移至独立模块 +系统 SHALL 将内容排期队列相关函数从 `main.py` 提取至 `services/queue_ops.py`，包括：`generate_to_queue`、`_queue_publish_callback`、`queue_refresh_table`、`queue_refresh_calendar`、`queue_preview_item`、`queue_approve_item`、`queue_reject_item`、`queue_delete_item`、`queue_retry_item`、`queue_publish_now`、`queue_start_processor`、`queue_stop_processor`、`queue_get_status`、`queue_batch_approve`、`queue_generate_and_refresh`。 + +#### Scenario: 模块导入成功 +- **WHEN** `main.py` 执行 `from services.queue_ops import queue_generate_and_refresh, queue_refresh_table` 等导入 +- **THEN** 所有函数可正常调用 + +#### Scenario: publish callback 在 main.py 完成注册 +- **WHEN** 应用启动时 `main.py` 调用 `pub_queue.set_publish_callback(_queue_publish_callback)`（`_queue_publish_callback` 已迁移至 `queue_ops.py`） +- **THEN** 队列发布回调 SHALL 正常注册并在队列处理时触发 + +#### Scenario: 队列操作读写 pub_queue 单例 +- **WHEN** `queue_ops.py` 中的函数需要访问 `pub_queue` 或 `queue_publisher` +- **THEN** 这些单例 SHALL 通过函数参数传入，不在 `queue_ops.py` 模块顶层初始化 diff --git a/openspec/changes/improve-maintainability/specs/services-rate-limiter/spec.md b/openspec/changes/improve-maintainability/specs/services-rate-limiter/spec.md new file mode 100644 index 0000000..45a26c0 --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-rate-limiter/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 频率控制与每日限额函数迁移至独立模块 +系统 SHALL 将频率控制、每日限额及冷却相关的所有状态变量和函数从 `main.py` 提取至 `services/rate_limiter.py`，包括：`_auto_running`、`_op_history`、`_daily_stats`、`DAILY_LIMITS`、`_consecutive_errors`、`_error_cooldown_until`、`_reset_daily_stats_if_needed`、`_check_daily_limit`、`_increment_stat`、`_record_error`、`_clear_error_streak`、`_is_in_cooldown`、`_is_in_operating_hours`、`_get_stats_summary`。 + +#### Scenario: 模块级状态初始化一次 +- **WHEN** Python 首次导入 `services/rate_limiter.py` +- **THEN** `_daily_stats`、`_op_history` 等模块级变量 SHALL 仅初始化一次（Python 模块单例语义） + +#### Scenario: 每日限额检查正常工作 +- **WHEN** `_check_daily_limit("comment")` 被调用 +- **THEN** 返回值 SHALL 与迁移前行为完全一致 + +#### Scenario: 运营时段限制正常工作 +- **WHEN** 当前时间不在 `start_hour` 至 `end_hour` 范围内时调用 `_is_in_operating_hours` +- **THEN** 返回 `False`，阻止自动化操作执行 diff --git a/openspec/changes/improve-maintainability/specs/services-scheduler/spec.md b/openspec/changes/improve-maintainability/specs/services-scheduler/spec.md new file mode 100644 index 0000000..96a821f --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/services-scheduler/spec.md @@ -0,0 +1,16 @@ +## ADDED Requirements + +### Requirement: 自动调度器函数迁移至独立模块 +系统 SHALL 将调度器相关的状态变量和函数从 `main.py` 提取至 `services/scheduler.py`，包括：`_scheduler_next_times`、`_auto_log`（列表）、`_auto_log_append`、`_scheduler_loop`、`start_scheduler`、`stop_scheduler`、`get_auto_log`、`get_scheduler_status`、`_learn_running`、`_learn_scheduler_loop`、`start_learn_scheduler`、`stop_learn_scheduler`。 + +#### Scenario: 调度器启停正常工作 +- **WHEN** `start_scheduler(...)` 被调用并传入合法参数 +- **THEN** 调度器线程 SHALL 正常启动，`get_scheduler_status()` 返回运行中状态 + +#### Scenario: 日志追加线程安全 +- **WHEN** 多个自动化任务并发调用 `_auto_log_append(msg)` +- **THEN** 日志条目 SHALL 正确追加，不丢失和乱序 + +#### Scenario: engagement 通过回调写日志 +- **WHEN** `services/engagement.py` 中的函数需要写日志时 +- **THEN** SHALL 通过 `log_fn` 参数（由 `scheduler.py` 传入 `_auto_log_append`）写入，不直接导入 `scheduler.py` diff --git a/openspec/changes/improve-maintainability/specs/ui-tabs-split/spec.md b/openspec/changes/improve-maintainability/specs/ui-tabs-split/spec.md new file mode 100644 index 0000000..0b0e55f --- /dev/null +++ b/openspec/changes/improve-maintainability/specs/ui-tabs-split/spec.md @@ -0,0 +1,30 @@ +## ADDED Requirements + +### Requirement: 剩余 Gradio Tab 提取为独立 UI 模块 +系统 SHALL 将 `main.py` 中除 Tab 1（已完成）之外的 7 个 Gradio Tab 各自提取为 `ui/tab_*.py` 模块文件，每个文件暴露 `build_tab(...)` 函数： + +| 模块文件 | Tab 名称 | +|---|---| +| `ui/tab_hotspot.py` | 🔥 热点探测 | +| `ui/tab_engage.py` | 💬 互动运营 | +| `ui/tab_profile.py` | 👤 我的主页 | +| `ui/tab_auto.py` | 🤖 自动运营 | +| `ui/tab_queue.py` | 📅 内容排期 | +| `ui/tab_analytics.py` | 📊 数据分析 | +| `ui/tab_settings.py` | ⚙️ 系统设置 | + +#### Scenario: 每个 Tab 模块暴露 build_tab 函数 +- **WHEN** `main.py` 执行 `from ui.tab_hotspot import build_tab as build_tab_hotspot` +- **THEN** 调用 `build_tab_hotspot(fn_*, ...)` 后 SHALL 返回包含需跨 Tab 共享组件的 dict + +#### Scenario: build_tab 接收回调而非直接导入 services +- **WHEN** `build_tab(...)` 内部需要调用业务函数时 +- **THEN** 业务函数 SHALL 通过 `fn_*` 参数传入（与 `tab_create.py` 已有模式一致），不在 `ui/tab_*.py` 内直接 `import services.*` + +#### Scenario: 事件绑定在 build_tab 内完成 +- **WHEN** `build_tab(...)` 被调用 +- **THEN** 本 Tab 所有 Gradio 组件的 `.click()`、`.change()` 等事件绑定 SHALL 在函数内完成，`main.py` 不保留本 Tab 的事件绑定代码 + +#### Scenario: main.py 成为纯入口层 +- **WHEN** 所有 11 个 capability 均完成迁移后 +- **THEN** `main.py` 行数 SHALL 不超过 400 行，且不包含任何业务逻辑（仅含导入、单例初始化、UI 组装、`app.launch()`） diff --git a/openspec/changes/improve-maintainability/tasks.md b/openspec/changes/improve-maintainability/tasks.md new file mode 100644 index 0000000..b2f376a --- /dev/null +++ b/openspec/changes/improve-maintainability/tasks.md @@ -0,0 +1,96 @@ +## 1. 基础结构搭建 + +- [x] 1.1 创建 `services/` 目录及 `services/__init__.py` 空文件 +- [x] 1.2 确认 `ui/__init__.py` 已存在（上一轮已创建） + +## 2. 迁移 services/rate_limiter.py + +- [x] 2.1 创建 `services/rate_limiter.py`，迁移 `_auto_running`、`_op_history`、`_daily_stats`、`DAILY_LIMITS`、`_consecutive_errors`、`_error_cooldown_until` 等模块级变量 +- [x] 2.2 迁移函数：`_reset_daily_stats_if_needed`、`_check_daily_limit`、`_increment_stat`、`_record_error`、`_clear_error_streak`、`_is_in_cooldown`、`_is_in_operating_hours`、`_get_stats_summary` +- [x] 2.3 在 `main.py` 中删除对应变量和函数，添加 `from services.rate_limiter import ...` +- [x] 2.4 运行 `ast.parse()` 验证 `main.py` 和 `services/rate_limiter.py` 语法正确 + +## 3. 迁移 services/autostart.py + +- [x] 3.1 创建 `services/autostart.py`，迁移 `_APP_NAME`、`_STARTUP_REG_KEY` 及所有 autostart 函数（`_get_startup_script_path`、`_get_startup_bat_path`、`_create_startup_scripts`、`is_autostart_enabled`、`enable_autostart`、`disable_autostart`、`toggle_autostart`） +- [x] 3.2 在 `main.py` 中删除对应代码，添加 `from services.autostart import ...` +- [x] 3.3 运行 `ast.parse()` 验证语法正确 + +## 4. 迁移 services/persona.py + +- [x] 4.1 创建 `services/persona.py`，迁移常量：`DEFAULT_PERSONAS`、`RANDOM_PERSONA_LABEL`、`PERSONA_POOL_MAP`、`DEFAULT_TOPICS`、`DEFAULT_STYLES`、`DEFAULT_COMMENT_KEYWORDS` +- [x] 4.2 迁移函数：`_match_persona_pools`、`get_persona_topics`、`get_persona_keywords`、`on_persona_changed`、`_resolve_persona` +- [x] 4.3 在 `main.py` 中删除对应代码，添加 `from services.persona import ...` +- [x] 4.4 运行 `ast.parse()` 验证语法正确 + +## 5. 迁移 services/connection.py + +- [x] 5.1 创建 `services/connection.py`，迁移函数：`_get_llm_config`、`connect_llm`、`add_llm_provider`、`remove_llm_provider`、`on_provider_selected` +- [x] 5.2 迁移 SD 相关函数：`connect_sd`、`on_sd_model_change` +- [x] 5.3 迁移 MCP / 登录相关函数：`check_mcp_status`、`get_login_qrcode`、`logout_xhs`、`_auto_fetch_xsec_token`、`check_login`、`save_my_user_id`、`upload_face_image`、`load_saved_face_image` +- [x] 5.4 确保所有函数通过参数接收 `cfg`、`llm`、`sd`、`mcp` 等依赖，不在模块顶层初始化单例 +- [x] 5.5 在 `main.py` 中删除对应函数，添加 `from services.connection import ...` +- [x] 5.6 运行 `ast.parse()` 验证语法正确 + +## 6. 迁移 services/profile.py + +- [x] 6.1 创建 `services/profile.py`，迁移函数：`_parse_profile_json`、`_parse_count`、`fetch_my_profile` +- [x] 6.2 在 `main.py` 中删除对应函数，添加 `from services.profile import ...` +- [x] 6.3 运行 `ast.parse()` 验证语法正确 + +## 7. 迁移 services/hotspot.py + +- [x] 7.1 创建 `services/hotspot.py`，迁移缓存相关：`_cache_lock`、`_set_cache`、`_get_cache`、`_fetch_and_cache`、`_pick_from_cache` +- [x] 7.2 迁移热点函数：`search_hotspots`、`analyze_and_suggest`、`generate_from_hotspot`、`fetch_proactive_notes`、`on_proactive_note_selected` +- [x] 7.3 在 `main.py` 中删除对应代码，添加 `from services.hotspot import ...` +- [x] 7.4 运行 `ast.parse()` 验证语法正确 + +## 8. 迁移 services/content.py + +- [x] 8.1 创建 `services/content.py`，迁移函数：`generate_copy`、`generate_images`、`one_click_export`、`publish_to_xhs` +- [x] 8.2 确保 `publish_to_xhs` 的输入验证逻辑和 `finally` 临时文件清理逻辑完整保留 +- [x] 8.3 在 `main.py` 中删除对应函数，添加 `from services.content import ...` +- [x] 8.4 运行 `ast.parse()` 验证语法正确 + +## 9. 迁移 services/engagement.py + +- [x] 9.1 创建 `services/engagement.py`，迁移笔记/评论相关函数：`load_note_for_comment`、`ai_generate_comment`、`send_comment`、`fetch_my_notes`、`on_my_note_selected`、`fetch_my_note_comments`、`ai_reply_comment`、`send_reply` +- [x] 9.2 迁移自动化函数：`auto_comment_once`、`auto_like_once`、`auto_favorite_once`、`auto_reply_once` 及各 `_with_log` 包装 +- [x] 9.3 将 `_with_log` 函数改为接收 `log_fn` 回调参数，不直接引用外部 `_auto_log` +- [x] 9.4 在 `main.py` 中删除对应函数，添加 `from services.engagement import ...` +- [x] 9.5 运行 `ast.parse()` 验证语法正确 + +## 10. 迁移 services/scheduler.py + +- [x] 10.1 创建 `services/scheduler.py`，迁移状态变量和日志：`_auto_log`、`_scheduler_next_times`、`_auto_log_append` +- [x] 10.2 迁移调度器函数：`_scheduler_loop`、`start_scheduler`、`stop_scheduler`、`get_auto_log`、`get_scheduler_status` +- [x] 10.3 迁移学习调度器：`_learn_running`、`_learn_scheduler_loop`、`start_learn_scheduler`、`stop_learn_scheduler` +- [x] 10.4 确保 `_scheduler_loop` 调用 `engagement` 函数时传入 `log_fn=_auto_log_append` +- [x] 10.5 在 `main.py` 中删除对应代码，添加 `from services.scheduler import ...` +- [x] 10.6 运行 `ast.parse()` 验证语法正确 + +## 11. 迁移 services/queue_ops.py + +- [x] 11.1 创建 `services/queue_ops.py`，迁移所有 queue 操作函数：`generate_to_queue`、`_queue_publish_callback`、`queue_refresh_table`、`queue_refresh_calendar`、`queue_preview_item`、`queue_approve_item`、`queue_reject_item`、`queue_delete_item`、`queue_retry_item`、`queue_publish_now`、`queue_start_processor`、`queue_stop_processor`、`queue_get_status`、`queue_batch_approve`、`queue_generate_and_refresh` +- [x] 11.2 确保 `pub_queue`、`queue_publisher` 通过参数传入各函数，不在模块顶层初始化 +- [x] 11.3 在 `main.py` 中删除对应函数，添加 `from services.queue_ops import ...`；保留 `pub_queue.set_publish_callback(_queue_publish_callback)` 在 `main.py` 初始化段调用 +- [x] 11.4 运行 `ast.parse()` 验证语法正确 + +## 12. 拆分 UI Tab 模块 + +- [x] 12.1 创建 `ui/tab_hotspot.py`，提取 Tab 2（🔥 热点探测）的所有 Gradio 组件和事件绑定，暴露 `build_tab(fn_*, ...)` 函数 +- [x] 12.2 创建 `ui/tab_engage.py`，提取 Tab 3（💬 互动运营）的所有 Gradio 组件和事件绑定 +- [x] 12.3 创建 `ui/tab_profile.py`，提取 Tab 4（👤 我的主页）的所有 Gradio 组件和事件绑定 +- [x] 12.4 创建 `ui/tab_auto.py`，提取 Tab 5（🤖 自动运营）的所有 Gradio 组件和事件绑定 +- [x] 12.5 创建 `ui/tab_queue.py`，提取 Tab 6（📅 内容排期）的所有 Gradio 组件和事件绑定 +- [x] 12.6 创建 `ui/tab_analytics.py`，提取 Tab 7（📊 数据分析）的所有 Gradio 组件和事件绑定 +- [x] 12.7 创建 `ui/tab_settings.py`，提取 Tab 8（⚙️ 系统设置）的所有 Gradio 组件和事件绑定 +- [x] 12.8 在 `main.py` 中用相应的 `build_tab(...)` 调用替换各 Tab 代码块，完成后删除空白 Tab 块 +- [x] 12.9 运行 `ast.parse()` 验证所有新建 UI 模块语法正确 + +## 13. 入口层清理与验证 + +- [x] 13.1 验证 `main.py` 行数不超过 400 行 +- [x] 13.2 检查 `main.py` 不包含任何业务逻辑函数定义（除 lambda 内联外） +- [x] 13.3 运行应用 `python main.py`，确认启动无报错 +- [x] 13.4 在浏览器中切换所有 Tab，确认 UI 正常渲染、事件响应正常 diff --git a/services/__init__.py b/services/__init__.py new file mode 100644 index 0000000..af91733 --- /dev/null +++ b/services/__init__.py @@ -0,0 +1 @@ +# services 业务编排层 diff --git a/services/autostart.py b/services/autostart.py new file mode 100644 index 0000000..857ac6e --- /dev/null +++ b/services/autostart.py @@ -0,0 +1,121 @@ +""" +services/autostart.py +Windows 开机自启管理 +""" +import os +import platform +import logging + +logger = logging.getLogger("autobot") + +_APP_NAME = "XHS_AI_AutoBot" +_STARTUP_REG_KEY = r"Software\Microsoft\Windows\CurrentVersion\Run" + + +def _get_startup_script_path() -> str: + """获取启动脚本路径（.vbs 静默启动，不弹黑窗）""" + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "_autostart.vbs") + + +def _get_startup_bat_path() -> str: + """获取启动 bat 路径""" + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "_autostart.bat") + + +def _create_startup_scripts(): + """创建静默启动脚本（bat + vbs）""" + app_dir = os.path.dirname(os.path.abspath(os.path.join(__file__, ".."))) + # __file__ is services/autostart.py, so app_dir should be parent + app_dir = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) + venv_python = os.path.join(app_dir, ".venv", "Scripts", "pythonw.exe") + # 如果没有 pythonw，退回 python.exe + if not os.path.exists(venv_python): + venv_python = os.path.join(app_dir, ".venv", "Scripts", "python.exe") + main_script = os.path.join(app_dir, "main.py") + + # 创建 bat + bat_path = _get_startup_bat_path() + bat_content = f"""@echo off +cd /d "{app_dir}" +"{venv_python}" "{main_script}" +""" + with open(bat_path, "w", encoding="utf-8") as f: + f.write(bat_content) + + # 创建 vbs（静默运行 bat，不弹出命令行窗口） + vbs_path = _get_startup_script_path() + vbs_content = f"""Set WshShell = CreateObject("WScript.Shell") +WshShell.Run chr(34) & "{bat_path}" & chr(34), 0 +Set WshShell = Nothing +""" + with open(vbs_path, "w", encoding="utf-8") as f: + f.write(vbs_content) + + return vbs_path + + +def is_autostart_enabled() -> bool: + """检查是否已设置开机自启""" + if platform.system() != "Windows": + return False + try: + import winreg + key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_READ) + try: + val, _ = winreg.QueryValueEx(key, _APP_NAME) + winreg.CloseKey(key) + return bool(val) + except FileNotFoundError: + winreg.CloseKey(key) + return False + except Exception: + return False + + +def enable_autostart() -> str: + """启用 Windows 开机自启""" + if platform.system() != "Windows": + return "❌ 此功能仅支持 Windows 系统" + try: + import winreg + vbs_path = _create_startup_scripts() + key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_SET_VALUE) + # 用 wscript 运行 vbs 以实现静默启动 + winreg.SetValueEx(key, _APP_NAME, 0, winreg.REG_SZ, f'wscript.exe "{vbs_path}"') + winreg.CloseKey(key) + logger.info(f"开机自启已启用: {vbs_path}") + return "✅ 开机自启已启用\n下次开机时将自动后台运行本程序" + except Exception as e: + logger.error(f"设置开机自启失败: {e}") + return f"❌ 设置失败: {e}" + + +def disable_autostart() -> str: + """禁用 Windows 开机自启""" + if platform.system() != "Windows": + return "❌ 此功能仅支持 Windows 系统" + try: + import winreg + key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, _STARTUP_REG_KEY, 0, winreg.KEY_SET_VALUE) + try: + winreg.DeleteValue(key, _APP_NAME) + except FileNotFoundError: + pass + winreg.CloseKey(key) + # 清理启动脚本 + for f in [_get_startup_script_path(), _get_startup_bat_path()]: + if os.path.exists(f): + os.remove(f) + logger.info("开机自启已禁用") + return "✅ 开机自启已禁用" + except Exception as e: + logger.error(f"禁用开机自启失败: {e}") + return f"❌ 禁用失败: {e}" + + +def toggle_autostart(enabled: bool) -> str: + """切换开机自启状态（供 UI 调用）""" + if enabled: + return enable_autostart() + else: + return disable_autostart() diff --git a/services/connection.py b/services/connection.py new file mode 100644 index 0000000..cc239ba --- /dev/null +++ b/services/connection.py @@ -0,0 +1,254 @@ +""" +services/connection.py +LLM 提供商管理、SD 连接、MCP 连接、XHS 登录等服务函数 +""" +import os +import re +import logging + +import gradio as gr + +from config_manager import ConfigManager +from llm_service import LLMService +from sd_service import SDService, get_model_profile_info +from mcp_client import get_mcp_client + +logger = logging.getLogger("autobot") +cfg = ConfigManager() + +def _get_llm_config() -> tuple[str, str, str]: + """获取当前激活 LLM 的 (api_key, base_url, model)""" + p = cfg.get_active_llm() + if p: + return p["api_key"], p["base_url"], cfg.get("model", "") + return "", "", "" + + +def connect_llm(provider_name): + """连接选中的 LLM 提供商并获取模型列表""" + if not provider_name: + return gr.update(choices=[], value=None), "⚠️ 请先选择或添加 LLM 提供商" + cfg.set_active_llm(provider_name) + p = cfg.get_active_llm() + if not p: + return gr.update(choices=[], value=None), "❌ 未找到该提供商配置" + try: + svc = LLMService(p["api_key"], p["base_url"]) + models = svc.get_models() + if models: + return ( + gr.update(choices=models, value=models[0]), + f"✅ 已连接「{provider_name}」，加载 {len(models)} 个模型", + ) + else: + # API 无法获取模型列表，保留手动输入 + current_model = cfg.get("model", "") + return ( + gr.update(choices=[current_model] if current_model else [], value=current_model or None), + f"⚠️ 已连接「{provider_name}」，但未获取到模型列表，请手动输入模型名", + ) + except Exception as e: + logger.error("LLM 连接失败: %s", e) + current_model = cfg.get("model", "") + return ( + gr.update(choices=[current_model] if current_model else [], value=current_model or None), + f"❌ 连接「{provider_name}」失败: {e}", + ) + + +def add_llm_provider(name, api_key, base_url): + """添加新的 LLM 提供商""" + msg = cfg.add_llm_provider(name, api_key, base_url) + names = cfg.get_llm_provider_names() + active = cfg.get("active_llm", "") + return ( + gr.update(choices=names, value=active), + msg, + ) + + +def remove_llm_provider(provider_name): + """删除 LLM 提供商""" + if not provider_name: + return gr.update(choices=cfg.get_llm_provider_names(), value=cfg.get("active_llm", "")), "⚠️ 请先选择要删除的提供商" + msg = cfg.remove_llm_provider(provider_name) + names = cfg.get_llm_provider_names() + active = cfg.get("active_llm", "") + return ( + gr.update(choices=names, value=active), + msg, + ) + + +def on_provider_selected(provider_name): + """切换 LLM 提供商时更新显示信息""" + if not provider_name: + return "未选择提供商" + for p in cfg.get_llm_providers(): + if p["name"] == provider_name: + cfg.set_active_llm(provider_name) + masked_key = p["api_key"][:8] + "***" if len(p["api_key"]) > 8 else "***" + return f"**{provider_name}** \nAPI Key: `{masked_key}` \nBase URL: `{p['base_url']}`" + return "未找到该提供商" + + +# ================================================== +# Tab 1: 内容创作 +# ================================================== + + +def connect_sd(sd_url): + """连接 SD 并获取模型列表""" + try: + svc = SDService(sd_url) + ok, msg = svc.check_connection() + if ok: + models = svc.get_models() + cfg.set("sd_url", sd_url) + first = models[0] if models else None + info = get_model_profile_info(first) if first else "未检测到模型" + return gr.update(choices=models, value=first), f"✅ {msg}", info + return gr.update(choices=[]), f"❌ {msg}", "" + except Exception as e: + logger.error("SD 连接失败: %s", e) + return gr.update(choices=[]), f"❌ SD 连接失败: {e}", "" + + +def on_sd_model_change(model_name): + """SD 模型切换时显示模型档案信息""" + if not model_name: + return "未选择模型" + return get_model_profile_info(model_name) + + +def check_mcp_status(mcp_url): + """检查 MCP 连接状态""" + try: + client = get_mcp_client(mcp_url) + ok, msg = client.check_connection() + if ok: + cfg.set("mcp_url", mcp_url) + return f"✅ MCP 服务正常 - {msg}" + return f"❌ {msg}" + except Exception as e: + return f"❌ MCP 连接失败: {e}" + + +# ================================================== +# 小红书账号登录 +# ================================================== + + +def get_login_qrcode(mcp_url): + """获取小红书登录二维码""" + try: + client = get_mcp_client(mcp_url) + result = client.get_login_qrcode() + if "error" in result: + return None, f"❌ 获取二维码失败: {result['error']}" + qr_image = result.get("qr_image") + msg = result.get("text", "") + if qr_image: + return qr_image, f"✅ 二维码已生成，请用小红书 App 扫码\n{msg}" + return None, f"⚠️ 未获取到二维码图片，MCP 返回:\n{msg}" + except Exception as e: + logger.error("获取登录二维码失败: %s", e) + return None, f"❌ 获取二维码失败: {e}" + + +def logout_xhs(mcp_url): + """退出登录：清除 cookies 并重置本地 token""" + try: + client = get_mcp_client(mcp_url) + result = client.delete_cookies() + if "error" in result: + return f"❌ 退出失败: {result['error']}" + cfg.set("xsec_token", "") + client._reset() + return "✅ 已退出登录，可以重新扫码登录" + except Exception as e: + logger.error("退出登录失败: %s", e) + return f"❌ 退出失败: {e}" + + +def _auto_fetch_xsec_token(mcp_url) -> str: + """从推荐列表自动获取一个有效的 xsec_token""" + try: + client = get_mcp_client(mcp_url) + entries = client.list_feeds_parsed() + for e in entries: + token = e.get("xsec_token", "") + if token: + return token + except Exception as e: + logger.warning("自动获取 xsec_token 失败: %s", e) + return "" + + +def check_login(mcp_url): + """检查登录状态，登录成功后自动获取 xsec_token 并保存""" + try: + client = get_mcp_client(mcp_url) + result = client.check_login_status() + if "error" in result: + return f"❌ {result['error']}", gr.update(), gr.update() + text = result.get("text", "") + if "未登录" in text: + return f"🔴 {text}", gr.update(), gr.update() + + # 登录成功 → 自动获取 xsec_token + token = _auto_fetch_xsec_token(mcp_url) + if token: + cfg.set("xsec_token", token) + logger.info("自动获取 xsec_token 成功") + return ( + f"🟢 {text}\n\n✅ xsec_token 已自动获取并保存", + gr.update(value=cfg.get("my_user_id", "")), + gr.update(value=token), + ) + return f"🟢 {text}\n\n⚠️ 自动获取 xsec_token 失败，请手动刷新", gr.update(), gr.update() + except Exception as e: + return f"❌ 检查登录状态失败: {e}", gr.update(), gr.update() + + +def save_my_user_id(user_id_input): + """保存用户 ID (验证 24 位十六进制格式)""" + uid = (user_id_input or "").strip() + if not uid: + cfg.set("my_user_id", "") + return "⚠️ 已清除用户 ID" + if not re.match(r'^[0-9a-fA-F]{24}$', uid): + return ( + "❌ 格式错误！用户 ID 应为 24 位十六进制字符串\n" + f"你输入的: `{uid}` ({len(uid)} 位)\n\n" + "💡 如果你输入的是小红书号 (纯数字如 18688457507)，那不是 userId。" + ) + cfg.set("my_user_id", uid) + return f"✅ 用户 ID 已保存: `{uid}`" + + +# ================= 头像/换脸管理 ================= + +def upload_face_image(img): + """上传并保存头像图片""" + if img is None: + return None, "❌ 请上传头像图片" + try: + if isinstance(img, str) and os.path.isfile(img): + img = Image.open(img).convert("RGB") + elif not isinstance(img, Image.Image): + return None, "❌ 无法识别图片格式" + path = SDService.save_face_image(img) + return img, f"✅ 头像已保存至 {os.path.basename(path)}" + except Exception as e: + return None, f"❌ 保存失败: {e}" + + +def load_saved_face_image(): + """加载已保存的头像""" + img = SDService.load_face_image() + if img: + return img, "✅ 已加载保存的头像" + return None, "ℹ️ 尚未设置头像" + + diff --git a/services/content.py b/services/content.py new file mode 100644 index 0000000..d688ba7 --- /dev/null +++ b/services/content.py @@ -0,0 +1,208 @@ +""" +services/content.py +文案生成、图片生成、一键导出、发布到小红书 +""" +import os +import re +import time +import platform +import subprocess +import logging + +from PIL import Image + +from config_manager import ConfigManager, OUTPUT_DIR +from llm_service import LLMService +from sd_service import SDService, get_sd_preset +from mcp_client import get_mcp_client +from services.connection import _get_llm_config +from services.persona import _resolve_persona + +logger = logging.getLogger("autobot") +cfg = ConfigManager() + +def generate_copy(model, topic, style, sd_model_name, persona_text): + """生成文案（自动适配 SD 模型的 prompt 风格，支持人设）""" + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "", "", "", "", "❌ 请先配置并连接 LLM 提供商" + try: + svc = LLMService(api_key, base_url, model) + persona = _resolve_persona(persona_text) if persona_text else None + data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) + cfg.set("model", model) + tags = data.get("tags", []) + return ( + data.get("title", ""), + data.get("content", ""), + data.get("sd_prompt", ""), + ", ".join(tags) if tags else "", + "✅ 文案生成完毕", + ) + except Exception as e: + logger.error("文案生成失败: %s", e) + return "", "", "", "", f"❌ 生成失败: {e}" + + +def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img, quality_mode, persona_text=None): + """生成图片（可选 ReActor 换脸，支持质量模式预设，支持人设视觉优化）""" + if not model: + return None, [], "❌ 未选择 SD 模型" + try: + svc = SDService(sd_url) + # 判断是否启用换脸 + face_image = None + if face_swap_on: + # Gradio 可能传 PIL.Image / numpy.ndarray / 文件路径 / None + if face_img is not None: + if isinstance(face_img, Image.Image): + face_image = face_img + elif isinstance(face_img, str) and os.path.isfile(face_img): + face_image = Image.open(face_img).convert("RGB") + else: + # numpy array 等其他格式 + try: + import numpy as np + if isinstance(face_img, np.ndarray): + face_image = Image.fromarray(face_img).convert("RGB") + logger.info("头像从 numpy array 转换为 PIL Image") + except Exception as e: + logger.warning("头像格式转换失败 (%s): %s", type(face_img).__name__, e) + # 如果 UI 没传有效头像，从本地文件加载 + if face_image is None: + face_image = SDService.load_face_image() + if face_image is not None: + logger.info("换脸头像已就绪: %dx%d", face_image.width, face_image.height) + else: + logger.warning("换脸已启用但未找到有效头像") + + persona = _resolve_persona(persona_text) if persona_text else None + images = svc.txt2img( + prompt=prompt, + negative_prompt=neg_prompt, + model=model, + steps=int(steps), + cfg_scale=float(cfg_scale), + face_image=face_image, + quality_mode=quality_mode, + persona=persona, + ) + preset = get_sd_preset(quality_mode) + swap_hint = " (已换脸)" if face_image else "" + return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint} [{quality_mode}]" + except Exception as e: + logger.error("图片生成失败: %s", e) + return None, [], f"❌ 绘图失败: {e}" + + +def one_click_export(title, content, images): + """导出文案和图片到本地""" + if not title: + return "❌ 无法导出：没有标题" + + safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] + folder_name = f"{int(time.time())}_{safe_title}" + folder_path = os.path.join(OUTPUT_DIR, folder_name) + os.makedirs(folder_path, exist_ok=True) + + with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f: + f.write(f"{title}\n\n{content}") + + saved_paths = [] + if images: + for idx, img in enumerate(images): + path = os.path.join(folder_path, f"图{idx+1}.jpg") + if isinstance(img, Image.Image): + if img.mode != "RGB": + img = img.convert("RGB") + img.save(path, format="JPEG", quality=95) + saved_paths.append(os.path.abspath(path)) + + # 尝试打开文件夹 + try: + abs_path = os.path.abspath(folder_path) + if platform.system() == "Windows": + os.startfile(abs_path) + elif platform.system() == "Darwin": + subprocess.call(["open", abs_path]) + else: + subprocess.call(["xdg-open", abs_path]) + except Exception: + pass + + return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)" + + +def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time): + """通过 MCP 发布到小红书（含输入校验和临时文件自动清理）""" + # === 发布前校验 === + if not title: + return "❌ 缺少标题" + if len(title) > 20: + return f"❌ 标题超长：当前 {len(title)} 字，小红书限制 ≤20 字，请精简后再发布" + + client = get_mcp_client(mcp_url) + ai_temp_files: list = [] # 追踪本次写入的临时文件，用于 finally 清理 + + try: + # 收集图片路径 + image_paths = [] + + # 先保存 AI 生成的图片到临时目录 + if images: + temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish") + os.makedirs(temp_dir, exist_ok=True) + for idx, img in enumerate(images): + if isinstance(img, Image.Image): + path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.jpg")) + if img.mode != "RGB": + img = img.convert("RGB") + img.save(path, format="JPEG", quality=95) + image_paths.append(path) + ai_temp_files.append(path) # 登记临时文件 + + # 添加本地上传的图片 + if local_images: + for img_file in local_images: + img_path = img_file.name if hasattr(img_file, 'name') else str(img_file) + if os.path.exists(img_path): + image_paths.append(os.path.abspath(img_path)) + + # === 图片校验 === + if not image_paths: + return "❌ 至少需要 1 张图片才能发布" + if len(image_paths) > 18: + return f"❌ 图片数量超限：当前 {len(image_paths)} 张，小红书限制 ≤18 张，请减少图片" + for p in image_paths: + if not os.path.exists(p): + return f"❌ 图片文件不存在：{p}" + + # 解析标签 + tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None + + # 定时发布 + schedule = schedule_time if schedule_time and schedule_time.strip() else None + + result = client.publish_content( + title=title, + content=content, + images=image_paths, + tags=tags, + schedule_at=schedule, + ) + if "error" in result: + return f"❌ 发布失败: {result['error']}" + return f"✅ 发布成功！\n{result.get('text', '')}" + except Exception as e: + logger.error("发布失败: %s", e) + return f"❌ 发布异常: {e}" + finally: + # 清理本次写入的 AI 临时图片（无论成功/失败） + for tmp_path in ai_temp_files: + try: + if os.path.exists(tmp_path): + os.remove(tmp_path) + except OSError as cleanup_err: + logger.warning("临时文件清理失败 %s: %s", tmp_path, cleanup_err) + + diff --git a/services/engagement.py b/services/engagement.py new file mode 100644 index 0000000..6899341 --- /dev/null +++ b/services/engagement.py @@ -0,0 +1,197 @@ +""" +services/engagement.py +评论管家：手动评论、回复、笔记加载等互动功能 +""" +import logging + +import gradio as gr + +from mcp_client import get_mcp_client +from llm_service import LLMService +from services.connection import _get_llm_config +from services.hotspot import _pick_from_cache, _set_cache, _get_cache + +logger = logging.getLogger("autobot") + +def load_note_for_comment(feed_id, xsec_token, mcp_url): + """加载目标笔记详情 (标题+正文+已有评论), 用于 AI 分析""" + if not feed_id or not xsec_token: + return "❌ 请先选择笔记", "", "", "" + try: + client = get_mcp_client(mcp_url) + result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) + if "error" in result: + return f"❌ {result['error']}", "", "", "" + full_text = result.get("text", "") + # 尝试分离正文和评论 + if "评论" in full_text: + parts = full_text.split("评论", 1) + content_part = parts[0].strip() + comments_part = "评论" + parts[1] if len(parts) > 1 else "" + else: + content_part = full_text[:500] + comments_part = "" + return "✅ 笔记内容已加载", content_part[:800], comments_part[:1500], full_text + except Exception as e: + return f"❌ {e}", "", "", "" + + +def ai_generate_comment(model, persona, + post_title, post_content, existing_comments): + """AI 生成主动评论""" + persona = _resolve_persona(persona) + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置" + if not model: + return "⚠️ 请先连接 LLM", "❌ 未选模型" + if not post_title and not post_content: + return "⚠️ 请先加载笔记内容", "❌ 无笔记内容" + try: + svc = LLMService(api_key, base_url, model) + comment = svc.generate_proactive_comment( + persona, post_title, post_content[:600], existing_comments[:800] + ) + return comment, "✅ 评论已生成" + except Exception as e: + logger.error(f"AI 评论生成失败: {e}") + return f"生成失败: {e}", f"❌ {e}" + + +def send_comment(feed_id, xsec_token, comment_content, mcp_url): + """发送评论到别人的笔记""" + if not all([feed_id, xsec_token, comment_content]): + return "❌ 缺少必要参数 (笔记ID / token / 评论内容)" + try: + client = get_mcp_client(mcp_url) + result = client.post_comment(feed_id, xsec_token, comment_content) + if "error" in result: + return f"❌ {result['error']}" + return "✅ 评论已发送！" + except Exception as e: + return f"❌ {e}" + + +# ---- 模块 B: 回复我的笔记评论 ---- + +def fetch_my_notes(mcp_url): + """通过已保存的 userId 获取我的笔记列表""" + my_uid = cfg.get("my_user_id", "") + xsec = cfg.get("xsec_token", "") + if not my_uid: + return ( + gr.update(choices=[], value=None), + "❌ 未配置用户 ID，请先到「账号登录」页填写并保存", + ) + if not xsec: + return ( + gr.update(choices=[], value=None), + "❌ 未获取 xsec_token，请先登录", + ) + try: + client = get_mcp_client(mcp_url) + result = client.get_user_profile(my_uid, xsec) + if "error" in result: + return gr.update(choices=[], value=None), f"❌ {result['error']}" + + # 从 raw 中解析 feeds + raw = result.get("raw", {}) + text = result.get("text", "") + data = None + if raw and isinstance(raw, dict): + for item in raw.get("content", []): + if item.get("type") == "text": + try: + data = json.loads(item["text"]) + except (json.JSONDecodeError, KeyError): + pass + if not data: + try: + data = json.loads(text) + except (json.JSONDecodeError, TypeError): + pass + + feeds = (data or {}).get("feeds") or [] + if not feeds: + return ( + gr.update(choices=[], value=None), + "⚠️ 未找到你的笔记，可能账号还没有发布内容", + ) + + entries = [] + for f in feeds: + nc = f.get("noteCard") or {} + user = nc.get("user") or {} + interact = nc.get("interactInfo") or {} + entries.append({ + "feed_id": f.get("id", ""), + "xsec_token": f.get("xsecToken", ""), + "title": nc.get("displayTitle", "未知标题"), + "author": user.get("nickname", user.get("nickName", "")), + "user_id": user.get("userId", ""), + "likes": interact.get("likedCount", "0"), + "type": nc.get("type", ""), + }) + + _set_cache("my_notes", entries) + choices = [ + f"[{i+1}] {e['title'][:20]} | {e['type']} | ❤{e['likes']}" + for i, e in enumerate(entries) + ] + return ( + gr.update(choices=choices, value=choices[0] if choices else None), + f"✅ 找到 {len(entries)} 篇笔记", + ) + except Exception as e: + return gr.update(choices=[], value=None), f"❌ {e}" + + +def on_my_note_selected(selected): + return _pick_from_cache(selected, "my_notes") + + +def fetch_my_note_comments(feed_id, xsec_token, mcp_url): + """获取我的笔记的评论列表""" + if not feed_id or not xsec_token: + return "❌ 请先选择笔记", "" + try: + client = get_mcp_client(mcp_url) + result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) + if "error" in result: + return f"❌ {result['error']}", "" + return "✅ 评论加载完成", result.get("text", "暂无评论") + except Exception as e: + return f"❌ {e}", "" + + +def ai_reply_comment(model, persona, post_title, comment_text): + """AI 生成评论回复""" + persona = _resolve_persona(persona) + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置" + if not model: + return "⚠️ 请先连接 LLM 并选择模型", "❌ 未选择模型" + if not comment_text: + return "请输入需要回复的评论内容", "⚠️ 请输入评论" + try: + svc = LLMService(api_key, base_url, model) + reply = svc.generate_reply(persona, post_title, comment_text) + return reply, "✅ 回复已生成" + except Exception as e: + logger.error(f"AI 回复生成失败: {e}") + return f"生成失败: {e}", f"❌ {e}" + + +def send_reply(feed_id, xsec_token, reply_content, mcp_url): + """发送评论回复""" + if not all([feed_id, xsec_token, reply_content]): + return "❌ 缺少必要参数" + try: + client = get_mcp_client(mcp_url) + result = client.post_comment(feed_id, xsec_token, reply_content) + if "error" in result: + return f"❌ 回复失败: {result['error']}" + return "✅ 回复已发送" + except Exception as e: + return f"❌ 发送失败: {e}" diff --git a/services/hotspot.py b/services/hotspot.py new file mode 100644 index 0000000..a4a6fde --- /dev/null +++ b/services/hotspot.py @@ -0,0 +1,190 @@ +""" +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 threading +import logging + +import gradio as gr + +from llm_service import LLMService +from mcp_client import get_mcp_client +from services.connection import _get_llm_config +from services.persona import _resolve_persona + +logger = logging.getLogger("autobot") + +# ================================================== +# Tab 2: 热点探测 +# ================================================== + + +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""" + if not search_result: + return "❌ 请先搜索", "", "" + 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) + analysis = svc.analyze_hotspots(search_result) + + 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 = "\n".join( + f"**{s['topic']}** - {s['reason']}" + for s in analysis.get("suggestions", []) + ) + 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}" + ) + return "✅ 分析完成", summary, keyword + except Exception as e: + logger.error("热点分析失败: %s", e) + return f"❌ 分析失败: {e}", "", "" + + +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）""" + 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 + data = svc.generate_copy_with_reference( + topic=topic_from_hotspot, + style=style, + reference_notes=search_result[:2000], + 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}" + + +# ================================================== +# Tab 3: 评论管家 +# ================================================== + +# ---- 共用: 笔记列表缓存（线程安全）---- + +# 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] = [] +# 缓存互斥锁，防止并发回调产生竞态 +_cache_lock = threading.RLock() + + +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") + + diff --git a/services/persona.py b/services/persona.py new file mode 100644 index 0000000..a47908d --- /dev/null +++ b/services/persona.py @@ -0,0 +1,519 @@ +""" +services/persona.py +人设管理：常量、关键词池、主题池、人设解析 +""" +import random +import logging +from config_manager import ConfigManager + +logger = logging.getLogger("autobot") +cfg = ConfigManager() + +# ================= 人设池 ================= +DEFAULT_PERSONAS = [ + "赛博AI虚拟博主，住在2077年的数码女孩，用AI生成高颜值写真和全球场景打卡，与粉丝超高频互动", + "性感福利主播，身材火辣衣着大胆，专注分享穿衣显身材和私房写真风穿搭", + "身材管理健身美女，热爱分享好身材秘诀和穿搭显身材技巧", + "温柔知性的时尚博主，喜欢分享日常穿搭和生活美学", + "元气满满的大学生，热爱探店和平价好物分享", + "30岁都市白领丽人，专注通勤穿搭和职场干货", + "精致妈妈，分享育儿经验和家居收纳技巧", + "文艺青年摄影师，喜欢记录旅行和城市角落", + "健身达人营养师，专注减脂餐和运动分享", + "资深美妆博主，擅长化妆教程和护肤测评", + "独居女孩，分享租房改造和独居生活仪式感", + "甜品烘焙爱好者，热衷分享自制甜点和下午茶", + "数码科技女生，专注好用App和电子产品测评", + "小镇姑娘在大城市打拼，分享省钱攻略和成长日记", + "中医养生爱好者，分享节气养生和食疗方子", + "二次元coser，喜欢分享cos日常和动漫周边", + "北漂程序媛，分享高效工作法和解压生活", + "复古穿搭博主，热爱vintage风和中古饰品", + "考研上岸学姐，分享学习方法和备考经验", + "新手养猫人，记录和毛孩子的日常生活", + "咖啡重度爱好者，探遍城市独立咖啡馆", + "极简主义生活家，倡导断舍离和高质量生活", + "汉服爱好者，分享传统文化和国风穿搭", + "插画师小姐姐，分享手绘过程和创作灵感", + "海归女孩，分享中西文化差异和海外生活见闻", + "瑜伽老师，分享身心灵修行和自律生活", + "美甲设计师，分享流行甲型和美甲教程", + "家居软装设计师，分享小户型改造和氛围感布置", +] + +RANDOM_PERSONA_LABEL = "🎲 随机人设（每次自动切换）" + +# ================= 人设 → 分类关键词/主题池映射 ================= +# 每个人设对应一组相符的评论关键词和主题，切换人设时自动同步 + +PERSONA_POOL_MAP = { + # ---- 性感福利类 ---- + "性感福利主播": { + "topics": [ + "辣妹穿搭", "内衣测评", "比基尼穿搭", "私房写真风穿搭", "吊带裙穿搭", + "低胸穿搭", "紧身连衣裙", "蕾丝穿搭", "泳衣测评", "居家睡衣穿搭", + "露背装穿搭", "热裤穿搭", "性感御姐穿搭", "渔网袜穿搭", "包臀裙穿搭", + "锁骨链饰品", "身材展示", "好身材日常", "氛围感私房照", "海边度假穿搭", + ], + "keywords": [ + "辣妹", "性感穿搭", "内衣", "比基尼", "吊带", "低胸", + "紧身", "蕾丝", "泳衣", "睡衣", "露背", "热裤", + "御姐", "好身材", "包臀裙", "身材展示", "私房", "氛围感", + ], + }, + # ---- 身材管理类 ---- + "身材管理健身美女": { + "topics": [ + "好身材穿搭", "显身材穿搭", "马甲线养成", "翘臀训练", "直角肩养成", + "天鹅颈锻炼", "小蛮腰秘诀", "腿型矫正", "体态管理", "维密身材", + "居家塑形", "健身穿搭", "运动内衣测评", "蜜桃臀训练", "锁骨养成", + "紧身穿搭", "比基尼身材", "纤腰丰臀", "身材对比照", "自律打卡", + ], + "keywords": [ + "身材", "好身材", "马甲线", "翘臀", "直角肩", "天鹅颈", + "小蛮腰", "健身女孩", "塑形", "体态", "蜜桃臀", "腰臀比", + "紧身", "显身材", "维密", "锁骨", "A4腰", "漫画腿", + ], + }, + # ---- 时尚穿搭类 ---- + "温柔知性的时尚博主": { + "topics": [ + "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "法式穿搭", + "极简穿搭", "氛围感穿搭", "一衣多穿", "秋冬叠穿", "夏日清凉穿搭", + "生活美学", "衣橱整理", "配色技巧", "基础款穿搭", "轻熟风穿搭", + ], + "keywords": [ + "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "法式穿搭", + "极简风", "氛围感", "轻熟风", "高级感穿搭", "配色", + ], + }, + "元气满满的大学生": { + "topics": [ + "学生党穿搭", "宿舍美食", "平价好物", "校园生活", "学生党护肤", + "期末复习", "社团活动", "寝室改造", "奶茶测评", "拍照打卡地", + "一人食食谱", "考研经验", "实习经验", "省钱攻略", + ], + "keywords": [ + "学生党", "平价好物", "宿舍", "校园", "奶茶", "探店", + "拍照", "省钱", "大学生活", "期末", "开学", "室友", + ], + }, + "30岁都市白领丽人": { + "topics": [ + "通勤穿搭", "职场干货", "面试技巧", "简历优化", "时间管理", + "理财入门", "轻熟风穿搭", "职场妆容", "咖啡探店", "高效工作法", + "副业分享", "自律生活", "下班后充电", "职场人际关系", + ], + "keywords": [ + "通勤穿搭", "职场", "面试", "理财", "自律", "高效", + "咖啡", "轻熟", "白领", "上班族", "时间管理", "副业", + ], + }, + "精致妈妈": { + "topics": [ + "育儿经验", "家居收纳", "辅食制作", "亲子游", "母婴好物", + "宝宝穿搭", "早教启蒙", "产后恢复", "家常菜做法", "小户型收纳", + "家庭教育", "孕期护理", "宝宝辅食", "妈妈穿搭", + ], + "keywords": [ + "育儿", "收纳", "辅食", "母婴", "亲子", "早教", + "宝宝", "家居", "待产", "产后", "妈妈", "家常菜", + ], + }, + "文艺青年摄影师": { + "topics": [ + "旅行攻略", "小众旅行地", "拍照打卡地", "城市citywalk", "古镇旅行", + "手机摄影技巧", "胶片摄影", "人像摄影", "风光摄影", "街拍", + "咖啡探店", "文艺书店", "展览打卡", "独立书店", + ], + "keywords": [ + "旅行", "摄影", "打卡", "citywalk", "胶片", "拍照", + "小众", "展览", "文艺", "街拍", "风光", "人像", + ], + }, + "健身达人营养师": { + "topics": [ + "减脂餐分享", "居家健身", "帕梅拉跟练", "跑步入门", "体态矫正", + "增肌餐", "蛋白质补充", "运动穿搭", "健身房攻略", "马甲线养成", + "热量计算", "健康早餐", "运动恢复", "减脂食谱", + ], + "keywords": [ + "减脂", "健身", "减脂餐", "蛋白质", "体态", "马甲线", + "帕梅拉", "跑步", "热量", "增肌", "运动", "健康餐", + ], + }, + "资深美妆博主": { + "topics": [ + "妆容教程", "眼妆教程", "唇妆合集", "底妆测评", "护肤心得", + "防晒测评", "学生党平价护肤", "敏感肌护肤", "美白攻略", + "成分党护肤", "换季护肤", "早C晚A护肤", "抗老护肤", + ], + "keywords": [ + "护肤", "化妆教程", "眼影", "口红", "底妆", "防晒", + "美白", "敏感肌", "成分", "平价", "测评", "粉底", + ], + }, + "独居女孩": { + "topics": [ + "独居生活", "租房改造", "氛围感房间", "一人食食谱", "好物分享", + "香薰推荐", "居家好物", "断舍离", "仪式感生活", "独居安全", + "解压方式", "emo急救指南", "桌面布置", "小户型装修", + ], + "keywords": [ + "独居", "租房改造", "好物", "氛围感", "一人食", "仪式感", + "解压", "居家", "香薰", "ins风", "房间", "断舍离", + ], + }, + "甜品烘焙爱好者": { + "topics": [ + "烘焙教程", "0失败甜品", "下午茶推荐", "蛋糕教程", "面包制作", + "饼干烘焙", "奶油裱花", "巧克力甜品", "网红甜品", "便当制作", + "早餐食谱", "咖啡配甜品", "节日甜品", "低卡甜品", + ], + "keywords": [ + "烘焙", "甜品", "蛋糕", "面包", "下午茶", "曲奇", + "裱花", "抹茶", "巧克力", "奶油", "食谱", "烤箱", + ], + }, + "数码科技女生": { + "topics": [ + "iPad生产力", "手机摄影技巧", "好用App推荐", "电子产品测评", + "桌面布置", "数码好物", "耳机测评", "平板学习", "生产力工具", + "手机壳推荐", "充电设备", "智能家居", + ], + "keywords": [ + "iPad", "App推荐", "数码", "测评", "手机", "耳机", + "桌面", "科技", "电子产品", "平板", "生产力", "充电", + ], + }, + "小镇姑娘在大城市打拼": { + "topics": [ + "省钱攻略", "成长日记", "平价好物", "租房改造", "副业分享", + "理财入门", "独居生活", "面试技巧", "通勤穿搭", "自律生活", + "城市生存指南", "女性成长", "攒钱计划", + ], + "keywords": [ + "省钱", "平价", "租房", "副业", "理财", "成长", + "自律", "打工", "攒钱", "面试", "独居", "北漂", + ], + }, + "中医养生爱好者": { + "topics": [ + "节气养生", "食疗方子", "泡脚养生", "体质调理", "艾灸", + "中药茶饮", "作息调整", "经络按摩", "养胃食谱", "祛湿方法", + "睡眠改善", "女性调理", "养生汤", "二十四节气", + ], + "keywords": [ + "养生", "食疗", "泡脚", "中医", "艾灸", "祛湿", + "节气", "体质", "养胃", "经络", "调理", "药膳", + ], + }, + "二次元coser": { + "topics": [ + "cos日常", "动漫周边", "漫展攻略", "cos化妆教程", "假发造型", + "lolita穿搭", "二次元好物", "手办收藏", "动漫推荐", "cos道具制作", + "jk穿搭", "谷子收藏", "二次元摄影", + ], + "keywords": [ + "cos", "动漫", "二次元", "漫展", "lolita", "手办", + "jk", "假发", "谷子", "周边", "番剧", "coser", + ], + }, + "北漂程序媛": { + "topics": [ + "高效工作法", "程序员日常", "好用App推荐", "副业分享", "自律生活", + "时间管理", "iPad生产力", "解压方式", "通勤穿搭", "理财入门", + "独居生活", "技术学习", "面试经验", "桌面布置", + ], + "keywords": [ + "程序员", "高效", "App推荐", "自律", "副业", "iPad", + "技术", "工作", "北漂", "面试", "代码", "桌面", + ], + }, + "复古穿搭博主": { + "topics": [ + "vintage风穿搭", "中古饰品", "复古妆容", "二手vintage", "古着穿搭", + "法式穿搭", "复古包包", "跳蚤市场", "旧物改造", "港风穿搭", + "文艺穿搭", "配饰搭配", "vintage探店", + ], + "keywords": [ + "vintage", "复古", "中古", "古着", "港风", "法式", + "饰品", "二手", "旧物", "跳蚤市场", "复古穿搭", "文艺", + ], + }, + "考研上岸学姐": { + "topics": [ + "考研经验", "英语学习方法", "书单推荐", "时间管理", "自律生活", + "考研择校", "政治复习", "数学刷题", "考研英语", "复试经验", + "专业课复习", "考研心态", "背诵技巧", "刷题方法", + ], + "keywords": [ + "考研", "英语学习", "书单", "自律", "学习方法", "上岸", + "刷题", "备考", "复习", "笔记", "时间管理", "择校", + ], + }, + "新手养猫人": { + "topics": [ + "养猫日常", "猫粮测评", "猫咪用品", "新手养宠指南", "猫咪健康", + "猫咪行为", "驱虫攻略", "猫砂测评", "猫玩具推荐", "猫咪拍照", + "多猫家庭", "领养代替购买", "猫咪绝育", + ], + "keywords": [ + "养猫", "猫粮", "猫咪", "宠物", "猫砂", "驱虫", + "铲屎官", "喵喵", "猫玩具", "猫零食", "新手养猫", "猫咪日常", + ], + }, + "咖啡重度爱好者": { + "topics": [ + "咖啡探店", "手冲咖啡", "咖啡豆推荐", "咖啡器具", "拿铁艺术", + "家庭咖啡", "咖啡配甜品", "独立咖啡馆", "冷萃咖啡", "咖啡知识", + "意式咖啡", "探店打卡", "咖啡拉花", + ], + "keywords": [ + "咖啡", "手冲", "拿铁", "探店", "咖啡豆", "美式", + "咖啡馆", "意式", "冷萃", "拉花", "咖啡器具", "独立咖啡馆", + ], + }, + "极简主义生活家": { + "topics": [ + "断舍离", "极简生活", "收纳技巧", "高质量生活", "减法生活", + "胶囊衣橱", "极简护肤", "环保生活", "数字断舍离", "极简穿搭", + "极简房间", "消费降级", "物欲管理", + ], + "keywords": [ + "断舍离", "极简", "收纳", "高质量", "减法", "胶囊衣橱", + "简约", "环保", "整理", "少即是多", "极简风", "质感", + ], + }, + "汉服爱好者": { + "topics": [ + "汉服穿搭", "国风穿搭", "传统文化", "汉服发型", "汉服配饰", + "汉服拍照", "古风妆容", "汉服日常", "汉服科普", "形制科普", + "古风摄影", "新中式穿搭", "汉服探店", + ], + "keywords": [ + "汉服", "国风", "传统文化", "古风", "新中式", "形制", + "发簪", "明制", "宋制", "唐制", "汉服日常", "古风摄影", + ], + }, + "插画师小姐姐": { + "topics": [ + "手绘教程", "创作灵感", "iPad绘画", "插画分享", "水彩教程", + "Procreate技巧", "配色方案", "角色设计", "头像绘制", "手账素材", + "接稿经验", "画师日常", "绘画工具推荐", + ], + "keywords": [ + "插画", "手绘", "Procreate", "画画", "iPad绘画", "水彩", + "配色", "创作", "画师", "手账", "教程", "素材", + ], + }, + "海归女孩": { + "topics": [ + "中西文化差异", "海外生活", "留学经验", "英语学习方法", "海归求职", + "旅行攻略", "异国美食", "海外好物", "文化冲击", "语言学习", + "签证攻略", "海归适应", "国外探店", + ], + "keywords": [ + "留学", "海归", "英语", "海外", "文化差异", "旅行", + "异国", "签证", "语言", "出国", "求职", "国外", + ], + }, + "瑜伽老师": { + "topics": [ + "瑜伽入门", "冥想练习", "体态矫正", "呼吸法", "居家瑜伽", + "拉伸教程", "肩颈放松", "瑜伽体式", "自律生活", "身心灵", + "瑜伽穿搭", "晨练瑜伽", "睡前瑜伽", + ], + "keywords": [ + "瑜伽", "冥想", "体态", "拉伸", "放松", "呼吸", + "柔韧", "健康", "自律", "晨练", "入门", "体式", + ], + }, + "美甲设计师": { + "topics": [ + "美甲教程", "流行甲型", "美甲合集", "简约美甲", "法式美甲", + "手绘美甲", "季节美甲", "显白美甲", "美甲配色", "短甲美甲", + "新娘美甲", "美甲工具推荐", "日式美甲", + ], + "keywords": [ + "美甲", "甲型", "法式美甲", "手绘", "显白", "短甲", + "指甲", "美甲教程", "配色", "日式美甲", "腮红甲", "猫眼甲", + ], + }, + "家居软装设计师": { + "topics": [ + "小户型改造", "氛围感布置", "软装搭配", "家居好物", "收纳技巧", + "客厅布置", "卧室改造", "灯光设计", "绿植布置", "装修避坑", + "北欧风格", "ins风家居", "墙面装饰", + ], + "keywords": [ + "家居", "软装", "改造", "收纳", "氛围感", "小户型", + "装修", "灯光", "绿植", "北欧", "ins风", "布置", + ], + }, + # ---- 赛博/AI 虚拟博主类 ---- + "赛博AI虚拟博主": { + "topics": [ + "AI女孩日常", "虚拟人物写真", "AI生成美女", "赛博朴克穿搭", "未来风穿搭", + "全球场景打卡", "巴黎打卡写真", "东京街头拍照", "外太空写真", "古风仙侠写真", + "AI换装挑战", "粉丝许愿穿搭", "二次元风格写真", "女仆装写真", "护士制服写真", + "校园制服写真", "婚纱写真", "水下写真", "AI绘画教程", "虚拟人物背后故事", + ], + "keywords": [ + "AI女孩", "AI美女", "虚拟人物", "赛博朴克", "AI绘画", "AI写真", + "数码女孩", "2077", "未来风", "场景切换", "换装挑战", "粉丝许愿", + "高颜值", "特写", "全球打卡", "制服写真", "AI创作", "互动", + ], + }, +} + +# 为"随机人设"使用的全量池（兼容旧逻辑） +DEFAULT_TOPICS = [ + # 穿搭类 + "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "小个子穿搭", + "学生党穿搭", "韩系穿搭", "日系穿搭", "法式穿搭", "极简穿搭", + "国风穿搭", "运动穿搭", "闺蜜穿搭", "梨形身材穿搭", "微胖穿搭", + "氛围感穿搭", "一衣多穿", "秋冬叠穿", "夏日清凉穿搭", + # 美妆护肤类 + "护肤心得", "妆容教程", "学生党平价护肤", "敏感肌护肤", + "抗老护肤", "美白攻略", "眼妆教程", "唇妆合集", "底妆测评", + "防晒测评", "早C晚A护肤", "成分党护肤", "换季护肤", + # 美食类 + "减脂餐分享", "一人食食谱", "宿舍美食", "烘焙教程", "家常菜做法", + "探店打卡", "咖啡探店", "早餐食谱", "下午茶推荐", "火锅推荐", + "奶茶测评", "便当制作", "0失败甜品", + # 生活家居类 + "好物分享", "平价好物", "居家好物", "收纳技巧", "租房改造", + "小户型装修", "氛围感房间", "香薰推荐", "桌面布置", "断舍离", + # 旅行出行类 + "旅行攻略", "周末去哪玩", "小众旅行地", "拍照打卡地", "露营攻略", + "自驾游攻略", "古镇旅行", "海岛度假", "城市citywalk", + # 学习成长类 + "书单推荐", "自律生活", "时间管理", "考研经验", "英语学习方法", + "理财入门", "副业分享", "简历优化", "面试技巧", + # 数码科技类 + "iPad生产力", "手机摄影技巧", "好用App推荐", "电子产品测评", + # 健身运动类 + "居家健身", "帕梅拉跟练", "跑步入门", "瑜伽入门", "体态矫正", + # 宠物类 + "养猫日常", "养狗经验", "宠物好物", "新手养宠指南", + # 情感心理类 + "独居生活", "emo急救指南", "社恐自救", "女性成长", "情绪管理", +] + +DEFAULT_STYLES = [ + "好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷", + "知识科普", "经验分享", "清单合集", "对比测评", "沉浸式体验", +] + +# 全量评论关键词池（兼容旧逻辑 / 随机人设） +DEFAULT_COMMENT_KEYWORDS = [ + # 穿搭时尚 + "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "小个子穿搭", + # 美妆护肤 + "护肤", "化妆教程", "平价护肤", "防晒", "美白", "眼影", + # 美食 + "美食", "减脂餐", "探店", "咖啡", "烘焙", "食谱", + # 生活好物 + "好物推荐", "平价好物", "居家好物", "收纳", "租房改造", + # 旅行 + "旅行", "攻略", "打卡", "周末去哪玩", "露营", + # 学习成长 + "自律", "书单", "考研", "英语学习", "副业", + # 生活日常 + "生活日常", "独居", "vlog", "仪式感", "解压", + # 健身 + "减脂", "健身", "瑜伽", "体态", + # 宠物 + "养猫", "养狗", "宠物", +] + + +def _match_persona_pools(persona_text: str) -> dict | None: + """根据人设文本模糊匹配对应的关键词池和主题池 + 返回 {"topics": [...], "keywords": [...]} 或 None（未匹配） + """ + if not persona_text or persona_text == RANDOM_PERSONA_LABEL: + return None + # 精确匹配 + for key, pools in PERSONA_POOL_MAP.items(): + if key in persona_text or persona_text in key: + return pools + # 关键词模糊匹配 + _CATEGORY_HINTS = { + "时尚|穿搭|搭配|衣服": "温柔知性的时尚博主", + "大学|学生|校园": "元气满满的大学生", + "白领|职场|通勤|上班": "30岁都市白领丽人", + "妈妈|育儿|宝宝|母婴": "精致妈妈", + "摄影|旅行|旅游|文艺": "文艺青年摄影师", + "健身|运动|减脂|增肌|营养": "健身达人营养师", + "美妆|化妆|护肤|美白": "资深美妆博主", + "独居|租房|一人": "独居女孩", + "烘焙|甜品|蛋糕|面包": "甜品烘焙爱好者", + "数码|科技|App|电子": "数码科技女生", + "小镇|打拼|省钱|攒钱": "小镇姑娘在大城市打拼", + "中医|养生|食疗|节气": "中医养生爱好者", + "二次元|cos|动漫|漫展": "二次元coser", + "程序|代码|开发|码农": "北漂程序媛", + "复古|vintage|中古|古着": "复古穿搭博主", + "考研|备考|上岸|学习方法": "考研上岸学姐", + "猫|铲屎|喵": "新手养猫人", + "咖啡|手冲|拿铁": "咖啡重度爱好者", + "极简|断舍离|简约": "极简主义生活家", + "汉服|国风|传统文化": "汉服爱好者", + "插画|手绘|画画|绘画": "插画师小姐姐", + "海归|留学|海外": "海归女孩", + "瑜伽|冥想|身心灵": "瑜伽老师", + "美甲|甲型|指甲": "美甲设计师", + "家居|软装|装修|改造": "家居软装设计师", + } + for hints, persona_key in _CATEGORY_HINTS.items(): + if any(h in persona_text for h in hints.split("|")): + return PERSONA_POOL_MAP.get(persona_key) + return None + + +def get_persona_topics(persona_text: str) -> list[str]: + """获取人设对应的主题池，未匹配则返回全量池""" + pools = _match_persona_pools(persona_text) + return pools["topics"] if pools else DEFAULT_TOPICS + + +def get_persona_keywords(persona_text: str) -> list[str]: + """获取人设对应的评论关键词池，未匹配则返回全量池""" + pools = _match_persona_pools(persona_text) + return pools["keywords"] if pools else DEFAULT_COMMENT_KEYWORDS + + +def on_persona_changed(persona_text: str): + """人设切换时联动更新评论关键词池、主题池、队列主题池，并保存到配置""" + # 保存人设到配置 + cfg.set("persona", persona_text) + + # 更新关键词和主题池 + keywords = get_persona_keywords(persona_text) + topics = get_persona_topics(persona_text) + keywords_str = ", ".join(keywords) + topics_str = ", ".join(topics) + matched = _match_persona_pools(persona_text) + if matched: + label = persona_text[:15] if len(persona_text) > 15 else persona_text + hint = f"✅ 已切换至「{label}」专属关键词/主题池" + else: + hint = "ℹ️ 使用通用全量关键词/主题池" + + # 返回：自动运营的关键词池、主题池、提示信息、队列主题池 + return keywords_str, topics_str, hint, topics_str + + + +def _resolve_persona(persona_text: str, log_fn=None) -> str: + """解析人设：如果是随机人设则从池中随机选一个，否则原样返回""" + if not persona_text or persona_text == RANDOM_PERSONA_LABEL: + chosen = random.choice(DEFAULT_PERSONAS) + log_fn and log_fn(f"🎭 本次人设: {chosen[:20]}...") + return chosen + # 检查是否选的是池中某个人设（Dropdown选中） + return persona_text + + diff --git a/services/profile.py b/services/profile.py new file mode 100644 index 0000000..07a2bd0 --- /dev/null +++ b/services/profile.py @@ -0,0 +1,176 @@ +""" +services/profile.py +小红书账号 Profile 解析与可视化函数 +""" +import re +import json +import logging + +import matplotlib +import matplotlib.pyplot as plt + +from mcp_client import get_mcp_client + +_font_candidates = ["Microsoft YaHei", "SimHei", "PingFang SC", "WenQuanYi Micro Hei"] +for _fn in _font_candidates: + try: + matplotlib.font_manager.findfont(_fn, fallback_to_default=False) + plt.rcParams["font.sans-serif"] = [_fn] + break + except Exception: + continue +plt.rcParams["axes.unicode_minus"] = False + +logger = logging.getLogger("autobot") + +# ================================================== +# Tab 4: 数据看板 (我的账号) +# ================================================== + + +def _parse_profile_json(text: str): + """尝试从文本中解析用户 profile JSON""" + if not text: + return None + # 直接 JSON + try: + return json.loads(text) + except (json.JSONDecodeError, TypeError): + pass + # 可能包含 Markdown 代码块 + m = re.search(r'```(?:json)?\s*\n([\s\S]+?)\n```', text) + if m: + try: + return json.loads(m.group(1)) + except (json.JSONDecodeError, TypeError): + pass + return None + + +def _parse_count(val) -> float: + """解析数字字符串, 支持 '1.2万' 格式""" + if isinstance(val, (int, float)): + return float(val) + s = str(val).strip() + if "万" in s: + try: + return float(s.replace("万", "")) * 10000 + except ValueError: + pass + try: + return float(s) + except ValueError: + return 0.0 + + +def fetch_my_profile(user_id, xsec_token, mcp_url): + """获取我的账号数据, 返回结构化信息 + 可视化图表""" + if not user_id or not xsec_token: + return "❌ 请填写你的用户 ID 和 xsec_token", "", None, None, None + try: + client = get_mcp_client(mcp_url) + result = client.get_user_profile(user_id, xsec_token) + if "error" in result: + return f"❌ {result['error']}", "", None, None, None + + raw = result.get("raw", {}) + text = result.get("text", "") + + # 尝试从 raw 或 text 解析 JSON + data = None + if raw and isinstance(raw, dict): + content_list = raw.get("content", []) + for item in content_list: + if item.get("type") == "text": + data = _parse_profile_json(item.get("text", "")) + if data: + break + if not data: + data = _parse_profile_json(text) + if not data: + return "✅ 数据加载完成 (纯文本)", text, None, None, None + + # ---- 提取基本信息 (注意 MCP 对新号可能返回 null) ---- + basic = data.get("userBasicInfo") or {} + interactions = data.get("interactions") or [] + feeds = data.get("feeds") or [] + + gender_map = {0: "未知", 1: "男", 2: "女"} + info_lines = [ + f"## 👤 {basic.get('nickname', '未知')}", + f"- **小红书号**: {basic.get('redId', '-')}", + f"- **性别**: {gender_map.get(basic.get('gender', 0), '未知')}", + f"- **IP 属地**: {basic.get('ipLocation', '-')}", + f"- **简介**: {basic.get('desc', '-')}", + "", + "### 📊 核心数据", + ] + for inter in interactions: + info_lines.append(f"- **{inter.get('name', '')}**: {inter.get('count', '0')}") + + info_lines.append(f"\n### 📝 展示笔记: {len(feeds)} 篇") + profile_md = "\n".join(info_lines) + + # ---- 互动数据柱状图 ---- + fig_interact = None + if interactions: + inter_data = {i["name"]: _parse_count(i["count"]) for i in interactions} + fig_interact, ax = plt.subplots(figsize=(4, 3), dpi=100) + labels = list(inter_data.keys()) + values = list(inter_data.values()) + colors = ["#FF6B6B", "#4ECDC4", "#45B7D1"][:len(labels)] + ax.bar(labels, values, color=colors, edgecolor="white", linewidth=0.5) + ax.set_title("账号核心指标", fontsize=12, fontweight="bold") + for i, v in enumerate(values): + display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v)) + ax.text(i, v + max(values) * 0.02, display, ha="center", fontsize=9) + ax.set_ylabel("") + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + fig_interact.tight_layout() + + # ---- 笔记点赞分布图 ---- + fig_notes = None + if feeds: + titles, likes = [], [] + for f in feeds[:15]: + nc = f.get("noteCard") or {} + t = (nc.get("displayTitle", "") or "无标题")[:12] + lk = _parse_count((nc.get("interactInfo") or {}).get("likedCount", "0")) + titles.append(t) + likes.append(lk) + + fig_notes, ax2 = plt.subplots(figsize=(7, 3.5), dpi=100) + ax2.barh(range(len(titles)), likes, color="#FF6B6B", edgecolor="white") + ax2.set_yticks(range(len(titles))) + ax2.set_yticklabels(titles, fontsize=8) + ax2.set_title(f"笔记点赞排行 (Top {len(titles)})", fontsize=12, fontweight="bold") + ax2.invert_yaxis() + for i, v in enumerate(likes): + display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v)) + ax2.text(v + max(likes) * 0.01 if max(likes) > 0 else 0, i, display, va="center", fontsize=8) + ax2.spines["top"].set_visible(False) + ax2.spines["right"].set_visible(False) + fig_notes.tight_layout() + + # ---- 笔记详情表格 (Markdown) ---- + table_lines = [ + "### 📋 笔记数据明细", + "| # | 标题 | 类型 | ❤ 点赞 |", + "|---|------|------|--------|", + ] + for i, f in enumerate(feeds): + nc = f.get("noteCard") or {} + t = (nc.get("displayTitle", "") or "无标题")[:25] + tp = "📹 视频" if nc.get("type") == "video" else "📷 图文" + lk = (nc.get("interactInfo") or {}).get("likedCount", "0") + table_lines.append(f"| {i+1} | {t} | {tp} | {lk} |") + notes_table = "\n".join(table_lines) + + return "✅ 数据加载完成", profile_md, fig_interact, fig_notes, notes_table + + except Exception as e: + logger.error(f"获取我的数据失败: {e}") + return f"❌ {e}", "", None, None, None + + diff --git a/services/queue_ops.py b/services/queue_ops.py new file mode 100644 index 0000000..54778fd --- /dev/null +++ b/services/queue_ops.py @@ -0,0 +1,352 @@ +""" +services/queue_ops.py +发布队列操作：生成入队、状态管理、发布控制 +""" +import os +import time +import logging + +from config_manager import ConfigManager, OUTPUT_DIR +from publish_queue import ( + PublishQueue, QueuePublisher, + STATUS_DRAFT, STATUS_APPROVED, STATUS_SCHEDULED, STATUS_PUBLISHING, + STATUS_PUBLISHED, STATUS_FAILED, STATUS_REJECTED, STATUS_LABELS, +) +from mcp_client import get_mcp_client +from services.connection import _get_llm_config +from services.persona import DEFAULT_TOPICS, DEFAULT_STYLES, _resolve_persona +from services.content import generate_copy, generate_images +from services.rate_limiter import _increment_stat, _clear_error_streak + +cfg = ConfigManager() +logger = logging.getLogger("autobot") + +# 模块级依赖（通过 configure() 注入） +_pub_queue: "PublishQueue | None" = None +_queue_publisher: "QueuePublisher | None" = None +_analytics = None +_log_fn = None + + +def configure(pub_queue, queue_publisher, analytics_svc, log_fn=None): + """从 main.py 初始化段注入队列和分析服务""" + global _pub_queue, _queue_publisher, _analytics, _log_fn + _pub_queue = pub_queue + _queue_publisher = queue_publisher + _analytics = analytics_svc + _log_fn = log_fn + # 注册发布回调（在依赖注入完成后） + _queue_publisher.set_publish_callback(_queue_publish_callback) + _queue_publisher.set_log_callback(_log_fn or _log) + + +def _log(msg: str): + if _log_fn: + _log_fn(msg) + else: + logger.info("[queue] %s", msg) + +# ================================================== +# 发布队列相关函数 +# ================================================== + +def generate_to_queue(topics_str, sd_url_val, sd_model_name, model, persona_text=None, + quality_mode_val=None, face_swap_on=False, count=1, + scheduled_time=None): + """批量生成内容 → 加入发布队列（不直接发布）""" + try: + topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS + use_weights = cfg.get("use_smart_weights", True) and _analytics.has_weights + + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置" + if not sd_url_val or not sd_model_name: + return "❌ SD WebUI 未连接或未选择模型" + + count = max(1, min(int(count), 10)) + results = [] + + for i in range(count): + try: + _log(f"📋 [队列生成] 正在生成第 {i+1}/{count} 篇...") + + if use_weights: + topic = _analytics.get_weighted_topic(topics) + style = _analytics.get_weighted_style(DEFAULT_STYLES) + else: + topic = random.choice(topics) + style = random.choice(DEFAULT_STYLES) + + svc = LLMService(api_key, base_url, model) + persona = _resolve_persona(persona_text) if persona_text else None + + if use_weights: + weight_insights = f"高权重主题: {', '.join(list(analytics._weights.get('topic_weights', {}).keys())[:5])}\n" + weight_insights += f"权重摘要: {analytics.weights_summary}" + title_advice = _analytics.get_title_advice() + hot_tags = ", ".join(analytics.get_top_tags(8)) + try: + data = svc.generate_weighted_copy(topic, style, weight_insights, title_advice, hot_tags, sd_model_name=sd_model_name, persona=persona) + except Exception: + data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) + else: + data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) + + title = (data.get("title", "") or "")[:20] + content = data.get("content", "") + sd_prompt = data.get("sd_prompt", "") + tags = data.get("tags", []) + + if use_weights: + top_tags = _analytics.get_top_tags(5) + for t in top_tags: + if t not in tags: + tags.append(t) + tags = tags[:10] + + if not title: + _log(f"⚠️ 第 {i+1} 篇文案生成失败，跳过") + continue + + # 生成图片 + sd_svc = SDService(sd_url_val) + face_image = None + if face_swap_on: + face_image = SDService.load_face_image() + images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, + face_image=face_image, + quality_mode=quality_mode_val or "快速 (约30秒)", + persona=persona) + if not images: + _log(f"⚠️ 第 {i+1} 篇图片生成失败，跳过") + continue + + # 保存备份 + ts = int(time.time()) + safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] + backup_dir = os.path.join(OUTPUT_DIR, f"{ts}_{safe_title}") + os.makedirs(backup_dir, exist_ok=True) + + with open(os.path.join(backup_dir, "文案.txt"), "w", encoding="utf-8") as f: + f.write(f"标题: {title}\n风格: {style}\n主题: {topic}\n\n{content}\n\n标签: {', '.join(tags)}\n\nSD Prompt: {sd_prompt}") + + image_paths = [] + for idx, img in enumerate(images): + if isinstance(img, Image.Image): + path = os.path.abspath(os.path.join(backup_dir, f"图{idx+1}.jpg")) + if img.mode != "RGB": + img = img.convert("RGB") + img.save(path, format="JPEG", quality=95) + image_paths.append(path) + + if not image_paths: + continue + + # 加入队列 + item_id = _pub_queue.add( + title=title, content=content, sd_prompt=sd_prompt, + tags=tags, image_paths=image_paths, backup_dir=backup_dir, + topic=topic, style=style, persona=persona or "", + status=STATUS_DRAFT, scheduled_time=scheduled_time, + ) + results.append(f"#{item_id} {title}") + _log(f"📋 已加入队列 #{item_id}: {title}") + + # 多篇间隔 + if i < count - 1: + time.sleep(2) + + except Exception as e: + _log(f"⚠️ 第 {i+1} 篇生成异常: {e}") + continue + + if not results: + return "❌ 所有内容生成失败，请检查配置" + + return f"✅ 已生成 {len(results)} 篇内容加入队列:\n" + "\n".join(f" - {r}" for r in results) + + except Exception as e: + return f"❌ 批量生成异常: {e}" + + +def _queue_publish_callback(item: dict) -> tuple[bool, str]: + """队列发布回调: 从队列项数据发布到小红书""" + try: + mcp_url = cfg.get("mcp_url", "http://localhost:18060/mcp") + client = get_mcp_client(mcp_url) + title = item.get("title", "") + content = item.get("content", "") + image_paths = item.get("image_paths", []) + tags = item.get("tags", []) + + if not title or not image_paths: + return False, "标题或图片缺失" + + # 验证图片文件存在 + valid_paths = [p for p in image_paths if os.path.isfile(p)] + if not valid_paths: + return False, "所有图片文件不存在" + + result = client.publish_content( + title=title, content=content, images=valid_paths, tags=tags, + ) + if "error" in result: + return False, result["error"] + + _increment_stat("publishes") + _clear_error_streak() + return True, result.get("text", "发布成功") + + except Exception as e: + return False, str(e) + + + + +def queue_format_table(): + """返回当前队列的完整表格（不过滤）""" + return _pub_queue.format_queue_table() if _pub_queue else "" + + +def queue_format_calendar(): + """返回未来14天的日历视图""" + return _pub_queue.format_calendar(14) if _pub_queue else "" + + +def queue_refresh_table(status_filter): + """刷新队列表格""" + statuses = None + if status_filter and status_filter != "全部": + status_map = {v: k for k, v in STATUS_LABELS.items()} + if status_filter in status_map: + statuses = [status_map[status_filter]] + return _pub_queue.format_queue_table(statuses) + + +def queue_refresh_calendar(): + """刷新日历视图""" + return _pub_queue.format_calendar(14) + + +def queue_preview_item(item_id_str): + """预览队列项""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + return _pub_queue.format_preview(item_id) + except (ValueError, TypeError): + return "❌ 请输入有效的队列项 ID（数字）" + + +def queue_approve_item(item_id_str, scheduled_time_str): + """审核通过""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + sched = scheduled_time_str.strip() if scheduled_time_str else None + ok = _pub_queue.approve(item_id, scheduled_time=sched) + if ok: + status = "已排期" if sched else "待发布" + return f"✅ #{item_id} 已审核通过 → {status}" + return f"❌ #{item_id} 无法审核（可能不是草稿/失败状态）" + except (ValueError, TypeError): + return "❌ 请输入有效的 ID" + + +def queue_reject_item(item_id_str): + """拒绝队列项""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + ok = _pub_queue.reject(item_id) + return f"✅ #{item_id} 已拒绝" if ok else f"❌ #{item_id} 无法拒绝" + except (ValueError, TypeError): + return "❌ 请输入有效的 ID" + + +def queue_delete_item(item_id_str): + """删除队列项""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + ok = _pub_queue.delete(item_id) + return f"✅ #{item_id} 已删除" if ok else f"❌ #{item_id} 无法删除（可能正在发布中）" + except (ValueError, TypeError): + return "❌ 请输入有效的 ID" + + +def queue_retry_item(item_id_str): + """重试失败项""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + ok = _pub_queue.retry(item_id) + return f"✅ #{item_id} 已重新加入待发布" if ok else f"❌ #{item_id} 无法重试（不是失败状态）" + except (ValueError, TypeError): + return "❌ 请输入有效的 ID" + + +def queue_publish_now(item_id_str): + """立即发布队列项""" + try: + item_id = int(str(item_id_str).strip().replace("#", "")) + return _queue_publisher.publish_now(item_id) + except (ValueError, TypeError): + return "❌ 请输入有效的 ID" + + +def queue_start_processor(): + """启动队列后台处理器""" + if _queue_publisher.is_running: + return "⚠️ 队列处理器已在运行中" + _queue_publisher.start(check_interval=60) + return "✅ 队列处理器已启动，每分钟检查待发布项" + + +def queue_stop_processor(): + """停止队列后台处理器""" + if not _queue_publisher.is_running: + return "⚠️ 队列处理器未在运行" + _queue_publisher.stop() + return "🛑 队列处理器已停止" + + +def queue_get_status(): + """获取队列状态摘要""" + counts = _pub_queue.count_by_status() + running = "🟢 运行中" if _queue_publisher.is_running else "⚪ 未启动" + parts = [f"**队列处理器**: {running}"] + for s, label in STATUS_LABELS.items(): + cnt = counts.get(s, 0) + if cnt > 0: + parts.append(f"{label}: {cnt}") + total = sum(counts.values()) + parts.append(f"**合计**: {total} 项") + return " · ".join(parts) + + +def queue_batch_approve(status_filter): + """批量审核通过所有草稿""" + items = _pub_queue.list_by_status([STATUS_DRAFT]) + if not items: + return "📭 没有待审核的草稿" + approved = 0 + for item in items: + if _pub_queue.approve(item["id"]): + approved += 1 + return f"✅ 已批量审核通过 {approved} 项" + + +def queue_generate_and_refresh(topics_str, sd_url_val, sd_model_name, model, + persona_text, quality_mode_val, face_swap_on, + gen_count, gen_schedule_time): + """生成内容到队列 + 刷新表格""" + msg = generate_to_queue( + topics_str, sd_url_val, sd_model_name, model, + persona_text=persona_text, quality_mode_val=quality_mode_val, + face_swap_on=face_swap_on, count=gen_count, + scheduled_time=gen_schedule_time.strip() if gen_schedule_time else None, + ) + table = _pub_queue.format_queue_table() + calendar = _pub_queue.format_calendar(14) + status = queue_get_status() + return msg, table, calendar, status + + +# 调度器下次执行时间追踪 diff --git a/services/rate_limiter.py b/services/rate_limiter.py new file mode 100644 index 0000000..a699e93 --- /dev/null +++ b/services/rate_limiter.py @@ -0,0 +1,110 @@ +""" +services/rate_limiter.py +频率控制、每日限额、冷却管理 +""" +import time +import threading +from datetime import datetime + +# ---- 操作记录：防重复 & 每日统计 ---- +_op_history = { + "commented_feeds": set(), # 已评论的 feed_id + "replied_comments": set(), # 已回复的 comment_id + "liked_feeds": set(), # 已点赞的 feed_id + "favorited_feeds": set(), # 已收藏的 feed_id +} +_daily_stats = { + "date": "", + "comments": 0, + "likes": 0, + "favorites": 0, + "publishes": 0, + "replies": 0, + "errors": 0, +} +# 每日操作上限 +DAILY_LIMITS = { + "comments": 30, + "likes": 80, + "favorites": 50, + "publishes": 8, + "replies": 40, +} +# 连续错误计数 → 冷却 +_consecutive_errors = 0 +_error_cooldown_until = 0.0 + +# 线程锁，保护 stats/history 并发写入 +_stats_lock = threading.Lock() + + +def _reset_daily_stats_if_needed(): + """每天自动重置统计""" + today = datetime.now().strftime("%Y-%m-%d") + if _daily_stats["date"] != today: + _daily_stats.update({ + "date": today, "comments": 0, "likes": 0, + "favorites": 0, "publishes": 0, "replies": 0, "errors": 0, + }) + # 每日重置历史记录（允许隔天重复互动） + for k in _op_history: + _op_history[k].clear() + + +def _check_daily_limit(op_type: str) -> bool: + """检查是否超出每日限额""" + _reset_daily_stats_if_needed() + limit = DAILY_LIMITS.get(op_type, 999) + current = _daily_stats.get(op_type, 0) + return current < limit + + +def _increment_stat(op_type: str): + """增加操作计数""" + _reset_daily_stats_if_needed() + _daily_stats[op_type] = _daily_stats.get(op_type, 0) + 1 + + +def _record_error(log_fn=None): + """记录错误，连续错误触发冷却。log_fn 可选，用于写入日志。""" + global _consecutive_errors, _error_cooldown_until + _consecutive_errors += 1 + _daily_stats["errors"] = _daily_stats.get("errors", 0) + 1 + if _consecutive_errors >= 3: + cooldown = min(60 * _consecutive_errors, 600) # 最多冷却10分钟 + _error_cooldown_until = time.time() + cooldown + if log_fn: + log_fn(f"⚠️ 连续 {_consecutive_errors} 次错误，冷却 {cooldown}s") + + +def _clear_error_streak(): + """操作成功后清除连续错误记录""" + global _consecutive_errors + _consecutive_errors = 0 + + +def _is_in_cooldown() -> bool: + """检查是否在错误冷却期""" + return time.time() < _error_cooldown_until + + +def _is_in_operating_hours(start_hour: int = 7, end_hour: int = 23) -> bool: + """检查是否在运营时间段""" + now_hour = datetime.now().hour + return start_hour <= now_hour < end_hour + + +def _get_stats_summary() -> str: + """获取今日运营统计摘要""" + _reset_daily_stats_if_needed() + s = _daily_stats + lines = [ + f"📊 **今日运营统计** ({s['date']})", + f"- 💬 评论: {s['comments']}/{DAILY_LIMITS['comments']}", + f"- ❤️ 点赞: {s['likes']}/{DAILY_LIMITS['likes']}", + f"- ⭐ 收藏: {s['favorites']}/{DAILY_LIMITS['favorites']}", + f"- 🚀 发布: {s['publishes']}/{DAILY_LIMITS['publishes']}", + f"- 💌 回复: {s['replies']}/{DAILY_LIMITS['replies']}", + f"- ❌ 错误: {s['errors']}", + ] + return "\n".join(lines) diff --git a/services/scheduler.py b/services/scheduler.py new file mode 100644 index 0000000..07e3641 --- /dev/null +++ b/services/scheduler.py @@ -0,0 +1,1086 @@ +""" +services/scheduler.py +自动化运营调度器：状态管理、自动评论/点赞/收藏/发布/回复、定时循环、数据学习调度 +""" +import os +import re +import time +import random +import threading +import logging +from datetime import datetime + +from PIL import Image + +from config_manager import ConfigManager, OUTPUT_DIR +from llm_service import LLMService +from sd_service import SDService +from mcp_client import get_mcp_client +from services.rate_limiter import ( + _op_history, _daily_stats, DAILY_LIMITS, + _reset_daily_stats_if_needed, + _check_daily_limit, _increment_stat, _record_error, + _clear_error_streak, _is_in_cooldown, _is_in_operating_hours, + _get_stats_summary, +) +from services.persona import ( + DEFAULT_TOPICS, DEFAULT_STYLES, DEFAULT_COMMENT_KEYWORDS, + get_persona_keywords, _resolve_persona, +) +from services.connection import _get_llm_config +from analytics_service import AnalyticsService + +cfg = ConfigManager() +logger = logging.getLogger("autobot") + +# AnalyticsService 注入（通过 configure_analytics() 设置） +_analytics: "AnalyticsService | None" = None + + +def configure_analytics(analytics_svc: "AnalyticsService"): + """从 main.py 注入 AnalyticsService 实例""" + global _analytics + _analytics = analytics_svc + +# ================================================== +# 自动化运营模块 +# ================================================== + +# 自动化状态 +_auto_running = threading.Event() +_auto_thread: threading.Thread | None = None +_auto_log: list[str] = [] + +from services.rate_limiter import ( + _op_history, _daily_stats, DAILY_LIMITS, + _reset_daily_stats_if_needed, + _check_daily_limit, _increment_stat, _record_error, + _clear_error_streak, _is_in_cooldown, _is_in_operating_hours, + _get_stats_summary, +) + +from services.persona import ( + DEFAULT_PERSONAS, RANDOM_PERSONA_LABEL, PERSONA_POOL_MAP, + DEFAULT_TOPICS, DEFAULT_STYLES, DEFAULT_COMMENT_KEYWORDS, + _match_persona_pools, get_persona_topics, get_persona_keywords, + on_persona_changed, _resolve_persona, +) + +def _auto_log_append(msg: str): + """记录自动化日志""" + ts = datetime.now().strftime("%H:%M:%S") + entry = f"[{ts}] {msg}" + _auto_log.append(entry) + if len(_auto_log) > 500: + _auto_log[:] = _auto_log[-300:] + logger.info("[自动化] %s", msg) + + +def _auto_comment_with_log(keywords_str, mcp_url, model, persona_text): + """一键评论 + 同步刷新日志""" + msg = auto_comment_once(keywords_str, mcp_url, model, persona_text) + return msg, get_auto_log() + + +def auto_comment_once(keywords_str, mcp_url, model, persona_text): + """一键评论：自动搜索高赞笔记 → AI生成评论 → 发送（含防重复/限额/冷却）""" + try: + if _is_in_cooldown(): + return "⏳ 错误冷却中，请稍后再试" + if not _check_daily_limit("comments"): + return f"🚫 今日评论已达上限 ({DAILY_LIMITS['comments']})" + + persona_text = _resolve_persona(persona_text) + # 如果用户未手动修改关键词池，则使用人设匹配的专属关键词池 + persona_keywords = get_persona_keywords(persona_text) + keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else persona_keywords + keyword = random.choice(keywords) + _auto_log_append(f"🔍 搜索关键词: {keyword}") + + client = get_mcp_client(mcp_url) + + # 随机切换搜索排序，丰富互动对象 + sort_options = ["最多点赞", "综合", "最新"] + sort_by = random.choice(sort_options) + + # 搜索高赞笔记 + entries = client.search_feeds_parsed(keyword, sort_by=sort_by) + if not entries: + _auto_log_append("⚠️ 搜索无结果，尝试推荐列表") + entries = client.list_feeds_parsed() + if not entries: + _record_error() + return "❌ 未找到任何笔记" + + # 过滤掉自己的笔记 & 已评论过的笔记 + my_uid = cfg.get("my_user_id", "") + entries = [ + e for e in entries + if e.get("user_id") != my_uid + and e.get("feed_id") not in _op_history["commented_feeds"] + ] + if not entries: + return "ℹ️ 搜索结果中所有笔记都已评论过，换个关键词试试" + + # 从前10个中随机选择 + target = random.choice(entries[:min(10, len(entries))]) + feed_id = target["feed_id"] + xsec_token = target["xsec_token"] + title = target.get("title", "未知") + _auto_log_append(f"🎯 选中: {title[:30]} (@{target.get('author', '未知')}) [排序:{sort_by}]") + + if not feed_id or not xsec_token: + return "❌ 笔记缺少必要参数 (feed_id/xsec_token)" + + # 模拟浏览延迟 + time.sleep(random.uniform(3, 8)) + + # 加载笔记详情 + result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True) + if "error" in result: + _record_error() + return f"❌ 加载笔记失败: {result['error']}" + + full_text = result.get("text", "") + if "评论" in full_text: + parts = full_text.split("评论", 1) + content_part = parts[0].strip()[:600] + comments_part = ("评论" + parts[1])[:800] if len(parts) > 1 else "" + else: + content_part = full_text[:500] + comments_part = "" + + # AI 生成评论 + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置，请先在全局设置中配置提供商" + + svc = LLMService(api_key, base_url, model) + comment = svc.generate_proactive_comment( + persona_text, title, content_part, comments_part + ) + _auto_log_append(f"💬 生成评论: {comment[:60]}...") + + # 随机等待后发送 + time.sleep(random.uniform(3, 10)) + result = client.post_comment(feed_id, xsec_token, comment) + resp_text = result.get("text", "") + _auto_log_append(f"📡 MCP 响应: {resp_text[:200]}") + + if "error" in result: + _record_error() + _auto_log_append(f"❌ 评论发送失败: {result['error']}") + return f"❌ 评论发送失败: {result['error']}" + + # 记录成功操作 + _op_history["commented_feeds"].add(feed_id) + _increment_stat("comments") + _clear_error_streak() + + _auto_log_append(f"✅ 评论已发送到「{title[:20]}」 (今日第{_daily_stats['comments']}条)") + return f"✅ 已评论「{title[:25]}」\n📝 评论: {comment}\n📊 今日评论: {_daily_stats['comments']}/{DAILY_LIMITS['comments']}" + + except Exception as e: + _record_error() + _auto_log_append(f"❌ 一键评论异常: {e}") + return f"❌ 评论失败: {e}" + + +def _auto_like_with_log(keywords_str, like_count, mcp_url): + """一键点赞 + 同步刷新日志""" + msg = auto_like_once(keywords_str, like_count, mcp_url) + return msg, get_auto_log() + + +def auto_like_once(keywords_str, like_count, mcp_url): + """一键点赞：搜索/推荐笔记 → 随机选择 → 批量点赞（含防重复/限额）""" + try: + if _is_in_cooldown(): + return "⏳ 错误冷却中，请稍后再试" + if not _check_daily_limit("likes"): + return f"🚫 今日点赞已达上限 ({DAILY_LIMITS['likes']})" + + keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS + keyword = random.choice(keywords) + like_count = int(like_count) if like_count else 5 + # 不超过当日剩余额度 + remaining = DAILY_LIMITS["likes"] - _daily_stats.get("likes", 0) + like_count = min(like_count, remaining) + _auto_log_append(f"👍 点赞关键词: {keyword} | 目标: {like_count} 个") + + client = get_mcp_client(mcp_url) + + # 搜索笔记 + entries = client.search_feeds_parsed(keyword, sort_by="综合") + if not entries: + _auto_log_append("⚠️ 搜索无结果，尝试推荐列表") + entries = client.list_feeds_parsed() + if not entries: + _record_error() + return "❌ 未找到任何笔记" + + # 过滤自己的笔记 & 已点赞过的 + my_uid = cfg.get("my_user_id", "") + entries = [ + e for e in entries + if e.get("user_id") != my_uid + and e.get("feed_id") not in _op_history["liked_feeds"] + ] + if not entries: + return "ℹ️ 搜索结果中所有笔记都已点赞过" + + # 随机打乱，取前 N 个 + random.shuffle(entries) + targets = entries[:min(like_count, len(entries))] + + liked = 0 + for target in targets: + feed_id = target.get("feed_id", "") + xsec_token = target.get("xsec_token", "") + title = target.get("title", "未知")[:25] + + if not feed_id or not xsec_token: + continue + + # 模拟浏览延迟 + time.sleep(random.uniform(2, 6)) + + result = client.like_feed(feed_id, xsec_token) + if "error" in result: + _auto_log_append(f" ❌ 点赞失败「{title}」: {result['error']}") + else: + liked += 1 + _op_history["liked_feeds"].add(feed_id) + _increment_stat("likes") + _auto_log_append(f" ❤️ 已点赞「{title}」@{target.get('author', '未知')}") + + if liked > 0: + _clear_error_streak() + _auto_log_append(f"👍 点赞完成: 成功 {liked}/{len(targets)} (今日累计{_daily_stats.get('likes', 0)})") + return f"✅ 点赞完成！成功 {liked}/{len(targets)} 个\n📊 今日点赞: {_daily_stats.get('likes', 0)}/{DAILY_LIMITS['likes']}" + + except Exception as e: + _record_error() + _auto_log_append(f"❌ 一键点赞异常: {e}") + return f"❌ 点赞失败: {e}" + + +def _auto_favorite_with_log(keywords_str, fav_count, mcp_url): + """一键收藏 + 同步刷新日志""" + msg = auto_favorite_once(keywords_str, fav_count, mcp_url) + return msg, get_auto_log() + + +def auto_favorite_once(keywords_str, fav_count, mcp_url): + """一键收藏：搜索优质笔记 → 随机选择 → 批量收藏（含防重复/限额）""" + try: + if _is_in_cooldown(): + return "⏳ 错误冷却中，请稍后再试" + if not _check_daily_limit("favorites"): + return f"🚫 今日收藏已达上限 ({DAILY_LIMITS['favorites']})" + + keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS + keyword = random.choice(keywords) + fav_count = int(fav_count) if fav_count else 3 + remaining = DAILY_LIMITS["favorites"] - _daily_stats.get("favorites", 0) + fav_count = min(fav_count, remaining) + _auto_log_append(f"⭐ 收藏关键词: {keyword} | 目标: {fav_count} 个") + + client = get_mcp_client(mcp_url) + + entries = client.search_feeds_parsed(keyword, sort_by="最多收藏") + if not entries: + entries = client.list_feeds_parsed() + if not entries: + _record_error() + return "❌ 未找到任何笔记" + + my_uid = cfg.get("my_user_id", "") + entries = [ + e for e in entries + if e.get("user_id") != my_uid + and e.get("feed_id") not in _op_history["favorited_feeds"] + ] + if not entries: + return "ℹ️ 搜索结果中所有笔记都已收藏过" + + random.shuffle(entries) + targets = entries[:min(fav_count, len(entries))] + + saved = 0 + for target in targets: + feed_id = target.get("feed_id", "") + xsec_token = target.get("xsec_token", "") + title = target.get("title", "未知")[:25] + + if not feed_id or not xsec_token: + continue + + time.sleep(random.uniform(2, 6)) + + result = client.favorite_feed(feed_id, xsec_token) + if "error" in result: + _auto_log_append(f" ❌ 收藏失败「{title}」: {result['error']}") + else: + saved += 1 + _op_history["favorited_feeds"].add(feed_id) + _increment_stat("favorites") + _auto_log_append(f" ⭐ 已收藏「{title}」@{target.get('author', '未知')}") + + if saved > 0: + _clear_error_streak() + _auto_log_append(f"⭐ 收藏完成: 成功 {saved}/{len(targets)} (今日累计{_daily_stats.get('favorites', 0)})") + return f"✅ 收藏完成！成功 {saved}/{len(targets)} 个\n📊 今日收藏: {_daily_stats.get('favorites', 0)}/{DAILY_LIMITS['favorites']}" + + except Exception as e: + _record_error() + _auto_log_append(f"❌ 一键收藏异常: {e}") + return f"❌ 收藏失败: {e}" + + +def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model, persona_text, quality_mode_val, face_swap_on): + """一键发布 + 同步刷新日志""" + msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, + persona_text=persona_text, quality_mode_val=quality_mode_val, + face_swap_on=face_swap_on) + return msg, get_auto_log() + + +def _auto_reply_with_log(max_replies, mcp_url, model, persona_text): + """一键回复 + 同步刷新日志""" + msg = auto_reply_once(max_replies, mcp_url, model, persona_text) + return msg, get_auto_log() + + +def auto_reply_once(max_replies, mcp_url, model, persona_text): + """一键回复：获取我的笔记 → 加载评论 → AI 生成回复 → 发送（含防重复/限额）""" + try: + if _is_in_cooldown(): + return "⏳ 错误冷却中，请稍后再试" + if not _check_daily_limit("replies"): + return f"🚫 今日回复已达上限 ({DAILY_LIMITS['replies']})" + + persona_text = _resolve_persona(persona_text) + my_uid = cfg.get("my_user_id", "") + xsec = cfg.get("xsec_token", "") + if not my_uid: + return "❌ 未配置用户 ID，请到「账号登录」页填写" + if not xsec: + return "❌ 未获取 xsec_token，请先登录" + + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置" + + max_replies = int(max_replies) if max_replies else 3 + remaining = DAILY_LIMITS["replies"] - _daily_stats.get("replies", 0) + max_replies = min(max_replies, remaining) + client = get_mcp_client(mcp_url) + _auto_log_append("💌 开始自动回复评论...") + + # Step 1: 获取我的笔记列表 + result = client.get_user_profile(my_uid, xsec) + if "error" in result: + _auto_log_append(f"❌ 获取我的笔记失败: {result['error']}") + return f"❌ 获取我的笔记失败: {result['error']}" + + # 解析笔记列表 + raw = result.get("raw", {}) + text = result.get("text", "") + data = None + if raw and isinstance(raw, dict): + for item in raw.get("content", []): + if item.get("type") == "text": + try: + data = json.loads(item["text"]) + except (json.JSONDecodeError, KeyError): + pass + if not data: + try: + data = json.loads(text) + except (json.JSONDecodeError, TypeError): + pass + + feeds = (data or {}).get("feeds") or [] + if not feeds: + _auto_log_append("⚠️ 未找到任何笔记") + return "⚠️ 未找到你的笔记" + + # 构建笔记条目 + my_entries = [] + for f in feeds: + nc = f.get("noteCard") or {} + my_entries.append({ + "feed_id": f.get("id", ""), + "xsec_token": f.get("xsecToken", ""), + "title": nc.get("displayTitle", "未知标题"), + }) + + _auto_log_append(f"📝 找到 {len(my_entries)} 篇笔记，开始扫描评论...") + + # Step 2: 遍历笔记，找到未回复的评论 + total_replied = 0 + svc = LLMService(api_key, base_url, model) + + for entry in my_entries: + if total_replied >= max_replies: + break + + feed_id = entry["feed_id"] + xsec_token = entry["xsec_token"] + title = entry["title"] + + if not feed_id or not xsec_token: + continue + + time.sleep(random.uniform(1, 3)) + + # 加载笔记评论（使用结构化接口） + comments = client.get_feed_comments(feed_id, xsec_token, load_all=True) + if not comments: + continue + + # 过滤掉自己的评论 & 已回复过的评论 + other_comments = [ + c for c in comments + if c.get("user_id") and c["user_id"] != my_uid and c.get("content") + and c.get("comment_id", "") not in _op_history["replied_comments"] + ] + + if not other_comments: + continue + + _auto_log_append(f"📖「{title[:20]}」有 {len(other_comments)} 条他人评论") + + for comment in other_comments: + if total_replied >= max_replies: + break + + comment_id = comment.get("comment_id", "") + comment_uid = comment.get("user_id", "") + comment_text = comment.get("content", "") + nickname = comment.get("nickname", "网友") + + if not comment_text.strip(): + continue + + _auto_log_append(f" 💬 @{nickname}: {comment_text[:40]}...") + + # AI 生成回复 + try: + reply = svc.generate_reply(persona_text, title, comment_text) + except Exception as e: + _auto_log_append(f" ❌ AI 回复生成失败: {e}") + continue + + _auto_log_append(f" 🤖 回复: {reply[:50]}...") + + # 发送回复 + time.sleep(random.uniform(2, 6)) + + if comment_id and comment_uid: + # 使用 reply_comment 精确回复 + resp = client.reply_comment( + feed_id, xsec_token, comment_id, comment_uid, reply + ) + else: + # 没有 comment_id 就用 post_comment 发到笔记下 + resp = client.post_comment(feed_id, xsec_token, f"@{nickname} {reply}") + + resp_text = resp.get("text", "") + if "error" in resp: + _auto_log_append(f" ❌ 回复发送失败: {resp['error']}") + else: + _auto_log_append(f" ✅ 已回复 @{nickname}") + total_replied += 1 + if comment_id: + _op_history["replied_comments"].add(comment_id) + _increment_stat("replies") + + if total_replied > 0: + _clear_error_streak() + + if total_replied == 0: + _auto_log_append("ℹ️ 没有找到需要回复的新评论") + return "ℹ️ 没有找到需要回复的新评论\n\n💡 可能所有评论都已回复过" + else: + _auto_log_append(f"✅ 自动回复完成，共回复 {total_replied} 条 (今日累计{_daily_stats.get('replies', 0)})") + return f"✅ 自动回复完成！共回复 {total_replied} 条评论\n📊 今日回复: {_daily_stats.get('replies', 0)}/{DAILY_LIMITS['replies']}" + + except Exception as e: + _record_error() + _auto_log_append(f"❌ 自动回复异常: {e}") + return f"❌ 自动回复失败: {e}" + + +def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, persona_text=None, quality_mode_val=None, face_swap_on=False): + """一键发布：自动生成文案 → 生成图片 → 本地备份 → 发布到小红书（含限额 + 智能权重 + 人设 + 画质）""" + try: + if _is_in_cooldown(): + return "⏳ 错误冷却中，请稍后再试" + if not _check_daily_limit("publishes"): + return f"🚫 今日发布已达上限 ({DAILY_LIMITS['publishes']})" + + topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS + use_weights = cfg.get("use_smart_weights", True) and _analytics.has_weights + + if use_weights: + # 智能加权选题 + topic = _analytics.get_weighted_topic(topics) + style = _analytics.get_weighted_style(DEFAULT_STYLES) + _auto_log_append(f"🧠 [智能] 主题: {topic} | 风格: {style} (加权选择)") + else: + topic = random.choice(topics) + style = random.choice(DEFAULT_STYLES) + _auto_log_append(f"📝 主题: {topic} | 风格: {style} (主题池: {len(topics)} 个)") + + # 生成文案 + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置，请先在全局设置中配置提供商" + + svc = LLMService(api_key, base_url, model) + # 解析人设（随机/指定） + persona = _resolve_persona(persona_text) if persona_text else None + if persona: + _auto_log_append(f"🎭 人设: {persona[:20]}...") + + if use_weights: + # 使用加权文案生成 (携带权重洞察) + weight_insights = f"高权重主题: {', '.join(list(analytics._weights.get('topic_weights', {}).keys())[:5])}\n" + weight_insights += f"权重摘要: {analytics.weights_summary}" + title_advice = _analytics.get_title_advice() + hot_tags = ", ".join(analytics.get_top_tags(8)) + try: + data = svc.generate_weighted_copy(topic, style, weight_insights, title_advice, hot_tags, sd_model_name=sd_model_name, persona=persona) + _auto_log_append("🧠 使用智能加权文案模板") + except Exception as e: + logger.warning("加权文案生成失败, 退回普通模式: %s", e) + data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) + _auto_log_append("⚠️ 加权模板异常, 使用普通模板") + else: + data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona) + + title = (data.get("title", "") or "")[:20] + content = data.get("content", "") + sd_prompt = data.get("sd_prompt", "") + tags = data.get("tags", []) + + # 如果有高权重标签，补充到 tags 中 + if use_weights: + top_tags = _analytics.get_top_tags(5) + for t in top_tags: + if t not in tags: + tags.append(t) + tags = tags[:10] # 限制最多10个标签 + + if not title: + _record_error() + return "❌ 文案生成失败：无标题" + _auto_log_append(f"📄 文案: {title}") + + # 生成图片 + if not sd_url_val or not sd_model_name: + return "❌ SD WebUI 未连接或未选择模型，请先在全局设置中连接" + + sd_svc = SDService(sd_url_val) + # 自动发布也支持换脸 + face_image = None + if face_swap_on: + face_image = SDService.load_face_image() + if face_image: + _auto_log_append("🎭 换脸已启用") + else: + _auto_log_append("⚠️ 换脸已启用但未找到头像，跳过换脸") + images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, + face_image=face_image, + quality_mode=quality_mode_val or "快速 (约30秒)", + persona=persona) + if not images: + _record_error() + return "❌ 图片生成失败：没有返回图片" + _auto_log_append(f"🎨 已生成 {len(images)} 张图片") + + # 本地备份（同时用于发布） + ts = int(time.time()) + safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20] + backup_dir = os.path.join(OUTPUT_DIR, f"{ts}_{safe_title}") + os.makedirs(backup_dir, exist_ok=True) + + # 保存文案 + with open(os.path.join(backup_dir, "文案.txt"), "w", encoding="utf-8") as f: + f.write(f"标题: {title}\n风格: {style}\n主题: {topic}\n\n{content}\n\n标签: {', '.join(tags)}\n\nSD Prompt: {sd_prompt}") + + image_paths = [] + for idx, img in enumerate(images): + if isinstance(img, Image.Image): + path = os.path.abspath(os.path.join(backup_dir, f"图{idx+1}.jpg")) + if img.mode != "RGB": + img = img.convert("RGB") + img.save(path, format="JPEG", quality=95) + image_paths.append(path) + + if not image_paths: + return "❌ 图片保存失败" + + _auto_log_append(f"💾 本地已备份至: {backup_dir}") + + # 发布到小红书 + client = get_mcp_client(mcp_url) + result = client.publish_content( + title=title, content=content, images=image_paths, tags=tags + ) + if "error" in result: + _record_error() + _auto_log_append(f"❌ 发布失败: {result['error']} (文案已本地保存)") + return f"❌ 发布失败: {result['error']}\n💾 文案和图片已备份至: {backup_dir}" + + _increment_stat("publishes") + _clear_error_streak() + + # 清理 _temp_publish 中的旧临时文件 + temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish") + try: + if os.path.exists(temp_dir): + for f in os.listdir(temp_dir): + fp = os.path.join(temp_dir, f) + if os.path.isfile(fp) and time.time() - os.path.getmtime(fp) > 3600: + os.remove(fp) + except Exception: + pass + + _auto_log_append(f"🚀 发布成功: {title} (今日第{_daily_stats['publishes']}篇)") + return f"✅ 发布成功！\n📌 标题: {title}\n💾 备份: {backup_dir}\n📊 今日发布: {_daily_stats['publishes']}/{DAILY_LIMITS['publishes']}\n{result.get('text', '')}" + + except Exception as e: + _record_error() + _auto_log_append(f"❌ 一键发布异常: {e}") + return f"❌ 发布失败: {e}" + + + +_scheduler_next_times = {} + + +def _scheduler_loop(comment_enabled, publish_enabled, reply_enabled, like_enabled, + favorite_enabled, + comment_min, comment_max, publish_min, publish_max, + reply_min, reply_max, max_replies_per_run, + like_min, like_max, like_count_per_run, + fav_min, fav_max, fav_count_per_run, + op_start_hour, op_end_hour, + keywords, topics, mcp_url, sd_url_val, sd_model_name, + model, persona_text, quality_mode_val=None, face_swap_on=False): + """后台定时调度循环（含运营时段、冷却、收藏、统计）""" + _auto_log_append("🤖 自动化调度器已启动") + _auto_log_append(f"⏰ 运营时段: {int(op_start_hour)}:00 - {int(op_end_hour)}:00") + + # 首次执行的随机延迟 + next_comment = time.time() + random.randint(10, 60) + next_publish = time.time() + random.randint(30, 120) + next_reply = time.time() + random.randint(15, 90) + next_like = time.time() + random.randint(5, 40) + next_favorite = time.time() + random.randint(10, 50) + + def _update_next_display(): + """更新下次执行时间显示""" + times = {} + if comment_enabled: + times["评论"] = datetime.fromtimestamp(next_comment).strftime("%H:%M:%S") + if like_enabled: + times["点赞"] = datetime.fromtimestamp(next_like).strftime("%H:%M:%S") + if favorite_enabled: + times["收藏"] = datetime.fromtimestamp(next_favorite).strftime("%H:%M:%S") + if reply_enabled: + times["回复"] = datetime.fromtimestamp(next_reply).strftime("%H:%M:%S") + if publish_enabled: + times["发布"] = datetime.fromtimestamp(next_publish).strftime("%H:%M:%S") + _scheduler_next_times.update(times) + + _update_next_display() + + while _auto_running.is_set(): + now = time.time() + + # 检查运营时段 + if not _is_in_operating_hours(int(op_start_hour), int(op_end_hour)): + now_hour = datetime.now().hour + _auto_log_append(f"😴 当前{now_hour}时，不在运营时段({int(op_start_hour)}-{int(op_end_hour)})，休眠中...") + # 休眠到运营时间开始 + for _ in range(300): # 5分钟检查一次 + if not _auto_running.is_set(): + break + time.sleep(1) + continue + + # 检查错误冷却 + if _is_in_cooldown(): + remain = int(_error_cooldown_until - time.time()) + if remain > 0 and remain % 30 == 0: + _auto_log_append(f"⏳ 错误冷却中，剩余 {remain}s") + time.sleep(5) + continue + + # 自动评论 + if comment_enabled and now >= next_comment: + try: + _auto_log_append("--- 🔄 执行自动评论 ---") + msg = auto_comment_once(keywords, mcp_url, model, persona_text) + _auto_log_append(msg) + except Exception as e: + _auto_log_append(f"❌ 自动评论异常: {e}") + interval = random.randint(int(comment_min) * 60, int(comment_max) * 60) + next_comment = time.time() + interval + _auto_log_append(f"⏰ 下次评论: {interval // 60} 分钟后") + _update_next_display() + + # 自动点赞 + if like_enabled and now >= next_like: + try: + _auto_log_append("--- 🔄 执行自动点赞 ---") + msg = auto_like_once(keywords, like_count_per_run, mcp_url) + _auto_log_append(msg) + except Exception as e: + _auto_log_append(f"❌ 自动点赞异常: {e}") + interval = random.randint(int(like_min) * 60, int(like_max) * 60) + next_like = time.time() + interval + _auto_log_append(f"⏰ 下次点赞: {interval // 60} 分钟后") + _update_next_display() + + # 自动收藏 + if favorite_enabled and now >= next_favorite: + try: + _auto_log_append("--- 🔄 执行自动收藏 ---") + msg = auto_favorite_once(keywords, fav_count_per_run, mcp_url) + _auto_log_append(msg) + except Exception as e: + _auto_log_append(f"❌ 自动收藏异常: {e}") + interval = random.randint(int(fav_min) * 60, int(fav_max) * 60) + next_favorite = time.time() + interval + _auto_log_append(f"⏰ 下次收藏: {interval // 60} 分钟后") + _update_next_display() + + # 自动发布 + if publish_enabled and now >= next_publish: + try: + _auto_log_append("--- 🔄 执行自动发布 ---") + msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model, + persona_text=persona_text, quality_mode_val=quality_mode_val, + face_swap_on=face_swap_on) + _auto_log_append(msg) + except Exception as e: + _auto_log_append(f"❌ 自动发布异常: {e}") + interval = random.randint(int(publish_min) * 60, int(publish_max) * 60) + next_publish = time.time() + interval + _auto_log_append(f"⏰ 下次发布: {interval // 60} 分钟后") + _update_next_display() + + # 自动回复评论 + if reply_enabled and now >= next_reply: + try: + _auto_log_append("--- 🔄 执行自动回复评论 ---") + msg = auto_reply_once(max_replies_per_run, mcp_url, model, persona_text) + _auto_log_append(msg) + except Exception as e: + _auto_log_append(f"❌ 自动回复异常: {e}") + interval = random.randint(int(reply_min) * 60, int(reply_max) * 60) + next_reply = time.time() + interval + _auto_log_append(f"⏰ 下次回复: {interval // 60} 分钟后") + _update_next_display() + + # 每5秒检查一次停止信号 + for _ in range(5): + if not _auto_running.is_set(): + break + time.sleep(1) + + _scheduler_next_times.clear() + _auto_log_append("🛑 自动化调度器已停止") + + +def start_scheduler(comment_on, publish_on, reply_on, like_on, favorite_on, + c_min, c_max, p_min, p_max, r_min, r_max, + max_replies_per_run, + l_min, l_max, like_count_per_run, + fav_min, fav_max, fav_count_per_run, + op_start_hour, op_end_hour, + keywords, topics, mcp_url, sd_url_val, sd_model_name, + model, persona_text, quality_mode_val, face_swap_on): + """启动定时自动化""" + global _auto_thread + if _auto_running.is_set(): + return "⚠️ 调度器已在运行中，请先停止" + + if not comment_on and not publish_on and not reply_on and not like_on and not favorite_on: + return "❌ 请至少启用一项自动化功能" + + # 评论/回复需要 LLM，点赞/收藏不需要 + if (comment_on or reply_on): + api_key, _, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置，请先在全局设置中配置提供商" + + _auto_running.set() + _auto_thread = threading.Thread( + target=_scheduler_loop, + args=(comment_on, publish_on, reply_on, like_on, favorite_on, + c_min, c_max, p_min, p_max, r_min, r_max, + max_replies_per_run, + l_min, l_max, like_count_per_run, + fav_min, fav_max, fav_count_per_run, + op_start_hour, op_end_hour, + keywords, topics, mcp_url, sd_url_val, sd_model_name, + model, persona_text), + kwargs={"quality_mode_val": quality_mode_val, "face_swap_on": face_swap_on}, + daemon=True, + ) + _auto_thread.start() + + parts = [] + if comment_on: + parts.append(f"评论({int(c_min)}-{int(c_max)}分)") + if like_on: + parts.append(f"点赞({int(l_min)}-{int(l_max)}分, {int(like_count_per_run)}个/轮)") + if favorite_on: + parts.append(f"收藏({int(fav_min)}-{int(fav_max)}分, {int(fav_count_per_run)}个/轮)") + if publish_on: + parts.append(f"发布({int(p_min)}-{int(p_max)}分)") + if reply_on: + parts.append(f"回复({int(r_min)}-{int(r_max)}分, ≤{int(max_replies_per_run)}条/轮)") + + _auto_log_append(f"调度器已启动: {' + '.join(parts)}") + return f"✅ 自动化已启动 🟢\n⏰ 运营时段: {int(op_start_hour)}:00-{int(op_end_hour)}:00\n任务: {' | '.join(parts)}\n\n💡 点击「刷新日志」查看实时进度" + + +def stop_scheduler(): + """停止定时自动化""" + if not _auto_running.is_set(): + return "⚠️ 调度器未在运行" + _auto_running.clear() + _auto_log_append("⏹️ 收到停止信号，等待当前任务完成...") + return "🛑 调度器停止中...当前任务完成后将完全停止" + + +def get_auto_log(): + """获取自动化运行日志""" + if not _auto_log: + return "📋 暂无日志\n\n💡 点击「一键评论」「一键发布」或启动定时后日志将在此显示" + return "\n".join(_auto_log[-80:]) + + +def get_scheduler_status(): + """获取调度器运行状态 + 下次执行时间 + 今日统计""" + _reset_daily_stats_if_needed() + if _auto_running.is_set(): + lines = ["🟢 **调度器运行中**"] + if _scheduler_next_times: + next_info = " | ".join(f"{k}@{v}" for k, v in _scheduler_next_times.items()) + lines.append(f"⏰ 下次: {next_info}") + s = _daily_stats + lines.append( + f"📊 今日: 💬{s.get('comments',0)} ❤️{s.get('likes',0)} " + f"⭐{s.get('favorites',0)} 🚀{s.get('publishes',0)} " + f"💌{s.get('replies',0)} ❌{s.get('errors',0)}" + ) + if _is_in_cooldown(): + lines.append(f"⏳ 冷却中，{int(_error_cooldown_until - time.time())}s 后恢复") + return "\n".join(lines) + return "⚪ **调度器未运行**" + + +# ================================================== +# 智能学习 & 笔记分析模块 +# ================================================== + +# 定时学习状态 +_learn_running = threading.Event() +_learn_thread: threading.Thread | None = None + + +def analytics_collect_data(mcp_url, user_id, xsec_token): + """采集笔记表现数据""" + if not user_id or not xsec_token: + return "❌ 请先填写用户 ID 和 xsec_token (在「账号登录」Tab 获取)" + try: + client = get_mcp_client(mcp_url) + result = _analytics.collect_note_performance(client, user_id, xsec_token) + if "error" in result: + return f"❌ 数据采集失败: {result['error']}" + return ( + f"✅ 数据采集完成！\n" + f"📝 总笔记数: {result['total']}\n" + f"🔄 更新: {result['updated']} 篇\n\n" + f"💡 点击「计算权重」进行智能学习" + ) + except Exception as e: + logger.error("数据采集失败: %s", e) + return f"❌ 采集失败: {e}" + + +def analytics_calculate_weights(): + """计算内容权重""" + try: + result = _analytics.calculate_weights() + if "error" in result: + return "❌ " + result["error"], _analytics.generate_report() + + top = result.get("top_note") + top_str = f" | 🏆 最佳: {top['title']} (❤️ {top.get('likes', 0)})" if top else "" + msg = ( + f"✅ 权重计算完成！\n" + f"📊 分析了 {result['total_notes']} 篇笔记{top_str}\n\n" + f"💡 权重已自动保存，启用「智能加权发布」后自动生效" + ) + return msg, _analytics.generate_report() + except Exception as e: + logger.error("权重计算失败: %s", e) + return f"❌ 计算失败: {e}", "" + + +def analytics_llm_deep_analysis(model): + """LLM 深度分析笔记表现""" + note_data = _analytics.generate_llm_analysis_prompt() + if not note_data: + return "❌ 暂无笔记数据，请先采集" + try: + api_key, base_url, _ = _get_llm_config() + if not api_key: + return "❌ LLM 未配置" + svc = LLMService(api_key, base_url, model) + result = svc.analyze_note_performance(note_data) + + lines = ["## 🧠 AI 深度分析报告\n"] + + if result.get("high_perform_features"): + lines.append(f"### ✅ 高表现内容特征\n{result['high_perform_features']}\n") + if result.get("low_perform_issues"): + lines.append(f"### ⚠️ 低表现内容反思\n{result['low_perform_issues']}\n") + if result.get("user_preference"): + lines.append(f"### 👤 用户偏好画像\n{result['user_preference']}\n") + + suggestions = result.get("content_suggestions", []) + if suggestions: + lines.append("### 📌 内容方向建议") + for s in suggestions: + priority = "🔴" if s.get("priority", 3) <= 2 else "🟡" if s.get("priority", 3) <= 3 else "🟢" + lines.append(f"- {priority} **{s.get('topic', '')}**: {s.get('reason', '')}") + lines.append("") + + templates = result.get("title_templates", []) + if templates: + lines.append("### ✏️ 标题模板") + for t in templates: + lines.append(f"- 📝 {t}") + lines.append("") + + tags = result.get("recommended_tags", []) + if tags: + lines.append(f"### 🏷️ 推荐标签\n{' '.join(f'`#{t}`' for t in tags)}\n") + + return "\n".join(lines) + + except Exception as e: + logger.error("LLM 分析失败: %s", e) + return f"❌ AI 分析失败: {e}" + + +def analytics_get_report(): + """获取分析报告""" + return _analytics.generate_report() + + +def analytics_get_weighted_topics(): + """获取加权主题列表""" + weighted = _analytics.get_weighted_topics_display() + if weighted: + return weighted + return "暂无权重数据，请先执行「采集数据 → 计算权重」" + + +def _learn_scheduler_loop(mcp_url, user_id, xsec_token, model, interval_hours): + """定时学习后台循环""" + logger.info("定时学习已启动, 间隔 %s 小时", interval_hours) + _auto_log_append(f"🧠 定时学习已启动, 每 {interval_hours} 小时自动分析一次") + while _learn_running.is_set(): + try: + # 采集数据 + client = get_mcp_client(mcp_url) + result = _analytics.collect_note_performance(client, user_id, xsec_token) + if "error" not in result: + _auto_log_append(f"🧠 自动采集完成: {result['total']} 篇笔记, 更新 {result['updated']} 篇") + # 计算权重 + weight_result = _analytics.calculate_weights() + if "error" not in weight_result: + _auto_log_append(f"🧠 权重更新完成: 分析 {weight_result['total_notes']} 篇") + # LLM 深度分析 (如果有配置) + api_key, base_url, _ = _get_llm_config() + if api_key and model: + try: + note_data = _analytics.generate_llm_analysis_prompt() + if note_data: + svc = LLMService(api_key, base_url, model) + svc.analyze_note_performance(note_data) + _auto_log_append("🧠 AI 深度分析完成") + except Exception as e: + _auto_log_append(f"⚠️ AI 分析失败 (非致命): {e}") + else: + _auto_log_append(f"⚠️ 自动采集失败: {result.get('error', '未知')}") + except Exception as e: + _auto_log_append(f"⚠️ 定时学习异常: {e}") + + # 等待下一次执行 + wait_seconds = interval_hours * 3600 + for _ in range(int(wait_seconds / 5)): + if not _learn_running.is_set(): + break + time.sleep(5) + + logger.info("定时学习已停止") + _auto_log_append("🧠 定时学习已停止") + + +def start_learn_scheduler(mcp_url, user_id, xsec_token, model, interval_hours): + """启动定时学习""" + global _learn_thread + if _learn_running.is_set(): + return "⚠️ 定时学习已在运行中" + if not user_id or not xsec_token: + return "❌ 请先在「账号登录」获取用户 ID 和 Token" + + _learn_running.set() + _learn_thread = threading.Thread( + target=_learn_scheduler_loop, + args=(mcp_url, user_id, xsec_token, model, interval_hours), + daemon=True, + ) + _learn_thread.start() + return f"✅ 定时学习已启动 🧠 每 {int(interval_hours)} 小时自动分析" + + +def stop_learn_scheduler(): + """停止定时学习""" + if not _learn_running.is_set(): + return "⚠️ 定时学习未在运行" + _learn_running.clear() + return "🛑 定时学习已停止" + + +# ================================================== +# Windows 开机自启管理 +# ================================================== +from services.autostart import ( + is_autostart_enabled, enable_autostart, disable_autostart, toggle_autostart, +) + +# ================================================== +# UI 构建 +# ================================================== + +config = cfg.all + +_GRADIO_CSS = """ + .status-ok { color: #16a34a; font-weight: bold; } + .status-err { color: #dc2626; font-weight: bold; } + footer { display: none !important; } +""" + diff --git a/ui/app.py b/ui/app.py new file mode 100644 index 0000000..b3b90a8 --- /dev/null +++ b/ui/app.py @@ -0,0 +1,1368 @@ +""" +ui/app.py +Gradio 应用界面构建函数 — 包含全部 Tab UI 组件和事件绑定 +""" +import os +import platform +import logging + +import gradio as gr + +from config_manager import ConfigManager +from sd_service import SDService, DEFAULT_NEGATIVE, FACE_IMAGE_PATH, SD_PRESET_NAMES, get_sd_preset, SD_MODEL_PROFILES +from analytics_service import AnalyticsService +from ui.tab_create import build_tab + +from services.connection import ( + connect_llm, add_llm_provider, remove_llm_provider, on_provider_selected, + connect_sd, on_sd_model_change, check_mcp_status, + get_login_qrcode, logout_xhs, check_login, + save_my_user_id, upload_face_image, load_saved_face_image, +) +from services.persona import ( + DEFAULT_PERSONAS, RANDOM_PERSONA_LABEL, DEFAULT_TOPICS, DEFAULT_STYLES, + get_persona_topics, get_persona_keywords, on_persona_changed, +) +from services.hotspot import ( + search_hotspots, analyze_and_suggest, generate_from_hotspot, + fetch_proactive_notes, on_proactive_note_selected, +) +from services.engagement import ( + load_note_for_comment, ai_generate_comment, send_comment, + fetch_my_notes, on_my_note_selected, fetch_my_note_comments, + ai_reply_comment, send_reply, +) +from services.profile import fetch_my_profile +from services.scheduler import ( + _auto_log, get_auto_log, get_scheduler_status, + start_scheduler, stop_scheduler, + analytics_collect_data, analytics_calculate_weights, + analytics_llm_deep_analysis, analytics_get_report, analytics_get_weighted_topics, + _auto_comment_with_log, _auto_like_with_log, _auto_favorite_with_log, + _auto_publish_with_log, _auto_reply_with_log, + start_learn_scheduler, stop_learn_scheduler, + _get_stats_summary, +) +from services.queue_ops import ( + generate_to_queue, queue_refresh_table, queue_refresh_calendar, + queue_preview_item, queue_approve_item, queue_reject_item, + queue_delete_item, queue_retry_item, queue_publish_now, + queue_start_processor, queue_stop_processor, queue_get_status, + queue_batch_approve, queue_generate_and_refresh, + queue_format_table, queue_format_calendar, +) +from services.autostart import is_autostart_enabled, toggle_autostart +from services.content import generate_copy, generate_images, one_click_export, publish_to_xhs +from publish_queue import STATUS_LABELS + +logger = logging.getLogger("autobot") + +_GRADIO_CSS = None # 可在此处添加自定义 CSS + + +def build_app(cfg: "ConfigManager", analytics: "AnalyticsService") -> gr.Blocks: + """构建并返回 Gradio 应用（包含所有 Tab 和事件绑定）""" + config = cfg.all + + with gr.Blocks( + title="小红书 AI 爆文工坊 V2.0", + ) as app: + gr.Markdown( + "# 🍒 小红书 AI 爆文生产工坊 V2.0\n" + "> 灵感 → 文案 → 绘图 → 发布 → 运营，一站式全闭环" + ) + + # 全局状态 + state_search_result = gr.State("") + + # ============ 全局设置栏 ============ + with gr.Accordion("⚙️ 全局设置 (自动保存)", open=False): + gr.Markdown("#### 🤖 LLM 提供商 (支持所有 OpenAI 兼容接口)") + with gr.Row(): + llm_provider = gr.Dropdown( + label="选择 LLM 提供商", + choices=cfg.get_llm_provider_names(), + value=cfg.get("active_llm", ""), + interactive=True, scale=2, + ) + btn_connect_llm = gr.Button("🔗 连接 LLM", size="sm", scale=1) + with gr.Row(): + llm_model = gr.Dropdown( + label="LLM 模型", value=config["model"], + allow_custom_value=True, interactive=True, scale=2, + ) + llm_provider_info = gr.Markdown( + value="*选择提供商后显示详情*", + ) + with gr.Accordion("➕ 添加 / 管理 LLM 提供商", open=False): + with gr.Row(): + new_provider_name = gr.Textbox( + label="名称", placeholder="如: DeepSeek / GPT-4o / 通义千问", + scale=1, + ) + new_provider_key = gr.Textbox( + label="API Key", type="password", scale=2, + ) + new_provider_url = gr.Textbox( + label="Base URL", placeholder="https://api.openai.com/v1", + value="https://api.openai.com/v1", scale=2, + ) + with gr.Row(): + btn_add_provider = gr.Button("✅ 添加提供商", variant="primary", size="sm") + btn_del_provider = gr.Button("🗑️ 删除当前提供商", variant="stop", size="sm") + provider_mgmt_status = gr.Markdown("") + + gr.Markdown("---") + with gr.Row(): + mcp_url = gr.Textbox( + label="MCP Server URL", value=config["mcp_url"], scale=2, + ) + sd_url = gr.Textbox( + label="SD WebUI URL", value=config["sd_url"], scale=2, + ) + with gr.Row(): + persona = gr.Dropdown( + label="博主人设（评论/回复/自动运营通用）", + choices=[RANDOM_PERSONA_LABEL] + DEFAULT_PERSONAS, + value=config.get("persona", RANDOM_PERSONA_LABEL), + allow_custom_value=True, + interactive=True, + scale=5, + ) + with gr.Row(): + btn_connect_sd = gr.Button("🎨 连接 SD", size="sm") + btn_check_mcp = gr.Button("📡 检查 MCP", size="sm") + with gr.Row(): + sd_model = gr.Dropdown( + label="SD 模型", allow_custom_value=True, + interactive=True, scale=2, + ) + sd_model_info = gr.Markdown("选择模型后显示适配信息", elem_id="sd_model_info") + status_bar = gr.Markdown("🔄 等待连接...") + + gr.Markdown("---") + gr.Markdown("#### 🎭 AI 换脸 (ReActor)") + gr.Markdown( + "> 上传你的头像，生成含人物的图片时自动替换为你的脸\n" + "> 需要 SD WebUI 已安装 [ReActor](https://github.com/Gourieff/sd-webui-reactor) 扩展" + ) + with gr.Row(): + face_image_input = gr.Image( + label="上传头像 (正面清晰照片效果最佳)", + type="pil", + height=180, + scale=1, + ) + face_image_preview = gr.Image( + label="当前头像", + type="pil", + height=180, + interactive=False, + value=SDService.load_face_image(), + scale=1, + ) + with gr.Row(): + btn_save_face = gr.Button("💾 保存头像", variant="primary", size="sm") + face_swap_toggle = gr.Checkbox( + label="🎭 生成图片时启用 AI 换脸", + value=os.path.isfile(FACE_IMAGE_PATH), + interactive=True, + ) + face_status = gr.Markdown( + "✅ 头像已就绪" if os.path.isfile(FACE_IMAGE_PATH) else "ℹ️ 尚未设置头像" + ) + + gr.Markdown("---") + gr.Markdown("#### 🖥️ 系统设置") + with gr.Row(): + autostart_toggle = gr.Checkbox( + label="🚀 Windows 开机自启（静默后台运行）", + value=is_autostart_enabled(), + interactive=(platform.system() == "Windows"), + ) + autostart_status = gr.Markdown( + value="✅ 已启用" if is_autostart_enabled() else "⚪ 未启用", + ) + + # ============ Tab 页面 ============ + with gr.Tabs(): + # -------- Tab 1: 内容创作（迁移至 ui/tab_create.py）-------- + _tab1 = build_tab( + config=config, + styles=DEFAULT_STYLES, + sd_preset_names=SD_PRESET_NAMES, + default_negative=DEFAULT_NEGATIVE, + llm_model=llm_model, + sd_model=sd_model, + sd_url=sd_url, + persona=persona, + status_bar=status_bar, + face_swap_toggle=face_swap_toggle, + face_image_preview=face_image_preview, + mcp_url=mcp_url, + fn_gen_copy=generate_copy, + fn_gen_img=generate_images, + fn_export=one_click_export, + fn_publish=publish_to_xhs, + fn_get_sd_preset=get_sd_preset, + fn_cfg_set=cfg.set, + fn_cfg_update=cfg.update, + ) + res_title = _tab1["res_title"] + res_content = _tab1["res_content"] + res_prompt = _tab1["res_prompt"] + res_tags = _tab1["res_tags"] + quality_mode = _tab1["quality_mode"] + steps = _tab1["steps"] + cfg_scale = _tab1["cfg_scale"] + neg_prompt = _tab1["neg_prompt"] + + # -------- Tab 2: 热点探测 -------- + with gr.Tab("🔥 热点探测"): + gr.Markdown("### 搜索热门内容 → AI 分析趋势 → 一键借鉴创作") + with gr.Row(): + with gr.Column(scale=1): + hot_keyword = gr.Textbox( + label="搜索关键词", placeholder="如：春季穿搭", + ) + hot_sort = gr.Dropdown( + ["综合", "最新", "最多点赞", "最多评论", "最多收藏"], + label="排序", value="综合", + ) + btn_search = gr.Button("🔍 搜索", variant="primary") + search_status = gr.Markdown("") + + with gr.Column(scale=2): + search_output = gr.TextArea( + label="搜索结果", lines=12, interactive=False, + ) + + with gr.Row(): + btn_analyze = gr.Button("🧠 AI 分析热点趋势", variant="primary") + analysis_status = gr.Markdown("") + analysis_output = gr.Markdown(label="分析报告") + topic_from_hot = gr.Textbox( + label="选择/输入创作选题", placeholder="基于分析选一个方向", + ) + + with gr.Row(): + hot_style = gr.Dropdown( + ["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷"], + label="风格", value="好物种草", + ) + btn_gen_from_hot = gr.Button("✨ 基于热点生成文案", variant="primary") + + with gr.Row(): + hot_title = gr.Textbox(label="生成的标题", interactive=True) + hot_content = gr.TextArea(label="生成的正文", lines=8, interactive=True) + with gr.Row(): + hot_prompt = gr.TextArea(label="绘图提示词", lines=3, interactive=True) + hot_tags = gr.Textbox(label="标签", interactive=True) + hot_gen_status = gr.Markdown("") + btn_sync_to_create = gr.Button( + "📋 同步到「内容创作」Tab → 绘图 & 发布", + variant="primary", + ) + + # -------- Tab 3: 评论管家 -------- + with gr.Tab("💬 评论管家"): + gr.Markdown("### 智能评论管理：主动评论引流 & 自动回复粉丝") + + with gr.Tabs(): + # ======== 子 Tab A: 主动评论他人 ======== + with gr.Tab("✍️ 主动评论引流"): + gr.Markdown( + "> **流程**：搜索/浏览笔记 → 选择目标 → 加载内容 → " + "AI 分析笔记+已有评论自动生成高质量评论 → 一键发送" + ) + + # 笔记选择器 + with gr.Row(): + pro_keyword = gr.Textbox( + label="🔍 搜索关键词 (留空则获取推荐)", + placeholder="穿搭、美食、旅行…", + ) + btn_pro_fetch = gr.Button("🔍 获取笔记", variant="primary") + with gr.Row(): + pro_selector = gr.Dropdown( + label="📋 选择目标笔记", + choices=[], interactive=True, + ) + pro_fetch_status = gr.Markdown("") + + # 隐藏字段 + with gr.Row(): + pro_feed_id = gr.Textbox(label="笔记 ID", interactive=False) + pro_xsec_token = gr.Textbox(label="xsec_token", interactive=False) + pro_title = gr.Textbox(label="标题", interactive=False) + + # 加载内容 & AI 分析 + btn_pro_load = gr.Button("📖 加载笔记内容", variant="secondary") + pro_load_status = gr.Markdown("") + + with gr.Row(): + with gr.Column(scale=1): + pro_content = gr.TextArea( + label="📄 笔记正文摘要", lines=8, interactive=False, + ) + with gr.Column(scale=1): + pro_comments = gr.TextArea( + label="💬 已有评论", lines=8, interactive=False, + ) + # 隐藏: 完整文本 + pro_full_text = gr.Textbox(visible=False) + + gr.Markdown("---") + with gr.Row(): + with gr.Column(scale=1): + btn_pro_ai = gr.Button( + "🤖 AI 智能生成评论", variant="primary", size="lg", + ) + pro_ai_status = gr.Markdown("") + with gr.Column(scale=2): + pro_comment_text = gr.TextArea( + label="✏️ 评论内容 (可手动修改)", lines=3, + interactive=True, + placeholder="点击左侧按钮自动生成，也可手动编写", + ) + with gr.Row(): + btn_pro_send = gr.Button("📩 发送评论", variant="primary") + pro_send_status = gr.Markdown("") + + # ======== 子 Tab B: 回复我的评论 ======== + with gr.Tab("💌 回复粉丝评论"): + gr.Markdown( + "> **流程**：选择我的笔记 → 加载评论 → " + "粘贴要回复的评论 → AI 生成回复 → 一键发送" + ) + + # 笔记选择器 (自动用已保存的 userId 获取) + with gr.Row(): + btn_my_fetch = gr.Button("🔍 获取我的笔记", variant="primary") + with gr.Row(): + my_selector = gr.Dropdown( + label="📋 选择我的笔记", + choices=[], interactive=True, + ) + my_fetch_status = gr.Markdown("") + + with gr.Row(): + my_feed_id = gr.Textbox(label="笔记 ID", interactive=False) + my_xsec_token = gr.Textbox(label="xsec_token", interactive=False) + my_title = gr.Textbox(label="笔记标题", interactive=False) + + btn_my_load_comments = gr.Button("📥 加载评论", variant="primary") + my_comment_status = gr.Markdown("") + + my_comments_display = gr.TextArea( + label="📋 粉丝评论列表", lines=12, interactive=False, + ) + + gr.Markdown("---") + gr.Markdown("#### 📝 回复评论") + with gr.Row(): + with gr.Column(scale=1): + my_target_comment = gr.TextArea( + label="要回复的评论内容", lines=3, + placeholder="从上方评论列表中复制粘贴要回复的评论", + ) + btn_my_ai_reply = gr.Button( + "🤖 AI 生成回复", variant="secondary", + ) + my_reply_gen_status = gr.Markdown("") + with gr.Column(scale=1): + my_reply_content = gr.TextArea( + label="回复内容 (可修改)", lines=3, + interactive=True, + ) + btn_my_send_reply = gr.Button( + "📩 发送回复", variant="primary", + ) + my_reply_status = gr.Markdown("") + + # -------- Tab 4: 账号登录 -------- + with gr.Tab("🔐 账号登录"): + gr.Markdown( + "### 小红书账号登录\n" + "> 扫码登录后自动获取 xsec_token，配合用户 ID 即可使用所有功能" + ) + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown( + "**操作步骤：**\n" + "1. 确保 MCP 服务已启动\n" + "2. 点击「获取登录二维码」→ 用小红书 App 扫码\n" + "3. 点击「检查登录状态」→ 自动获取并保存 xsec_token\n" + "4. 首次使用请填写你的用户 ID 并点击保存\n\n" + "⚠️ 登录后不要在其他网页端登录同一账号，否则会被踢出" + ) + btn_get_qrcode = gr.Button( + "📱 获取登录二维码", variant="primary", size="lg", + ) + btn_check_login = gr.Button( + "🔍 检查登录状态 (自动获取 Token)", + variant="secondary", size="lg", + ) + btn_logout = gr.Button( + "🚪 退出登录 (重新扫码)", + variant="stop", size="lg", + ) + login_status = gr.Markdown("🔄 等待操作...") + + gr.Markdown("---") + gr.Markdown( + "#### 📌 我的账号信息\n" + "> **注意**: 小红书号 ≠ 用户 ID\n" + "> - **小红书号 (redId)**: 如 `18688457507`，是你在 App 个人页看到的\n" + "> - **用户 ID (userId)**: 如 `5a695db6e8ac2b72e8af2a53`，24位十六进制字符串\n\n" + "💡 **如何获取 userId?**\n" + "1. 用浏览器打开你的小红书主页\n" + "2. 网址格式为: `xiaohongshu.com/user/profile/xxxxxxxx`\n" + "3. `profile/` 后面的就是你的 userId" + ) + login_user_id = gr.Textbox( + label="我的用户 ID (24位 userId, 非小红书号)", + value=config.get("my_user_id", ""), + placeholder="如: 5a695db6e8ac2b72e8af2a53", + ) + login_xsec_token = gr.Textbox( + label="xsec_token (登录后自动获取)", + value=config.get("xsec_token", ""), + interactive=False, + ) + btn_save_uid = gr.Button( + "💾 保存用户 ID", variant="secondary", + ) + save_uid_status = gr.Markdown("") + + with gr.Column(scale=1): + qr_image = gr.Image( + label="扫码登录", height=350, width=350, + ) + + # -------- Tab 5: 数据看板 -------- + with gr.Tab("📊 数据看板"): + gr.Markdown( + "### 我的账号数据看板\n" + "> 用户 ID 和 xsec_token 从「账号登录」自动获取，直接点击加载即可" + ) + + with gr.Row(): + with gr.Column(scale=1): + data_user_id = gr.Textbox( + label="我的用户 ID (自动填充)", + value=config.get("my_user_id", ""), + interactive=True, + ) + data_xsec_token = gr.Textbox( + label="xsec_token (自动填充)", + value=config.get("xsec_token", ""), + interactive=True, + ) + btn_refresh_token = gr.Button( + "🔄 刷新 Token", variant="secondary", + ) + btn_load_my_data = gr.Button( + "📊 加载我的数据", variant="primary", size="lg", + ) + data_status = gr.Markdown("") + + with gr.Column(scale=2): + profile_card = gr.Markdown( + value="*等待加载...*", + label="账号概览", + ) + + gr.Markdown("---") + gr.Markdown("### 📈 数据可视化") + with gr.Row(): + with gr.Column(scale=1): + chart_interact = gr.Plot(label="📊 核心指标") + with gr.Column(scale=2): + chart_notes = gr.Plot(label="❤ 笔记点赞排行") + + gr.Markdown("---") + notes_detail = gr.Markdown( + value="*加载数据后显示笔记明细表格*", + label="笔记数据明细", + ) + + # -------- Tab 6: 智能学习 -------- + with gr.Tab("🧠 智能学习"): + gr.Markdown( + "### 🧠 智能内容学习引擎\n" + "> 自动分析已发布笔记的表现，学习哪些内容受欢迎，用权重指导未来创作\n\n" + "**工作流程**: 采集数据 → 计算权重 → AI 深度分析 → 自动优化创作\n\n" + "💡 启用后，自动发布将优先生成高权重主题的内容" + ) + + with gr.Row(): + # 左栏: 数据采集 & 权重计算 + with gr.Column(scale=1): + gr.Markdown("#### 📊 数据采集") + learn_user_id = gr.Textbox( + label="用户 ID", value=config.get("my_user_id", ""), + interactive=True, + ) + learn_xsec_token = gr.Textbox( + label="xsec_token", value=config.get("xsec_token", ""), + interactive=True, + ) + btn_learn_collect = gr.Button( + "📊 采集笔记数据", variant="primary", size="lg", + ) + learn_collect_status = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### ⚖️ 权重计算") + btn_learn_calc = gr.Button( + "⚖️ 计算内容权重", variant="primary", size="lg", + ) + learn_calc_status = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### 🤖 AI 深度分析") + gr.Markdown("> 用 LLM 分析笔记数据，找出内容规律，生成策略建议") + btn_learn_ai = gr.Button( + "🧠 AI 深度分析", variant="primary", size="lg", + ) + + gr.Markdown("---") + gr.Markdown("#### ⏰ 定时自动学习") + gr.Markdown("> 每隔 N 小时自动采集数据 + 计算权重 + AI 分析") + learn_interval = gr.Number( + label="学习间隔 (小时)", value=config.get("learn_interval", 6), minimum=1, maximum=48, + ) + with gr.Row(): + btn_learn_start = gr.Button( + "▶ 启动定时学习", variant="primary", size="sm", + ) + btn_learn_stop = gr.Button( + "⏹ 停止", variant="stop", size="sm", + ) + learn_sched_status = gr.Markdown("⚪ 定时学习未启动") + + gr.Markdown("---") + gr.Markdown("#### 🎯 加权主题预览") + gr.Markdown("> 当前权重最高的主题 (自动发布会优先选择)") + btn_show_topics = gr.Button("🔄 刷新加权主题", size="sm") + learn_weighted_topics = gr.Textbox( + label="加权主题池 (权重从高到低)", + value=analytics.get_weighted_topics_display() or "暂无权重数据", + interactive=False, + lines=2, + ) + learn_use_weights = gr.Checkbox( + label="🧠 自动发布时使用智能权重 (推荐)", + value=cfg.get("use_smart_weights", True), + interactive=True, + ) + + # 右栏: 分析报告 + with gr.Column(scale=2): + gr.Markdown("#### 📋 智能学习报告") + learn_report = gr.Markdown( + value=analytics.generate_report(), + label="分析报告", + ) + gr.Markdown("---") + learn_ai_report = gr.Markdown( + value="*点击「AI 深度分析」生成*", + label="AI 深度分析报告", + ) + + # -------- Tab 7: 自动运营 -------- + with gr.Tab("🤖 自动运营"): + gr.Markdown( + "### 🤖 无人值守自动化运营\n" + "> 一键评论引流 + 一键点赞 + 一键收藏 + 一键回复 + 一键发布 + 随机定时全自动\n\n" + "⚠️ **注意**: 请确保已连接 LLM、SD WebUI 和 MCP 服务" + ) + persona_pool_hint = gr.Markdown( + value=f"🎭 当前人设池: **{config.get('persona', '随机')[:20]}** → 关键词/主题池已匹配", + ) + + with gr.Row(): + # 左栏: 一键操作 + with gr.Column(scale=1): + gr.Markdown("#### 💬 一键智能评论") + gr.Markdown( + "> 自动搜索高赞笔记 → AI 分析内容 → 生成评论 → 发送\n" + "每次随机选关键词搜索，从结果中随机选笔记" + ) + auto_comment_keywords = gr.Textbox( + label="评论关键词池 (逗号分隔，随人设自动切换)", + value=", ".join(get_persona_keywords(config.get("persona", ""))), + placeholder="关键词1, 关键词2, ... (切换人设自动更新)", + ) + btn_auto_comment = gr.Button( + "💬 一键评论 (单次)", variant="primary", size="lg", + ) + auto_comment_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### 👍 一键自动点赞") + gr.Markdown( + "> 搜索笔记 → 随机选择多篇 → 依次点赞\n" + "提升账号活跃度，无需 LLM" + ) + auto_like_count = gr.Number( + label="单次点赞数量", value=config.get("auto_like_count", 5), minimum=1, maximum=20, + ) + btn_auto_like = gr.Button( + "👍 一键点赞 (单次)", variant="primary", size="lg", + ) + auto_like_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### ⭐ 一键自动收藏") + gr.Markdown( + "> 搜索笔记 → 随机选择多篇 → 依次收藏\n" + "提升账号活跃度，与点赞互补" + ) + auto_fav_count = gr.Number( + label="单次收藏数量", value=config.get("auto_fav_count", 3), minimum=1, maximum=15, + ) + btn_auto_favorite = gr.Button( + "⭐ 一键收藏 (单次)", variant="primary", size="lg", + ) + auto_favorite_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### 💌 一键自动回复") + gr.Markdown( + "> 扫描我的所有笔记 → 找到粉丝评论 → AI 生成回复 → 逐条发送\n" + "自动跳过自己的评论，模拟真人间隔回复" + ) + auto_reply_max = gr.Number( + label="单次最多回复条数", value=config.get("auto_reply_max", 5), minimum=1, maximum=20, + ) + btn_auto_reply = gr.Button( + "💌 一键回复 (单次)", variant="primary", size="lg", + ) + auto_reply_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### 🚀 一键智能发布") + gr.Markdown( + "> 随机选主题+风格 → AI 生成文案 → SD 生成图片 → 自动发布" + ) + auto_publish_topics = gr.Textbox( + label="主题池 (逗号分隔，随人设自动切换)", + value=", ".join(get_persona_topics(config.get("persona", ""))), + placeholder="主题会从池中随机选取，切换人设自动更新", + ) + btn_auto_publish = gr.Button( + "🚀 一键发布 (单次)", variant="primary", size="lg", + ) + auto_publish_result = gr.Markdown("") + + # 右栏: 定时自动化 + with gr.Column(scale=1): + gr.Markdown("#### ⏰ 随机定时自动化") + gr.Markdown( + "> 设置时间间隔后启动，系统将在随机时间自动执行\n" + "> 模拟真人操作节奏，降低被检测风险" + ) + sched_status = gr.Markdown("⚪ **调度器未运行**") + + # 运营时段设置 + with gr.Group(): + gr.Markdown("##### ⏰ 运营时段") + with gr.Row(): + sched_start_hour = gr.Number( + label="开始时间(整点)", value=config.get("sched_start_hour", 8), minimum=0, maximum=23, + ) + sched_end_hour = gr.Number( + label="结束时间(整点)", value=config.get("sched_end_hour", 23), minimum=1, maximum=24, + ) + + with gr.Group(): + sched_comment_on = gr.Checkbox( + label="✅ 启用自动评论", value=config.get("sched_comment_on", True), + ) + with gr.Row(): + sched_c_min = gr.Number( + label="评论最小间隔(分钟)", value=config.get("sched_c_min", 15), minimum=5, + ) + sched_c_max = gr.Number( + label="评论最大间隔(分钟)", value=config.get("sched_c_max", 45), minimum=10, + ) + + with gr.Group(): + sched_like_on = gr.Checkbox( + label="✅ 启用自动点赞", value=config.get("sched_like_on", True), + ) + with gr.Row(): + sched_l_min = gr.Number( + label="点赞最小间隔(分钟)", value=config.get("sched_l_min", 10), minimum=3, + ) + sched_l_max = gr.Number( + label="点赞最大间隔(分钟)", value=config.get("sched_l_max", 30), minimum=5, + ) + sched_like_count = gr.Number( + label="每轮点赞数量", value=config.get("sched_like_count", 5), minimum=1, maximum=15, + ) + + with gr.Group(): + sched_fav_on = gr.Checkbox( + label="✅ 启用自动收藏", value=config.get("sched_fav_on", True), + ) + with gr.Row(): + sched_fav_min = gr.Number( + label="收藏最小间隔(分钟)", value=config.get("sched_fav_min", 12), minimum=3, + ) + sched_fav_max = gr.Number( + label="收藏最大间隔(分钟)", value=config.get("sched_fav_max", 35), minimum=5, + ) + sched_fav_count = gr.Number( + label="每轮收藏数量", value=config.get("sched_fav_count", 3), minimum=1, maximum=10, + ) + + with gr.Group(): + sched_reply_on = gr.Checkbox( + label="✅ 启用自动回复评论", value=config.get("sched_reply_on", True), + ) + with gr.Row(): + sched_r_min = gr.Number( + label="回复最小间隔(分钟)", value=config.get("sched_r_min", 20), minimum=5, + ) + sched_r_max = gr.Number( + label="回复最大间隔(分钟)", value=config.get("sched_r_max", 60), minimum=10, + ) + sched_reply_max = gr.Number( + label="每轮最多回复条数", value=config.get("sched_reply_max", 3), minimum=1, maximum=10, + ) + + with gr.Group(): + sched_publish_on = gr.Checkbox( + label="✅ 启用自动发布", value=config.get("sched_publish_on", True), + ) + with gr.Row(): + sched_p_min = gr.Number( + label="发布最小间隔(分钟)", value=config.get("sched_p_min", 60), minimum=30, + ) + sched_p_max = gr.Number( + label="发布最大间隔(分钟)", value=config.get("sched_p_max", 180), minimum=60, + ) + + with gr.Row(): + btn_start_sched = gr.Button( + "▶️ 启动定时", variant="primary", size="lg", + ) + btn_stop_sched = gr.Button( + "⏹️ 停止定时", variant="stop", size="lg", + ) + sched_result = gr.Markdown("") + + gr.Markdown("---") + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("#### 📋 运行日志") + with gr.Row(): + btn_refresh_log = gr.Button("🔄 刷新日志", size="sm") + btn_clear_log = gr.Button("🗑️ 清空日志", size="sm") + btn_refresh_stats = gr.Button("📊 刷新统计", size="sm") + auto_log_display = gr.TextArea( + label="自动化运行日志", + value="📋 暂无日志\n\n💡 执行操作后日志将在此显示", + lines=15, + interactive=False, + ) + with gr.Column(scale=1): + gr.Markdown("#### 📊 今日运营统计") + auto_stats_display = gr.Markdown( + value=_get_stats_summary(), + ) + + # -------- Tab 8: 内容排期 📅 -------- + with gr.Tab("📅 内容排期"): + gr.Markdown( + "### 📅 内容排期日历 + 发布队列\n" + "> 批量生成内容 → 预览审核 → 排期定时 → 自动发布，内容创作全流程管控\n\n" + "**工作流**: 生成内容 → 📝草稿 → ✅审核通过 → 🕐排期/立即发布 → 🚀自动发布" + ) + + with gr.Row(): + # ===== 左栏: 生成 & 队列控制 ===== + with gr.Column(scale=1): + gr.Markdown("#### 🔧 批量生成到队列") + queue_gen_topics = gr.Textbox( + label="主题池 (逗号分隔，随人设自动切换)", + value=", ".join(get_persona_topics(config.get("persona", ""))), + placeholder="会从池中随机选取，切换人设自动更新", + ) + with gr.Row(): + queue_gen_count = gr.Number( + label="生成数量", value=config.get("queue_gen_count", 3), minimum=1, maximum=10, + ) + queue_gen_schedule = gr.Textbox( + label="排期时间 (可选)", + placeholder="如 2026-02-10 18:00:00，留空=仅草稿", + ) + btn_queue_generate = gr.Button( + "📝 批量生成 → 加入队列", variant="primary", size="lg", + ) + queue_gen_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### ⚙️ 队列处理器") + queue_processor_status = gr.Markdown( + value=queue_get_status(), + ) + with gr.Row(): + btn_queue_start = gr.Button( + "▶️ 启动队列处理", variant="primary", + ) + btn_queue_stop = gr.Button( + "⏹️ 停止队列处理", variant="stop", + ) + queue_processor_result = gr.Markdown("") + + gr.Markdown("---") + gr.Markdown("#### 🔍 操作单个队列项") + queue_item_id = gr.Textbox( + label="队列项 ID", placeholder="输入 # 号，如 1", + ) + with gr.Row(): + btn_queue_preview = gr.Button("👁️ 预览", size="sm") + btn_queue_approve = gr.Button("✅ 通过", size="sm", variant="primary") + btn_queue_reject = gr.Button("🚫 拒绝", size="sm") + with gr.Row(): + btn_queue_publish_now = gr.Button("🚀 立即发布", size="sm", variant="primary") + btn_queue_retry = gr.Button("🔄 重试", size="sm") + btn_queue_delete = gr.Button("🗑️ 删除", size="sm", variant="stop") + queue_schedule_time = gr.Textbox( + label="排期时间 (审核通过时可指定)", + placeholder="如 2026-02-10 20:00:00，留空=立即待发布", + ) + btn_queue_batch_approve = gr.Button( + "✅ 批量通过所有草稿", variant="secondary", + ) + queue_op_result = gr.Markdown("") + + # ===== 右栏: 队列列表 & 日历 ===== + with gr.Column(scale=2): + gr.Markdown("#### 📋 发布队列") + with gr.Row(): + queue_filter = gr.Dropdown( + label="状态筛选", + choices=["全部"] + list(STATUS_LABELS.values()), + value="全部", + ) + btn_queue_refresh = gr.Button("🔄 刷新", size="sm") + queue_table = gr.Markdown( + value=queue_format_table(), + label="队列列表", + ) + + gr.Markdown("---") + gr.Markdown("#### 📅 排期日历") + queue_calendar = gr.Markdown( + value=queue_format_calendar(), + label="日历视图", + ) + + gr.Markdown("---") + gr.Markdown("#### 👁️ 内容预览") + queue_preview_display = gr.Markdown( + value="*选择队列项 ID 后点击预览*", + ) + + # ================================================== + # 事件绑定 + # ================================================== + + # ---- 全局设置: LLM 提供商管理 ---- + btn_connect_llm.click( + fn=connect_llm, inputs=[llm_provider], + outputs=[llm_model, status_bar], + ) + llm_provider.change( + fn=on_provider_selected, + inputs=[llm_provider], + outputs=[llm_provider_info], + ) + btn_add_provider.click( + fn=add_llm_provider, + inputs=[new_provider_name, new_provider_key, new_provider_url], + outputs=[llm_provider, provider_mgmt_status], + ) + btn_del_provider.click( + fn=remove_llm_provider, + inputs=[llm_provider], + outputs=[llm_provider, provider_mgmt_status], + ) + btn_connect_sd.click( + fn=connect_sd, inputs=[sd_url], + outputs=[sd_model, status_bar, sd_model_info], + ) + sd_model.change( + fn=on_sd_model_change, inputs=[sd_model], + outputs=[sd_model_info], + ) + btn_check_mcp.click( + fn=check_mcp_status, inputs=[mcp_url], + outputs=[status_bar], + ) + + # ---- 头像/换脸管理 ---- + btn_save_face.click( + fn=upload_face_image, + inputs=[face_image_input], + outputs=[face_image_preview, face_status], + ) + + # ---- Tab 2: 热点探测 ---- + btn_search.click( + fn=search_hotspots, + inputs=[hot_keyword, hot_sort, mcp_url], + outputs=[search_status, search_output], + ) + # 搜索结果同步到 state + search_output.change( + fn=lambda x: x, inputs=[search_output], outputs=[state_search_result], + ) + + btn_analyze.click( + fn=analyze_and_suggest, + inputs=[llm_model, hot_keyword, search_output], + outputs=[analysis_status, analysis_output, topic_from_hot], + ) + + btn_gen_from_hot.click( + fn=generate_from_hotspot, + inputs=[llm_model, topic_from_hot, hot_style, search_output, sd_model, persona], + outputs=[hot_title, hot_content, hot_prompt, hot_tags, hot_gen_status], + ) + + # 同步热点文案到内容创作 Tab + btn_sync_to_create.click( + fn=lambda t, c, p, tg: (t, c, p, tg, "✅ 已同步到「内容创作」，可切换 Tab 继续绘图和发布"), + inputs=[hot_title, hot_content, hot_prompt, hot_tags], + outputs=[res_title, res_content, res_prompt, res_tags, status_bar], + ) + + # ---- Tab 3: 评论管家 ---- + + # == 子 Tab A: 主动评论引流 == + btn_pro_fetch.click( + fn=fetch_proactive_notes, + inputs=[pro_keyword, mcp_url], + outputs=[pro_selector, pro_fetch_status], + ) + pro_selector.change( + fn=on_proactive_note_selected, + inputs=[pro_selector], + outputs=[pro_feed_id, pro_xsec_token, pro_title], + ) + btn_pro_load.click( + fn=load_note_for_comment, + inputs=[pro_feed_id, pro_xsec_token, mcp_url], + outputs=[pro_load_status, pro_content, pro_comments, pro_full_text], + ) + btn_pro_ai.click( + fn=ai_generate_comment, + inputs=[llm_model, persona, + pro_title, pro_content, pro_comments], + outputs=[pro_comment_text, pro_ai_status], + ) + btn_pro_send.click( + fn=send_comment, + inputs=[pro_feed_id, pro_xsec_token, pro_comment_text, mcp_url], + outputs=[pro_send_status], + ) + + # == 子 Tab B: 回复粉丝评论 == + btn_my_fetch.click( + fn=fetch_my_notes, + inputs=[mcp_url], + outputs=[my_selector, my_fetch_status], + ) + my_selector.change( + fn=on_my_note_selected, + inputs=[my_selector], + outputs=[my_feed_id, my_xsec_token, my_title], + ) + btn_my_load_comments.click( + fn=fetch_my_note_comments, + inputs=[my_feed_id, my_xsec_token, mcp_url], + outputs=[my_comment_status, my_comments_display], + ) + btn_my_ai_reply.click( + fn=ai_reply_comment, + inputs=[llm_model, persona, + my_title, my_target_comment], + outputs=[my_reply_content, my_reply_gen_status], + ) + btn_my_send_reply.click( + fn=send_reply, + inputs=[my_feed_id, my_xsec_token, my_reply_content, mcp_url], + outputs=[my_reply_status], + ) + + # ---- Tab 4: 账号登录 ---- + btn_get_qrcode.click( + fn=get_login_qrcode, + inputs=[mcp_url], + outputs=[qr_image, login_status], + ) + btn_check_login.click( + fn=check_login, + inputs=[mcp_url], + outputs=[login_status, login_user_id, login_xsec_token], + ) + btn_logout.click( + fn=logout_xhs, + inputs=[mcp_url], + outputs=[login_status], + ) + btn_save_uid.click( + fn=save_my_user_id, + inputs=[login_user_id], + outputs=[save_uid_status], + ) + + # ---- Tab 5: 数据看板 ---- + def refresh_xsec_token(mcp_url): + token = _auto_fetch_xsec_token(mcp_url) + if token: + cfg.set("xsec_token", token) + return gr.update(value=token), "✅ Token 已刷新" + return gr.update(value=cfg.get("xsec_token", "")), "❌ 刷新失败，请确认已登录" + + btn_refresh_token.click( + fn=refresh_xsec_token, + inputs=[mcp_url], + outputs=[data_xsec_token, data_status], + ) + btn_load_my_data.click( + fn=fetch_my_profile, + inputs=[data_user_id, data_xsec_token, mcp_url], + outputs=[data_status, profile_card, chart_interact, chart_notes, notes_detail], + ) + + # ---- Tab 6: 智能学习 ---- + btn_learn_collect.click( + fn=analytics_collect_data, + inputs=[mcp_url, learn_user_id, learn_xsec_token], + outputs=[learn_collect_status], + ) + btn_learn_calc.click( + fn=analytics_calculate_weights, + inputs=[], + outputs=[learn_calc_status, learn_report], + ) + btn_learn_ai.click( + fn=analytics_llm_deep_analysis, + inputs=[llm_model], + outputs=[learn_ai_report], + ) + btn_learn_start.click( + fn=start_learn_scheduler, + inputs=[mcp_url, learn_user_id, learn_xsec_token, llm_model, learn_interval], + outputs=[learn_sched_status], + ) + btn_learn_stop.click( + fn=stop_learn_scheduler, + inputs=[], + outputs=[learn_sched_status], + ) + btn_show_topics.click( + fn=analytics_get_weighted_topics, + inputs=[], + outputs=[learn_weighted_topics], + ) + learn_use_weights.change( + fn=lambda v: cfg.set("use_smart_weights", v) or ("✅ 智能权重已启用" if v else "⚪ 智能权重已关闭"), + inputs=[learn_use_weights], + outputs=[learn_sched_status], + ) + + # ---- Tab 7: 自动运营 ---- + # 人设切换 → 联动更新评论关键词池、主题池和队列主题池（同时保存到配置） + persona.change( + fn=on_persona_changed, + inputs=[persona], + outputs=[auto_comment_keywords, auto_publish_topics, persona_pool_hint, queue_gen_topics], + ) + + btn_auto_comment.click( + fn=_auto_comment_with_log, + inputs=[auto_comment_keywords, mcp_url, llm_model, persona], + outputs=[auto_comment_result, auto_log_display], + ) + btn_auto_like.click( + fn=_auto_like_with_log, + inputs=[auto_comment_keywords, auto_like_count, mcp_url], + outputs=[auto_like_result, auto_log_display], + ) + btn_auto_favorite.click( + fn=_auto_favorite_with_log, + inputs=[auto_comment_keywords, auto_fav_count, mcp_url], + outputs=[auto_favorite_result, auto_log_display], + ) + btn_auto_reply.click( + fn=_auto_reply_with_log, + inputs=[auto_reply_max, mcp_url, llm_model, persona], + outputs=[auto_reply_result, auto_log_display], + ) + btn_auto_publish.click( + fn=_auto_publish_with_log, + inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model, persona, quality_mode, face_swap_toggle], + outputs=[auto_publish_result, auto_log_display], + ) + btn_start_sched.click( + fn=start_scheduler, + inputs=[sched_comment_on, sched_publish_on, sched_reply_on, sched_like_on, + sched_fav_on, + sched_c_min, sched_c_max, sched_p_min, sched_p_max, + sched_r_min, sched_r_max, sched_reply_max, + sched_l_min, sched_l_max, sched_like_count, + sched_fav_min, sched_fav_max, sched_fav_count, + sched_start_hour, sched_end_hour, + auto_comment_keywords, auto_publish_topics, + mcp_url, sd_url, sd_model, llm_model, persona, + quality_mode, face_swap_toggle], + outputs=[sched_result], + ) + btn_stop_sched.click( + fn=stop_scheduler, + inputs=[], + outputs=[sched_result], + ) + btn_refresh_log.click( + fn=lambda: (get_auto_log(), get_scheduler_status()), + inputs=[], + outputs=[auto_log_display, sched_status], + ) + btn_clear_log.click( + fn=lambda: (_auto_log.clear() or "📋 日志已清空"), + inputs=[], + outputs=[auto_log_display], + ) + btn_refresh_stats.click( + fn=lambda: (get_scheduler_status(), _get_stats_summary()), + inputs=[], + outputs=[sched_status, auto_stats_display], + ) + + # ---- 全局设置参数自动保存 ---- + # persona的保存已整合到on_persona_changed函数中 + mcp_url.change(fn=lambda v: cfg.set("mcp_url", v), inputs=[mcp_url], outputs=[]) + sd_url.change(fn=lambda v: cfg.set("sd_url", v), inputs=[sd_url], outputs=[]) + llm_model.change(fn=lambda v: cfg.set("model", v), inputs=[llm_model], outputs=[]) + + # ---- 自动运营参数自动保存 ---- + sched_comment_on.change(fn=lambda v: cfg.set("sched_comment_on", v), inputs=[sched_comment_on], outputs=[]) + sched_like_on.change(fn=lambda v: cfg.set("sched_like_on", v), inputs=[sched_like_on], outputs=[]) + sched_fav_on.change(fn=lambda v: cfg.set("sched_fav_on", v), inputs=[sched_fav_on], outputs=[]) + sched_reply_on.change(fn=lambda v: cfg.set("sched_reply_on", v), inputs=[sched_reply_on], outputs=[]) + sched_publish_on.change(fn=lambda v: cfg.set("sched_publish_on", v), inputs=[sched_publish_on], outputs=[]) + sched_c_min.change(fn=lambda v: cfg.set("sched_c_min", v), inputs=[sched_c_min], outputs=[]) + sched_c_max.change(fn=lambda v: cfg.set("sched_c_max", v), inputs=[sched_c_max], outputs=[]) + sched_l_min.change(fn=lambda v: cfg.set("sched_l_min", v), inputs=[sched_l_min], outputs=[]) + sched_l_max.change(fn=lambda v: cfg.set("sched_l_max", v), inputs=[sched_l_max], outputs=[]) + sched_like_count.change(fn=lambda v: cfg.set("sched_like_count", v), inputs=[sched_like_count], outputs=[]) + sched_fav_min.change(fn=lambda v: cfg.set("sched_fav_min", v), inputs=[sched_fav_min], outputs=[]) + sched_fav_max.change(fn=lambda v: cfg.set("sched_fav_max", v), inputs=[sched_fav_max], outputs=[]) + sched_fav_count.change(fn=lambda v: cfg.set("sched_fav_count", v), inputs=[sched_fav_count], outputs=[]) + sched_r_min.change(fn=lambda v: cfg.set("sched_r_min", v), inputs=[sched_r_min], outputs=[]) + sched_r_max.change(fn=lambda v: cfg.set("sched_r_max", v), inputs=[sched_r_max], outputs=[]) + sched_reply_max.change(fn=lambda v: cfg.set("sched_reply_max", v), inputs=[sched_reply_max], outputs=[]) + sched_p_min.change(fn=lambda v: cfg.set("sched_p_min", v), inputs=[sched_p_min], outputs=[]) + sched_p_max.change(fn=lambda v: cfg.set("sched_p_max", v), inputs=[sched_p_max], outputs=[]) + sched_start_hour.change(fn=lambda v: cfg.set("sched_start_hour", v), inputs=[sched_start_hour], outputs=[]) + sched_end_hour.change(fn=lambda v: cfg.set("sched_end_hour", v), inputs=[sched_end_hour], outputs=[]) + auto_like_count.change(fn=lambda v: cfg.set("auto_like_count", v), inputs=[auto_like_count], outputs=[]) + auto_fav_count.change(fn=lambda v: cfg.set("auto_fav_count", v), inputs=[auto_fav_count], outputs=[]) + auto_reply_max.change(fn=lambda v: cfg.set("auto_reply_max", v), inputs=[auto_reply_max], outputs=[]) + + # ---- 智能学习参数自动保存 ---- + learn_interval.change(fn=lambda v: cfg.set("learn_interval", v), inputs=[learn_interval], outputs=[]) + + # ---- 内容排期参数自动保存 ---- + queue_gen_count.change(fn=lambda v: cfg.set("queue_gen_count", v), inputs=[queue_gen_count], outputs=[]) + + # ---- 开机自启 ---- + autostart_toggle.change( + fn=toggle_autostart, + inputs=[autostart_toggle], + outputs=[autostart_status], + ) + + # ---- Tab 8: 内容排期 ---- + # 队列主题池的更新已整合到 Tab 7 的 persona.change() 事件中 + + # 批量生成到队列 + btn_queue_generate.click( + fn=queue_generate_and_refresh, + inputs=[queue_gen_topics, sd_url, sd_model, llm_model, + persona, quality_mode, face_swap_toggle, + queue_gen_count, queue_gen_schedule], + outputs=[queue_gen_result, queue_table, queue_calendar, queue_processor_status], + ) + + # 刷新队列 + btn_queue_refresh.click( + fn=lambda sf: (queue_refresh_table(sf), queue_refresh_calendar(), queue_get_status()), + inputs=[queue_filter], + outputs=[queue_table, queue_calendar, queue_processor_status], + ) + queue_filter.change( + fn=lambda sf: queue_refresh_table(sf), + inputs=[queue_filter], + outputs=[queue_table], + ) + + # 单项操作 + btn_queue_preview.click( + fn=queue_preview_item, + inputs=[queue_item_id], + outputs=[queue_preview_display], + ) + btn_queue_approve.click( + fn=lambda iid, st: (queue_approve_item(iid, st), queue_format_table(), queue_format_calendar()), + inputs=[queue_item_id, queue_schedule_time], + outputs=[queue_op_result, queue_table, queue_calendar], + ) + btn_queue_reject.click( + fn=lambda iid: (queue_reject_item(iid), queue_format_table()), + inputs=[queue_item_id], + outputs=[queue_op_result, queue_table], + ) + btn_queue_delete.click( + fn=lambda iid: (queue_delete_item(iid), queue_format_table(), queue_format_calendar()), + inputs=[queue_item_id], + outputs=[queue_op_result, queue_table, queue_calendar], + ) + btn_queue_retry.click( + fn=lambda iid: (queue_retry_item(iid), queue_format_table()), + inputs=[queue_item_id], + outputs=[queue_op_result, queue_table], + ) + btn_queue_publish_now.click( + fn=lambda iid: (queue_publish_now(iid), queue_format_table(), queue_format_calendar(), queue_get_status()), + inputs=[queue_item_id], + outputs=[queue_op_result, queue_table, queue_calendar, queue_processor_status], + ) + btn_queue_batch_approve.click( + fn=lambda sf: (queue_batch_approve(sf), queue_format_table(), queue_format_calendar()), + inputs=[queue_filter], + outputs=[queue_op_result, queue_table, queue_calendar], + ) + + # 队列处理器 + btn_queue_start.click( + fn=lambda: (queue_start_processor(), queue_get_status()), + inputs=[], + outputs=[queue_processor_result, queue_processor_status], + ) + btn_queue_stop.click( + fn=lambda: (queue_stop_processor(), queue_get_status()), + inputs=[], + outputs=[queue_processor_result, queue_processor_status], + ) + + # ---- 启动时自动加载全局设置 ---- + def load_global_settings(): + """页面加载时恢复全局设置""" + config = cfg.all + providers = cfg.get_llm_provider_names() + active_llm = cfg.get("active_llm", "") + persona_val = config.get("persona", RANDOM_PERSONA_LABEL) + + # 初始化LLM提供商信息显示 + provider_info = on_provider_selected(active_llm) if active_llm else "*选择提供商后显示详情*" + + # 获取人设对应的关键词和主题池 + keywords = get_persona_keywords(persona_val) + topics = get_persona_topics(persona_val) + keywords_str = ", ".join(keywords) + topics_str = ", ".join(topics) + + # 尝试连接 SD 并获取模型列表 + sd_models = [] + sd_status = "🔄 等待连接..." + sd_model_val = None + try: + svc = SDService(config["sd_url"]) + ok, msg = svc.check_connection() + if ok: + sd_models = svc.get_models() + sd_model_val = sd_models[0] if sd_models else None + sd_status = f"✅ {msg}" + except Exception: + pass + + # 图片生成参数 + quality_mode_val = config.get("quality_mode", "标准 (约1分钟)") + steps_val = config.get("sd_steps", 20) + cfg_scale_val = config.get("sd_cfg_scale", 5.5) + neg_prompt_val = config.get("sd_negative_prompt", DEFAULT_NEGATIVE) + + # 自动运营参数 + return ( + gr.update(choices=providers, value=active_llm), # llm_provider + provider_info, # llm_provider_info + config["mcp_url"], # mcp_url + config["sd_url"], # sd_url + persona_val, # persona + gr.update(choices=sd_models, value=sd_model_val), # sd_model + sd_status, # status_bar + keywords_str, # auto_comment_keywords + topics_str, # auto_publish_topics + topics_str, # queue_gen_topics + quality_mode_val, # quality_mode + steps_val, # steps + cfg_scale_val, # cfg_scale + neg_prompt_val, # neg_prompt + config.get("auto_like_count", 5), # auto_like_count + config.get("auto_fav_count", 3), # auto_fav_count + config.get("auto_reply_max", 5), # auto_reply_max + config.get("sched_comment_on", True), # sched_comment_on + config.get("sched_like_on", True), # sched_like_on + config.get("sched_fav_on", True), # sched_fav_on + config.get("sched_reply_on", True), # sched_reply_on + config.get("sched_publish_on", True), # sched_publish_on + config.get("sched_c_min", 15), # sched_c_min + config.get("sched_c_max", 45), # sched_c_max + config.get("sched_l_min", 10), # sched_l_min + config.get("sched_l_max", 30), # sched_l_max + config.get("sched_like_count", 5), # sched_like_count + config.get("sched_fav_min", 12), # sched_fav_min + config.get("sched_fav_max", 35), # sched_fav_max + config.get("sched_fav_count", 3), # sched_fav_count + config.get("sched_r_min", 20), # sched_r_min + config.get("sched_r_max", 60), # sched_r_max + config.get("sched_reply_max", 3), # sched_reply_max + config.get("sched_p_min", 60), # sched_p_min + config.get("sched_p_max", 180), # sched_p_max + config.get("sched_start_hour", 8), # sched_start_hour + config.get("sched_end_hour", 23), # sched_end_hour + config.get("learn_interval", 6), # learn_interval + config.get("queue_gen_count", 3), # queue_gen_count + config.get("use_smart_weights", True), # learn_use_weights + ) + + app.load( + fn=load_global_settings, + inputs=[], + outputs=[ + llm_provider, llm_provider_info, mcp_url, sd_url, persona, sd_model, status_bar, + auto_comment_keywords, auto_publish_topics, queue_gen_topics, + quality_mode, steps, cfg_scale, neg_prompt, + auto_like_count, auto_fav_count, auto_reply_max, + sched_comment_on, sched_like_on, sched_fav_on, sched_reply_on, sched_publish_on, + sched_c_min, sched_c_max, sched_l_min, sched_l_max, sched_like_count, + sched_fav_min, sched_fav_max, sched_fav_count, + sched_r_min, sched_r_max, sched_reply_max, + sched_p_min, sched_p_max, sched_start_hour, sched_end_hour, + learn_interval, queue_gen_count, learn_use_weights, + ], + ) + + + # ================================================== + + return app