zhoujie
1ec520b47e
- 新增智能选题引擎 `TopicEngine`，整合热点数据与历史权重，提供多维度评分和创作角度建议 - 新增内容模板系统 `ContentTemplate`，支持从 JSON 文件加载模板并应用于文案生成 - 新增批量创作功能 `batch_generate_copy`，支持串行生成多篇文案并自动入草稿队列 - 升级文案质量流水线：实现 Prompt 分层架构（基础层 + 风格层 + 人设层）、LLM 自检与改写机制、深度去 AI 化后处理 - 优化图文协同：新增封面图策略选择、SD prompt 与文案语义联动、图文匹配度评估 - 集成数据闭环：在文案生成中自动注入 `AnalyticsService` 权重数据，实现发布 → 数据回收 → 优化创作的完整循环 - 更新 UI 组件：新增选题推荐展示区、批量创作折叠面板、封面图策略选择器和图文匹配度评分展示 ♻️ refactor(llm): 重构 Prompt 架构并增强去 AI 化处理 - 将 `PROMPT_COPYWRITING` 拆分为分层架构（基础层 + 风格层 + 人设层），提高维护性和灵活性 - 增强 `_humanize_content` 方法：新增语气词注入、标点不规范化、段落节奏打散和 emoji 密度控制 - 新增 `_self_check` 和 `_self_check_rewrite` 方法，实现文案 AI 痕迹自检与自动改写 - 新增 `evaluate_image_text_match` 方法，支持文案与 SD prompt 的语义匹配度评估（可选，失败不阻塞） - 新增封面图策略配置 `COVER_STRATEGIES` 和情感基调映射 `EMOTION_SD_MAP` 📝 docs(openspec): 归档内容创作优化提案和详细规格 - 新增 `openspec/changes/archive/2026-02-28-optimize-content-creation/` 目录，包含设计文档、提案、规格说明和任务清单 - 新增 `openspec/specs/` 下的批量创作、文案质量流水线、图文协同、服务内容和智能选题引擎规格文档 - 更新 `openspec/specs/services-content/spec.md`，反映新增的批量创作和智能选题入口函数 🔧 chore(config): 更新服务配置和 UI 集成 - 在 `services/content.py` 中集成权重数据自动注入逻辑，实现数据驱动创作 - 在 `ui/app.py` 中新增选题推荐、批量生成和图文匹配度评估的回调函数 - 在 `ui/tab_create.py` 中新增智能选题推荐区、批量创作面板和图文匹配度评估组件 - 修复 `services/sd_service.py` 中的头像文件路径问题，确保目录存在
408 lines
14 KiB
Python
408 lines
14 KiB
Python
"""
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services/content.py
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文案生成、图片生成、一键导出、发布到小红书
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"""
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import os
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import re
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import time
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import platform
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import subprocess
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import logging
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from PIL import Image
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from .config_manager import ConfigManager, OUTPUT_DIR
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from .llm_service import LLMService
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from .sd_service import SDService, get_sd_preset
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from .mcp_client import get_mcp_client
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from .connection import _get_llm_config
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from .persona import _resolve_persona
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logger = logging.getLogger("autobot")
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cfg = ConfigManager()
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def generate_copy(model, topic, style, sd_model_name, persona_text):
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"""生成文案（自动适配 SD 模型，支持人设，自动注入权重数据）"""
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api_key, base_url, _ = _get_llm_config()
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if not api_key:
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return "", "", "", "", "❌ 请先配置并连接 LLM 提供商"
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try:
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svc = LLMService(api_key, base_url, model)
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persona = _resolve_persona(persona_text) if persona_text else None
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# 尝试自动注入权重数据（数据闭环 9.1）
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data = None
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try:
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from .analytics_service import AnalyticsService
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analytics = AnalyticsService()
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if analytics.has_weights:
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weight_insights = analytics.weights_summary
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title_advice = analytics.get_title_advice()
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hot_tags = ", ".join(analytics.get_top_tags(8))
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data = svc.generate_weighted_copy(
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topic, style,
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weight_insights=weight_insights,
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title_advice=title_advice,
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hot_tags=hot_tags,
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sd_model_name=sd_model_name,
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persona=persona,
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)
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logger.info("使用加权文案生成路径（权重数据已注入）")
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except Exception as e:
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logger.debug("权重数据注入跳过: %s", e)
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# 无权重或权重路径失败时，退回基础生成
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if data is None:
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data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona)
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cfg.set("model", model)
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tags = data.get("tags", [])
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return (
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data.get("title", ""),
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data.get("content", ""),
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data.get("sd_prompt", ""),
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", ".join(tags) if tags else "",
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"✅ 文案生成完毕",
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)
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except Exception as e:
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logger.error("文案生成失败: %s", e)
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return "", "", "", "", f"❌ 生成失败: {e}"
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def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img, quality_mode, persona_text=None, enhance_level: float = 1.0):
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"""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，支持美化增强）"""
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if not model:
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return None, [], "❌ 未选择 SD 模型"
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try:
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svc = SDService(sd_url)
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# 判断是否启用换脸
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face_image = None
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if face_swap_on:
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# Gradio 可能传 PIL.Image / numpy.ndarray / 文件路径 / None
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if face_img is not None:
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if isinstance(face_img, Image.Image):
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face_image = face_img
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elif isinstance(face_img, str) and os.path.isfile(face_img):
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face_image = Image.open(face_img).convert("RGB")
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else:
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# numpy array 等其他格式
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try:
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import numpy as np
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if isinstance(face_img, np.ndarray):
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face_image = Image.fromarray(face_img).convert("RGB")
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logger.info("头像从 numpy array 转换为 PIL Image")
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except Exception as e:
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logger.warning("头像格式转换失败 (%s): %s", type(face_img).__name__, e)
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# 如果 UI 没传有效头像，从本地文件加载
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if face_image is None:
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face_image = SDService.load_face_image()
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if face_image is not None:
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logger.info("换脸头像已就绪: %dx%d", face_image.width, face_image.height)
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else:
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logger.warning("换脸已启用但未找到有效头像")
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persona = _resolve_persona(persona_text) if persona_text else None
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images = svc.txt2img(
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prompt=prompt,
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negative_prompt=neg_prompt,
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model=model,
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steps=int(steps),
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cfg_scale=float(cfg_scale),
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face_image=face_image,
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quality_mode=quality_mode,
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persona=persona,
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enhance_level=float(enhance_level),
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)
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preset = get_sd_preset(quality_mode)
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swap_hint = " (已换脸)" if face_image else ""
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return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint} [{quality_mode}]"
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except Exception as e:
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logger.error("图片生成失败: %s", e)
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return None, [], f"❌ 绘图失败: {e}"
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def one_click_export(title, content, images):
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"""导出文案和图片到本地"""
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if not title:
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return "❌ 无法导出：没有标题"
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safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20]
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folder_name = f"{int(time.time())}_{safe_title}"
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folder_path = os.path.join(OUTPUT_DIR, folder_name)
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os.makedirs(folder_path, exist_ok=True)
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with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f:
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f.write(f"{title}\n\n{content}")
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saved_paths = []
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if images:
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for idx, img in enumerate(images):
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path = os.path.join(folder_path, f"图{idx+1}.jpg")
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if isinstance(img, Image.Image):
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if img.mode != "RGB":
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img = img.convert("RGB")
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img.save(path, format="JPEG", quality=95)
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saved_paths.append(os.path.abspath(path))
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# 尝试打开文件夹
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try:
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abs_path = os.path.abspath(folder_path)
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if platform.system() == "Windows":
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os.startfile(abs_path)
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elif platform.system() == "Darwin":
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subprocess.call(["open", abs_path])
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else:
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subprocess.call(["xdg-open", abs_path])
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except Exception:
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pass
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return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)"
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def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time):
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"""通过 MCP 发布到小红书（含输入校验和临时文件自动清理）"""
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# === 发布前校验 ===
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if not title:
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return "❌ 缺少标题"
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if len(title) > 20:
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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"
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client = get_mcp_client(mcp_url)
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ai_temp_files: list = [] # 追踪本次写入的临时文件，用于 finally 清理
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try:
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# 收集图片路径
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image_paths = []
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# 先保存 AI 生成的图片到临时目录
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if images:
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temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
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os.makedirs(temp_dir, exist_ok=True)
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for idx, img in enumerate(images):
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if isinstance(img, Image.Image):
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path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.jpg"))
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if img.mode != "RGB":
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img = img.convert("RGB")
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img.save(path, format="JPEG", quality=95)
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image_paths.append(path)
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ai_temp_files.append(path) # 登记临时文件
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# 添加本地上传的图片
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if local_images:
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for img_file in local_images:
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img_path = img_file.name if hasattr(img_file, 'name') else str(img_file)
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if os.path.exists(img_path):
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image_paths.append(os.path.abspath(img_path))
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# === 图片校验 ===
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if not image_paths:
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return "❌ 至少需要 1 张图片才能发布"
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if len(image_paths) > 18:
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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"
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for p in image_paths:
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if not os.path.exists(p):
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return f"❌ 图片文件不存在：{p}"
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# 解析标签
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tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None
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# 定时发布
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schedule = schedule_time if schedule_time and schedule_time.strip() else None
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result = client.publish_content(
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title=title,
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content=content,
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images=image_paths,
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tags=tags,
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schedule_at=schedule,
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)
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if "error" in result:
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return f"❌ 发布失败: {result['error']}"
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return f"✅ 发布成功！\n{result.get('text', '')}"
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except Exception as e:
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logger.error("发布失败: %s", e)
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return f"❌ 发布异常: {e}"
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finally:
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# 清理本次写入的 AI 临时图片（无论成功/失败）
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for tmp_path in ai_temp_files:
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try:
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if os.path.exists(tmp_path):
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os.remove(tmp_path)
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except OSError as cleanup_err:
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logger.warning("临时文件清理失败 %s: %s", tmp_path, cleanup_err)
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# ========== 批量创作 ==========
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def batch_generate_copy(
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model: str,
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topics: list[str],
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style: str,
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sd_model_name: str = "",
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persona_text: str = "",
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template_name: str = "",
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publish_queue=None,
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) -> tuple[list[dict], str]:
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"""
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批量生成多篇文案（串行），自动插入发布队列草稿
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Args:
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model: LLM 模型名
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topics: 主题列表 (最多 10 个)
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style: 写作风格
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sd_model_name: SD 模型名
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persona_text: 人设文本
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template_name: 可选的模板名
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publish_queue: 可选的 PublishQueue 实例
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Returns:
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(results_list, status_msg)
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"""
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if not topics:
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return [], "❌ 请输入至少一个主题"
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if len(topics) > 10:
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return [], "❌ 批量生成最多支持 10 个主题，请减少数量"
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api_key, base_url, _ = _get_llm_config()
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if not api_key:
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return [], "❌ 请先配置并连接 LLM 提供商"
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# 加载模板覆盖
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prompt_override = ""
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tags_preset = []
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if template_name:
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try:
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from .content_template import ContentTemplate
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ct = ContentTemplate()
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override = ct.apply_template(template_name)
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style = override.get("style") or style
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prompt_override = override.get("prompt_override", "")
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tags_preset = override.get("tags_preset", [])
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except Exception as e:
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logger.warning("模板加载失败，使用默认参数: %s", e)
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svc = LLMService(api_key, base_url, model)
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persona = _resolve_persona(persona_text) if persona_text else None
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results = []
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success_count = 0
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fail_count = 0
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for idx, topic in enumerate(topics):
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topic = topic.strip()
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if not topic:
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continue
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try:
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data = svc.generate_copy(
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topic, style,
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sd_model_name=sd_model_name,
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persona=persona,
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)
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# 如有模板 prompt_override，它已通过风格参数间接生效
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# 合并模板标签
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tags = data.get("tags", [])
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if tags_preset:
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existing = set(tags)
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for t in tags_preset:
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if t not in existing:
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tags.append(t)
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data["tags"] = tags
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data["batch_index"] = idx
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results.append(data)
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success_count += 1
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# 自动入队为草稿
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if publish_queue:
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try:
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publish_queue.add(
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title=data.get("title", ""),
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content=data.get("content", ""),
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sd_prompt=data.get("sd_prompt", ""),
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tags=data.get("tags", []),
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topic=topic,
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style=style,
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persona=persona_text if persona_text else "",
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status="draft",
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)
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except Exception as e:
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logger.warning("批量草稿入队失败 #%d: %s", idx, e)
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logger.info("批量生成 %d/%d 完成: %s", idx + 1, len(topics), topic[:20])
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except Exception as e:
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logger.error("批量生成 %d/%d 失败 [%s]: %s", idx + 1, len(topics), topic[:20], e)
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results.append({
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"batch_index": idx,
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"topic": topic,
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"error": str(e),
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})
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fail_count += 1
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status = f"✅ 批量生成完成: {success_count} 成功"
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if fail_count:
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status += f", {fail_count} 失败"
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if publish_queue and success_count:
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status += f" | {success_count} 篇已入草稿队列"
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return results, status
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def generate_copy_with_topic_engine(
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model: str,
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style: str,
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sd_model_name: str = "",
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persona_text: str = "",
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count: int = 1,
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hotspot_data: dict = None,
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publish_queue=None,
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) -> tuple[list[dict], str]:
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"""
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使用智能选题引擎自动选题 + 生成文案
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Args:
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model: LLM 模型名
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style: 写作风格
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sd_model_name: SD 模型名
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persona_text: 人设文本
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count: 生成篇数
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hotspot_data: 可选的热点分析数据
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publish_queue: 可选的 PublishQueue 实例
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Returns:
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(results_list, status_msg)
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"""
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try:
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from .analytics_service import AnalyticsService
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from .topic_engine import TopicEngine
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analytics = AnalyticsService()
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engine = TopicEngine(analytics)
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recommendations = engine.recommend_topics(count=count, hotspot_data=hotspot_data)
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if not recommendations:
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return [], "❌ 选题引擎未找到推荐主题，请先进行热点搜索或积累数据"
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topics = [r["topic"] for r in recommendations]
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results, status = batch_generate_copy(
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model=model,
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topics=topics,
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style=style,
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sd_model_name=sd_model_name,
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persona_text=persona_text,
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publish_queue=publish_queue,
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)
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# 把选题推荐信息附加到结果
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for result in results:
|
||
idx = result.get("batch_index", -1)
|
||
if 0 <= idx < len(recommendations):
|
||
result["topic_recommendation"] = recommendations[idx]
|
||
|
||
return results, status
|
||
|
||
except Exception as e:
|
||
logger.error("智能选题生成失败: %s", e)
|
||
return [], f"❌ 智能选题生成失败: {e}" |