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
345 lines
14 KiB
Python
345 lines
14 KiB
Python
"""
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内容创作 Tab UI 模块
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包含 Tab 1「✨ 内容创作」的所有 Gradio 组件定义和事件绑定
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"""
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import logging
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import gradio as gr
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logger = logging.getLogger("autobot")
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def build_tab(
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config: dict,
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styles: list,
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sd_preset_names: list,
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default_negative: str,
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# 共享的 Gradio 组件（由 main.py 创建并传入）
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llm_model,
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sd_model,
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sd_url,
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persona,
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status_bar,
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face_swap_toggle,
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face_image_preview,
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mcp_url,
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# 业务处理函数（由 main.py 传入，避免循环导入）
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fn_gen_copy,
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fn_gen_img,
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fn_export,
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fn_publish,
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fn_get_sd_preset,
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fn_cfg_set,
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fn_cfg_update,
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# 新增: 批量创作 & 选题推荐回调
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fn_batch_generate=None,
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fn_topic_recommend=None,
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fn_evaluate_match=None,
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):
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"""
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构建「✨ 内容创作」Tab，注册所有事件绑定。
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Parameters
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----------
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config : 当前配置字典（用于读取初始值）
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styles : 文案风格列表
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sd_preset_names : SD 生成模式名称列表
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default_negative: 默认反向提示词
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llm_model / sd_model / ... : 由全局设置栏创建的共享组件引用
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fn_* : 业务逻辑回调函数
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Returns
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-------
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None —— 所有事件绑定均在此函数内完成，无需外部再次绑定。
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"""
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with gr.Tab("✨ 内容创作"):
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# 本 Tab 私有的图片状态（不被其他 Tab 使用）
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state_images = gr.State([])
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with gr.Row():
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# ---- 左栏：输入 ----
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with gr.Column(scale=3):
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gr.Markdown("### 💡 构思")
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# === 智能选题推荐 ===
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with gr.Accordion("🧠 智能选题推荐", open=False):
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btn_recommend = gr.Button("🔍 获取推荐选题", variant="secondary", size="sm")
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topic_recommendations = gr.Markdown(
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value="点击上方按钮获取推荐选题",
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label="推荐选题",
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)
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topic = gr.Textbox(label="笔记主题", placeholder="例如：优衣库早春穿搭")
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style = gr.Dropdown(
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styles,
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label="风格", value="好物种草",
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)
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btn_gen_copy = gr.Button("✨ 第一步：生成文案", variant="primary")
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gr.Markdown("---")
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gr.Markdown("### 🎨 绘图参数")
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# 封面图策略选择
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cover_strategy = gr.Radio(
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["人物特写", "场景展示", "对比图", "文字卡片"],
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label="封面图策略",
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value="人物特写",
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info="影响 SD 构图和尺寸",
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)
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quality_mode = gr.Radio(
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sd_preset_names,
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label="生成模式",
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value=config.get("quality_mode", "标准 (约1分钟)"),
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info="快速≈30s 标准≈1min 精细≈2-3min (SDXL)",
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)
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with gr.Accordion("高级设置 (覆盖预设)", open=False):
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neg_prompt = gr.Textbox(
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label="反向提示词",
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value=config.get("sd_negative_prompt", default_negative),
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lines=2,
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)
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steps = gr.Slider(8, 50, value=config.get("sd_steps", 20), step=1, label="步数")
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cfg_scale = gr.Slider(1, 15, value=config.get("sd_cfg_scale", 5.5), step=0.5, label="CFG Scale")
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enhance_level = gr.Slider(
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0.0, 2.0,
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value=config.get("enhance_level", 1.0),
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step=0.1,
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label="美化强度",
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info="0=关闭 1=默认 2=强化（锐化+皮肤校色+通透感）",
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)
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btn_gen_img = gr.Button("🎨 第二步：生成图片", variant="primary")
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# ---- 中栏：文案编辑 ----
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with gr.Column(scale=4):
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gr.Markdown("### 📝 文案编辑")
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res_title = gr.Textbox(
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label="标题",
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interactive=True,
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info="小红书限制 ≤20 字，超出将无法发布",
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)
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res_content = gr.TextArea(
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label="正文 (可手动修改)", lines=12, interactive=True,
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)
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res_prompt = gr.TextArea(
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label="绘图提示词", lines=3, interactive=True,
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)
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res_tags = gr.Textbox(
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label="话题标签 (逗号分隔)", interactive=True,
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placeholder="穿搭, 春季, 好物种草",
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)
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# ---- 右栏：预览 & 发布 ----
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with gr.Column(scale=3):
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gr.Markdown("### 🖼️ 视觉预览")
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gallery = gr.Gallery(label="AI 生成图片", columns=2, height=300)
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local_images = gr.File(
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label="📁 上传本地图片（可混排）",
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file_count="multiple",
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file_types=["image"],
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)
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gr.Markdown("### 🚀 发布")
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schedule_time = gr.Textbox(
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label="定时发布 (可选, ISO8601格式)",
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placeholder="如 2026-02-08T18:00:00+08:00，留空=立即发布",
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)
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with gr.Row():
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btn_export = gr.Button("📂 导出本地", variant="secondary")
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btn_publish = gr.Button("🚀 发布到小红书", variant="primary")
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publish_msg = gr.Markdown("")
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# === 图文匹配度评分 ===
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with gr.Accordion("📊 图文匹配度", open=False):
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btn_eval_match = gr.Button("评估匹配度", variant="secondary", size="sm")
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match_score_display = gr.Markdown("点击按钮评估文案与图片的匹配度")
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# === 批量创作面板 ===
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with gr.Accordion("📦 批量创作", open=False):
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with gr.Row():
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with gr.Column(scale=2):
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batch_topics = gr.TextArea(
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label="批量主题 (每行一个，最多10个)",
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placeholder="优衣库早春穿搭\n百元床品测评\n新手养宠攻略",
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lines=5,
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)
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with gr.Column(scale=1):
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batch_template = gr.Dropdown(
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choices=["(不使用模板)", "好物种草", "日常分享", "攻略教程"],
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value="(不使用模板)",
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label="内容模板",
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)
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btn_batch_gen = gr.Button("🚀 批量生成", variant="primary")
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btn_smart_gen = gr.Button("🧠 智能选题+生成", variant="secondary")
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batch_result = gr.Markdown("")
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# ---- 事件绑定 ----
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btn_gen_copy.click(
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fn=fn_gen_copy,
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inputs=[llm_model, topic, style, sd_model, persona],
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outputs=[res_title, res_content, res_prompt, res_tags, status_bar],
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)
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def save_quality_mode(mode):
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fn_cfg_set("quality_mode", mode)
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p = fn_get_sd_preset(mode)
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return p["steps"], p["cfg_scale"]
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quality_mode.change(
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fn=save_quality_mode,
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inputs=[quality_mode],
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outputs=[steps, cfg_scale],
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)
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steps.change(fn=lambda s: fn_cfg_set("sd_steps", s), inputs=[steps], outputs=[])
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cfg_scale.change(fn=lambda c: fn_cfg_set("sd_cfg_scale", c), inputs=[cfg_scale], outputs=[])
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neg_prompt.change(fn=lambda n: fn_cfg_set("sd_negative_prompt", n), inputs=[neg_prompt], outputs=[])
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enhance_level.change(fn=lambda v: fn_cfg_set("enhance_level", v), inputs=[enhance_level], outputs=[])
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btn_gen_img.click(
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fn=fn_gen_img,
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inputs=[sd_url, res_prompt, neg_prompt, sd_model, steps, cfg_scale,
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face_swap_toggle, face_image_preview, quality_mode, persona,
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enhance_level],
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outputs=[gallery, state_images, status_bar],
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)
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btn_export.click(
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fn=fn_export,
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inputs=[res_title, res_content, state_images],
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outputs=[publish_msg],
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)
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btn_publish.click(
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fn=fn_publish,
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inputs=[res_title, res_content, res_tags, state_images,
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local_images, mcp_url, schedule_time],
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outputs=[publish_msg],
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)
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# ---- 新增事件绑定 ----
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# 智能选题推荐
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def _on_recommend(model_name):
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if not fn_topic_recommend:
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return "⚠️ 选题推荐功能未连接"
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try:
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recommendations = fn_topic_recommend(model_name)
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if not recommendations:
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return "暂无推荐选题，请先搜索热点或积累数据"
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lines = []
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for i, r in enumerate(recommendations, 1):
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angles_str = "、".join(r.get("angles", [])[:2])
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lines.append(
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f"**{i}. {r['topic']}** (评分: {r['score']})\n"
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f" {r.get('reason', '')}\n"
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f" 💡 角度: {angles_str}"
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)
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return "\n\n".join(lines)
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except Exception as e:
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logger.error("选题推荐失败: %s", e)
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return f"❌ 推荐失败: {e}"
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btn_recommend.click(
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fn=_on_recommend,
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inputs=[llm_model],
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outputs=[topic_recommendations],
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)
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# 图文匹配度评估
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def _on_eval_match(model_name, content, sd_prompt):
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if not fn_evaluate_match:
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return "⚠️ 图文匹配度评估功能未连接"
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if not content or not sd_prompt:
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return "请先生成文案和图片后再评估"
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try:
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result = fn_evaluate_match(model_name, content, sd_prompt)
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if result.get("skipped"):
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return "⚠️ 评估超时或失败，已跳过"
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score = result.get("match_score", 0)
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suggestions = result.get("suggestions", [])
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icon = "🟢" if score >= 80 else ("🟡" if score >= 50 else "🔴")
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text = f"{icon} 匹配度: **{score}/100**"
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if suggestions:
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text += "\n\n改进建议:\n" + "\n".join(f"- {s}" for s in suggestions)
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if score < 50:
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text += "\n\n⚠️ 匹配度较低，建议重新生成图片提示词"
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return text
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except Exception as e:
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return f"评估失败: {e}"
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btn_eval_match.click(
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fn=_on_eval_match,
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inputs=[llm_model, res_content, res_prompt],
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outputs=[match_score_display],
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)
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# 批量生成
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def _on_batch_generate(model_name, topics_text, style_val, sd_model_name, persona_text, template):
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if not fn_batch_generate:
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return "⚠️ 批量创作功能未连接"
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topics = [t.strip() for t in topics_text.strip().split("\n") if t.strip()]
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if not topics:
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return "❌ 请输入至少一个主题（每行一个）"
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template_name = template if template != "(不使用模板)" else ""
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try:
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results, status = fn_batch_generate(
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model_name, topics, style_val, sd_model_name, persona_text, template_name
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)
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lines = [f"### {status}\n"]
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for r in results:
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if "error" in r:
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lines.append(f"❌ **{r.get('topic', '未知')}**: {r['error']}")
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else:
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lines.append(f"✅ **{r.get('title', '无标题')}** — {r.get('topic', '')}")
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return "\n\n".join(lines)
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except Exception as e:
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return f"❌ 批量生成失败: {e}"
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btn_batch_gen.click(
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fn=_on_batch_generate,
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inputs=[llm_model, batch_topics, style, sd_model, persona, batch_template],
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outputs=[batch_result],
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)
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# 智能选题+生成
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def _on_smart_generate(model_name, style_val, sd_model_name, persona_text):
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if not fn_topic_recommend or not fn_batch_generate:
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return "⚠️ 智能选题功能未连接"
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try:
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recommendations = fn_topic_recommend(model_name)
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if not recommendations:
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return "❌ 选题引擎未找到推荐主题"
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# 取前 3 个推荐
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topics = [r["topic"] for r in recommendations[:3]]
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results, status = fn_batch_generate(
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model_name, topics, style_val, sd_model_name, persona_text, ""
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)
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lines = [f"### {status}\n", "**使用推荐选题:**"]
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for r in results:
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if "error" in r:
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lines.append(f"❌ **{r.get('topic', '未知')}**: {r['error']}")
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else:
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lines.append(f"✅ **{r.get('title', '无标题')}**")
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return "\n\n".join(lines)
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except Exception as e:
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return f"❌ 智能生成失败: {e}"
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btn_smart_gen.click(
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fn=_on_smart_generate,
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inputs=[llm_model, style, sd_model, persona],
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outputs=[batch_result],
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)
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# 返回可能被其他 Tab 引用的组件
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return {
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"res_title": res_title,
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"res_content": res_content,
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"res_prompt": res_prompt,
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"res_tags": res_tags,
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"quality_mode": quality_mode,
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"steps": steps,
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"cfg_scale": cfg_scale,
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"neg_prompt": neg_prompt,
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"enhance_level": enhance_level,
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"cover_strategy": cover_strategy,
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}
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