feat(short_drama_editing): 添加强短剧混剪剪辑脚本生成完整功能

- 新增短剧混剪脚本生成专用提示词类并完成注册
- 优化merge_script工具函数，支持多视频路径输入、自动填充视频信息
- 扩展SDP处理流水线，新增直接基于剧情分析和字幕生成剪辑脚本的逻辑
- 更新WebUI相关组件与工具函数，适配新的短剧混剪脚本生成流程
- 添加字幕时间戳校验与路径规范化工具，确保生成脚本合法性

app/services/SDP/generate_script_short.py

@@ -1,6 +1,8 @@
 """
 视频脚本生成pipeline，串联各个处理步骤
 """
+import json
+import os
 from typing import Any, Dict, Optional
 from loguru import logger
 
@@ -16,6 +18,10 @@ def generate_script_result(
     base_url: str = None,
     custom_clips: int = 5,
     provider: str = None,
+    video_paths=None,
+    plot_analysis: Optional[str] = None,
+    short_name: str = "",
+    drama_genre: str = "",
     *,
     srt_path: Optional[str] = None,
     subtitle_content: Optional[str] = None,
@@ -30,6 +36,10 @@ def generate_script_result(
         base_url: API基础URL，可选
         custom_clips: 自定义片段数量，默认5
         provider: LLM服务提供商，可选
+        video_paths: 原始视频路径列表，用于生成 video_id/video_name
+        plot_analysis: 已完成的剧情理解文本，提供时会跳过混剪内部剧情理解
+        short_name: 短剧名称
+        drama_genre: 短剧类型
         srt_path: 字幕文件路径（向后兼容）
         subtitle_content: 字幕文本内容
         subtitle_file_path: 字幕文件路径（推荐）
@@ -56,10 +66,23 @@ def generate_script_result(
             provider=provider,
             srt_path=resolved_path,
             subtitle_content=resolved_content,
+            plot_analysis=plot_analysis,
+            video_paths=video_paths,
+            short_name=short_name,
+            drama_genre=drama_genre,
         )
 
-        adjusted_results = openai_analysis['plot_points']
-        final_script = merge_script(adjusted_results, output_path)
+        if openai_analysis.get("script_items"):
+            final_script = openai_analysis["script_items"]
+            if not output_path or not str(output_path).strip():
+                raise ValueError("output_path不能为空")
+            os.makedirs(os.path.dirname(str(output_path)) or ".", exist_ok=True)
+            with open(output_path, "w", encoding="utf-8") as f:
+                json.dump(final_script, f, ensure_ascii=False, indent=4)
+            logger.info(f"短剧混剪脚本生成完成：{output_path}")
+        else:
+            adjusted_results = openai_analysis['plot_points']
+            final_script = merge_script(adjusted_results, output_path, video_paths=video_paths)
 
         return {"status": "success", "script": final_script}
 
@@ -79,6 +102,10 @@ def generate_script(
     base_url: str = None,
     custom_clips: int = 5,
     provider: str = None,
+    video_paths=None,
+    plot_analysis: Optional[str] = None,
+    short_name: str = "",
+    drama_genre: str = "",
     *,
     subtitle_content: Optional[str] = None,
     subtitle_file_path: Optional[str] = None,
@@ -93,6 +120,10 @@ def generate_script(
         base_url: API基础URL，可选
         custom_clips: 自定义片段数量，默认5
         provider: LLM服务提供商，可选
+        video_paths: 原始视频路径列表，用于生成 video_id/video_name
+        plot_analysis: 已完成的剧情理解文本
+        short_name: 短剧名称
+        drama_genre: 短剧类型
         subtitle_content: 字幕文本内容（可选）
         subtitle_file_path: 字幕文件路径（推荐使用，可选）
 
@@ -110,6 +141,10 @@ def generate_script(
         base_url=base_url,
         custom_clips=custom_clips,
         provider=provider,
+        video_paths=video_paths,
+        plot_analysis=plot_analysis,
+        short_name=short_name,
+        drama_genre=drama_genre,
         srt_path=srt_path,
         subtitle_content=subtitle_content,
         subtitle_file_path=subtitle_file_path,

app/services/SDP/utils/step1_subtitle_analyzer_openai.py

@@ -1,11 +1,16 @@
 """
 使用统一LLM服务，分析字幕文件，返回剧情梗概和爆点
 """
+import os
 import traceback
 import json
 from loguru import logger
 
 from app.services.subtitle_text import has_timecodes, normalize_subtitle_text, read_subtitle_text
+from app.services.short_drama_narration_validation import (
+    build_subtitle_index,
+    parse_script_timestamp_range,
+)
 # 导入新的提示词管理系统
 from app.services.prompts import PromptManager
 # 导入统一LLM服务
@@ -14,6 +19,176 @@ from app.services.llm.unified_service import UnifiedLLMService
 from app.services.llm.migration_adapter import _run_async_safely
 
 
+def _normalize_paths(paths):
+    if isinstance(paths, str):
+        paths = [paths]
+    if not paths:
+        return []
+
+    normalized_paths = []
+    seen = set()
+    for path in paths:
+        if not isinstance(path, str):
+            continue
+        path = path.strip()
+        if not path or path in seen:
+            continue
+        normalized_paths.append(path)
+        seen.add(path)
+    return normalized_paths
+
+
+def _coerce_positive_int(value):
+    try:
+        number = int(value)
+    except (TypeError, ValueError):
+        return None
+    return number if number > 0 else None
+
+
+def _match_video_id_by_name(video_name, video_paths):
+    video_name = os.path.basename(str(video_name or "").strip())
+    if not video_name:
+        return None
+
+    for index, video_path in enumerate(video_paths, start=1):
+        if os.path.basename(video_path) == video_name:
+            return index
+    return None
+
+
+def _default_video_name(video_id, video_paths):
+    if 1 <= video_id <= len(video_paths):
+        return os.path.basename(video_paths[video_id - 1])
+    return ""
+
+
+def _normalize_short_mix_items(items, video_paths, subtitle_content):
+    if not isinstance(items, list) or not items:
+        raise ValueError("短剧混剪脚本 items 必须是非空数组")
+
+    normalized_video_paths = _normalize_paths(video_paths)
+    subtitle_index = build_subtitle_index(subtitle_content, normalized_video_paths)
+    available_video_ids = {cue.video_id for cue in subtitle_index}
+    if normalized_video_paths:
+        available_video_ids.update(range(1, len(normalized_video_paths) + 1))
+
+    normalized_items = []
+    ranges_by_video = {}
+    for index, raw_item in enumerate(items, start=1):
+        if not isinstance(raw_item, dict):
+            raise ValueError(f"第 {index} 个混剪片段必须是对象")
+
+        item_id = index
+        video_id = (
+            _match_video_id_by_name(raw_item.get("video_name") or raw_item.get("source_video"), normalized_video_paths)
+            or _coerce_positive_int(raw_item.get("video_id") or raw_item.get("video_index"))
+            or 1
+        )
+        if available_video_ids and video_id not in available_video_ids:
+            raise ValueError(f"片段 {item_id} 的 video_id={video_id} 不在已选视频范围内")
+
+        try:
+            start_ms, end_ms, timestamp = parse_script_timestamp_range(raw_item.get("timestamp", ""))
+        except ValueError as exc:
+            raise ValueError(f"片段 {item_id}: {exc}") from exc
+        if start_ms >= end_ms:
+            raise ValueError(f"片段 {item_id} 的开始时间必须早于结束时间")
+
+        video_cues = [cue for cue in subtitle_index if cue.video_id == video_id]
+        if video_cues:
+            min_start = min(cue.start_ms for cue in video_cues)
+            max_end = max(cue.end_ms for cue in video_cues)
+            if start_ms < min_start or end_ms > max_end:
+                raise ValueError(f"片段 {item_id} 的时间戳不在视频 {video_id} 的字幕范围内")
+            if not any(start_ms < cue.end_ms and end_ms > cue.start_ms for cue in video_cues):
+                raise ValueError(f"片段 {item_id} 的时间戳没有命中视频 {video_id} 的字幕内容")
+
+        picture = str(
+            raw_item.get("picture")
+            or raw_item.get("title")
+            or raw_item.get("narrative_function")
+            or raw_item.get("intent")
+            or raw_item.get("story_role")
+            or ""
+        ).strip()
+        if not picture:
+            raise ValueError(f"片段 {item_id} 的 picture 不能为空")
+
+        video_name = str(raw_item.get("video_name") or "").strip()
+        if normalized_video_paths:
+            video_name = _default_video_name(video_id, normalized_video_paths)
+
+        normalized_items.append(
+            {
+                "_id": item_id,
+                "video_id": video_id,
+                "video_name": video_name,
+                "timestamp": timestamp,
+                "picture": picture,
+                "narration": f"播放原片{item_id}",
+                "OST": 1,
+            }
+        )
+        ranges_by_video.setdefault(video_id, []).append((start_ms, end_ms, item_id))
+
+    for video_id, ranges in ranges_by_video.items():
+        ranges = sorted(ranges, key=lambda item: (item[0], item[1], item[2]))
+        previous_start, previous_end, previous_id = ranges[0]
+        for start_ms, end_ms, item_id in ranges[1:]:
+            if start_ms < previous_end:
+                raise ValueError(f"视频 {video_id} 的片段 {item_id} 与片段 {previous_id} 时间戳重叠")
+            if end_ms > previous_end:
+                previous_start, previous_end, previous_id = start_ms, end_ms, item_id
+
+    return normalized_items
+
+
+def _generate_short_mix_script(
+    *,
+    subtitle_content,
+    plot_analysis,
+    custom_clips,
+    provider,
+    model_name,
+    api_key,
+    base_url,
+    video_paths=None,
+    short_name="",
+    drama_genre="",
+):
+    script_generation_prompt = PromptManager.get_prompt(
+        category="short_drama_editing",
+        name="script_generation",
+        parameters={
+            "drama_name": short_name or "短剧",
+            "drama_genre": drama_genre or "短剧",
+            "plot_analysis": plot_analysis,
+            "subtitle_content": subtitle_content,
+            "custom_clips": int(custom_clips or 5),
+        },
+    )
+
+    response = _run_async_safely(
+        UnifiedLLMService.generate_text,
+        prompt=script_generation_prompt,
+        provider=provider,
+        model=model_name,
+        api_key=api_key,
+        base_url=base_url,
+        temperature=0.1,
+        max_tokens=4000,
+    )
+
+    from webui.tools.generate_short_summary import parse_and_fix_json
+    script_data = parse_and_fix_json(response)
+    if not script_data:
+        raise ValueError("无法解析短剧混剪脚本JSON")
+
+    script_items = script_data.get("items") or script_data.get("segments") or script_data.get("plot_points")
+    return _normalize_short_mix_items(script_items, video_paths, subtitle_content)
+
+
 def analyze_subtitle(
     model_name: str,
     api_key: str = None,
@@ -21,7 +196,11 @@ def analyze_subtitle(
     custom_clips: int = 5,
     provider: str = None,
     srt_path: str = None,
-    subtitle_content: str = None
+    subtitle_content: str = None,
+    plot_analysis: str = None,
+    video_paths=None,
+    short_name: str = "",
+    drama_genre: str = "",
 ) -> dict:
     """分析字幕内容，返回完整的分析结果
 
@@ -33,6 +212,10 @@ def analyze_subtitle(
         provider (str, optional): LLM服务提供商. Defaults to None.
         srt_path (str, optional): SRT字幕文件路径（与subtitle_content二选一）
         subtitle_content (str, optional): SRT字幕文本内容（与srt_path二选一）
+        plot_analysis (str, optional): 已审核/缓存的剧情理解文本，提供时直接进入混剪脚本生成
+        video_paths (list, optional): 原始视频路径列表，用于补齐 video_id/video_name
+        short_name (str, optional): 短剧名称
+        drama_genre (str, optional): 短剧类型
 
     Returns:
         dict: 包含剧情梗概和结构化的时间段分析的字典
@@ -87,6 +270,27 @@ def analyze_subtitle(
 
         logger.info(f"使用LLM服务分析字幕，提供商: {provider}, 模型: {model_name}")
 
+        if plot_analysis and str(plot_analysis).strip():
+            logger.info("使用已有剧情理解直接生成短剧混剪脚本")
+            script_items = _generate_short_mix_script(
+                subtitle_content=subtitle_content,
+                plot_analysis=str(plot_analysis).strip(),
+                custom_clips=custom_clips,
+                provider=provider,
+                model_name=model_name,
+                api_key=api_key,
+                base_url=base_url,
+                video_paths=video_paths,
+                short_name=short_name,
+                drama_genre=drama_genre,
+            )
+            return {
+                "summary": str(plot_analysis).strip(),
+                "plot_titles": [],
+                "plot_points": [],
+                "script_items": script_items,
+            }
+
         # 使用新的提示词管理系统
         subtitle_analysis_prompt = PromptManager.get_prompt(
             category="short_drama_editing",
@@ -120,6 +324,28 @@ def analyze_subtitle(
         logger.info(f"字幕分析完成，找到 {len(summary_data.get('plot_titles', []))} 个关键情节")
         logger.debug(json.dumps(summary_data, indent=4, ensure_ascii=False))
 
+        try:
+            script_items = _generate_short_mix_script(
+                subtitle_content=subtitle_content,
+                plot_analysis=json.dumps(summary_data, ensure_ascii=False, indent=2),
+                custom_clips=custom_clips,
+                provider=provider,
+                model_name=model_name,
+                api_key=api_key,
+                base_url=base_url,
+                video_paths=video_paths,
+                short_name=short_name,
+                drama_genre=drama_genre,
+            )
+            return {
+                "summary": summary_data.get("summary", ""),
+                "plot_titles": summary_data.get("plot_titles", []),
+                "plot_points": [],
+                "script_items": script_items,
+            }
+        except Exception as direct_script_error:
+            logger.warning(f"直接生成短剧混剪脚本失败，回退到时间段定位: {direct_script_error}")
+
         # 构建爆点标题列表
         plot_titles_text = ""
         logger.info(f"找到 {len(summary_data.get('plot_titles', []))} 个片段")

app/services/SDP/utils/step5_merge_script.py

@@ -8,7 +8,8 @@ from typing import Dict, List
 
 def merge_script(
         plot_points: List[Dict],
-        output_path: str
+        output_path: str,
+        video_paths=None,
 ):
     """合并生成最终脚本
 
@@ -19,6 +20,10 @@ def merge_script(
     Returns:
         str: 最终合并的脚本
     """
+    if isinstance(video_paths, str):
+        video_paths = [video_paths]
+    video_paths = [path for path in (video_paths or []) if isinstance(path, str) and path.strip()]
+
     # 创建包含所有信息的临时列表
     final_script = []
 
@@ -29,9 +34,12 @@ def merge_script(
             "_id": number,
             "timestamp": plot_point["timestamp"],
             "picture": plot_point["picture"],
-            "narration": f"播放原生_{os.urandom(4).hex()}",
+            "narration": f"播放原片{number}",
             "OST": 1,  # OST=0 仅保留解说 OST=2 保留解说和原声
         }
+        if video_paths:
+            script_item["video_id"] = 1
+            script_item["video_name"] = os.path.basename(video_paths[0])
         final_script.append(script_item)
         number += 1
 

app/services/prompts/short_drama_editing/__init__.py

@@ -11,6 +11,7 @@
 
 from .subtitle_analysis import SubtitleAnalysisPrompt
 from .plot_extraction import PlotExtractionPrompt
+from .script_generation import ScriptGenerationPrompt
 from ..manager import PromptManager
 
 
@@ -25,9 +26,14 @@ def register_prompts():
     plot_extraction_prompt = PlotExtractionPrompt()
     PromptManager.register_prompt(plot_extraction_prompt, is_default=True)
 
+    # 注册混剪脚本生成提示词
+    script_generation_prompt = ScriptGenerationPrompt()
+    PromptManager.register_prompt(script_generation_prompt, is_default=True)
+
 
 __all__ = [
     "SubtitleAnalysisPrompt",
-    "PlotExtractionPrompt", 
+    "PlotExtractionPrompt",
+    "ScriptGenerationPrompt",
     "register_prompts"
 ]

app/services/prompts/short_drama_editing/script_generation.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+"""
+@Project: 短剧混剪-剪辑脚本生成
+@File   : script_generation.py
+@Description: 基于剧情理解和字幕直接生成短剧混剪脚本
+"""
+
+from ..base import ParameterizedPrompt, PromptMetadata, ModelType, OutputFormat
+
+
+class ScriptGenerationPrompt(ParameterizedPrompt):
+    """短剧混剪脚本生成提示词"""
+
+    def __init__(self):
+        metadata = PromptMetadata(
+            name="script_generation",
+            category="short_drama_editing",
+            version="v1.0",
+            description="基于剧情理解和原始字幕直接生成短剧混剪脚本，不生成解说文案",
+            model_type=ModelType.TEXT,
+            output_format=OutputFormat.JSON,
+            tags=["短剧", "混剪", "剪辑脚本", "时间戳", "多视频", "原声"],
+            parameters=[
+                "drama_name",
+                "drama_genre",
+                "plot_analysis",
+                "subtitle_content",
+                "custom_clips",
+            ],
+        )
+        super().__init__(
+            metadata,
+            required_parameters=["plot_analysis", "subtitle_content", "custom_clips"],
+        )
+
+        self._system_prompt = (
+            "你是一名专业短剧混剪剪辑师。你必须严格输出JSON，"
+            "只从字幕中选择真实存在的可剪辑原声片段，不生成解说文案。"
+        )
+
+    def get_template(self) -> str:
+        return """# 短剧混剪脚本生成任务
+
+## 目标
+根据剧情理解和原始字幕，为短剧《${drama_name}》生成一份可直接裁剪的混剪 JSON 脚本。
+
+短剧混剪与短剧解说的区别：
+- 不生成解说文案。
+- 不需要用户审核旁白。
+- 直接从剧情理解中选择能串成故事线的原片片段。
+- 每个片段默认保留原声，OST 必须为 1。
+
+## 用户选择的短剧类型
+<drama_genre>
+${drama_genre}
+</drama_genre>
+
+## 需要生成的片段数量
+<custom_clips>
+${custom_clips}
+</custom_clips>
+
+## 剧情理解材料
+<plot>
+${plot_analysis}
+</plot>
+
+## 原始字幕（含视频编号和局部时间戳）
+<subtitles>
+${subtitle_content}
+</subtitles>
+
+## 选择原则
+1. 选择 ${custom_clips} 个片段，尽量形成“开端 -> 冲突升级 -> 高潮/反转 -> 悬念或阶段结果”的完整观看路径。
+2. 只能使用原始字幕中真实存在的视频编号、视频文件名和时间范围。
+3. timestamp 必须是对应 video_id 内部的局部时间戳，格式为 "HH:MM:SS,mmm-HH:MM:SS,mmm"。
+4. 同一个 video_id 内的片段不得交叉或重叠；整体顺序要服务剧情理解，单个视频内尽量按时间顺序。
+5. 优先选择关键对白、身份揭露、情绪爆发、反转、冲突升级和能看懂前因后果的片段。
+6. 单个片段建议 5-45 秒；不要只截 1-2 秒的孤立金句，也不要截过长的流水账。
+7. 如果两个关键剧情之间跳跃太大，优先选择包含上下文的连续时间段，而不是硬切爆点。
+8. picture 要描述画面中人物、动作、情绪、场景和该片段的剧情作用。
+9. narration 字段必须写成“播放原片+_id”，例如 _id 为 3 时写“播放原片3”。
+10. OST 必须为 1，表示保留原片原声。
+
+## 字段规则
+- _id：从 1 开始连续递增。
+- video_id：来自字幕分段标题，例如“视频 2”就填 2；单视频填 1。
+- video_name：对应视频文件名，必须从字幕分段标题提取；单视频也要填写。
+- timestamp：必须来自对应视频字幕时间轴。
+- picture：非空字符串。
+- narration：固定为“播放原片+_id”。
+- OST：固定为 1。
+
+## 输出格式
+只输出严格 JSON：
+
+{
+  "items": [
+    {
+      "_id": 1,
+      "video_id": 1,
+      "video_name": "1.mp4",
+      "timestamp": "00:00:01,000-00:00:12,500",
+      "picture": "女主被当众羞辱仍然强撑，冲突正式爆发，为后续逆袭埋下情绪钩子",
+      "narration": "播放原片1",
+      "OST": 1
+    }
+  ]
+}
+
+现在请生成短剧混剪脚本。"""

webui/components/script_settings.py

@@ -26,6 +26,7 @@ from webui.tools.generate_short_summary import (
 
 
 SCRIPT_TABLE_BASE_COLUMNS = ["_id", "video_id", "video_name", "timestamp", "picture", "narration", "OST"]
+SCRIPT_TABLE_TEXT_COLUMNS = {"video_name", "timestamp", "picture", "narration", "value"}
 MODE_FILE = "file_selection"
 MODE_AUTO = "auto"
 MODE_SHORT = "short"
@@ -666,16 +667,42 @@ def render_short_generate_options(tr):
     渲染Short Generate模式下的特殊选项
     在Short Generate模式下，替换原有的输入框为自定义片段选项
     """
-    summary_narration_panel(tr, SUMMARY_MODE_CONFIGS[MODE_SHORT_SUMMARY])
-    # 显示自定义片段数量选择器
-    custom_clips = st.number_input(
-        tr("自定义片段"),
-        min_value=1,
-        max_value=20,
-        value=st.session_state.get('custom_clips', 5),
-        help=tr("设置需要生成的短视频片段数量"),
-        key="custom_clips_input"
-    )
+    summary_config = SUMMARY_MODE_CONFIGS[MODE_SHORT_SUMMARY]
+    summary_narration_panel(tr, summary_config)
+
+    type_option_key = _summary_state_key(summary_config, "type_option")
+    custom_type_key = _summary_state_key(summary_config, "custom_type")
+    type_options = [code for code, _ in summary_config["type_options"]]
+    if st.session_state.get(type_option_key) not in type_options:
+        st.session_state[type_option_key] = summary_config["default_type"]
+
+    show_custom_type = st.session_state.get(type_option_key, summary_config["default_type"]) == "custom"
+    option_cols = st.columns([1.1, 1.1, 1], vertical_alignment="bottom") if show_custom_type else st.columns([1.1, 1], vertical_alignment="bottom")
+    with option_cols[0]:
+        st.selectbox(
+            tr(summary_config["type_label_key"]),
+            options=type_options,
+            format_func=lambda code: tr(dict(summary_config["type_options"]).get(code, code)),
+            key=type_option_key,
+        )
+    option_index = 1
+    if show_custom_type:
+        with option_cols[option_index]:
+            st.text_input(
+                tr(summary_config["custom_type_label_key"]),
+                key=custom_type_key,
+                placeholder=tr(summary_config["custom_type_placeholder_key"]),
+            )
+        option_index += 1
+    with option_cols[option_index]:
+        custom_clips = st.number_input(
+            tr("自定义片段"),
+            min_value=1,
+            max_value=20,
+            value=st.session_state.get('custom_clips', 5),
+            help=tr("设置需要生成的短视频片段数量"),
+            key="custom_clips_input"
+        )
     st.session_state['custom_clips'] = custom_clips
 
 
@@ -729,6 +756,7 @@ def summary_narration_panel(tr, summary_config):
     plot_analysis_key = _summary_state_key(summary_config, "plot_analysis")
     plot_source_key = _summary_state_key(summary_config, "plot_analysis_subtitle_path")
     plot_signature_key = _summary_state_key(summary_config, "plot_analysis_signature")
+    pending_plot_key = _summary_state_key(summary_config, "pending_plot_analysis")
 
     st.markdown(
         f"""
@@ -815,6 +843,15 @@ def summary_narration_panel(tr, summary_config):
         st.session_state[plot_analysis_key] = ""
         st.session_state[plot_source_key] = ""
         st.session_state[plot_signature_key] = ""
+        st.session_state.pop(pending_plot_key, None)
+    else:
+        pending_plot = st.session_state.pop(pending_plot_key, None)
+        if isinstance(pending_plot, dict) and pending_plot.get("signature") == current_signature:
+            pending_analysis = str(pending_plot.get("plot_analysis") or "")
+            if pending_analysis:
+                st.session_state[plot_analysis_key] = pending_analysis
+                st.session_state[plot_source_key] = pending_plot.get("subtitle_path") or current_subtitle_path
+                st.session_state[plot_signature_key] = current_signature
 
     if analyze_plot_clicked:
         with st.spinner(tr("Analyzing plot...")):
@@ -1003,10 +1040,17 @@ def _script_json_to_table(script_data):
             {"value": json.dumps(item, ensure_ascii=False)}
             for item in script_data
         ]
-        return pd.DataFrame(rows, columns=["value"])
+        return _normalize_script_table_types(pd.DataFrame(rows, columns=["value"]))
 
     columns = _ordered_script_columns(script_data)
-    return pd.DataFrame(script_data, columns=columns)
+    return _normalize_script_table_types(pd.DataFrame(script_data, columns=columns))
+
+
+def _normalize_script_table_types(table_data):
+    for column in SCRIPT_TABLE_TEXT_COLUMNS:
+        if column in table_data.columns:
+            table_data[column] = table_data[column].where(table_data[column].notna(), "").astype(str).astype("object")
+    return table_data
 
 
 def _normalize_script_table_value(column, value):
@@ -1723,8 +1767,66 @@ def render_script_buttons(tr, params):
             generate_script_docu(params, tr)
         elif script_path == "short":
             # 执行 短剧混剪 脚本生成
+            summary_config = SUMMARY_MODE_CONFIGS[MODE_SHORT_SUMMARY]
+            type_option_key = _summary_state_key(summary_config, "type_option")
+            custom_type_key = _summary_state_key(summary_config, "custom_type")
+            web_search_key = _summary_state_key(summary_config, "web_search_enabled")
+            plot_analysis_key = _summary_state_key(summary_config, "plot_analysis")
+            plot_source_key = _summary_state_key(summary_config, "plot_analysis_subtitle_path")
+            plot_signature_key = _summary_state_key(summary_config, "plot_analysis_signature")
+            pending_plot_key = _summary_state_key(summary_config, "pending_plot_analysis")
+            if (
+                st.session_state.get(type_option_key) == "custom"
+                and not str(st.session_state.get(custom_type_key, '') or '').strip()
+            ):
+                st.error(tr(summary_config["custom_type_empty_key"]))
+                st.stop()
+
+            subtitle_paths = _selected_subtitle_paths()
+            subtitle_path = subtitle_paths[0] if subtitle_paths else None
+            video_theme = st.session_state.get('video_theme')
+            web_search_enabled = bool(st.session_state.get(web_search_key, False))
+            current_signature = _short_drama_plot_analysis_signature(
+                subtitle_paths,
+                video_theme,
+                web_search_enabled,
+                _selected_video_paths(),
+            )
+            plot_analysis = ""
+            if st.session_state.get(plot_signature_key) == current_signature:
+                plot_analysis = st.session_state.get(plot_analysis_key, '')
+            elif (
+                not web_search_enabled
+                and st.session_state.get(plot_source_key) == subtitle_path
+            ):
+                plot_analysis = st.session_state.get(plot_analysis_key, '')
+
             custom_clips = st.session_state.get('custom_clips')
-            generate_script_short(tr, params, custom_clips)
+            short_result = generate_script_short(
+                tr,
+                params,
+                custom_clips,
+                subtitle_paths=subtitle_paths,
+                video_theme=video_theme,
+                temperature=st.session_state.get('temperature', 0.7),
+                plot_analysis=plot_analysis,
+                subtitle_content=st.session_state.get('subtitle_content', ''),
+                enable_web_search=web_search_enabled,
+                video_paths=_selected_video_paths(),
+                drama_genre=_resolve_short_drama_type(),
+                prompt_category=summary_config["prompt_category"],
+                search_keywords=summary_config["search_keywords"],
+                empty_title_message_key=summary_config["empty_title_message_key"],
+                web_search_context_description=summary_config["web_search_context_description"],
+            )
+            if short_result and short_result.get("plot_analysis"):
+                st.session_state[pending_plot_key] = {
+                    "plot_analysis": short_result["plot_analysis"],
+                    "subtitle_path": subtitle_path,
+                    "signature": current_signature,
+                }
+                st.session_state[plot_source_key] = subtitle_path
+                st.session_state[plot_signature_key] = current_signature
         else:
             load_script(tr, script_path)
 

webui/tools/generate_script_short.py

@@ -8,9 +8,32 @@ from loguru import logger
 from app.config import config
 from app.services.upload_validation import ensure_existing_file, InputValidationError
 from app.utils import utils
+from webui.tools.generate_short_summary import (
+    SHORT_DRAMA_PROMPT_CATEGORY,
+    SHORT_DRAMA_SEARCH_KEYWORDS,
+    _build_combined_subtitle_content,
+    _normalize_paths,
+    analyze_short_drama_plot,
+)
 
 
-def generate_script_short(tr, params, custom_clips=5):
+def generate_script_short(
+    tr,
+    params,
+    custom_clips=5,
+    subtitle_paths=None,
+    video_theme=None,
+    temperature=0.7,
+    plot_analysis=None,
+    subtitle_content=None,
+    enable_web_search=False,
+    video_paths=None,
+    drama_genre="逆袭/复仇",
+    prompt_category=SHORT_DRAMA_PROMPT_CATEGORY,
+    search_keywords=SHORT_DRAMA_SEARCH_KEYWORDS,
+    empty_title_message_key="Please enter short drama name before web search",
+    web_search_context_description="短剧名称、人物关系、剧情背景和公开剧情梗概",
+):
     """
     生成短视频脚本
     
@@ -18,6 +41,14 @@ def generate_script_short(tr, params, custom_clips=5):
         tr: 翻译函数
         params: 视频参数对象
         custom_clips: 自定义片段数量，默认为5
+        subtitle_paths: 已转写/上传/翻译/校准后的字幕路径列表
+        video_theme: 短剧名称
+        temperature: LLM温度
+        plot_analysis: 已完成的剧情理解文本
+        subtitle_content: 已合并的字幕文本
+        enable_web_search: 是否在剧情理解前联网搜索
+        video_paths: 原始视频路径列表
+        drama_genre: 用户选择的短剧类型
     """
     progress_bar = st.progress(0)
     status_text = st.empty()
@@ -33,38 +64,47 @@ def generate_script_short(tr, params, custom_clips=5):
         with st.spinner(tr("Generating script...")):
             # ========== 严格验证：必须上传视频和字幕（与短剧解说保持一致）==========
             # 1. 验证视频文件
-            video_path = getattr(params, "video_origin_path", None)
-            if not video_path or not str(video_path).strip():
+            selected_video_paths = _normalize_paths(
+                video_paths
+                or getattr(params, "video_origin_paths", [])
+                or getattr(params, "video_origin_path", "")
+            )
+            if not selected_video_paths:
                 st.error(tr("Please select video file first"))
                 st.stop()
 
-            try:
-                ensure_existing_file(
-                    str(video_path),
-                    label=tr("Video"),
-                    allowed_exts=(".mp4", ".mov", ".avi", ".flv", ".mkv"),
-                )
-            except InputValidationError as e:
-                st.error(str(e))
-                st.stop()
+            for video_path in selected_video_paths:
+                try:
+                    ensure_existing_file(
+                        str(video_path),
+                        label=tr("Video"),
+                        allowed_exts=(".mp4", ".mov", ".avi", ".flv", ".mkv"),
+                    )
+                except InputValidationError as e:
+                    st.error(str(e))
+                    st.stop()
 
             # 2. 验证字幕文件（移除推断逻辑，必须上传）
-            subtitle_path = st.session_state.get("subtitle_path")
-            if not subtitle_path or not str(subtitle_path).strip():
+            subtitle_paths = _normalize_paths(subtitle_paths or st.session_state.get("subtitle_paths") or st.session_state.get("subtitle_path"))
+            if not subtitle_paths:
                 st.error(tr("Please upload subtitle file first"))
                 st.stop()
 
+            validated_subtitle_paths = []
             try:
-                subtitle_path = ensure_existing_file(
-                    str(subtitle_path),
-                    label=tr("Subtitle"),
-                    allowed_exts=(".srt",),
-                )
+                for subtitle_path in subtitle_paths:
+                    validated_subtitle_paths.append(
+                        ensure_existing_file(
+                            str(subtitle_path),
+                            label=tr("Subtitle"),
+                            allowed_exts=(".srt",),
+                        )
+                    )
             except InputValidationError as e:
                 st.error(str(e))
                 st.stop()
 
-            logger.info(f"使用用户上传的字幕文件: {subtitle_path}")
+            logger.info(f"使用用户处理后的字幕文件: {validated_subtitle_paths}")
 
             # ========== 获取 LLM 配置 ==========
             text_provider = config.app.get('text_llm_provider', 'gemini').lower()
@@ -80,18 +120,40 @@ def generate_script_short(tr, params, custom_clips=5):
 
             update_progress(20, tr("Preparing script generation"))
 
+            subtitle_content = str(subtitle_content or "").strip() or _build_combined_subtitle_content(
+                validated_subtitle_paths,
+                selected_video_paths,
+            )
+            if not subtitle_content:
+                st.error(tr("Subtitle file is empty or unreadable"))
+                st.stop()
+
+            plot_analysis = str(plot_analysis or "").strip()
+            if not plot_analysis:
+                update_progress(35, tr("Analyzing subtitles with model..."))
+                plot_analysis = analyze_short_drama_plot(
+                    validated_subtitle_paths,
+                    temperature,
+                    tr,
+                    subtitle_content=subtitle_content,
+                    short_name=video_theme,
+                    enable_web_search=enable_web_search,
+                    video_paths=selected_video_paths,
+                    prompt_category=prompt_category,
+                    search_keywords=search_keywords,
+                    empty_title_message_key=empty_title_message_key,
+                    web_search_context_description=web_search_context_description,
+                )
+                if not plot_analysis:
+                    st.error(tr("Script generation failed check logs"))
+                    st.stop()
+
             # ========== 调用后端生成脚本 ==========
             from app.services.SDP.generate_script_short import generate_script_result
 
             output_path = os.path.join(utils.script_dir(), "merged_subtitle.json")
 
-            subtitle_content = st.session_state.get("subtitle_content")
-            subtitle_kwargs = (
-                {"subtitle_content": str(subtitle_content)}
-                if subtitle_content is not None and str(subtitle_content).strip()
-                else {"subtitle_file_path": subtitle_path}
-            )
-
+            update_progress(55, tr("Generating script..."))
             result = generate_script_result(
                 api_key=text_api_key,
                 model_name=text_model,
@@ -99,7 +161,11 @@ def generate_script_short(tr, params, custom_clips=5):
                 base_url=text_base_url,
                 custom_clips=custom_clips,
                 provider=text_provider,
-                **subtitle_kwargs,
+                subtitle_content=subtitle_content,
+                video_paths=selected_video_paths,
+                plot_analysis=plot_analysis,
+                short_name=video_theme or "",
+                drama_genre=drama_genre or "",
             )
 
             if result.get("status") != "success":
@@ -120,8 +186,14 @@ def generate_script_short(tr, params, custom_clips=5):
         progress_bar.progress(100)
         status_text.text(tr("Script generation completed!"))
         st.success(tr("Video script generated successfully"))
+        return {
+            "script": st.session_state.get('video_clip_json', []),
+            "plot_analysis": plot_analysis,
+            "subtitle_content": subtitle_content,
+        }
 
     except Exception as err:
         progress_bar.progress(100)
         st.error(f"{tr('Generation error')}: {str(err)}")
         logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
+        return None