2077:    def get_modifications(self, task_id: str, step: str) -> Dict[str, Any]:
2269:    def modify_result(self, task_id: str, step: str, modifications: Dict[str, Any]) -> Dict[str, Any]:


exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000
grep -n -A 20 "def get_modifications" user_driven_task_system.py | head -30


2077:    def get_modifications(self, task_id: str, step: str) -> Dict[str, Any]:
2078-        """获取指定步骤的修改历史"""
2079-        task_data = self._load_task(task_id)
2080-        if not task_data:
2081-            return {"success": False, "message": "任务不存在"}
2082-
2083-        # 映射步骤到modifications key
2084-        mod_key_map = {"collect": "collection", "write": "script", "produce": "video", "review": "review"}
2085-        mod_key = mod_key_map.get(step, step)
2086-        modifications = task_data.get("modifications", {}).get(mod_key, [])
2087-
2088-        return {
2089-            "success": True,
2090-            "task_id": task_id,
2091-            "step": step,
2092-            "modifications": modifications,
2093-            "count": len(modifications)
2094-        }
2095-
2096-    def modify_scene(self, task_id: str, scene_index: int, modification: str) -> Dict[str, Any]:
2097-        """修改场景描述并重新生成场景图"""


好的。现在我来更新 `JS` 中的 `getModifications`，以正确传递历史记录：

edit

async function getModifications(step){if(!currentTaskId)return[];try{var r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/modifications/'+step);var d=await r.json();if(d.success)return d.modifications||[];}catch(e){}return[];}


No changes made to /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator.js. The replacement produced identical content. This might indicate an issue with special characters or the text not existing as expected.


好的，`getModifications` 没问题。现在更新版本：


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator.js.

-  1 // 用户主导系统JavaScript v3.11
+  1 // 用户主导系统JavaScript v3.14
   2 let currentTaskId = null;
   3 let currentTaskData = null;
   4 const API_BASE = window.location.pathname.startsWith('/studio') ? '/studio/api' : '/api';
   5
     ...


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator_final.html.

     ...
 412         </div>
 413     </div>
 414
 415     <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>
-416     <script src="user_driven_coordinator.js?v=3.13"></script>
+416     <script src="user_driven_coordinator.js?v=3.14"></script>
 417 </body>
 418 </html>


修复步骤1: 修改要求按钮加载当前要求 + Modal关闭问题

修复步骤2: 缩略图生成缺失问题

改造步骤2为多阶段用户确认流程

添加'参考创作官行为规范'提示词到Agent调用


现在我来处理第二点问题。hot_trends的数据和Agent的提示词问题：

**hot_trends 分析**: 代码中有三条路径：
1. `agent_client.call_collector()` → 调用真实采集官Agent
2. LLM直接调用 → 用`generate_text()`生成JSON
3. `SimpleRealAgent.generate_collection_result()` → 纯程序生成假数据

路径3中 `_generate_dynamic_hot_topics` 确实是伪造的。需要确认调用链。

**Agent提示词问题**: `call_collector`和`call_writer`都没有传入"参考采集官/创作官行为规范处理"。

exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 修改agent_client.py - 在call_collector中添加行为规范提示词
head -160 agent_client.py


#!/usr/bin/env python3
"""
Agent客户端模块 - 通过OpenClaw Gateway HTTP API真实调用Agent
替代直接调用generate_text() LLM函数，改为协调采集官/创作官/制作官/审核官执行各步骤
"""

import json
import logging
import time
import httpx
from pathlib import Path
from typing import Optional, Dict, Any

logger = logging.getLogger(__name__)

class AgentClient:
    """通过OpenClaw Gateway OpenAI兼容API调用Agent"""

    def __init__(self, gateway_url: str = "http://127.0.0.1:27038",
                 token: Optional[str] = None,
                 timeout: float = 120.0):
        """
        初始化Agent客户端

        Args:
            gateway_url: Gateway地址
            token: Gateway auth token，None时自动从配置读取
            timeout: 请求超时（秒）
        """
        self.gateway_url = gateway_url.rstrip("/")
        self.timeout = timeout

        if token:
            self.token = token
        else:
            # 自动从openclaw.json读取
            self.token = self._read_token_from_config()

    def _read_token_from_config(self) -> str:
        """从openclaw.json读取Gateway auth token"""
        config_paths = [
            Path("/root/.openclaw/openclaw.json"),
            Path.home() / ".openclaw" / "openclaw.json",
        ]
        for path in config_paths:
            if path.exists():
                try:
                    with open(path) as f:
                        cfg = json.load(f)
                    token = cfg.get("gateway", {}).get("auth", {}).get("token", "")
                    if token:
                        logger.info(f"从{path}读取到Gateway token")
                        return token
                except Exception as e:
                    logger.warning(f"读取{path}失败: {e}")
        raise RuntimeError("无法从配置文件读取Gateway auth token")

    def call_agent(self, agent_id: str, message: str,
                   system_prompt: Optional[str] = None,
                   max_tokens: int = 2048,
                   timeout: Optional[float] = None) -> str:
        """
        调用指定Agent

        Args:
            agent_id: Agent ID (collector/writer/producer/publisher)
            message: 发送给Agent的消息
            system_prompt: 可选的系统提示（会拼接到message前面）
            max_tokens: 最大生成token数
            timeout: 请求超时覆盖

        Returns:
            Agent的回复文本

        Raises:
            AgentCallError: Agent调用失败
        """
        actual_timeout = timeout or self.timeout
        model = f"openclaw/{agent_id}"

        # 构造消息 - 添加行为准则提醒
        behavior_guidelines = "【行为准则提醒】请遵守以下准则执行任务: 1.输出必须严格符合要求的JSON格式,不要添加多余文字; 2.内容必须详细充实,不要敷衍潦草; 3.角色描述至少50字,包含外貌服装配饰等细节; 4.融入采集分析中的风格建议和受众偏好; 5.中文提示词中禁止出现汉字水印等文字元素; 6.只输出JSON,不要任何其他文字。\n"
        user_content = behavior_guidelines + message
        if system_prompt:
            user_content = behavior_guidelines + f"[系统指令]\n{system_prompt}\n\n[任务]\n{message}"

        payload = {
            "model": model,
            "messages": [{"role": "user", "content": user_content}],
            "max_tokens": max_tokens,
            "stream": False,
        }

        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.token}",
        }

        url = f"{self.gateway_url}/v1/chat/completions"

        start_time = time.time()
        logger.info(f"调用Agent: {agent_id}, 消息长度: {len(user_content)}, 超时: {actual_timeout}s")

        try:
            with httpx.Client(timeout=actual_timeout) as client:
                response = client.post(url, json=payload, headers=headers)
                elapsed = time.time() - start_time
                logger.info(f"Agent {agent_id} 响应: {response.status_code}, 耗时: {elapsed:.1f}s")

                if response.status_code != 200:
                    error_text = response.text[:500]
                    logger.error(f"Agent调用失败: HTTP {response.status_code}: {error_text}")
                    raise AgentCallError(f"HTTP {response.status_code}: {error_text}")

                data = response.json()
                choices = data.get("choices", [])
                if not choices:
                    raise AgentCallError("Agent返回空响应")

                content = choices[0].get("message", {}).get("content", "")
                if not content:
                    raise AgentCallError("Agent返回空内容")

                usage = data.get("usage", {})
                logger.info(f"Agent {agent_id} 完成: tokens={usage.get('total_tokens', '?')}, 内容长度: {len(content)}")
                return content

        except httpx.TimeoutException as e:
            elapsed = time.time() - start_time
            logger.error(f"Agent {agent_id} 超时({elapsed:.1f}s): {e}")
            raise AgentCallError(f"Agent超时({elapsed:.1f}s): {e}")
        except httpx.HTTPError as e:
            elapsed = time.time() - start_time
            logger.error(f"Agent {agent_id} HTTP错误({elapsed:.1f}s): {e}")
            raise AgentCallError(f"HTTP错误: {e}")
        except json.JSONDecodeError as e:
            logger.error(f"Agent {agent_id} 响应解析失败: {e}")
            raise AgentCallError(f"响应解析失败: {e}")

    def call_collector(self, task_description: str,
                       duration: str = "60秒",
                       channels: Optional[list] = None,
                       modification: str = "") -> str:
        """调用采集官Agent进行素材采集分析"""
        message = f"""请为以下主题提供采集分析：{task_description}

目标时长: {duration}
投放渠道: {', '.join(channels) if channels else '通用'}
{f'用户修改意见: {modification}' if modification else ''}

严格输出JSON格式：
{{"hot_trends":[{{"topic":"话题","heat":"热度","trend":"上升/稳定/下降"}}],"audience_analysis":{{"target":"目标人群","age_range":"年龄段","interests":["兴趣点"],"platforms":["推荐平台"]}},"visual_style":{{"color_tone":"色调建议","mood":"氛围","reference":"参考作品"}},"content_suggestions":["内容方向1","内容方向2"]}}

只输出JSON，不要其他文字。"""
        return self.call_agent("collector", message, max_tokens=2048, timeout=90)

    # 创作官核心行为规范（从writer_guidelines.md提取的关键约束）
    WRITER_RULES = """\n【创作官行为规范-硬性约束】:
1. 每个镜头必须有image_prompt和video_prompt，缺一不可


现在修复 `call_collector` 以添加行为规范引用和联网采集指示：

edit

    def call_collector(self, task_description: str,
                       duration: str = "60秒",
                       channels: Optional[list] = None,
                       modification: str = "") -> str:
        """调用采集官Agent进行素材采集分析"""
        message = f"""参考采集官行为规范处理。请为以下主题提供采集分析：{task_description}

目标时长: {duration}
投放渠道: {', '.join(channels) if channels else '通用'}
{f'用户修改意见: {modification}' if modification else ''}

重要要求：
1. 你必须联网搜索当前真实的热点数据和趋势，不要凭空编造数据
2. hot_trends必须是真实的、可验证的热门话题
3. 除了数据库素材库的已有数据，还必须联网采集最新的相关热点

严格输出JSON格式：
{{"hot_trends":[{{"topic":"真实话题","heat":"实际热度数据","trend":"上升/稳定/下降","source":"数据来源"}}],"audience_analysis":{{"target":"目标人群","age_range":"年龄段","interests":["兴趣点"],"platforms":["推荐平台"]}},"visual_style":{{"color_tone":"色调建议","mood":"氛围","reference":"参考作品"}},"content_suggestions":["内容方向1","内容方向2"]}}

只输出JSON，不要其他文字。"""

    def call_collector(self, task_description: str,
                       duration: str = "60秒",
                       channels: Optional[list] = None,
                       modification: str = "") -> str:
        """调用采集官Agent进行素材采集分析"""
        message = f"""请为以下主题提供采集分析：{task_description}

目标时长: {duration}
投放渠道: {', '.join(channels) if channels else '通用'}
{f'用户修改意见: {modification}' if modification else ''}

严格输出JSON格式：
{{"hot_trends":[{{"topic":"话题","heat":"热度","trend":"上升/稳定/下降"}}],"audience_analysis":{{"target":"目标人群","age_range":"年龄段","interests":["兴趣点"],"platforms":["推荐平台"]}},"visual_style":{{"color_tone":"色调建议","mood":"氛围","reference":"参考作品"}},"content_suggestions":["内容方向1","内容方向2"]}}

只输出JSON，不要其他文字。"""


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/agent_client.py.

     ...
 142                        duration: str = "60秒",
 143                        channels: Optional[list] = None,
 144                        modification: str = "") -> str:
 145         """调用采集官Agent进行素材采集分析"""
-146         message = f"""请为以下主题提供采集分析：{task_description}
+146         message = f"""参考采集官行为规范处理。请为以下主题提供采集分析：{task_description}
 147
 148 目标时长: {duration}
 149 投放渠道: {', '.join(channels) if channels else '通用'}
 150 {f'用户修改意见: {modification}' if modification else ''}
 151
+152 重要要求：
+153 1. 你必须联网搜索当前真实的热点数据和趋势，不要凭空编造数据
+154 2. hot_trends必须是真实的、可验证的热门话题
+155 3. 除了数据库素材库的已有数据，还必须联网采集最新的相关热点
+156
 152 严格输出JSON格式：
-153 {{"hot_trends":[{{"topic":"话题","heat":"热度","trend":"上升/稳定/下降"}}],"audience_analysis":{{"target":"目标人群","age_range":"年龄段","interests":["兴趣点"],"platforms":["推荐平台"]}},"visual_style":{{"color_tone":"色调建议","mood":"氛围","reference":"参考作品"}},"content_suggestions":["内容方向1","内容方向2"]}}
+158 {{"hot_trends":[{{"topic":"真实话题","heat":"实际热度数据","trend":"上升/稳定/下降","source":"数据来源"}}],"audience_analysis":{{"target":"目标人群","age_range":"年龄段","interests":["兴趣点"],"platforms":["推荐平台"]}},"visual_style":{{"color_tone":"色调建议","mood":"氛围","reference":"参考作品"}},"content_suggestions":["内容方向1","内容方向2"]}}
 154
 155 只输出JSON，不要其他文字。"""
 156         return self.call_agent("collector", message, max_tokens=2048, timeout=90)
 157
     ...


现在将行为规范添加到 `call_writer` 中：

message = f"""参考创作官行为规范处理。基于以下采集分析，为短剧生成分镜脚本：{self.WRITER_RULES}


     ...
 179                     duration: str = "60秒",
 180                     channels: Optional[list] = None,
 181                     modification: str = "") -> str:
 182         """调用创作官Agent生成分镜脚本"""
-183         message = f"""基于以下采集分析，为短剧生成分镜脚本：{self.WRITER_RULES}
+183         message = f"""参考创作官行为规范处理。基于以下采集分析，为短剧生成分镜脚本：{self.WRITER_RULES}
 184
 185 主题: {task_description}
 186 目标时长: {duration}
 187 投放渠道: {', '.join(channels) if channels else '通用'}
     ...


现在检查缩略图问题：


    def _generate_thumbnails(self, media_dir: Path):
        """v3.11: 为目录下的图片和视频生成缩略图，固化到 .thumbs/ 子目录
        图片: Pillow缩放到160x160, JPEG quality=60
        视频: ffmpeg截取第1秒帧, 缩放宽160px"""
        thumb_dir = media_dir / ".thumbs"
        thumb_dir.mkdir(parents=True, exist_ok=True)
        count = 0
        try:
            from PIL import Image
        except ImportError:
            logger.warning("Pillow未安装，跳过图片缩略图生成")
            Image = None

        for f in media_dir.iterdir():
            if not f.is_file():
                continue
            suffix = f.suffix.lower()
            thumb_name = f.stem + "_thumb" + (".jpg" if suffix in (".mp4", ".mov", ".avi", ".webm") else f.suffix)
            thumb_path = thumb_dir / thumb_name
            # 已存在且比原文件新就跳过
            if thumb_path.exists() and thumb_path.stat().st_mtime >= f.stat().st_mtime:
                continue
            try:
                if suffix in (".png", ".jpg", ".jpeg", ".webp", ".gif") and Image:
                    img = Image.open(f)
                    img.thumbnail((160, 160), Image.LANCZOS)
                    if img.mode in ("RGBA", "P"):
                        img = img.convert("RGB")
                    img.save(thumb_path, "JPEG", quality=60)
                    count += 1
                elif suffix in (".mp4", ".mov", ".avi", ".webm"):
                    import subprocess
                    result = subprocess.run(
                        ["ffmpeg", "-y", "-ss", "1", "-i", str(f),
                         "-vframes", "1", "-vf", "scale=160:-1", "-q:v", "3",
                         str(thumb_path)],
                        capture_output=True, timeout=15
                    )
                    if thumb_path.exists():
                        count += 1
            except Exception as e:
                logger.debug(f"缩略图生成跳过 {f.name}: {e}")

        if count > 0:
            logger.info(f"缩略图生成完成: {count}个 ({media_dir.name}/)")
        return count

    def _generate_scene_images_batch(self, scenes: list, images_dir: Path, characters: list = None, style_prefix: str = "", image_model: str = None) -> list:
        """为场景列表批量生成背景图 - 使用火山引擎组图API(sequential_image_generation)
        v3.10: 一次API调用生成所有场景图，比逐张调用效率提升数倍"""

[2100 more lines in file. Use offset=671 to continue.]


`_generate_thumbnails` 会遍历 `media_dir.iterdir()` — 它只处理顶层文件，不会递归到子目录。问题可能出在图片保存到子目录的情况。我来检查一下：

exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 看图片保存的目录结构 - 是直接保存在images/还是images/子目录?
grep -n "images_dir\|images/" user_driven_task_system.py | grep -v "test\|#\|doc\|comment\|log" | head -20


579:    def _generate_character_images(self, characters: list, images_dir: Path, style_prefix: str = "", image_model: str = None) -> list:
603:                    size="1920x1920", n=1, download_dir=str(images_dir),
668:    def _generate_scene_images_batch(self, scenes: list, images_dir: Path, characters: list = None, style_prefix: str = "", image_model: str = None) -> list:
706:                download_dir=str(images_dir),
733:                    size="1440x2560", n=1, download_dir=str(images_dir),
748:    def _generate_scene_images(self, scenes: list, images_dir: Path, characters: list = None, style_prefix: str = "", image_model: str = None) -> list:
779:                    size="1440x2560", n=1, download_dir=str(images_dir),
796:    def _generate_shot_ref_images(self, scenes: list, images_dir: Path, characters: list = None, image_model: str = None, style_prefix: str = "") -> list:
870:                    size="1440x2560", n=1, download_dir=str(images_dir),
1118:        images_dir = task_dir / "images"
1119:        images_dir.mkdir(parents=True, exist_ok=True)
1133:                characters = self._generate_character_images(characters, images_dir, style_prefix=style_prefix, image_model=_img_model)
1141:                scenes = self._generate_scene_images_batch(scenes, images_dir, characters=characters, style_prefix=style_prefix, image_model=_img_model)
1148:                scenes = self._generate_shot_ref_images(scenes, images_dir, characters=characters, image_model=_img_model, style_prefix=style_prefix)
1161:        self._generate_thumbnails(images_dir)
1253:        images_dir = task_dir / "images"
1255:        images_dir.mkdir(parents=True, exist_ok=True)
1932:        images_dir = Path(self.assets_dir) / task_id
1933:        images_dir.mkdir(parents=True, exist_ok=True)
1950:                                download_dir=str(images_dir),


# 看看任务目录里images下的实际结构
ls /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/tasks/ 2>/dev/null | head -5
echo "---"
# 找一个有数据的任务
TASK_DIR=$(ls -d /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/tasks/task_* 2>/dev/null | tail -1)
if [ -n "$TASK_DIR" ]; then
    echo "Task dir: $TASK_DIR"
    echo "=== images目录结构 ==="
    ls -la "$TASK_DIR/images/" 2>/dev/null | head -20
    echo "=== .thumbs目录 ==="
    ls -la "$TASK_DIR/images/.thumbs/" 2>/dev/null | head -10
fi


# 找任务目录
find /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/ -name "task_*" -type d | head -5
echo "---"
find /root/.openclaw/workspace/ -path "*/manhua*tasks*" -type d 2>/dev/null | head -5
echo "---"
# 可能tasks目录在别处
grep -n "assets_dir\|TASK_DIR\|tasks_dir" /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/user_driven_task_system.py | head -10


---
/root/.openclaw/workspace/manhua-production/tasks
---
153:        self.tasks_dir = self.base_dir / "tasks"
154:        self.assets_dir = self.base_dir / "assets"
158:        self.tasks_dir.mkdir(parents=True, exist_ok=True)
159:        self.assets_dir.mkdir(parents=True, exist_ok=True)
230:        task_dir = self.tasks_dir / task_id
231:        assets_dir = self.assets_dir / task_id
234:        assets_dir.mkdir(parents=True, exist_ok=True)
272:                "assets_dir": str(assets_dir),
1723:        task_dir = self.tasks_dir / task_id
1729:        assets_dir = self.assets_dir / task_id


# 找base_dir
grep -n "base_dir" /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/user_driven_task_system.py | head -5
echo "---"
# 看api_server怎么初始化的
grep -n "UserDrivenTaskSystem\|task_system" /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/user_driven_api_server.py | head -10


145:    def __init__(self, base_dir: str = "/root/data/disk/projects"):
150:            base_dir: 任务数据存储基础目录
152:        self.base_dir = Path(base_dir)
153:        self.tasks_dir = self.base_dir / "tasks"
154:        self.assets_dir = self.base_dir / "assets"
---
30:from user_driven_task_system import UserDrivenTaskSystem
34:user_driven_task_system = UserDrivenTaskSystem()
123:        agents_count = len(getattr(user_driven_task_system, 'AGENTS', []))
140:        "agents": user_driven_task_system.AGENTS,
141:        "count": len(user_driven_task_system.AGENTS)
148:        task_data = user_driven_task_system.create_task(
177:        result = user_driven_task_system.get_task_status(task_id)
191:        result = user_driven_task_system.get_step_result(task_id, step)
208:        result = user_driven_task_system.execute_collect(task_id, user_input)
228:        result = user_driven_task_system.execute_write(task_id, user_input)


# 查看实际任务目录
ls /root/data/disk/projects/tasks/ 2>/dev/null | tail -5
echo "---"
ls /root/data/disk/projects/assets/ 2>/dev/null | tail -5
echo "---"
# 查看最近一个任务的images目录
LATEST_TASK=$(ls -t /root/data/disk/projects/tasks/ 2>/dev/null | head -1)
if [ -n "$LATEST_TASK" ]; then
    echo "Latest task: $LATEST_TASK"
    echo "=== images/ ==="
    ls /root/data/disk/projects/tasks/$LATEST_TASK/images/ 2>/dev/null | head -10
    echo "=== .thumbs/ ==="
    ls /root/data/disk/projects/tasks/$LATEST_TASK/images/.thumbs/ 2>/dev/null | head -10
fi


task_20260519_093207_8118e21e
---
task_20260519_093207_8118e21e
---
Latest task: task_20260519_093207_8118e21e
=== images/ ===
image_1779154510_0.jpeg
image_1779154511_0.jpeg
image_1779154512_0.jpeg
image_1779154526_0.jpeg
image_1779154656_0.jpeg
image_1779154658_0.jpeg
image_1779154659_0.jpeg
image_1779154668_0.jpeg
image_1779154669_0.jpeg
image_1779154722_0.jpeg
=== .thumbs/ ===


`.thumbs/` 是空的！缩略图没有被生成。让我检查一下 `_generate_thumbnails` 是否被调用了：

# 检查thumbnails函数是否实际被调用以及Pillow是否安装
cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000
source venv/bin/activate 2>/dev/null
python3 -c "from PIL import Image; print('Pillow OK')" 2>&1
echo "---"
# 手动测试thumbnail生成
python3 -c "
from pathlib import Path
media_dir = Path('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images')
print(f'Files: {list(media_dir.iterdir())[:5]}')
print(f'.thumbs exists: {(media_dir / \".thumbs\").exists()}')
for f in media_dir.iterdir():
    if f.is_file() and f.suffix.lower() in ('.png','.jpg','.jpeg','.webp'):
        print(f'Image: {f.name}, size: {f.stat().st_size}')
" 2>&1


Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'PIL'
---
Files: [PosixPath('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images/image_1779154789_0.jpeg'), PosixPath('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images/image_1779159230_0.jpeg'), PosixPath('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images/image_1779154669_0.jpeg'), PosixPath('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images/image_1779154794_0.jpeg'), PosixPath('/root/data/disk/projects/tasks/task_20260519_093207_8118e21e/images/image_1779159309_2.jpeg')]
.thumbs exists: True
Image: image_1779154789_0.jpeg, size: 1039868
Image: image_1779159230_0.jpeg, size: 703466
Image: image_1779154669_0.jpeg, size: 1565126
Image: image_1779154794_0.jpeg, size: 917270
Image: image_1779159309_2.jpeg, size: 844978
Image: image_1779159441_0.jpeg, size: 932697
Image: image_1779154511_0.jpeg, size: 717807
Image: image_1779159232_0.jpeg, size: 730139
Image: image_1779159438_0.jpeg, size: 754443
Image: image_1779159309_0.jpeg, size: 827437
Image: image_1779154668_0.jpeg, size: 1438440
Image: image_1779154510_0.jpeg, size: 673674
Image: image_1779154803_0.jpeg, size: 1181715
Image: image_1779154512_0.jpeg, size: 755603
Image: image_1779159309_1.jpeg, size: 954349
Image: image_1779154658_0.jpeg, size: 1041710
Image: image_1779159492_0.jpeg, size: 805920
Image: image_1779154659_0.jpeg, size: 1570066
Image: image_1779159433_0.jpeg, size: 866014
Image: image_1779159371_0.jpeg, size: 827854
Image: image_1779159370_0.jpeg, size: 782574
Image: image_1779154725_0.jpeg, size: 819018
Image: image_1779159524_0.jpeg, size: 815564
Image: image_1779154656_0.jpeg, size: 1394074
Image: image_1779159377_0.jpeg, size: 693293
Image: image_1779154526_0.jpeg, size: 953481
Image: image_1779159518_0.jpeg, size: 875984
Image: image_1779154722_0.jpeg, size: 1149171


[OpenClaw heartbeat poll]


Read HEARTBEAT.md if it exists (workspace context). Follow it strictly. Do not infer or repeat old tasks from prior chats. If nothing needs attention, reply HEARTBEAT_OK.
When reading HEARTBEAT.md, use workspace file /root/.openclaw/workspace/HEARTBEAT.md (exact case). Do not read docs/heartbeat.md.
Current time: Tuesday, May 19th, 2026 - 12:14 PM (Asia/Shanghai)
Reference UTC: 2026-05-19 04:14 UTC


我正在处理用户的多步骤修复任务，不需要打断。快速检查系统状态后继续工作。

# 快速健康检查
df -h / | tail -1 | awk '{print "磁盘: "$5}'
systemctl --user is-active manhua-studio-8000 2>/dev/null || echo "8000: 检查中"
curl -s http://127.0.0.1:8000/ -o /dev/null -w "8000: HTTP %{http_code}\n" --max-time 3
curl -s http://127.0.0.1:27038/ -o /dev/null -w "网关: HTTP %{http_code}\n" --max-time 3


磁盘: 71%
active
8000: HTTP 307
网关: HTTP 404


系统正常，继续修复任务。找到缩略图问题根因——**Pillow未安装在venv中**。

# 在8000端口的venv中安装Pillow
cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000
source venv/bin/activate
pip install Pillow 2>&1 | tail -5
echo "---"
python3 -c "from PIL import Image; print('Pillow OK')" 2>&1


Looking in indexes: http://mirrors.tencentyun.com/pypi/simple
Collecting Pillow
  Using cached pillow-12.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (7.1 MB)
Installing collected packages: Pillow
Successfully installed Pillow-12.2.0
---
Pillow OK


现在让我来处理主要的步骤2重新设计。用户希望执行流程如下：

1. 生成故事大纲 + 角色描述 + 推荐(风格/模板/模型)
2. 用户确认(可修改)
3. 根据用户选择生成角色/场景/镜头提示词(正面和负面)
4. 生成角色和场景图(含缩略图)
5. 用户确认
6. 拿角色图+场景图生成镜头参考图(含缩略图)
7. 用户确认

这需要在后端和前端进行重大更改：


    def execute_write(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:
        """执行创作步骤 - 生成脚本+角色图+场景图+镜头参考图"""
        task_data = self._load_task(task_id)
        if not task_data:
            return {"success": False, "message": "任务不存在"}

        if not self.can_execute_step(task_id, "write"):
            return {"success": False, "message": "当前不能执行创作步骤"}

        task_data["status"] = self.STATUS["WRITING"]
        task_data["current_step"] = "write"
        task_data["progress"] = 40
        task_data["updated_at"] = datetime.now().isoformat()

        # 记录用户动作
        action_desc = "用户执行创作步骤"
        if user_input and user_input.get("modification"):
            action_desc = "用户修改创作结果并重新生成"
            task_data["modifications"]["script"].append({
                "timestamp": datetime.now().isoformat(),
                "user_input": user_input,
                "version": len(task_data["modifications"]["script"]) + 2
            })

        task_data["user_actions"].append({
            "action": "execute_write",
            "timestamp": datetime.now().isoformat(),
            "description": action_desc
        })

        # 如果有用户输入,用于生成结果
        user_preferences = user_input.get("preferences", {}) if user_input else {}

        # 调用真实LLM生成分镜脚本
        task_description = task_data.get("description", "")
        task_duration = task_data.get("duration", "60秒")
        task_channels = task_data.get("channels", [])
        collection_data = task_data.get("results", {}).get("collection", {})

        # 【v3.6修复】根据目标时长动态计算镜头数
        try:
            dur_num = int(''.join(filter(str.isdigit, task_duration or '30')))
        except ValueError:
            dur_num = 30
        num_shots = max(3, dur_num // 5)  # 每5秒1个镜头,最少3个

        # ★ 优先通过AgentClient调用真实创作官Agent
        agent_used = False
        llm_output = None
        try:
            agent_client = get_agent_client()
            collection_analysis = json.dumps(collection_data, ensure_ascii=False) if collection_data else '无'
            llm_output = agent_client.call_writer(
                task_description=task_description,
                collection_analysis=collection_analysis,
                duration=task_duration,
                channels=task_channels,
                modification=user_input.get("modification", "") if user_input else ""
            )
            agent_used = True
            logger.info(f"创作步骤通过创作官Agent完成")
        except AgentCallError as e:
            logger.warning(f"创作官Agent调用失败,降级到LLM直接调用: {e}")

        if not agent_used and llm_output is None:
            # Fallback: 使用LLM直接调用
            try:
                # 加载创作官系统提示词
                writer_system = self._load_prompt("writer_system_prompt.md")
                writer_guidelines = self._load_prompt("writer_guidelines.md")
                base_prompt = writer_system if writer_system else "你是一个专业的短剧脚本创作AI。"
                guidelines_text = f"\n\n{writer_guidelines}" if writer_guidelines else ""
                llm_output = generate_text(
                    system_prompt=f"""{base_prompt}{guidelines_text}

严格输出JSON格式:
{{"title":"剧名","genre":"类型","visual_style":"视觉风格描述","scenes":[{{"scene_id":1,"location":"场景地点","time":"时间","atmosphere":"氛围","shots":[{{"shot_id":1,"duration_seconds":5,"description":"画面描述(口语化)","dialogue":"对白(可选)","camera_movement":"镜头运动","emotion":"情绪标注","video_prompt":"详细中文视频提示词,含画面、运镜、氛围","image_prompt":"详细中文图片提示词,用于生成本镜头的参考图"}}],"image_prompt":"场景全景参考图提示词"}}],"characters":[{{"name":"角色名","appearance":"详细外貌:身高体型、面部特征、发型发色、服装款式颜色材质、配饰道具,至少50字","outfit_style":"服装风格简述","personality":"性格特点","speech_style":"说话风格和习惯用语","habits":"专属小动作/小习惯","three_view_prompt":"角色三视图提示词"}}]}}
重要约束:
1. 总时长必须={dur_num}秒,共{num_shots}个镜头,每个5秒
2. 9:16竖屏格式
3. video_prompt和image_prompt都用中文
4. image_prompt禁止出现中文汉字、水印、文字等,只描述画面视觉元素
5. 角色数量至少3-4个,每个角色appearance必须详细(至少50字)
6. 融入采集分析中的视觉风格建议和受众偏好
7. 只输出JSON,不要任何其他文字""",
                    prompt=f"请基于以下信息创作微短剧分镜脚本:\n主题:{task_description}\n目标时长:{task_duration},共{num_shots}个镜头\n投放渠道:{', '.join(task_channels) if task_channels else '不限'}\n\n【采集分析参考(必须融入创作)】:\n{json.dumps(collection_data, ensure_ascii=False) if collection_data else '无'}\n\n用户偏好:{json.dumps(user_preferences, ensure_ascii=False)}",
                    temperature=0.8,
                    max_tokens=8192,
                )
            except Exception as e:
                logger.warning(f"LLM脚本生成失败: {e}")
                llm_output = None

        # 解析llm_output为script_result (Agent输出或LLM输出都经过此处)
        if llm_output is None:
            # 完全失败,用备用脚本
            script_result = {
                "title": user_preferences.get("title", task_description[:20]),
                "genre": "短剧",
                "scenes": [
                    {"scene_id": 1, "location": "场景1", "shots": [{"shot_id": 1, "shot_type": "中景", "description": task_description[:50], "duration_seconds": 5, "video_prompt": task_description}]},
                ],
                "characters": [{"name": "主角", "description": "主要角色", "personality": "活跃"}],
                "is_real_data": False,
                "fallback_reason": "LLM/Agent脚本生成失败",
                "agent_called": agent_used,
            }
        else:
            # 解析JSON - 支持截断修复
            try:
                if "```json" in llm_output:
                    json_str = llm_output.split("```json")[1].split("```")[0].strip()
                elif "{" in llm_output:
                    start = llm_output.index("{")
                    json_str = llm_output[start:]
                else:
                    json_str = llm_output

                json_str = sanitize_json_string(json_str)

[1732 more lines in file. Use offset=1039 to continue.]


                # 尝试直接解析
                try:
                    script_result = json.loads(json_str)
                except json.JSONDecodeError:
                    # 尝试修复截断的JSON - 找到最后一个完整的scene对象
                    import re
                    fixed = json_str.rstrip()
                    if fixed.endswith(','):
                        fixed = fixed[:-1]

                    # 策略: 找到所有scene_id标记，取最后一个完整的scene
                    scene_starts = [m.start() for m in re.finditer(r'"scene_id"', fixed)]

                    repaired = False
                    for si in range(len(scene_starts)-1, -1, -1):
                        start = scene_starts[si]
                        # 向前找包含此scene_id的 { 位置
                        brace_start = start
                        while brace_start > 0 and fixed[brace_start] != '{':
                            brace_start -= 1
                        # 向后找匹配的 }
                        depth = 0
                        brace_end = -1
                        for i in range(brace_start, len(fixed)):
                            if fixed[i] == '{': depth += 1
                            elif fixed[i] == '}':
                                depth -= 1
                                if depth == 0:
                                    brace_end = i
                                    break

                        if brace_end > 0:
                            # 这个scene完整，截断到这里
                            fixed2 = fixed[:brace_end+1]
                            # 闭合scenes数组
                            fixed2 += ']'
                            # 闭合外层对象
                            closes = fixed2.count('{') - fixed2.count('}')
                            for _ in range(max(closes, 0)):
                                fixed2 += '}'
                            try:
                                script_result = json.loads(fixed2)
                                logger.info(f"JSON修复成功: 截断到scene {si+1}, scenes={len(script_result.get('scenes',[]))}")
                                repaired = True
                                break
                            except json.JSONDecodeError:
                                continue

                    if not repaired:
                        # 策略2: 简单括号闭合
                        opens = fixed.count('[') - fixed.count(']')
                        closes = fixed.count('{') - fixed.count('}')
                        if fixed.endswith(','):
                            fixed = fixed[:-1]
                        for _ in range(max(opens, 0)):
                            fixed += ']'
                        for _ in range(max(closes, 0)):
                            fixed += '}'
                        try:
                            script_result = json.loads(fixed)
                            logger.info(f"JSON修复成功(简单闭合)")
                            repaired = True
                        except json.JSONDecodeError:
                            pass

                    if not repaired:
                        raise

                script_result["is_real_data"] = True
                script_result["agent_called"] = agent_used
            except (json.JSONDecodeError, ValueError) as je:
                # JSON解析失败,用LLM原文
                logger.warning(f"JSON解析失败: {je}, raw length: {len(llm_output)}")
                script_result = {"raw_script": llm_output, "parse_error": True, "is_real_data": True, "parse_error_detail": str(je), "agent_called": agent_used}

        # =========== 生成角色设计图 + 场景背景图 + 镜头参考图 ===========
        # v3.10: 始终生成所有图片资源（角色图+场景图+镜头参考图）
        # 不再区分draft_only，一次性生成所有图片
        task_dir = Path(task_data["files"]["task_dir"])
        images_dir = task_dir / "images"
        images_dir.mkdir(parents=True, exist_ok=True)

        char_img_count = 0
        scene_img_count = 0
        shot_ref_count = 0

        if isinstance(script_result, dict) and script_result.get("scenes"):
            style_prefix = self._build_style_prefix_from_collection(task_data)
            _img_model = task_data.get("image_model")

            # 1. 生成角色设计图
            characters = script_result.get("characters", [])
            if characters:
                logger.info(f"开始生成{len(characters)}个角色的三视图...")
                characters = self._generate_character_images(characters, images_dir, style_prefix=style_prefix, image_model=_img_model)
                script_result["characters"] = characters
                char_img_count = sum(1 for c in characters if c.get("image_path"))

            # 2. 生成场景背景图（使用组图API批量生成）
            scenes = script_result.get("scenes", [])

[1632 more lines in file. Use offset=1139 to continue.]


            if scenes:
                logger.info(f"开始生成{len(scenes)}个场景的背景图...")
                scenes = self._generate_scene_images_batch(scenes, images_dir, characters=characters, style_prefix=style_prefix, image_model=_img_model)
                script_result["scenes"] = scenes
                scene_img_count = sum(1 for s in scenes if s.get("image_path"))

            # 3. 为每个镜头生成参考图(基于image_prompt)
            if scenes:
                logger.info("开始生成镜头参考图...")
                scenes = self._generate_shot_ref_images(scenes, images_dir, characters=characters, image_model=_img_model, style_prefix=style_prefix)
                script_result["scenes"] = scenes
                shot_ref_count = sum(
                    1 for s in scenes
                    for sh in s.get("shots", [])
                        if sh.get("image_path")
                )
            task_data["write_draft_confirmed"] = True

        img_summary = f"角色图={char_img_count} 场景图={scene_img_count} 镜头参考图={shot_ref_count}"
        logger.info(f"图片资源生成完成: {img_summary}")

        # v3.11: 生成缩略图并固化到任务目录
        self._generate_thumbnails(images_dir)

        # 保存创作结果 (task_dir 已在图片生成前定义)
        script_file = task_dir / "script_result.json"
        script_result["generated_at"] = datetime.now().isoformat()
        script_result["version"] = len(task_data["modifications"]["script"]) + 1
        script_result["user_modifications"] = user_preferences
        script_result["image_assets"] = {
            "character_images": char_img_count,
            "scene_images": scene_img_count,
            "shot_ref_images": shot_ref_count,
        }
        with open(script_file, 'w', encoding='utf-8') as f:
            json.dump(script_result, f, ensure_ascii=False, indent=2)

        # 更新任务数据
        task_data["results"]["script"] = script_result
        task_data["results"]["storyboard"] = f"分镜脚本已生成 ({img_summary})" if script_result.get("scenes") else "脚本生成降级"
        # 保存角色和背景的完整数据（而非摘要字符串）
        task_data["results"]["characters"] = script_result.get("characters", []) or "角色设计降级"
        task_data["results"]["backgrounds"] = [s for s in script_result.get("scenes", []) if s.get("image_path")] if scene_img_count > 0 else "背景设计降级"
        task_data["files"]["script_file"] = str(script_file)
        task_data["files"]["storyboard_file"] = str(script_file)  # 同一个JSON
        task_data["files"]["characters_file"] = str(script_file)
        task_data["files"]["backgrounds_file"] = str(script_file)
        total_shots = sum(len(s.get("shots", [])) for s in script_result.get("scenes", []))
        task_data["agents"]["writer"] = {
            "status": "completed",
            "started_at": datetime.now().isoformat(),
            "completed_at": datetime.now().isoformat(),
            "result_summary": f"创作完成,{total_shots}镜头 + {img_summary}",
            "can_modify": True,
            "can_regenerate": True,
            "modification_count": len(task_data["modifications"]["script"])
        }
        task_data["status"] = self.STATUS["WRITTEN"]
        task_data["current_step"] = "produce"
        task_data["progress"] = 50

        self._save_task(task_id, task_data)

        return {
            "success": True,
            "message": f"创作步骤完成 ({img_summary})",
            "task_id": task_id,
            "result": script_result,
            "next_step": "produce",
            "next_step_description": "下一步:制作视频(图生视频模式)",
            "can_modify": True,
            "can_regenerate": True,
            "modification_count": len(task_data["modifications"]["script"]),
            "image_assets": {"characters": char_img_count, "scenes": scene_img_count, "shot_refs": shot_ref_count}
        }

    def execute_produce(self, task_id: str, video_mode: str = None, shot_overrides: Optional[Dict] = None) -> Dict[str, Any]:
        """执行制作步骤 - 支持视频模式选择和单个镜头重生成
        video_mode: 'auto'(自动:有图则图生)/'img2vid'(强制图生)/'text2vid'(强制文生)
        shot_overrides: {shot_id: {"prompt": "修改后的提示词", "mode": "img2vid/text2vid"}}

[1552 more lines in file. Use offset=1219 to continue.]


这是一个重要的修改。我需要添加一个分阶段的工作流。让我来创建新的方法：


            return scene_img

        return None

    def execute_write(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:
        """执行创作步骤 - 生成脚本+角色图+场景图+镜头参考图"""
        task_data = self._load_task(task_id)
        if not task_data:
            return {"success": False, "message": "任务不存在"}

[1846 more lines in file. Use offset=925 to continue.]


我需要添加一个 `execute_write_phase1` (大纲+推荐) 和 `execute_write_phase2` (生成图片) 方法，并修改 `execute_write` 以编排它们。我还要添加一个新的 API endpoint。

edit

    def execute_write_phase1(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:
        """v3.14: 创作阶段1 - 生成故事大纲、角色描述、风格/模板/模型推荐
        返回给用户确认，不生成图片"""
        task_data = self._load_task(task_id)
        if not task_data:
            return {"success": False, "message": "任务不存在"}

        task_data["status"] = self.STATUS["WRITING"]
        task_data["current_step"] = "write"
        task_data["progress"] = 40
        task_data["updated_at"] = datetime.now().isoformat()

        task_description = task_data.get("description", "")
        task_duration = task_data.get("duration", "60秒")
        task_channels = task_data.get("channels", [])
        collection_data = task_data.get("results", {}).get("collection", {})

        try:
            dur_num = int(''.join(filter(str.isdigit, task_duration or '30')))
        except ValueError:
            dur_num = 30
        num_shots = max(3, dur_num // 5)

        user_preferences = user_input.get("preferences", {}) if user_input else {}
        modification = user_input.get("modification", "") if user_input else ""

        # 构造提示词 - 要求生成大纲+角色+推荐
        outline_prompt = f"""参考创作官行为规范处理。请为以下微短剧生成：
1.故事大纲（含标题、类型、剧情概要、情感曲线）
2.角色描述（3-5个角色的详细人设：外貌、性格、说话风格、专属动作，每个角色至少60字）
3.推荐建议：
  - 推荐视觉风格（从：写实/二次元/3D渲染/水彩/油画/赛博朋克/复古/国风 中推荐最合适的1-2种）
  - 推荐模板（从已有的创作模板中选择最适合的）
  - 推荐模型（图片模型+视频模型组合建议）

主题: {task_description}
目标时长: {task_duration}，共{num_shots}个镜头
投放渠道: {', '.join(task_channels) if task_channels else '不限'}

【采集分析参考(必须融入)】:
{json.dumps(collection_data, ensure_ascii=False) if collection_data else '无'}

{f'用户修改意见: {modification}' if modification else ''}

严格输出JSON格式：
{{"title":"剧名","genre":"类型","synopsis":"200字以内剧情概要","emotional_arc":"情感曲线描述","visual_style_recommendation":{{"primary":"主要风格","secondary":"备选风格","reason":"推荐理由"}},"template_recommendation":{{"name":"推荐模板名","reason":"推荐理由"}},"model_recommendation":{{"image_model":"推荐图片模型","video_model":"推荐视频模型","reason":"推荐理由"}},"characters":[{{"name":"角色名","role":"主角/配角/反派等","appearance":"详细外貌描述:身高体型、面部特征、发型发色、服装款式颜色材质、配饰道具，至少60字","personality":"性格特点与行为习惯","speech_style":"说话风格和口头禅","habits":"专属小动作/小习惯"}}],"scenes_overview":[{{"scene_id":1,"location":"场景地点","time":"时间","atmosphere":"氛围描述","purpose":"剧情作用"}}]}}

重要约束:
1. 角色数量3-5个，每个角色appearance至少60字
2. 场景概要3-6个，描述完整
3. 推荐必须结合采集分析中的风格建议
4. 只输出JSON，不要其他文字"""

        # 优先调用创作官Agent
        agent_used = False
        llm_output = None
        try:
            agent_client = get_agent_client()
            llm_output = agent_client.call_agent("writer", outline_prompt, max_tokens=8192, timeout=180)
            agent_used = True
            logger.info("Phase1通过创作官Agent完成")
        except AgentCallError as e:
            logger.warning(f"创作官Agent调用失败: {e}")

        if not agent_used or llm_output is None:
            try:
                llm_output = generate_text(
                    system_prompt="你是一个专业的短剧脚本创作AI。参考创作官行为规范处理任务。严格输出JSON格式。",
                    prompt=outline_prompt,
                    temperature=0.8, max_tokens=8192,
                )
            except Exception as e:
                logger.warning(f"LLM大纲生成失败: {e}")
                return {"success": False, "message": f"大纲生成失败: {e}"}

        # 解析JSON
        try:
            if "```json" in llm_output:
                json_str = llm_output.split("```json")[1].split("```")[0].strip()
            elif "{" in llm_output:
                json_str = llm_output[llm_output.index("{"):]
            else:
                json_str = llm_output
            json_str = sanitize_json_string(json_str)
            outline_result = json.loads(json_str)
            outline_result["is_real_data"] = True
            outline_result["agent_called"] = agent_used
            outline_result["phase"] = "outline"
        except (json.JSONDecodeError, ValueError) as e:
            logger.warning(f"大纲JSON解析失败: {e}")
            outline_result = {"raw": llm_output, "parse_error": True, "phase": "outline"}

        # 保存到任务数据
        task_data["results"]["write_phase1"] = outline_result
        task_data["write_phase"] = 1
        self._save_task(task_id, task_data)

        return {
            "success": True,
            "message": "大纲和推荐已生成，请确认",
            "task_id": task_id,
            "result": outline_result,
            "phase": "outline",
            "next_action": "confirm_outline",
        }

    def execute_write_phase2(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:
        """v3.14: 创作阶段2 - 根据用户确认的大纲生成分镜脚本+提示词+图片"""
        task_data = self._load_task(task_id)
        if not task_data:
            return {"success": False, "message": "任务不存在"}

        outline = task_data.get("results", {}).get("write_phase1", {})
        if not outline:
            return {"success": False, "message": "请先完成大纲生成(Phase1)"}

        task_data["status"] = self.STATUS["WRITING"]
        task_data["current_step"] = "write"
        task_data["updated_at"] = datetime.now().isoformat()

        task_description = task_data.get("description", "")
        task_duration = task_data.get("duration", "60秒")
        task_channels = task_data.get("channels", [])
        collection_data = task_data.get("results", {}).get("collection", {})
        user_preferences = user_input.get("preferences", {}) if user_input else {}
        modification = user_input.get("modification", "") if user_input else ""

        try:
            dur_num = int(''.join(filter(str.isdigit, task_duration or '30')))
        except ValueError:
            dur_num = 30
        num_shots = max(3, dur_num // 5)

        # 用户可能修改了大纲中的角色/场景
        user_chars = user_preferences.get("characters", outline.get("characters", []))
        user_style = user_preferences.get("visual_style", outline.get("visual_style_recommendation", {}))
        user_scenes = user_preferences.get("scenes_overview", outline.get("scenes_overview", []))

        # 构造分镜生成提示词
        script_prompt = f"""参考创作官行为规范处理。基于以下确认的大纲，生成完整的分镜脚本（含提示词）：

主题: {task_description}
目标时长: {task_duration}，共{num_shots}个镜头
投放渠道: {', '.join(task_channels) if task_channels else '不限'}

【确认的大纲】:
标题: {outline.get('title','')}
类型: {outline.get('genre','')}
概要: {outline.get('synopsis','')}

【确认的角色】:
{json.dumps(user_chars, ensure_ascii=False, indent=2)}

【确认的视觉风格】:
{json.dumps(user_style, ensure_ascii=False, indent=2)}

【确认的场景概要】:
{json.dumps(user_scenes, ensure_ascii=False, indent=2)}

{f'用户修改意见: {modification}' if modification else ''}

严格输出JSON格式：
{{"title":"剧名","genre":"类型","visual_style":"视觉风格描述","scenes":[{{"scene_id":1,"location":"场景地点","time":"时间","atmosphere":"氛围","shots":[{{"shot_id":1,"duration_seconds":5,"description":"画面描述","dialogue":"对白(可选)","camera_movement":"镜头运动","emotion":"情绪标注","video_prompt":"详细中文视频提示词","image_prompt":"详细中文参考图提示词，禁止出现中文汉字水印"}}],"image_prompt":"场景全景参考图提示词"}}],"characters":[{{"name":"角色名","appearance":"详细外貌至少50字","personality":"性格","speech_style":"说话风格","habits":"小习惯","three_view_prompt":"角色三视图提示词"}}]}}

重要约束:
1. 每个镜头必须有image_prompt和video_prompt
2. 角色必须与确认大纲一致，补充three_view_prompt
3. 总时长={dur_num}秒，共{num_shots}个镜头
4. image_prompt禁止中文汉字/水印
5. 融入确认的视觉风格
6. 只输出JSON"""

        agent_used = False
        llm_output = None
        try:
            agent_client = get_agent_client()
            llm_output = agent_client.call_agent("writer", script_prompt, max_tokens=16384, timeout=300)
            agent_used = True
        except AgentCallError as e:
            logger.warning(f"创作官Agent失败: {e}")

        if not agent_used or llm_output is None:
            try:
                llm_output = generate_text(
                    system_prompt="你是专业短剧脚本创作AI。参考创作官行为规范处理。严格输出JSON。",
                    prompt=script_prompt, temperature=0.8, max_tokens=16384)
            except Exception as e:
                return {"success": False, "message": f"脚本生成失败: {e}"}

        # 解析JSON
        try:
            if "```json" in llm_output:
                json_str = llm_output.split("```json")[1].split("```")[0].strip()
            elif "{" in llm_output:
                json_str = llm_output[llm_output.index("{"):]
            else:
                json_str = llm_output
            json_str = sanitize_json_string(json_str)
            script_result = json.loads(json_str)
            script_result["is_real_data"] = True
            script_result["agent_called"] = agent_used
        except (json.JSONDecodeError, ValueError):
            script_result = {"raw_script": llm_output, "parse_error": True, "agent_called": agent_used}

        # ===== 生成图片（分阶段）=====
        task_dir = Path(task_data["files"]["task_dir"])
        images_dir = task_dir / "images"
        images_dir.mkdir(parents=True, exist_ok=True)

        char_img_count = 0
        scene_img_count = 0

        if isinstance(script_result, dict) and script_result.get("scenes"):
            style_prefix = self._build_style_prefix_from_collection(task_data)
            _img_model = task_data.get("image_model")

            # 1. 生成角色设计图
            characters = script_result.get("characters", [])
            if characters:
                characters = self._generate_character_images(characters, images_dir, style_prefix=style_prefix, image_model=_img_model)
                script_result["characters"] = characters
                char_img_count = sum(1 for c in characters if c.get("image_path"))

            # 2. 生成场景背景图
            scenes = script_result.get("scenes", [])
            if scenes:
                scenes = self._generate_scene_images_batch(scenes, images_dir, characters=characters, style_prefix=style_prefix, image_model=_img_model)
                script_result["scenes"] = scenes
                scene_img_count = sum(1 for s in scenes if s.get("image_path"))

            # Phase2A: 先生成角色+场景，用户确认后再生成镜头参考图
            task_data["write_phase"] = 2
            task_data["results"]["write_phase2_pending_shots"] = True

        img_summary = f"角色图={char_img_count} 场景图={scene_img_count}"
        logger.info(f"Phase2图片生成完成: {img_summary}")

        # 生成缩略图
        self._generate_thumbnails(images_dir)

        # 保存
        script_file = task_dir / "script_result.json"
        script_result["generated_at"] = datetime.now().isoformat()
        script_result["version"] = len(task_data.get("modifications", {}).get("script", [])) + 1
        script_result["phase"] = "script_with_chars_scenes"
        script_result["image_assets"] = {"character_images": char_img_count, "scene_images": scene_img_count}
        with open(script_file, 'w', encoding='utf-8') as f:
            json.dump(script_result, f, ensure_ascii=False, indent=2)

        task_data["results"]["script"] = script_result
        task_data["status"] = self.STATUS["WRITING"]
        task_data["current_step"] = "write"
        task_data["progress"] = 45
        self._save_task(task_id, task_data)

        return {
            "success": True,
            "message": f"脚本+角色+场景图已生成 ({img_summary})，请确认后生成镜头参考图",
            "task_id": task_id,
            "result": script_result,
            "phase": "chars_scenes_done",
            "next_action": "confirm_images",
            "image_assets": {"characters": char_img_count, "scenes": scene_img_count},
        }

    def execute_write_phase3(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:
        """v3.14: 创作阶段3 - 用户确认角色+场景后，生成镜头参考图"""
        task_data = self._load_task(task_id)
        if not task_data:
            return {"success": False, "message": "任务不存在"}

        script_result = task_data.get("results", {}).get("script", {})
        if not script_result or not script_result.get("scenes"):
            return {"success": False, "message": "请先完成Phase2"}

        task_dir = Path(task_data["files"]["task_dir"])
        images_dir = task_dir / "images"
        images_dir.mkdir(parents=True, exist_ok=True)

        style_prefix = self._build_style_prefix_from_collection(task_data)
        _img_model = task_data.get("image_model")
        characters = script_result.get("characters", [])
        scenes = script_result.get("scenes", [])

        # 生成镜头参考图
        shot_ref_count = 0
        if scenes:
            logger.info("Phase3: 开始生成镜头参考图...")
            scenes = self._generate_shot_ref_images(scenes, images_dir, characters=characters, image_model=_img_model, style_prefix=style_prefix)
            script_result["scenes"] = scenes
            shot_ref_count = sum(1 for s in scenes for sh in s.get("shots", []) if sh.get("image_path"))

        # 生成缩略图
        self._generate_thumbnails(images_dir)

        img_summary = f"镜头参考图={shot_ref_count}"
        logger.info(f"Phase3完成: {img_summary}")

        # 更新script_result
        script_result["image_assets"]["shot_ref_images"] = shot_ref_count
        script_file = task_dir / "script_result.json"
        with open(script_file, 'w', encoding='utf-8') as f:
            json.dump(script_result, f, ensure_ascii=False, indent=2)

        # 更新任务状态为完成
        task_data["results"]["script"] = script_result
        task_data["results"]["write_phase2_pending_shots"] = False
        task_data["write_phase"] = 3
        task_data["write_draft_confirmed"] = True
        total_shots = sum(len(s.get("shots", [])) for s in scenes)
        task_data["agents"]["writer"] = {
            "status": "completed",
            "started_at": datetime.now().isoformat(),
            "completed_at": datetime.now().isoformat(),
            "result_summary": f"创作完成，{total_shots}镜头",
            "can_modify": True, "can_regenerate": True,
        }
        task_data["status"] = self.STATUS["WRITTEN"]
        task_data["current_step"] = "produce"
        task_data["progress"] = 50
        self._save_task(task_id, task_data)

        return {
            "success": True,
            "message": f"镜头参考图生成完成 ({img_summary})",
            "task_id": task_id,
            "result": script_result,
            "phase": "complete",
            "next_step": "produce",
            "image_assets": {"shot_refs": shot_ref_count},
        }

    def execute_write(self, task_id: str, user_input: Optional[Dict] = None) -> Dict[str, Any]:


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/user_driven_task_system.py.


现在，为各个阶段添加 API 端点：

exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 找到write步骤的路由定义位置
grep -n "steps/write\|execute_write\|execute_step" user_driven_api_server.py | head -20


218:@app.post("/api/tasks/{task_id}/steps/write")
219:async def execute_write(task_id: str, preferences: Optional[Dict] = None):
228:        result = user_driven_task_system.execute_write(task_id, user_input)


@app.post("/api/tasks/{task_id}/steps/write")
async def execute_write(task_id: str, preferences: Optional[Dict] = None):
    """执行创作步骤"""
    try:
        user_input = None
        if preferences:
            user_input = {
                "preferences": preferences.dict(exclude_none=True)
            }

        result = user_driven_task_system.execute_write(task_id, user_input)
        if not result["success"]:
            raise HTTPException(status_code=400, detail=result["message"])
        return result
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"执行创作步骤失败: {str(e)}")
        raise HTTPException(status_code=500, detail=f"执行创作步骤失败: {str(e)}")

@app.post("/api/tasks/{task_id}/confirm-draft")
async def confirm_draft(task_id: str):
    """确认创作草稿，生成场景图和镜头参考图"""
    try:
        result = user_driven_task_system.confirm_write_draft(task_id)
        if not result["success"]:
            raise HTTPException(status_code=400, detail=result["message"])
        return result
    except HTTPException:
        raise

[542 more lines in file. Use offset=248 to continue.]


edit

@app.post("/api/tasks/{task_id}/steps/write")
async def execute_write(task_id: str, preferences: Optional[Dict] = None):
    """执行创作步骤 - v3.14支持多阶段"""
    try:
        user_input = None
        if preferences:
            user_input = {
                "preferences": preferences.dict(exclude_none=True)
            }

        # v3.14: 检查是否指定了phase
        phase = None
        if preferences and hasattr(preferences, 'dict'):
            d = preferences.dict(exclude_none=True)
            phase = d.get('phase', None)

        if phase == 'outline':
            result = user_driven_task_system.execute_write_phase1(task_id, user_input)
        elif phase == 'confirm_outline':
            result = user_driven_task_system.execute_write_phase2(task_id, user_input)
        elif phase == 'confirm_images':
            result = user_driven_task_system.execute_write_phase3(task_id, user_input)
        else:
            # 默认: 一次性执行全部(兼容旧逻辑)
            result = user_driven_task_system.execute_write(task_id, user_input)

        if not result["success"]:
            raise HTTPException(status_code=400, detail=result["message"])
        return result
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"执行创作步骤失败: {str(e)}")
        raise HTTPException(status_code=500, detail=f"执行创作步骤失败: {str(e)}")

@app.post("/api/tasks/{task_id}/steps/write")
async def execute_write(task_id: str, preferences: Optional[Dict] = None):
    """执行创作步骤"""
    try:
        user_input = None
        if preferences:
            user_input = {
                "preferences": preferences.dict(exclude_none=True)
            }

        result = user_driven_task_system.execute_write(task_id, user_input)
        if not result["success"]:
            raise HTTPException(status_code=400, detail=result["message"])
        return result
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"执行创作步骤失败: {str(e)}")
        raise HTTPException(status_code=500, detail=f"执行创作步骤失败: {str(e)}")


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/user_driven_api_server.py.

     ...
 216         raise HTTPException(status_code=500, detail=f"执行采集步骤失败: {str(e)}")
 217
 218 @app.post("/api/tasks/{task_id}/steps/write")
 219 async def execute_write(task_id: str, preferences: Optional[Dict] = None):
-220     """执行创作步骤"""
+220     """执行创作步骤 - v3.14支持多阶段"""
 221     try:
 222         user_input = None
 223         if preferences:
 224             user_input = {
 225                 "preferences": preferences.dict(exclude_none=True)
 226             }
-227
-228         result = user_driven_task_system.execute_write(task_id, user_input)
+227
+228         # v3.14: 检查是否指定了phase
+229         phase = None
+230         if preferences and hasattr(preferences, 'dict'):
+231             d = preferences.dict(exclude_none=True)
+232             phase = d.get('phase', None)
+233
+234         if phase == 'outline':
+235             result = user_driven_task_system.execute_write_phase1(task_id, user_input)
+236         elif phase == 'confirm_outline':
+237             result = user_driven_task_system.execute_write_phase2(task_id, user_input)
+238         elif phase == 'confirm_images':
+239             result = user_driven_task_system.execute_write_phase3(task_id, user_input)
+240         else:
+241             # 默认: 一次性执行全部(兼容旧逻辑)
+242             result = user_driven_task_system.execute_write(task_id, user_input)
+243
 229         if not result["success"]:
 230             raise HTTPException(status_code=400, detail=result["message"])
 231         return result
 232     except HTTPException:
     ...


现在更新前端，使其使用多阶段工作流：


async function refreshTaskList(){try{const r=await fetch(`${API_BASE}/tasks`);const d=await r.json();if(d.success&&d.tasks.length){let h='';d.tasks.forEach(t=>{const act=t.task_id===currentTaskId;const sb=t.status==='completed'||t.status==='reviewed'?'bg-success':t.status==='produced'||t.status==='written'||t.status==='collected'?'bg-info':'bg-secondary';h+=`

${escapeHtml(t.description||t.task_id)}
${t.task_id.slice(0,20)}... | ${t.current_step||t.status}
⏱${t.duration||'60秒'}🎬${t.video_mode||'img2vid'}🖼${t.image_model||'auto'}📹${t.video_model||'auto'}${(t.channels&&t.channels.length)?'📢'+t.channels.join(',')+'':''}

${t.progress}%

`;});document.getElementById('taskList').innerHTML=h;}else document.getElementById('taskList').innerHTML='
暂无任务
';}catch(e){}}
async function switchTask(taskId){stopAutoRefresh();currentTaskId=taskId;document.getElementById('taskInfo').style.display='block';document.getElementById('currentTaskId').textContent=taskId;addLog('info','切换到任务: '+taskId);['collect','write','produce','review'].forEach(s=>{const c=document.getElementById(s+'ResultContent');if(c)c.innerHTML='';const b=document.getElementById(s+'Body');if(b)b.classList.remove('expanded');});const resp=await fetch(`${API_BASE}/tasks/${taskId}/status`);if(resp.ok){const data=await resp.json();if(data.success){currentTaskData=data.task;updateTaskInfo(data.task);/* v3.10: 回显任务配置到侧边配置面板 */const cfgMap={cfgDuration:'duration',cfgVideoMode:'video_mode',cfgImageModel:'image_model',cfgVideoModel:'video_model'};Object.entries(cfgMap).forEach(([elId,key])=>{const el=document.getElementById(elId);if(el&&data.task[key])el.value=data.task[key];});const chs=data.task.channels||[];document.querySelectorAll('.cfg-ch').forEach(c=>{c.checked=chs.includes(c.value);});const step=data.task.current_step;const steps=['collect','write','produce','review'];const isCompleted=step==='complete'||step==='completed'||step==='reviewed'||data.task.status==='completed'||data.task.progress>=100;const idx=isCompleted?steps.length:steps.indexOf(step);const done=steps.slice(0,idx);steps.forEach((s,i)=>{if(i<idx)updateStepStatus(s,'completed',true,true,true);else if(i===idx&&idx<steps.length)updateStepStatus(s,'pending',true);else if(!isCompleted)updateStepStatus(s,'pending',false);else updateStepStatus(s,'completed',true,true,true);});for(const s of done){try{const r=await fetch(`${API_BASE}/tasks/${taskId}/steps/${s}`);if(r.ok){const d=await r.json();if(d.success&&d.result){const fn={collect:displayCollectionResult,write:displayWriteResult,produce:displayProduceResult,review:displayReviewResult}[s];if(fn)fn(d.result,true);}}}catch(e){}}refreshTaskList();const isInProgress=!isCompleted&&step!=='complete'&&step!=='completed'&&step!=='reviewed'&&data.task.status!=='completed'&&data.task.progress<100;if(isInProgress)startAutoRefresh();}}}

// Event bindings

[55 more lines in file. Use offset=283 to continue.]


现在我需要更新 `btnExecuteWrite` 事件，使其首先使用第一阶段：

document.getElementById('btnExecuteWrite')?.addEventListener('click',async()=>{
    if(!currentTaskId){alert('请先选择任务');return;}
    const btn=document.getElementById('btnExecuteWrite');
    btn.disabled=true;btn.innerHTML=' 生成大纲中...';
    addLog('info','开始生成故事大纲和推荐...');
    try{
        const r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({phase:'outline'})});
        const d=await r.json();
        if(d.success&&d.result){
            displayWritePhase1Result(d.result);
            addLog('success','大纲生成完成，请确认后继续');
        }else{
            addLog('error',d.detail||d.message||'大纲生成失败');
            btn.disabled=false;btn.innerHTML='执行创作';
        }
    }catch(e){addLog('error','大纲生成失败: '+e.message);btn.disabled=false;btn.innerHTML='执行创作';}
});


Successfully replaced 1 block(s) in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator.js.

...
 297 });
 298 document.getElementById('btnCancelCollectModification')?.addEventListener('click',()=>document.getElementById('collectModificationForm').style.display='none');
 299 document.getElementById('btnSubmitCollectModification')?.addEventListener('click',async()=>{const fb=document.getElementById('collectModificationText').value.trim();if(!fb){alert('请输入修改意见');return;}document.getElementById('collectModificationForm').style.display='none';addLog('info','修改采集: '+fb);try{const r=await fetch(`${API_BASE}/tasks/${currentTaskId}/steps/collect`,{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({modification:fb,preferences:{user_feedback:fb}})});const d=await r.json();if(d.success){displayCollectionResult(d.result);updateStepStatus('collect','completed',false,true,true);addLog('success','修改后重新采集完成');await refreshTaskStatus();}else addLog('error',d.message||'修改采集失败');}catch(e){addLog('error','修改采集失败: '+e.message);}});
 300
-301 document.getElementById('btnExecuteWrite')?.addEventListener('click',()=>executeStep('write','btnExecuteWrite','创作中','执行创作'));
+301 document.getElementById('btnExecuteWrite')?.addEventListener('click',async()=>{
+302     if(!currentTaskId){alert('请先选择任务');return;}
+303     const btn=document.getElementById('btnExecuteWrite');
+304     btn.disabled=true;btn.innerHTML=' 生成大纲中...';
+305     addLog('info','开始生成故事大纲和推荐...');
+306     try{
+307         const r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({phase:'outline'})});
+308         const d=await r.json();
+309         if(d.success&&d.result){
+310             displayWritePhase1Result(d.result);
+311             addLog('success','大纲生成完成，请确认后继续');
+312         }else{
+313             addLog('error',d.detail||d.message||'大纲生成失败');
+314             btn.disabled=false;btn.innerHTML='执行创作';
+315         }
+316     }catch(e){addLog('error','大纲生成失败: '+e.message);btn.disabled=false;btn.innerHTML='执行创作';}
+317 });
 302 document.getElementById('btnViewWrite')?.addEventListener('click',()=>viewStepResult('write'));
 303 document.getElementById('btnModifyWrite')?.addEventListener('click',async()=>{var hist=await getModifications('write');var p=await showPromptEditor('修改创作要求','',hist);if(p!==null){addLog('info','修改创作: '+p);try{var r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({modification:p,preferences:{user_feedback:p}})});var d=await r.json();if(d.success&&d.result){displayWriteResult(d.result);updateStepStatus('write','completed',false,true,true);addLog('success','修改后重新创作完成');await refreshTaskStatus();}}catch(e){addLog('error','修改创作失败: '+e.message);}}});
 304 document.getElementById('btnCancelModification')?.addEventListener('click',()=>document.getElementById('writeModificationForm').style.display='none');
 305 document.getElementById('btnSaveModification')?.addEventListener('click',async()=>{const title=document.getElementById('modifyTitle').value.trim();const style=document.getElementById('modifyStyle').value;const reqs=document.getElementById('modifyRequirements').value.trim();const fb=[title?'标题:'+title:'',style?'风格:'+style:'',reqs].filter(Boolean).join('；');if(!fb){alert('请输入至少一项修改');return;}document.getElementById('writeModificationForm').style.display='none';addLog('info','修改创作: '+fb);try{const r=await fetch(`${API_BASE}/tasks/${currentTaskId}/steps/write`,{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({modification:fb,preferences:{title,style,user_feedback:reqs}})});const d=await r.json();if(d.success&&d.result){displayWriteResult(d.result);updateStepStatus('write','completed',false,true,true);addLog('success','修改后重新创作完成');await refreshTaskStatus();}}catch(e){addLog('error','修改创作失败: '+e.message);}});
     ...


现在添加 `displayWritePhase1Result` 函数和“确认大纲”/“确认图像”按钮处理程序：

// v3.14: 多阶段创作流程显示函数
function displayWritePhase1Result(result){
    const c=document.getElementById('writeResultContent');
    if(!result){c.innerHTML='
暂无
';return;}
    let h='';
    if(result.agent_called)h+='
🤖 由创作官Agent执行
';
    h+='

📋 Phase 1: 大纲和推荐

';
    if(result.title)h+=`<h5 class="mb-2">📖 ${escapeHtml(result.title)}</h5>`;
    if(result.genre)h+=`<span class="badge bg-secondary me-1">类型: ${escapeHtml(result.genre)}</span> `;
    if(result.synopsis)h+=`<div class="alert alert-light mb-2"><strong>剧情概要:</strong> ${escapeHtml(result.synopsis)}</div>`;
    if(result.emotional_arc)h+=`<div class="mb-2"><strong>情感曲线:</strong> ${escapeHtml(result.emotional_arc)}</div>`;
    // 视觉风格推荐
    if(result.visual_style_recommendation){
        const vs=result.visual_style_recommendation;
        h+=`<div class="card mb-2"><div class="card-header py-1">🎨 推荐视觉风格</div><div class="card-body p-2">`;
        h+=`<p class="mb-1"><strong>主要风格:</strong> ${escapeHtml(vs.primary||'')}</p>`;
        h+=`<p class="mb-1"><strong>备选风格:</strong> ${escapeHtml(vs.secondary||'')}</p>`;
        h+=`<p class="mb-0 small text-muted">${escapeHtml(vs.reason||'')}</p></div></div>`;
    }
    // 模型推荐
    if(result.model_recommendation){
        const mr=result.model_recommendation;
        h+=`<div class="card mb-2"><div class="card-header py-1">🤖 推荐模型</div><div class="card-body p-2">`;
        h+=`<p class="mb-1">图片模型: <strong>${escapeHtml(mr.image_model||'')}</strong></p>`;
        h+=`<p class="mb-1">视频模型: <strong>${escapeHtml(mr.video_model||'')}</strong></p>`;
        h+=`<p class="mb-0 small text-muted">${escapeHtml(mr.reason||'')}</p></div></div>`;
    }
    // 角色描述
    if(result.characters&&result.characters.length){
        h+=`<h6 class="mt-3">👥 角色设定 (${result.characters.length}个)</h6><div class="row mb-2">`;
        result.characters.forEach(ch=>{
            h+=`

            ${escapeHtml(ch.name)} ${escapeHtml(ch.role||'')}

${escapeHtml(ch.appearance||'')}

性格: ${escapeHtml(ch.personality||'')}

说话: ${escapeHtml(ch.speech_style||'')}

`;
        });
        h+='
';
    }
    // 场景概要
    if(result.scenes_overview&&result.scenes_overview.length){
        h+=`<h6>🎬 场景概要 (${result.scenes_overview.length}个)</h6>`;
        result.scenes_overview.forEach(sc=>{
            h+=`

            场景${sc.scene_id}: ${escapeHtml(sc.location||'')}
            ${escapeHtml(sc.time||'')}

${escapeHtml(sc.atmosphere||'')} - ${escapeHtml(sc.purpose||'')}

`;
        });
    }
    // 确认按钮
    h+=`

    <button class="btn btn-success" id="btnConfirmOutline" onclick="confirmWriteOutline()">确认大纲，继续生成</button>
    <button class="btn btn-outline-warning" id="btnModifyOutline" onclick="modifyWriteOutline()">修改大纲</button>

`;
    c.innerHTML=h;
    expandStep('write');
}

// 确认大纲 -> Phase2
async function confirmWriteOutline(){
    if(!currentTaskId)return;
    const btn=document.getElementById('btnConfirmOutline');
    btn.disabled=true;btn.innerHTML=' 生成脚本和图片中...';
    addLog('info','确认大纲，开始生成脚本+角色+场景图...');
    try{
        const r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({phase:'confirm_outline'})});
        const d=await r.json();
        if(d.success&&d.result){
            displayWritePhase2Result(d.result);
            addLog('success',`脚本+图片生成完成: ${d.message}`);
        }else{addLog('error',d.detail||d.message||'生成失败');btn.disabled=false;btn.innerHTML='确认大纲，继续生成';}
    }catch(e){addLog('error','生成失败: '+e.message);btn.disabled=false;btn.innerHTML='确认大纲，继续生成';}
}

// 修改大纲
async function modifyWriteOutline(){
    if(!currentTaskId)return;
    var p=await showPromptEditor('修改大纲要求','');
    if(p===null)return;
    addLog('info','修改大纲: '+p);
    try{
        const r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({phase:'outline',modification:p,preferences:{user_feedback:p}})});
        const d=await r.json();
        if(d.success&&d.result){displayWritePhase1Result(d.result);addLog('success','大纲重新生成完成');}
        else addLog('error',d.detail||d.message||'修改失败');
    }catch(e){addLog('error','修改失败: '+e.message);}
}

// Phase2结果显示(脚本+角色图+场景图，等待确认后生成镜头图)
function displayWritePhase2Result(result){
    displayWriteResult(result);
    // 追加确认按钮
    const c=document.getElementById('writeResultContent');
    const existingBtn=c.querySelector('#btnConfirmImages');
    if(!existingBtn){
        const div=document.createElement('div');
        div.className='mt-3 d-flex gap-2';
        div.innerHTML=`<button class="btn btn-success" id="btnConfirmImages" onclick="confirmWriteImages()"><i class="bi bi-check-circle me-1"></i>确认图片，生成镜头参考图</button>`;
        c.appendChild(div);
    }
}

// 确认图片 -> Phase3(生成镜头参考图)
async function confirmWriteImages(){
    if(!currentTaskId)return;
    const btn=document.getElementById('btnConfirmImages');
    btn.disabled=true;btn.innerHTML=' 生成镜头参考图中...';
    addLog('info','确认图片，开始生成镜头参考图...');
    try{
        const r=await fetch(API_BASE+'/tasks/'+currentTaskId+'/steps/write',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({phase:'confirm_images'})});
        const d=await r.json();
        if(d.success&&d.result){
            displayWriteResult(d.result);
            updateStepStatus('write','completed',false,true,true);
            addLog('success',`镜头参考图生成完成: ${d.message}`);
            await refreshTaskStatus();
        }else{addLog('error',d.detail||d.message||'生成失败');btn.disabled=false;btn.innerHTML='确认图片，生成镜头参考图';}
    }catch(e){addLog('error','生成失败: '+e.message);btn.disabled=false;btn.innerHTML='确认图片，生成镜头参考图';}
}


现在重启服务并验证：

exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 验证Python语法
source venv/bin/activate
python3 -c "import py_compile; py_compile.compile('user_driven_task_system.py', doraise=True); print('task_system OK')" 2>&1
python3 -c "import py_compile; py_compile.compile('agent_client.py', doraise=True); print('agent_client OK')" 2>&1
python3 -c "import py_compile; py_compile.compile('user_driven_api_server.py', doraise=True); print('api_server OK')" 2>&1


task_system OK
agent_client OK
api_server OK


# 重启8000端口服务
systemctl --user restart manhua-studio-8000
sleep 3
systemctl --user status manhua-studio-8000 --no-pager | head -15
echo "---"
curl -s http://127.0.0.1:8000/ -o /dev/null -w "HTTP %{http_code}\n" --max-time 5
echo "---"
# 验证HTML版本号
curl -s http://127.0.0.1:8000/static/user_driven_coordinator_final.html | grep "v=3.14"


● manhua-studio-8000.service - Manhua Studio API Server (port 8000)
     Loaded: loaded (/root/.config/systemd/user/manhua-studio-8000.service; enabled; preset: enabled)
     Active: active (running) since Tue 2026-05-19 12:19:04 CST; 3s ago
   Main PID: 1099655 (python)
      Tasks: 1 (limit: 4369)
     Memory: 33.1M (peak: 33.3M)
        CPU: 462ms
     CGroup: /user.slice/user-0.slice/user@0.service/app.slice/manhua-studio-8000.service
             └─1099655 /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/bin/python -m uvicorn api_server:app --host 0.0.0.0 --port 8000 --log-level info

May 19 12:19:04 VM-0-12-ubuntu systemd[3282]: Started manhua-studio-8000.service - Manhua Studio API Server (port 8000).
May 19 12:19:04 VM-0-12-ubuntu manhua-studio-8000[1099655]: INFO:     Started server process [1099655]
May 19 12:19:04 VM-0-12-ubuntu manhua-studio-8000[1099655]: INFO:     Waiting for application startup.
May 19 12:19:04 VM-0-12-ubuntu manhua-studio-8000[1099655]: INFO:     Application startup complete.
May 19 12:19:04 VM-0-12-ubuntu manhua-studio-8000[1099655]: INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
---
HTTP 307
---
    <script src="user_driven_coordinator.js?v=3.14"></script>


# 为已有的任务生成缩略图(之前Pillow未安装导致空的)
cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000
source venv/bin/activate

python3 -c "
from pathlib import Path
from PIL import Image
import subprocess

for task_dir in Path('/root/data/disk/projects/tasks').iterdir():
    if not task_dir.is_dir(): continue
    images_dir = task_dir / 'images'
    if not images_dir.exists(): continue
    thumb_dir = images_dir / '.thumbs'
    thumb_dir.mkdir(exist_ok=True)
    count = 0
    for f in images_dir.iterdir():
        if not f.is_file(): continue
        suffix = f.suffix.lower()
        thumb_name = f.stem + '_thumb' + ('.jpg' if suffix in ('.mp4','.mov','.avi','.webm') else f.suffix)
        thumb_path = thumb_dir / thumb_name
        if thumb_path.exists() and thumb_path.stat().st_mtime >= f.stat().st_mtime:
            continue
        try:
            if suffix in ('.png','.jpg','.jpeg','.webp','.gif'):
                img = Image.open(f)
                img.thumbnail((160, 160), Image.LANCZOS)
                if img.mode in ('RGBA','P'): img = img.convert('RGB')
                img.save(thumb_path, 'JPEG', quality=60)
                count += 1
            elif suffix in ('.mp4','.mov','.avi','.webm'):
                r = subprocess.run(['ffmpeg','-y','-ss','1','-i',str(f),'-vframes','1','-vf','scale=160:-1','-q:v','3',str(thumb_path)], capture_output=True, timeout=15)
                if thumb_path.exists(): count += 1
        except: pass
    if count > 0:
        print(f'{task_dir.name}: {count} thumbnails generated')
print('Done')
" 2>&1


task_20260519_093207_8118e21e: 28 thumbnails generated
Done


修复步骤1: 修改要求按钮加载当前要求 + Modal关闭问题

修复步骤2: 缩略图生成缺失问题

改造步骤2为多阶段用户确认流程

添加'参考创作官行为规范'提示词到Agent调用


全部修复完成。总结一下：

---

## v3.14 修复汇总

### 步骤1修复

**1. 修改要求按钮没加载当前要求** ✅
- 现在点击"修改要求"时，会先加载当前任务描述或上次采集分析内容作为默认值
- 同时显示历史修改记录

**2. Modal无法关闭** ✅
- 原因：`backdrop: 'static'` + `keyboard: false` 导致无法点外部或按Esc关闭
- 已改为 `backdrop: true` + `keyboard: true`

**3. hot_trends数据是否伪造** ✅ 已确认
- 三条路径：真实Agent调用 → LLM生成 → SimpleRealAgent伪造
- **当Agent和LLM都失败时，确实会用SimpleRealAgent生成假数据**（标记为`is_real_data: False`）
- 已在call_collector提示词中明确要求："你必须联网搜索当前真实的热点数据，不要凭空编造"

**4. Agent调用提示词** ✅
- call_collector已添加"参考采集官行为规范处理"
- call_writer已添加"参考创作官行为规范处理"
- 都加入了联网采集指令

### 步骤2修复

**5. 缩略图没生成** ✅
- **根因：Pillow未安装在venv中**
- 已安装Pillow，并为所有已有任务补生成缩略图（28张）

**6. 多阶段用户确认流程** ✅ 完整改造
新的执行流程：
1. **点击"执行创作"** → 生成大纲+角色描述+推荐(风格/模板/模型)
2. **用户确认** → 可修改大纲 → 确认后生成角色图+场景图+缩略图
3. **用户确认图片** → 生成镜头参考图+缩略图
4. 全部确认 → 进入制作步骤

**7. Agent提示词** ✅
- 所有Agent调用都添加了"参考创作官/采集官行为规范处理"


[Tue 2026-05-19 12:52 GMT+8] 参考CTO行为规范处理
不要生成假数据，没有查到就是没查到。这条加到通用的行为规范中
创建完任务 进度为undefined%
任务列表里面的模型显示都是auto
多阶段用户确认流程中 大纲一直再生成中； 我只看到了角色设定没看到 故事大纲内容和推荐(风格/模板/模型) ；


System (untrusted): [2026-05-19 12:30:19 GMT+8] zod模块健康检查通过，状态正常，无需修复，不发送通知。


禁止假数据：删除SimpleRealAgent伪造逻辑，加到通用规范

修复创建任务后进度显示undefined%

修复任务列表模型显示都是auto

修复多阶段大纲生成卡住+显示不完整


cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 1. 删除SimpleRealAgent伪造逻辑 - execute_collect中的fallback
grep -n "SimpleRealAgent\|generate_collection_result\|is_real_data.*False\|fallback_reason" user_driven_task_system.py | head -20


25:from simple_real_agent import SimpleRealAgent
452:                collection_result = SimpleRealAgent.generate_collection_result(task_description)
453:                collection_result["is_real_data"] = False
454:                collection_result["fallback_reason"] = "LLM采集失败"
1354:                "is_real_data": False,
1355:                "fallback_reason": "LLM/Agent脚本生成失败",


                    )
                except Exception as e:
                    logger.warning(f"LLM采集失败,降级到基础采集: {e}")
                    analysis = None

            if analysis is None:
                # 完全降级到基础采集
                collection_result = SimpleRealAgent.generate_collection_result(task_description)
                collection_result["is_real_data"] = False
                collection_result["fallback_reason"] = "LLM采集失败"
                collection_result["from_db"] = False
                collection_result["agent_called"] = False
            else:
                # 解析JSON
                try:
                    if "```json" in analysis:
                        json_str = analysis.split("```json")[1].split("```")[0].strip()
                    elif "{" in analysis:
                        json_str = analysis[analysis.index("{"):]
                    else:
                        json_str = analysis
                    json_str = sanitize_json_string(json_str)
                    parsed = json.loads(json_str)
                    collection_result = {
                        "status": "succeeded",
                        "task_description": task_description,
                        "analysis": json.dumps(parsed, ensure_ascii=False, indent=2),
                        "parsed": parsed,
                        "is_real_data": True,
                        "from_db": False,

[2628 more lines in file. Use offset=475 to continue.]


edit

            if analysis is None:
                # 完全降级到基础采集
                collection_result = SimpleRealAgent.generate_collection_result(task_description)
                collection_result["is_real_data"] = False
                collection_result["fallback_reason"] = "LLM采集失败"
                collection_result["from_db"] = False
                collection_result["agent_called"] = False

            if analysis is None:
                # v3.14: 不再生成假数据，采集失败就返回失败
                collection_result = {
                    "status": "failed",
                    "task_description": task_description,
                    "analysis": "采集失败：Agent和LLM均未返回有效数据",
                    "is_real_data": False,
                    "from_db": False,
                    "agent_called": False,
                    "error": "采集失败，请稍后重试或修改要求"
                }


      ...
  447                     logger.warning(f"LLM采集失败,降级到基础采集: {e}")
  448                     analysis = None
  449
  450             if analysis is None:
- 451                 # 完全降级到基础采集
- 452                 collection_result = SimpleRealAgent.generate_collection_result(task_description)
- 453                 collection_result["is_real_data"] = False
- 454                 collection_result["fallback_reason"] = "LLM采集失败"
- 455                 collection_result["from_db"] = False
- 456                 collection_result["agent_called"] = False
+ 451                 # v3.14: 不再生成假数据，采集失败就返回失败
+ 452                 collection_result = {
+ 453                     "status": "failed",
+ 454                     "task_description": task_description,
+ 455                     "analysis": "采集失败：Agent和LLM均未返回有效数据",
+ 456                     "is_real_data": False,
+ 457                     "from_db": False,
+ 458                     "agent_called": False,
+ 459                     "error": "采集失败，请稍后重试或修改要求"
+ 460                 }
  457             else:
  458                 # 解析JSON
  459                 try:
  460                     if "```json" in analysis:
      ...


现在修复写入回退逻辑：


                logger.warning(f"LLM脚本生成失败: {e}")
                llm_output = None

        # 解析llm_output为script_result (Agent输出或LLM输出都经过此处)
        if llm_output is None:
            # 完全失败,用备用脚本
            script_result = {
                "title": user_preferences.get("title", task_description[:20]),
                "genre": "短剧",
                "scenes": [
                    {"scene_id": 1, "location": "场景1", "shots": [{"shot_id": 1, "shot_type": "中景", "description": task_description[:50], "duration_seconds": 5, "video_prompt": task_description}]},
                ],
                "characters": [{"name": "主角", "description": "主要角色", "personality": "活跃"}],
                "is_real_data": False,
                "fallback_reason": "LLM/Agent脚本生成失败",

[1747 more lines in file. Use offset=1360 to continue.]


        # 解析llm_output为script_result (Agent输出或LLM输出都经过此处)
        if llm_output is None:
            # v3.14: 不再生成假数据，脚本生成失败就返回失败
            task_data["status"] = self.STATUS["FAILED"] if "FAILED" in self.STATUS else "failed"
            self._save_task(task_id, task_data)
            return {
                "success": False,
                "message": "脚本生成失败：Agent和LLM均未返回有效数据，请稍后重试",
                "task_id": task_id,
            }

        # 解析llm_output为script_result (Agent输出或LLM输出都经过此处)
        if llm_output is None:
            # 完全失败,用备用脚本
            script_result = {
                "title": user_preferences.get("title", task_description[:20]),
                "genre": "短剧",
                "scenes": [
                    {"scene_id": 1, "location": "场景1", "shots": [{"shot_id": 1, "shot_type": "中景", "description": task_description[:50], "duration_seconds": 5, "video_prompt": task_description}]},
                ],
                "characters": [{"name": "主角", "description": "主要角色", "personality": "活跃"}],
                "is_real_data": False,
                "fallback_reason": "LLM/Agent脚本生成失败",


      ...
 1346                 llm_output = None
 1347
 1348         # 解析llm_output为script_result (Agent输出或LLM输出都经过此处)
 1349         if llm_output is None:
-1350             # 完全失败,用备用脚本
-1351             script_result = {
-1352                 "title": user_preferences.get("title", task_description[:20]),
-1353                 "genre": "短剧",
-1354                 "scenes": [
-1355                     {"scene_id": 1, "location": "场景1", "shots": [{"shot_id": 1, "shot_type": "中景", "description": task_description[:50], "duration_seconds": 5, "video_prompt": task_description}]},
-1356                 ],
-1357                 "characters": [{"name": "主角", "description": "主要角色", "personality": "活跃"}],
-1358                 "is_real_data": False,
-1359                 "fallback_reason": "LLM/Agent脚本生成失败",
+1350             # v3.14: 不再生成假数据，脚本生成失败就返回失败
+1351             task_data["status"] = self.STATUS["FAILED"] if "FAILED" in self.STATUS else "failed"
+1352             self._save_task(task_id, task_data)
+1353             return {
+1354                 "success": False,
+1355                 "message": "脚本生成失败：Agent和LLM均未返回有效数据，请稍后重试",
+1356                 "task_id": task_id,
+1357             }
 1360                 "agent_called": agent_used,
 1361             }
 1362         else:
 1363             # 解析JSON - 支持截断修复
      ...


现在将规则添加到 MEMORY.md:


- API部署前: `curl -s -L http://127.0.0.1:port/ | grep "DOCTYPE html"`

**系统稳定性**:
- pnpm copy模式下文件是独立副本，drop_caches+网关冷启动=模块损坏
- nohup启动的服务会被systemctl restart的SIGTERM波及，必须用独立systemd服务

[58 more lines in file. Use offset=63 to continue.]


**禁止生成假数据**:
- 采集/创作/制作步骤中，当Agent和LLM均失败时，不得用SimpleRealAgent等伪造数据
- 没查到就是没查到，返回失败让用户重试，不要编造内容
- 所有fallback必须明确标记失败原因，不允许静默降级到假数据


Successfully replaced 1 block(s) in /root/.openclaw/workspace/MEMORY.md.

     ...
  56 - 根路径(`/`)必须返回HTML或重定向，不能返回JSON
  57 - 图片/视频必须有前端可访问的URL路径(`/api/files`)
  58 - API部署前: `curl -s -L http://127.0.0.1:port/ | grep "DOCTYPE html"`
  59
- 60 **系统稳定性**:
+ 60 **禁止生成假数据**:
+ 61 - 采集/创作/制作步骤中，当Agent和LLM均失败时，不得用SimpleRealAgent等伪造数据
+ 62 - 没查到就是没查到，返回失败让用户重试，不要编造内容
+ 63 - 所有fallback必须明确标记失败原因，不允许静默降级到假数据
  61 - pnpm copy模式下文件是独立副本，drop_caches+网关冷启动=模块损坏
  62 - nohup启动的服务会被systemctl restart的SIGTERM波及，必须用独立systemd服务
  63 - 两套并行代码(task_system.py vs user_driven_task_system.py)是最大架构债务
  64 - 同步阻塞API(视频生成3-5分钟)不能在async函数中直接调用
     ...


现在修复“undefined%”的进度问题。检查 `create_task`：

exec

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 找create_task方法中的progress字段
grep -n -A 50 "def create_task" user_driven_task_system.py | head -60


216:    def create_task(self, description: str, user_id: str = "default",
217-                     duration: str = "60秒", channels: list = None,
218-                     video_mode: str = "img2vid",
219-                     image_model: str = "auto",
220-                     video_model: str = "auto") -> Dict[str, Any]:
221-        """创建新任务
222-        Args:
223-            duration: 视频时长(30秒/60秒/90秒/120秒)
224-            channels: 投放渠道列表(抖音/快手/小红书/B站/微信视频号)
225-            video_mode: 视频生成模式(img2vid/text2vid/auto)
226-            image_model: 图片生成模型(auto/doubao-seedream-4-5-251128/doubao-seedream-4-0-250828)
227-            video_model: 视频生成模型(auto/doubao-seedance-1-5-pro-251215/doubao-seedance-1-0-pro-250528/doubao-seedance-1-0-pro-fast-251015)
228-        """
229-        task_id = f"task_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
230-        task_dir = self.tasks_dir / task_id
231-        assets_dir = self.assets_dir / task_id
232-
233-        task_dir.mkdir(parents=True, exist_ok=True)
234-        assets_dir.mkdir(parents=True, exist_ok=True)
235-
236-        task_data = {
237-            "task_id": task_id,
238-            "description": description,
239-            "user_id": user_id,
240-            "duration": duration,
241-            "channels": channels or [],
242-            "video_mode": video_mode,
243-            "image_model": image_model,
244-            "video_model": video_model,
245-            "status": self.STATUS["CREATED"],
246-            "created_at": datetime.now().isoformat(),
247-            "updated_at": datetime.now().isoformat(),
248-            "current_step": "collect",
249-            "progress": 0,
250-            "user_actions": [],
251-            "agents": {},
252-            "results": {
253-                "collection": None,
254-                "script": None,
255-                "storyboard": None,
256-                "characters": None,
257-                "backgrounds": None,
258-                "video": None,
259-                "review": None
260-            },
261-            "modifications": {
262-                "collection": [],
263-                "script": [],
264-                "storyboard": [],
265-                "characters": [],
266-                "backgrounds": [],


cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 检查前端updateTaskInfo - progress显示逻辑
grep -n "progress\|undefined" static/user_driven_coordinator.js | head -15


110:    document.getElementById('taskProgress').textContent=d.progress+'%';
111:    document.getElementById('taskProgressBar').style.width=d.progress+'%';
209:if(r.quality_assessment){const qa=r.quality_assessment;h+='
📊 质量评估

';[{key:'overall_quality',label:'总体',color:'#0066cc'},{key:'creativity',label:'创意',color:'#9c27b0'},{key:'technical_execution',label:'技术',color:'#ff9800'},{key:'storytelling',label:'叙事',color:'#4caf50'},{key:'visual_impact',label:'视觉',color:'#e91e63'}].forEach(d=>{const v=qa[d.key];if(v!==undefined){const pct=Math.round(v*10);const bc=pct>=70?'border-color:'+d.color:pct>=50?'border-color:#ffc107':'border-color:#dc3545';h+=`<div class="gauge-container"><div class="gauge-ring" style="border:3px solid;${bc};">${pct}</div><small>${d.label}</small></div>`;}});h+='
';}
211:if(r.score!==undefined){const sc=r.score>=80?'bg-success':r.score>=60?'bg-warning':'bg-danger';h+=`<div class="alert ${sc} text-white py-2 mb-3">📊 综合评分: <strong>${r.score}/100</strong></div>`;}
214:if(r.publish_recommendation){const pr=r.publish_recommendation;h+='
🚀 发布建议

';if(pr.recommended!==undefined)h+=`<p><strong>推荐发布:</strong> <span class="badge ${pr.recommended?'bg-success':'bg-warning'}">${pr.recommended?'✅ 是':'⚠ 待改进'}</span></p>`;if(pr.platforms&&pr.platforms.length)h+=`<p><strong>推荐平台:</strong> ${pr.platforms.map(p=>'<span class="badge bg-info me-1">'+escapeHtml(p)+'</span>').join('')}</p>`;if(pr.publish_time)h+=`<p><strong>最佳发布时间:</strong> ${escapeHtml(pr.publish_time)}</p>`;if(pr.target_audience)h+=`<p><strong>目标受众:</strong> ${escapeHtml(pr.target_audience)}</p>`;if(pr.content_strategy)h+=`<p><strong>运营建议:</strong> ${escapeHtml(pr.content_strategy)}</p>`;h+='

';}
215:if(r.final_verdict){const fv=r.final_verdict;const stColor={approved:'bg-success',revision_needed:'bg-warning',rejected:'bg-danger'}[fv.status]||'bg-secondary';h+=`<div class="card border-2 ${fv.status==='approved'?'border-success':fv.status==='rejected'?'border-danger':'border-warning'}"><div class="card-body">`;h+=`<h6>⚖ 最终裁决</h6><p><span class="badge ${stColor}">${fv.status||'未知'}</span></p>`;if(fv.message)h+=`<p>${escapeHtml(fv.message)}</p>`;if(fv.publish_ready!==undefined)h+=`<p><strong>可发布:</strong> ${fv.publish_ready?'✅ 是':'❌ 否'}</p>`;h+='

';}
261:    if(p!==null)doRegenShot(id,p||undefined);
263:function doRegenShotText(id){doRegenShot(id,undefined,'text2vid');}
279:async function refreshTaskList(){try{const r=await fetch(`${API_BASE}/tasks`);const d=await r.json();if(d.success&&d.tasks.length){let h='';d.tasks.forEach(t=>{const act=t.task_id===currentTaskId;const sb=t.status==='completed'||t.status==='reviewed'?'bg-success':t.status==='produced'||t.status==='written'||t.status==='collected'?'bg-info':'bg-secondary';h+=`

${escapeHtml(t.description||t.task_id)}
${t.task_id.slice(0,20)}... | ${t.current_step||t.status}
⏱${t.duration||'60秒'}🎬${t.video_mode||'img2vid'}🖼${t.image_model||'auto'}📹${t.video_model||'auto'}${(t.channels&&t.channels.length)?'📢'+t.channels.join(',')+'':''}

${t.progress}%

`;});document.getElementById('taskList').innerHTML=h;}else document.getElementById('taskList').innerHTML='
暂无任务
';}catch(e){}}
280:async function switchTask(taskId){stopAutoRefresh();currentTaskId=taskId;document.getElementById('taskInfo').style.display='block';document.getElementById('currentTaskId').textContent=taskId;addLog('info','切换到任务: '+taskId);['collect','write','produce','review'].forEach(s=>{const c=document.getElementById(s+'ResultContent');if(c)c.innerHTML='';const b=document.getElementById(s+'Body');if(b)b.classList.remove('expanded');});const resp=await fetch(`${API_BASE}/tasks/${taskId}/status`);if(resp.ok){const data=await resp.json();if(data.success){currentTaskData=data.task;updateTaskInfo(data.task);/* v3.10: 回显任务配置到侧边配置面板 */const cfgMap={cfgDuration:'duration',cfgVideoMode:'video_mode',cfgImageModel:'image_model',cfgVideoModel:'video_model'};Object.entries(cfgMap).forEach(([elId,key])=>{const el=document.getElementById(elId);if(el&&data.task[key])el.value=data.task[key];});const chs=data.task.channels||[];document.querySelectorAll('.cfg-ch').forEach(c=>{c.checked=chs.includes(c.value);});const step=data.task.current_step;const steps=['collect','write','produce','review'];const isCompleted=step==='complete'||step==='completed'||step==='reviewed'||data.task.status==='completed'||data.task.progress>=100;const idx=isCompleted?steps.length:steps.indexOf(step);const done=steps.slice(0,idx);steps.forEach((s,i)=>{if(i<idx)updateStepStatus(s,'completed',true,true,true);else if(i===idx&&idx<steps.length)updateStepStatus(s,'pending',true);else if(!isCompleted)updateStepStatus(s,'pending',false);else updateStepStatus(s,'completed',true,true,true);});for(const s of done){try{const r=await fetch(`${API_BASE}/tasks/${taskId}/steps/${s}`);if(r.ok){const d=await r.json();if(d.success&&d.result){const fn={collect:displayCollectionResult,write:displayWriteResult,produce:displayProduceResult,review:displayReviewResult}[s];if(fn)fn(d.result,true);}}}catch(e){}}refreshTaskList();const isInProgress=!isCompleted&&step!=='complete'&&step!=='completed'&&step!=='reviewed'&&data.task.status!=='completed'&&data.task.progress<100;if(isInProgress)startAutoRefresh();}}}


第110行：`d.progress+'%'` — 如果API返回时未包含 `progress` 字段，它会显示为 `undefined%`。我来检查一下任务列表的API：

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 检查list_tasks和get_task_status返回的数据
grep -n -A 20 "def list_tasks" user_driven_task_system.py | head -30


3007:    def list_tasks(self) -> Dict[str, Any]:
3008-        """列出所有任务"""
3009-        tasks = []
3010-        if self.tasks_dir.exists():
3011-            for task_dir in sorted(self.tasks_dir.iterdir(), key=lambda x: x.stat().st_mtime, reverse=True):
3012-                if task_dir.is_dir():
3013-                    task_file = task_dir / "task.json"
3014-                    if task_file.exists():
3015-                        try:
3016-                            with open(task_file, 'r', encoding='utf-8') as f:
3017-                                td = json.load(f)
3018-                            tasks.append({
3019-                                "task_id": td.get("task_id", task_dir.name),
3020-                                "description": td.get("description", "")[:50],
3021-                                "status": td.get("status", "unknown"),
3022-                                "current_step": td.get("current_step", ""),
3023-                                "progress": td.get("progress", 0),
3024-                                "created_at": td.get("created_at", ""),
3025-                                "updated_at": td.get("updated_at", ""),
3026-                                # v3.10: 显示创建参数
3027-                                "duration": td.get("duration", "60秒"),


好的 — `list_tasks` 返回 `progress: td.get("progress", 0)`。问题可能在于 `create_task` 在保存到磁盘之前返回时没有 `progress`。让我检查一下 `API`：

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 检查创建任务的API返回
grep -n -A 25 "async def create_task" user_driven_api_server.py | head -30


145:async def create_task(request: CreativeDescription):
146-    """创建新任务"""
147-    try:
148-        task_data = user_driven_task_system.create_task(
149-            description=request.description,
150-            user_id=request.user_id,
151-            duration=request.duration,
152-            channels=request.channels,
153-            video_mode=request.video_mode,
154-            image_model=request.image_model,
155-            video_model=request.video_model
156-        )
157-
158-        return {
159-            "success": True,
160-            "message": "任务创建成功",
161-            "task_id": task_data["task_id"],
162-            "status": task_data["status"],
163-            "current_step": task_data["current_step"],
164-            "progress": task_data["progress"],
165-            "created_at": task_data["created_at"],
166-            "next_step": "collect",
167-            "next_step_description": "第一步：采集热点素材"
168-        }
169-    except Exception as e:
170-        logger.error(f"创建任务失败: {str(e)}")


创建时，`progress` 是从 `task_data["progress"]` 获取的，而 `task_data` 是 `0`。但是，在保存 `JSON` 后，如果前端直接调用 `switchTask`，`progress` 可能是未定义的。让我检查一下前端的 `create` 流程：

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 搜索创建任务的前端代码
grep -n "createTask\|create.*task\|btnCreateTask\|btnNewTask\|创建任务\|创建.*Modal" static/user_driven_coordinator.js | head -15


找到了！第 404 行：创建后，`updateTaskInfo(d)` 将 API 响应作为 `d` 传递。响应中包含 `progress: 0`，这是正确的。但问题可能在于 `updateTaskInfo` 读取 `d.progress` 时返回的是 `0`，而不是 `undefined`。让我更仔细地检查一下 `task status` API：

cd /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000

# 检查get_task_status
grep -n -A 30 "def get_task_status" user_driven_task_system.py | head -40