feat: 完成布局优化 - 操作列固定、批量按钮自适应、分类标签带数量
优化内容: 1. 表格布局: - 使用 calc(100vw - 160px) 确保表格不超出视口 - 操作列 fixed='right' 固定在右侧，宽度 300px - 按钮 3 个后自动换行 (max-width: 200px) - 恢复合理列宽，不再过度压缩 2. 批量操作区域: - 容器改为 inline-block，宽度自适应按钮内容 - 背景宽度与按钮总宽度匹配 3. 分类标签: - 显示数量 (如 '待处理 (20)') - 点击切换筛选，去掉误导的 'X' 图标 4. 删除功能: - 操作列增加删除按钮 - 删除前弹出确认对话框 5. 系统日志: - 修复后端日志路径 (parents[4]) - 404 时显示友好提示 6. 其他: - 左侧菜单宽度 160px - 所有功能保留 (登录、用户管理、批量操作等)
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lt
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"""
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审计日志记录模块
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用法：
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from .audit_logger import audit_log
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audit_log(action="create_user", user=current_user, details={...}, request=request, db=db)
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"""
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from sqlalchemy.orm import Session
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from ..models import AuditLog
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from typing import Optional, Dict, Any
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from datetime import datetime
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def audit_log(
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action: str,
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*,
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user=None, # User 对象或 None
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username: Optional[str] = None,
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resource_type: Optional[str] = None,
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resource_id: Optional[str] = None,
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details: Optional[Dict[str, Any]] = None,
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ip_address: Optional[str] = None,
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user_agent: Optional[str] = None,
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db: Session = None
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) -> None:
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"""
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记录审计日志
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参数：
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action: 操作类型（必填），如 "login", "create_user", "delete_user", "publish"
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user: 操作用户的 User 对象（可选，如果提供则自动填充 user_id 和 username）
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username: 直接指定用户名（如果 user 为 None 则必须提供）
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resource_type: 资源类型，如 "user", "topic", "publish_record"
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resource_id: 资源ID
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details: 操作详情字典（如变更前后的值）
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ip_address: IP 地址
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user_agent: User-Agent
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db: 数据库会话（必填）
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"""
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if db is None:
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raise ValueError("db session is required")
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# 确定 user_id 和 username
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user_id = None
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if user is not None:
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user_id = getattr(user, 'id', None)
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username = getattr(user, 'username', username)
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if not username:
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username = "anonymous"
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log = AuditLog(
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user_id=user_id,
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username=username,
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action=action,
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resource_type=resource_type,
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resource_id=resource_id,
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details=details or {},
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ip_address=ip_address,
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user_agent=user_agent,
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created_at=datetime.utcnow()
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)
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db.add(log)
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db.commit()
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# 不抛出异常，避免影响主流程
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@@ -6,7 +6,7 @@ import os
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logger = logging.getLogger(__name__)
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# 计算项目根目录（从本文件位置上升4层）
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PROJECT_ROOT = Path(__file__).resolve().parents[4]
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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# 允许环境变量覆盖（适合容器部署）
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if os.getenv('PROJECT_ROOT'):
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PROJECT_ROOT = Path(os.getenv('PROJECT_ROOT'))
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@@ -26,7 +26,7 @@ def run_creator(topic_id: str = None):
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cwd=str(PROJECT_ROOT),
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capture_output=True,
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text=True,
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timeout=300 # 5分钟超时
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timeout=1800 # 30分钟超时，避免AI撰写超时
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)
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if result.returncode != 0:
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logger.error(f"Creator failed: {result.stderr}")
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@@ -0,0 +1,118 @@
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""" ModelScope 专用 LLM 客户端 """
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import requests
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import json
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from typing import Optional
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class LLMError(Exception):
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pass
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# 临时使用 NVIDIA 端点（ModelScope Key 已失效）
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CONFIG = {
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"base_url": "https://integrate.api.nvidia.com/v1",
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"api_key": "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",
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"model": "stepfun-ai/step-3.5-flash",
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}
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def call_llm(
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prompt: str,
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system_prompt: str = "你是一个专业的内容创作助手。",
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temperature: float = 0.7,
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max_tokens: int = 2000,
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stream: bool = False,
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) -> str:
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"""调用 ModelScope LLM 生成文本"""
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endpoint = f"{CONFIG['base_url'].rstrip('/')}/chat/completions"
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headers = {
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"Authorization": f"Bearer {CONFIG['api_key']}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": CONFIG["model"],
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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],
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": stream,
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}
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try:
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resp = requests.post(endpoint, json=payload, headers=headers, timeout=120, stream=stream)
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if resp.status_code != 200:
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raise LLMError(f"HTTP {resp.status_code}: {resp.text[:200]}")
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if stream:
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full = []
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for line in resp.iter_lines():
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if not line:
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continue
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if line.startswith(b'data: '):
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data = line[6:]
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if data == b'[DONE]':
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break
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try:
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chunk = json.loads(data)
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delta = chunk['choices'][0]['delta']
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if 'reasoning_content' in delta and delta['reasoning_content']:
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full.append(delta['reasoning_content'])
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if 'content' in delta and delta['content']:
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full.append(delta['content'])
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except Exception:
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continue
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return "".join(full)
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else:
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data = resp.json()
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msg = data["choices"][0]["message"]
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content = msg.get('content') or msg.get('reasoning') or msg.get('reasoning_content')
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return content.strip() if content else ''
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except requests.RequestException as e:
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raise LLMError(f"Request failed: {e}")
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def expand_content_with_llm(
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topic: dict,
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section_title: str,
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section_content: str,
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context: str = ""
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) -> str:
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"""扩写大纲章节，返回包含 ## 标题的完整 Markdown"""
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prompt = f"""你是一个专业的内容创作者。请将以下大纲扩展为完整的文章章节。
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# 选题信息
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- 标题：{topic.get('title')}
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- 领域：{topic.get('field')}
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- 核心观点：{topic.get('core_concept', '')}
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- 受众痛点：{topic.get('audience_pain', '')}
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- 独特视角：{topic.get('unique_angle', '')}
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# 当前章节
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## {section_title}
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{section_content}
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# 要求
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- 以 `## {section_title}` 作为章节标题开头
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- 字数：300-500 字
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- 风格：客观、专业、易懂
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- 使用 Markdown 格式
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- 包含具体数据或案例（如果有）
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- 保持与整体文章调性一致
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直接输出完整的 Markdown 章节（包括 ## 标题和正文段落）。"""
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if context:
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prompt = f"# 参考资料\n{context}\n\n{prompt}"
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try:
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result = call_llm(prompt, temperature=0.8, max_tokens=2000)
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return result.strip()
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except Exception as e:
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return f"## {section_title}\n\n（LLM 调用失败：{e}，请手动补充）"
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# 测试
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if __name__ == "__main__":
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try:
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print(f"[modelscope_client] 使用模型：{CONFIG['model']}")
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resp = call_llm("你好，请用一句话介绍你自己。", max_tokens=50)
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print(f"[modelscope_client] 响应：{resp}")
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except Exception as e:
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print(f"[modelscope_client] 错误：{e}")
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@@ -9,7 +9,7 @@ from typing import List
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logger = logging.getLogger(__name__)
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# 计算项目根目录（从本文件位置上升4层）
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PROJECT_ROOT = Path(__file__).resolve().parents[4]
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if os.getenv('PROJECT_ROOT'):
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PROJECT_ROOT = Path(os.getenv('PROJECT_ROOT'))
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