mirror of
https://github.com/JefferyHcool/BiliNote.git
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c105342ded
### 性能优化 - 后端任务执行从串行锁改为 ThreadPoolExecutor 并发执行（默认3线程） - 添加 GZipMiddleware 响应压缩 + Nginx gzip 配置 - 数据库连接池参数优化（pool_size=10, max_overflow=20） - 视频帧提取并行化（ThreadPoolExecutor） - LLM 重试配置缓存到实例，避免每次请求读 env var - 前端路由级代码拆分（React.lazy + Suspense） - Vite manualChunks 拆分 markdown/markmap/vendor - MarkdownViewer 用 React.memo + useMemo 减少不必要渲染 - NoteHistory Fuse.js 实例 useMemo 缓存 - useTaskPolling 无待处理任务时跳过轮询 - 移除 antd 依赖（NoteForm Alert、modelForm Tag），改用 shadcn/ui ### 前端转写器配置（新功能） - 新增 TranscriberConfigManager（JSON 文件存储，替代环境变量） - 新增 GET/POST /transcriber_config API 端点 - 新增 GET /transcriber_models_status 模型下载状态查询 - 新增 POST /transcriber_download 后台模型下载触发 - 前端转写器设置页面：引擎选择、模型大小选择、模型下载管理 - deploy_status 端点同步从配置文件读取 ### Bug 修复 - 修复任务进行中切换页面后进度丢失：Home.tsx status 派生逻辑补全中间状态 - 修复 MLX Whisper 静默回退 fast-whisper：移除环境变量门控，macOS 下自动尝试导入 - MLX Whisper 不可用时抛出 RuntimeError 而非静默回退 - 前端展示 MLX Whisper 可用性状态，不可用时禁用保存 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
642 lines
26 KiB
Python
642 lines
26 KiB
Python
import json
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import logging
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import os
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from dataclasses import asdict
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from pathlib import Path
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from typing import List, Optional, Tuple, Union, Any
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from fastapi import HTTPException
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from pydantic import HttpUrl
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from dotenv import load_dotenv
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from app.downloaders.base import Downloader
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from app.downloaders.bilibili_downloader import BilibiliDownloader
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from app.downloaders.douyin_downloader import DouyinDownloader
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from app.downloaders.local_downloader import LocalDownloader
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from app.downloaders.youtube_downloader import YoutubeDownloader
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from app.db.video_task_dao import delete_task_by_video, insert_video_task
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from app.enmus.exception import NoteErrorEnum, ProviderErrorEnum
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from app.enmus.task_status_enums import TaskStatus
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from app.enmus.note_enums import DownloadQuality
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from app.exceptions.note import NoteError
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from app.exceptions.provider import ProviderError
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from app.gpt.base import GPT
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from app.gpt.gpt_factory import GPTFactory
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from app.models.audio_model import AudioDownloadResult
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from app.models.gpt_model import GPTSource
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from app.models.model_config import ModelConfig
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from app.models.notes_model import AudioDownloadResult, NoteResult
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from app.models.transcriber_model import TranscriptResult, TranscriptSegment
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from app.services.constant import SUPPORT_PLATFORM_MAP
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from app.services.provider import ProviderService
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from app.transcriber.base import Transcriber
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from app.transcriber.transcriber_provider import get_transcriber, _transcribers
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from app.utils.note_helper import replace_content_markers, prepend_source_link
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from app.utils.screenshot_marker import extract_screenshot_timestamps
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from app.utils.status_code import StatusCode
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from app.utils.video_helper import generate_screenshot
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from app.utils.video_reader import VideoReader
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# ------------------ 环境变量与全局配置 ------------------
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# 从 .env 文件中加载环境变量
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load_dotenv()
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# 后端 API 地址与端口（若有需要可以在代码其他部分使用 BACKEND_BASE_URL）
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API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost")
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BACKEND_PORT = os.getenv("BACKEND_PORT", "8483")
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BACKEND_BASE_URL = f"{API_BASE_URL}:{BACKEND_PORT}"
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# 输出目录（用于缓存音频、转写、Markdown 文件，以及存储截图）
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NOTE_OUTPUT_DIR = Path(os.getenv("NOTE_OUTPUT_DIR", "note_results"))
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NOTE_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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IMAGE_OUTPUT_DIR = os.getenv("OUT_DIR", "./static/screenshots")
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# 图片基础 URL（用于生成 Markdown 中的图片链接，需前端静态目录对应）
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IMAGE_BASE_URL = os.getenv("IMAGE_BASE_URL", "/static/screenshots")
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# 日志配置
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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class NoteGenerator:
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"""
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NoteGenerator 用于执行视频/音频下载、转写、GPT 生成笔记、插入截图/链接、
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以及将任务信息写入状态文件与数据库等功能。
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"""
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def __init__(self):
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from app.services.transcriber_config_manager import TranscriberConfigManager
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config_manager = TranscriberConfigManager()
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self.model_size: str = config_manager.get_whisper_model_size()
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self.device: Optional[str] = None
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self.transcriber_type: str = config_manager.get_transcriber_type()
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self.transcriber: Transcriber = self._init_transcriber()
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self.video_path: Optional[Path] = None
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self.video_img_urls=[]
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logger.info("NoteGenerator 初始化完成")
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# ---------------- 公有方法 ----------------
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def generate(
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self,
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video_url: Union[str, HttpUrl],
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platform: str,
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quality: DownloadQuality = DownloadQuality.medium,
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task_id: Optional[str] = None,
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model_name: Optional[str] = None,
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provider_id: Optional[str] = None,
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link: bool = False,
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screenshot: bool = False,
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_format: Optional[List[str]] = None,
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style: Optional[str] = None,
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extras: Optional[str] = None,
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output_path: Optional[str] = None,
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video_understanding: bool = False,
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video_interval: int = 0,
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grid_size: Optional[List[int]] = None,
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) -> NoteResult | None:
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"""
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主流程：按步骤依次下载、转写、GPT 总结、截图/链接处理、存库、返回 NoteResult。
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:param video_url: 视频或音频链接
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:param platform: 平台名称，对应 SUPPORT_PLATFORM_MAP 中的键
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:param quality: 下载音频的质量枚举
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:param task_id: 用于标识本次任务的唯一 ID，亦用于状态文件和缓存文件命名
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:param model_name: GPT 模型名称
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:param provider_id: 模型供应商 ID
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:param link: 是否在笔记中插入视频片段链接
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:param screenshot: 是否在笔记中替换 Screenshot 标记为图片
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:param _format: 包含 'link' 或 'screenshot' 等字符串的列表，决定后续处理
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:param style: GPT 生成笔记的风格
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:param extras: 额外参数，传递给 GPT
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:param output_path: 下载输出目录（可选）
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:param video_understanding: 是否需要视频拼图理解（生成缩略图）
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:param video_interval: 视频帧截取间隔（秒），仅在 video_understanding 为 True 时生效
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:param grid_size: 生成缩略图时的网格大小，如 [3, 3]
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:return: NoteResult 对象，包含 markdown 文本、转写结果和音频元信息
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"""
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if grid_size is None:
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grid_size = []
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try:
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logger.info(f"开始生成笔记 (task_id={task_id})")
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self._update_status(task_id, TaskStatus.PARSING)
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# 获取下载器与 GPT 实例
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downloader = self._get_downloader(platform)
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gpt = self._get_gpt(model_name, provider_id)
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# 缓存文件路径
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audio_cache_file = NOTE_OUTPUT_DIR / f"{task_id}_audio.json"
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transcript_cache_file = NOTE_OUTPUT_DIR / f"{task_id}_transcript.json"
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markdown_cache_file = NOTE_OUTPUT_DIR / f"{task_id}_markdown.md"
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print(audio_cache_file)
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# 1. 下载音频/视频
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audio_meta = self._download_media(
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downloader=downloader,
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video_url=video_url,
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quality=quality,
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audio_cache_file=audio_cache_file,
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status_phase=TaskStatus.DOWNLOADING,
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platform=platform,
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output_path=output_path,
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screenshot=screenshot,
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video_understanding=video_understanding,
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video_interval=video_interval,
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grid_size=grid_size,
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)
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# 2. 获取字幕/转写文字
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# 优先尝试获取平台字幕，没有再 fallback 到音频转写
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transcript = self._get_transcript(
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downloader=downloader,
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video_url=video_url,
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audio_file=audio_meta.file_path,
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transcript_cache_file=transcript_cache_file,
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status_phase=TaskStatus.TRANSCRIBING,
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task_id=task_id,
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)
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# 3. GPT 总结
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markdown = self._summarize_text(
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audio_meta=audio_meta,
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transcript=transcript,
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gpt=gpt,
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markdown_cache_file=markdown_cache_file,
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link=link,
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screenshot=screenshot,
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formats=_format or [],
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style=style,
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extras=extras,
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video_img_urls=self.video_img_urls,
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)
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# 4. 截图 & 链接替换
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if _format:
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markdown = self._post_process_markdown(
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markdown=markdown,
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video_path=self.video_path,
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formats=_format,
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audio_meta=audio_meta,
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platform=platform,
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)
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markdown = prepend_source_link(markdown, str(video_url))
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# 5. 保存记录到数据库
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self._update_status(task_id, TaskStatus.SAVING)
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self._save_metadata(video_id=audio_meta.video_id, platform=platform, task_id=task_id)
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# 6. 完成
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self._update_status(task_id, TaskStatus.SUCCESS)
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logger.info(f"笔记生成成功 (task_id={task_id})")
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return NoteResult(markdown=markdown, transcript=transcript, audio_meta=audio_meta)
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except Exception as exc:
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logger.error(f"生成笔记流程异常 (task_id={task_id})：{exc}", exc_info=True)
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self._update_status(task_id, TaskStatus.FAILED, message=str(exc))
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return None
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@staticmethod
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def delete_note(video_id: str, platform: str) -> int:
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"""
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删除数据库中对应 video_id 与 platform 的任务记录
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:param video_id: 视频 ID
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:param platform: 平台标识
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:return: 删除的记录数
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"""
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logger.info(f"删除笔记记录 (video_id={video_id}, platform={platform})")
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return delete_task_by_video(video_id, platform)
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# ---------------- 私有方法 ----------------
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def _init_transcriber(self) -> Transcriber:
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"""
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根据环境变量 TRANSCRIBER_TYPE 动态获取并实例化转写器
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"""
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if self.transcriber_type not in _transcribers:
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logger.error(f"未找到支持的转写器：{self.transcriber_type}")
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raise Exception(f"不支持的转写器：{self.transcriber_type}")
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logger.info(f"使用转写器：{self.transcriber_type}")
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return get_transcriber(transcriber_type=self.transcriber_type)
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def _get_gpt(self, model_name: Optional[str], provider_id: Optional[str]) -> GPT:
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"""
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根据 provider_id 获取对应的 GPT 实例
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:param model_name: GPT 模型名称
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:param provider_id: 供应商 ID
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:return: GPT 实例
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"""
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provider = ProviderService.get_provider_by_id(provider_id)
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if not provider:
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logger.error(f"[get_gpt] 未找到模型供应商: provider_id={provider_id}")
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raise ProviderError(code=ProviderErrorEnum.NOT_FOUND,message=ProviderErrorEnum.NOT_FOUND.message)
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logger.info(f"创建 GPT 实例 {provider_id}")
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config = ModelConfig(
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api_key=provider["api_key"],
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base_url=provider["base_url"],
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model_name=model_name,
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provider=provider["type"],
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name=provider["name"],
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)
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return GPTFactory().from_config(config)
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def _get_downloader(self, platform: str) -> Downloader:
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"""
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根据平台名称获取对应的下载器实例
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:param platform: 平台标识，需在 SUPPORT_PLATFORM_MAP 中
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:return: 对应的 Downloader 子类实例
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"""
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downloader_cls = SUPPORT_PLATFORM_MAP.get(platform)
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logger.debug(f"实例化下载器 - {platform}")
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instance = None
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if not downloader_cls:
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logger.error(f"不支持的平台：{platform}")
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raise NoteError(code=NoteErrorEnum.PLATFORM_NOT_SUPPORTED.code,
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message=NoteErrorEnum.PLATFORM_NOT_SUPPORTED.message)
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try:
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instance = downloader_cls
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except Exception as e:
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logger.error(f"实例化下载器失败：{e}")
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logger.info(f"使用下载器：{downloader_cls.__class__}")
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return instance
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def _update_status(self, task_id: Optional[str], status: Union[str, TaskStatus], message: Optional[str] = None):
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"""
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创建或更新 {task_id}.status.json，记录当前任务状态
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:param task_id: 任务唯一 ID
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:param status: TaskStatus 枚举或自定义状态字符串
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:param message: 可选消息，用于记录失败原因等
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"""
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if not task_id:
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return
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NOTE_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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status_file = NOTE_OUTPUT_DIR / f"{task_id}.status.json"
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print(f"写入状态文件: {status_file} 当前状态: {status}")
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data = {"status": status.value if isinstance(status, TaskStatus) else status}
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if message:
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data["message"] = message
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try:
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# First create a temporary file
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temp_file = status_file.with_suffix('.tmp')
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# Write to temporary file
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with temp_file.open('w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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# Atomic rename operation
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temp_file.replace(status_file)
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print(f"状态文件写入成功: {status_file}")
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except Exception as e:
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logger.error(f"写入状态文件失败 (task_id={task_id})：{e}")
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# Try to write error to file directly as fallback
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try:
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with status_file.open('w', encoding='utf-8') as f:
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f.write(f"Error writing status: {str(e)}")
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except:
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logger.error(f"写入错误 {e}")
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def _handle_exception(self, task_id, exc):
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logger.error(f"任务异常 (task_id={task_id})", exc_info=True)
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error_message = getattr(exc, 'detail', str(exc))
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if isinstance(error_message, dict):
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try:
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error_message = json.dumps(error_message, ensure_ascii=False)
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except:
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error_message = str(error_message)
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self._update_status(task_id, TaskStatus.FAILED, message=error_message)
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def _download_media(
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self,
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downloader: Downloader,
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video_url: Union[str, HttpUrl],
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quality: DownloadQuality,
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audio_cache_file: Path,
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status_phase: TaskStatus,
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platform: str,
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output_path: Optional[str],
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screenshot: bool,
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video_understanding: bool,
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video_interval: int,
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grid_size: List[int],
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) -> AudioDownloadResult | None:
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"""
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1. 检查音频缓存；若不存在，则根据需要下载音频或视频（若需截图/可视化）。
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2. 如果需要视频，则先下载视频并生成缩略图集，再下载音频。
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3. 返回 AudioDownloadResult
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:param downloader: Downloader 实例
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:param video_url: 视频/音频链接
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:param quality: 音频下载质量
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:param audio_cache_file: 本地缓存 JSON 文件路径
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:param status_phase: 对应的状态枚举，如 TaskStatus.DOWNLOADING
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:param platform: 平台标识
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:param output_path: 下载输出目录（可为 None）
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:param screenshot: 是否需要在笔记中插入截图
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:param video_understanding: 是否需要生成缩略图
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:param video_interval: 视频截帧间隔
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:param grid_size: 缩略图网格尺寸
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:return: AudioDownloadResult 对象
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"""
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task_id = audio_cache_file.stem.split("_")[0]
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self._update_status(task_id, status_phase)
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# 判断是否需要下载视频
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need_video = screenshot or video_understanding
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if screenshot and not grid_size:
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grid_size = [2, 2]
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frame_interval = video_interval if video_interval and video_interval > 0 else 6
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if need_video:
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try:
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logger.info("开始下载视频")
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video_path_str = downloader.download_video(video_url)
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self.video_path = Path(video_path_str)
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logger.info(f"视频下载完成：{self.video_path}")
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# 若指定了 grid_size，则生成缩略图
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if grid_size:
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self.video_img_urls=VideoReader(
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video_path=str(self.video_path),
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grid_size=tuple(grid_size),
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frame_interval=frame_interval,
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unit_width=960,
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unit_height=540,
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save_quality=80,
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).run()
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else:
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logger.info("未指定 grid_size，跳过缩略图生成")
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except Exception as exc:
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logger.error(f"视频下载失败：{exc}")
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self._handle_exception(task_id, exc)
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raise
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# 已有缓存，尝试加载
|
||
if audio_cache_file.exists():
|
||
logger.info(f"检测到音频缓存 ({audio_cache_file})，直接读取")
|
||
try:
|
||
data = json.loads(audio_cache_file.read_text(encoding="utf-8"))
|
||
return AudioDownloadResult(**data)
|
||
except Exception as e:
|
||
logger.warning(f"读取音频缓存失败，将重新下载：{e}")
|
||
# 下载音频
|
||
try:
|
||
logger.info("开始下载音频")
|
||
audio = downloader.download(
|
||
video_url=video_url,
|
||
quality=quality,
|
||
output_dir=output_path,
|
||
need_video=need_video,
|
||
)
|
||
# 缓存 audio 元信息到本地 JSON
|
||
audio_cache_file.write_text(json.dumps(asdict(audio), ensure_ascii=False, indent=2), encoding="utf-8")
|
||
logger.info(f"音频下载并缓存成功 ({audio_cache_file})")
|
||
return audio
|
||
except Exception as exc:
|
||
logger.error(f"音频下载失败：{exc}")
|
||
self._handle_exception(task_id, exc)
|
||
raise
|
||
|
||
|
||
def _get_transcript(
|
||
self,
|
||
downloader: Downloader,
|
||
video_url: str,
|
||
audio_file: str,
|
||
transcript_cache_file: Path,
|
||
status_phase: TaskStatus,
|
||
task_id: Optional[str] = None,
|
||
) -> TranscriptResult | None:
|
||
"""
|
||
优先获取平台字幕，没有则 fallback 到音频转写
|
||
|
||
:param downloader: 下载器实例
|
||
:param video_url: 视频链接
|
||
:param audio_file: 音频文件路径（用于 fallback 转写）
|
||
:param transcript_cache_file: 缓存文件路径
|
||
:param status_phase: 状态枚举
|
||
:param task_id: 任务 ID
|
||
:return: TranscriptResult 对象
|
||
"""
|
||
self._update_status(task_id, status_phase)
|
||
|
||
# 已有缓存，直接返回
|
||
if transcript_cache_file.exists():
|
||
logger.info(f"检测到转写缓存 ({transcript_cache_file})，尝试读取")
|
||
try:
|
||
data = json.loads(transcript_cache_file.read_text(encoding="utf-8"))
|
||
segments = [TranscriptSegment(**seg) for seg in data.get("segments", [])]
|
||
return TranscriptResult(language=data.get("language"), full_text=data["full_text"], segments=segments)
|
||
except Exception as e:
|
||
logger.warning(f"加载转写缓存失败，将重新获取：{e}")
|
||
|
||
# 1. 先尝试获取平台字幕
|
||
logger.info("尝试获取平台字幕...")
|
||
try:
|
||
transcript = downloader.download_subtitles(video_url)
|
||
if transcript and transcript.segments:
|
||
logger.info(f"成功获取平台字幕，共 {len(transcript.segments)} 段")
|
||
# 缓存结果
|
||
transcript_cache_file.write_text(
|
||
json.dumps(asdict(transcript), ensure_ascii=False, indent=2),
|
||
encoding="utf-8"
|
||
)
|
||
return transcript
|
||
else:
|
||
logger.info("平台无可用字幕，将使用音频转写")
|
||
except Exception as e:
|
||
logger.warning(f"获取平台字幕失败: {e}，将使用音频转写")
|
||
|
||
# 2. Fallback 到音频转写
|
||
return self._transcribe_audio(
|
||
audio_file=audio_file,
|
||
transcript_cache_file=transcript_cache_file,
|
||
status_phase=status_phase,
|
||
)
|
||
|
||
def _transcribe_audio(
|
||
self,
|
||
audio_file: str,
|
||
transcript_cache_file: Path,
|
||
status_phase: TaskStatus,
|
||
) -> TranscriptResult | None:
|
||
"""
|
||
1. 检查转写缓存；若存在则尝试加载，否则调用转写器生成并缓存。
|
||
2. 返回 TranscriptResult 对象
|
||
|
||
:param audio_file: 音频文件本地路径
|
||
:param transcript_cache_file: 转写结果缓存路径
|
||
:param status_phase: 对应的状态枚举，如 TaskStatus.TRANSCRIBING
|
||
:return: TranscriptResult 对象
|
||
"""
|
||
task_id = transcript_cache_file.stem.split("_")[0]
|
||
self._update_status(task_id, status_phase)
|
||
|
||
# 已有缓存，尝试加载
|
||
if transcript_cache_file.exists():
|
||
logger.info(f"检测到转写缓存 ({transcript_cache_file})，尝试读取")
|
||
try:
|
||
data = json.loads(transcript_cache_file.read_text(encoding="utf-8"))
|
||
segments = [TranscriptSegment(**seg) for seg in data.get("segments", [])]
|
||
return TranscriptResult(language=data["language"], full_text=data["full_text"], segments=segments)
|
||
except Exception as e:
|
||
logger.warning(f"加载转写缓存失败，将重新转写：{e}")
|
||
|
||
# 调用转写器
|
||
try:
|
||
logger.info("开始转写音频")
|
||
transcript = self.transcriber.transcript(file_path=audio_file)
|
||
transcript_cache_file.write_text(json.dumps(asdict(transcript), ensure_ascii=False, indent=2), encoding="utf-8")
|
||
logger.info(f"转写并缓存成功 ({transcript_cache_file})")
|
||
return transcript
|
||
except Exception as exc:
|
||
logger.error(f"音频转写失败：{exc}")
|
||
self._handle_exception(task_id, exc)
|
||
raise
|
||
|
||
def _summarize_text(
|
||
self,
|
||
audio_meta: AudioDownloadResult,
|
||
transcript: TranscriptResult,
|
||
gpt: GPT,
|
||
markdown_cache_file: Path,
|
||
link: bool,
|
||
screenshot: bool,
|
||
formats: List[str],
|
||
style: Optional[str],
|
||
extras: Optional[str],
|
||
video_img_urls: List[str],
|
||
) -> str | None:
|
||
"""
|
||
调用 GPT 对转写结果进行总结，生成 Markdown 文本并缓存。
|
||
|
||
:param audio_meta: AudioDownloadResult 元信息
|
||
:param transcript: TranscriptResult 转写结果
|
||
:param gpt: GPT 实例
|
||
:param markdown_cache_file: Markdown 缓存路径
|
||
:param link: 是否在笔记中插入链接
|
||
:param screenshot: 是否在笔记中生成截图占位
|
||
:param formats: 包含 'link' 或 'screenshot' 的列表
|
||
:param style: GPT 输出风格
|
||
:param extras: GPT 额外参数
|
||
:return: 生成的 Markdown 字符串
|
||
"""
|
||
task_id = markdown_cache_file.stem
|
||
self._update_status(task_id, TaskStatus.SUMMARIZING)
|
||
|
||
source = GPTSource(
|
||
title=audio_meta.title,
|
||
segment=transcript.segments,
|
||
tags=audio_meta.raw_info.get("tags", []),
|
||
screenshot=screenshot,
|
||
video_img_urls=video_img_urls,
|
||
link=link,
|
||
_format=formats,
|
||
style=style,
|
||
extras=extras,
|
||
checkpoint_key=task_id,
|
||
)
|
||
|
||
try:
|
||
markdown = gpt.summarize(source)
|
||
markdown_cache_file.write_text(markdown, encoding="utf-8")
|
||
logger.info(f"GPT 总结并缓存成功 ({markdown_cache_file})")
|
||
return markdown
|
||
except Exception as exc:
|
||
logger.error(f"GPT 总结失败：{exc}")
|
||
self._handle_exception(task_id, exc)
|
||
raise
|
||
|
||
def _post_process_markdown(
|
||
self,
|
||
markdown: str,
|
||
video_path: Optional[Path],
|
||
formats: List[str],
|
||
audio_meta: AudioDownloadResult,
|
||
platform: str,
|
||
) -> str:
|
||
"""
|
||
对生成的 Markdown 做后期处理：插入截图和/或插入链接。
|
||
|
||
:param markdown: 原始 Markdown 字符串
|
||
:param video_path: 本地视频路径（可为 None）
|
||
:param formats: 包含 'link' 或 'screenshot' 的列表
|
||
:param audio_meta: AudioDownloadResult 元信息，用于链接替换
|
||
:param platform: 平台标识，用于链接替换
|
||
:return: 处理后的 Markdown 字符串
|
||
"""
|
||
if "screenshot" in formats and video_path:
|
||
try:
|
||
markdown = self._insert_screenshots(markdown, video_path)
|
||
except Exception as exc:
|
||
logger.warning("截图插入失败，跳过该步骤")
|
||
|
||
if "link" in formats:
|
||
try:
|
||
markdown = replace_content_markers(markdown, video_id=audio_meta.video_id, platform=platform)
|
||
except Exception as e:
|
||
logger.warning(f"链接插入失败，跳过该步骤：{e}")
|
||
|
||
return markdown
|
||
|
||
def _insert_screenshots(self, markdown: str, video_path: Path) -> str | None | Any:
|
||
"""
|
||
扫描 Markdown 文本中所有 Screenshot 标记，并替换为实际生成的截图链接。
|
||
|
||
:param markdown: 含有 *Screenshot-mm:ss 或 Screenshot-[mm:ss] 标记的 Markdown 文本
|
||
:param video_path: 本地视频文件路径
|
||
:return: 替换后的 Markdown 字符串
|
||
"""
|
||
matches: List[Tuple[str, int]] = extract_screenshot_timestamps(markdown)
|
||
for idx, (marker, ts) in enumerate(matches):
|
||
try:
|
||
img_path = generate_screenshot(str(video_path), str(IMAGE_OUTPUT_DIR), ts, idx)
|
||
filename = Path(img_path).name
|
||
# 构建前端可访问的 URL，例如 /static/screenshots/{filename}
|
||
img_url = f"{IMAGE_BASE_URL.rstrip('/')}/{filename}"
|
||
markdown = markdown.replace(marker, f"", 1)
|
||
except Exception as exc:
|
||
logger.error(f"生成截图失败 (timestamp={ts})：{exc}")
|
||
# self._handle_exception(task_id, exc)
|
||
return None
|
||
return markdown
|
||
|
||
@staticmethod
|
||
def _extract_screenshot_timestamps(markdown: str) -> List[Tuple[str, int]]:
|
||
"""
|
||
从 Markdown 文本中提取所有 '*Screenshot-mm:ss' 或 'Screenshot-[mm:ss]' 标记，
|
||
返回 [(原始标记文本, 时间戳秒数), ...] 列表。
|
||
|
||
:param markdown: 原始 Markdown 文本
|
||
:return: 标记与对应时间戳秒数的列表
|
||
"""
|
||
return extract_screenshot_timestamps(markdown)
|
||
|
||
def _save_metadata(self, video_id: str, platform: str, task_id: str) -> None:
|
||
"""
|
||
将生成的笔记任务记录插入数据库
|
||
|
||
:param video_id: 视频 ID
|
||
:param platform: 平台标识
|
||
:param task_id: 任务 ID
|
||
"""
|
||
try:
|
||
insert_video_task(video_id=video_id, platform=platform, task_id=task_id)
|
||
logger.info(f"已保存任务记录到数据库 (video_id={video_id}, platform={platform}, task_id={task_id})")
|
||
except Exception as e:
|
||
logger.error(f"保存任务记录失败：{e}")
|