Фоновые задачи: плейлисты, длинные лекции, вкладка настроек

- таблица jobs + очередь с воркером: POST /jobs сразу отвечает, элементы
  плейлиста обрабатываются последовательно, есть отмена и живой прогресс
  (скачивание %, минуты распознанного Vosk-аудио)
- expand_url: плейлист раскрывается в список видео
- keep_video=false теперь качает только аудио (для 4-5ч лекций)
- настройки (LLM-ключ, модель, длина выжимки, хранение видео, путь Vosk)
  хранятся в Postgres: переживают перезапуск и перезагрузку страницы
- длина выжимки прокидывается в LLM-промпт ({n} предложений)
- UI: вкладки «Главная»/«Настройки», карточка задач с автообновлением
  каждые 3 сек, прогресс-бар, отмена, библиотека обновляется по мере
  готовности элементов

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -48,19 +48,38 @@ OpenAI-совместимый эндпоинт. Два способа:
 Если LLM не настроена, работает встроенная экстрактивная суммаризация
 (выбор ключевых предложений, без нейросети).
 
+## Плейлисты и длинные лекции
+
+Обработка идёт через фоновые задачи: `POST /jobs` сразу возвращает id, элементы
+плейлиста обрабатываются по очереди, прогресс виден в UI (скачивание %,
+минуты распознанного аудио, номер видео в плейлисте). Если в настройках
+выключено «Сохранять видео», для лекций качается только аудиодорожка —
+в разы быстрее и легче.
+
+Настройки (ключ LLM, модель, длина выжимки, хранение видео) сохраняются
+в Postgres — переживают перезапуск контейнеров и перезагрузку страницы.
+Вкладка «Настройки» в UI.
+
 ## API
 
 ```bash
-# обработать видео целиком
+# фоновая задача: видео или плейлист
+curl -X POST localhost:8000/jobs/ \
+  -H 'Content-Type: application/json' \
+  -d '{"url": "https://www.youtube.com/playlist?list=..."}'
+curl localhost:8000/jobs/            # статусы и прогресс
+
+# синхронно, одно видео (для коротких роликов)
 curl -X POST localhost:8000/pipeline/process \
   -H 'Content-Type: application/json' \
   -d '{"url": "https://youtu.be/...", "summary_max_sentences": 10}'
 
-# каталог обработанных видео
+# каталог обработанных видео (фильтры: ?q=, ?section_id=, ?method=)
 curl localhost:8000/videos/
 
-# проверить связь с LLM
+# настройки
-curl -X POST localhost:8000/llm/test
+curl -X PUT localhost:8000/settings/ -H 'Content-Type: application/json' \
+  -d '{"llm_base_url": "https://openrouter.ai/api/v1", "llm_api_key": "sk-...", "llm_model": "..."}'
 ```
 
 ## Заметки

api/db/connection.py

@@ -8,7 +8,9 @@ from sqlalchemy.orm import sessionmaker
 
 from api.config import settings
 from api.models.base import Base
+import api.models.job  # noqa: F401  - register model with Base.metadata
 import api.models.section  # noqa: F401  - register model with Base.metadata
+import api.models.setting  # noqa: F401  - register model with Base.metadata
 import api.models.video  # noqa: F401  - register model with Base.metadata
 
 
@@ -69,3 +71,6 @@ async def init_db() -> None:
                 "REFERENCES sections(id) ON DELETE SET NULL"
             )
         )
+        await conn.execute(
+            text("ALTER TABLE videos ADD COLUMN IF NOT EXISTS job_id VARCHAR")
+        )

api/db/job.py

@@ -0,0 +1,53 @@
+from sqlalchemy import select, update
+
+from api.db.connection import SessionLocal
+from api.models.job import Job
+
+
+async def create_job(job_id: str, url: str) -> Job:
+    async with SessionLocal() as session:
+        j = Job(id=job_id, url=url, status="queued")
+        session.add(j)
+        await session.commit()
+        await session.refresh(j)
+        return j
+
+
+async def get_job(job_id: str) -> Job | None:
+    async with SessionLocal() as session:
+        return await session.get(Job, job_id)
+
+
+async def list_jobs(limit: int = 50) -> list[Job]:
+    async with SessionLocal() as session:
+        res = await session.execute(
+            select(Job).order_by(Job.created_at.desc()).limit(limit)
+        )
+        return list(res.scalars().all())
+
+
+async def update_job(job_id: str, **fields) -> None:
+    async with SessionLocal() as session:
+        await session.execute(update(Job).where(Job.id == job_id).values(**fields))
+        await session.commit()
+
+
+async def delete_job(job_id: str) -> bool:
+    async with SessionLocal() as session:
+        j = await session.get(Job, job_id)
+        if j is None:
+            return False
+        await session.delete(j)
+        await session.commit()
+        return True
+
+
+async def mark_interrupted() -> None:
+    """На старте приложения: задачи, шедшие до перезапуска, отмечаем прерванными."""
+    async with SessionLocal() as session:
+        await session.execute(
+            update(Job)
+            .where(Job.status.in_(("queued", "running")))
+            .values(status="interrupted", current_item="")
+        )
+        await session.commit()

api/db/setting.py

@@ -0,0 +1,21 @@
+from sqlalchemy import select
+
+from api.db.connection import SessionLocal
+from api.models.setting import Setting
+
+
+async def get_all_settings() -> dict[str, str]:
+    async with SessionLocal() as session:
+        res = await session.execute(select(Setting))
+        return {s.key: s.value for s in res.scalars().all()}
+
+
+async def set_settings(values: dict[str, str]) -> None:
+    async with SessionLocal() as session:
+        for key, value in values.items():
+            existing = await session.get(Setting, key)
+            if existing is None:
+                session.add(Setting(key=key, value=str(value)))
+            else:
+                existing.value = str(value)
+        await session.commit()

api/db/video.py

@@ -14,12 +14,14 @@ async def create_video(
     text_summary_path: str,
     transcription_method: str,
     section_id: int | None = None,
+    job_id: str | None = None,
 ) -> Video:
     async with SessionLocal() as session:
         v = Video(
             uuid=uuid,
             source_url=source_url,
             section_id=section_id,
+            job_id=job_id,
             title=title or "",
             video_path=video_path or "",
             text_full_path=text_full_path or "",

api/lib/jobqueue.py

@@ -0,0 +1,119 @@
+"""Очередь фоновых задач: одно видео или плейлист, элементы обрабатываются
+последовательно (чтобы не долбить YouTube и LLM параллельными запросами).
+
+Живой прогресс держится в памяти (_live) и подмешивается в GET /jobs;
+устойчивое состояние (счётчики, статус) — в таблице jobs.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+
+from api.db.job import get_job, update_job
+from api.lib.processing import process_single_video
+from api.lib.youtube import expand_url
+
+
+logger = logging.getLogger("jobs")
+
+_queue: asyncio.Queue[str] = asyncio.Queue()
+_live: dict[str, str] = {}
+_cancel: set[str] = set()
+_params: dict[str, dict] = {}
+
+
+def submit(job_id: str, params: dict) -> None:
+    _params[job_id] = params
+    _queue.put_nowait(job_id)
+
+
+def live_status(job_id: str) -> str:
+    return _live.get(job_id, "")
+
+
+def request_cancel(job_id: str) -> None:
+    _cancel.add(job_id)
+
+
+def is_cancelled(job_id: str) -> bool:
+    return job_id in _cancel
+
+
+async def worker() -> None:
+    """Единственный воркер; запускается на старте приложения."""
+    logger.info("job worker started")
+    while True:
+        job_id = await _queue.get()
+        try:
+            await _run_job(job_id)
+        except Exception:
+            logger.exception("job %s crashed", job_id)
+            try:
+                await update_job(job_id, status="error", current_item="")
+            except Exception:
+                pass
+        finally:
+            _live.pop(job_id, None)
+            _cancel.discard(job_id)
+            _params.pop(job_id, None)
+            _queue.task_done()
+
+
+async def _run_job(job_id: str) -> None:
+    job = await get_job(job_id)
+    if job is None or job.status == "cancelled":
+        return
+    params = _params.get(job_id, {})
+    loop = asyncio.get_running_loop()
+
+    await update_job(job_id, status="running", current_item="анализ ссылки…")
+    _live[job_id] = "анализ ссылки…"
+
+    plan = await loop.run_in_executor(None, expand_url, job.url)
+    entries = plan["entries"]
+    if not entries:
+        await update_job(job_id, status="error", error="Плейлист пуст", current_item="")
+        return
+    await update_job(
+        job_id, kind=plan["kind"], title=plan["title"], total_items=len(entries)
+    )
+
+    done = failed = 0
+    errors: list[str] = []
+    n = len(entries)
+
+    for i, entry in enumerate(entries, start=1):
+        if is_cancelled(job_id):
+            await update_job(job_id, status="cancelled", current_item="")
+            logger.info("job %s cancelled at item %d/%d", job_id, i, n)
+            return
+
+        label = f"{i}/{n} · {entry['title']}"
+        await update_job(job_id, current_item=label)
+        _live[job_id] = label
+
+        def _notify(msg: str, _label: str = label) -> None:
+            _live[job_id] = f"{_label} — {msg}"
+
+        try:
+            await process_single_video(
+                entry["url"],
+                section_id=params.get("section_id"),
+                model_path=params.get("model_path"),
+                summary_max_sentences=params.get("summary_max_sentences"),
+                keep_video=params.get("keep_video"),
+                job_id=job_id,
+                progress=_notify,
+            )
+            done += 1
+        except Exception as e:
+            failed += 1
+            errors.append(f"{entry['title']}: {e}")
+            logger.warning("job %s item %d/%d failed: %s", job_id, i, n, e)
+        await update_job(
+            job_id, done_items=done, failed_items=failed, error="\n".join(errors)[:4000]
+        )
+
+    status = "error" if done == 0 and failed > 0 else "done"
+    await update_job(job_id, status=status, current_item="")
+    logger.info("job %s finished: %d ok, %d failed", job_id, done, failed)

api/lib/processing.py

@@ -0,0 +1,229 @@
+"""Ядро пайплайна: одно видео от URL до записи в БД.
+
+Используется и синхронным роутом /pipeline/process, и фоновыми задачами /jobs.
+Все дефолты (LLM, длина выжимки, хранение видео, Vosk-модель) берутся из
+настроек в БД (таблица settings) с фолбэком на переменные окружения.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+import uuid as _uuid
+from pathlib import Path
+from typing import Callable, Optional
+
+from api.config import settings
+from api.db.setting import get_all_settings
+from api.db.video import create_video
+from api.lib.llm import LLMConfig
+from api.lib.summarize import summarize
+from api.lib.transcribe import transcribe_via_ffmpeg
+from api.lib.youtube import download_video_with_subs, vtt_to_text
+
+
+logger = logging.getLogger("processing")
+
+_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
+_VIDEO_DIR = _PROJECT_ROOT / "data" / "videos"
+_TEXT_DIR = _PROJECT_ROOT / "data" / "text"
+_MODELS_DIR = _PROJECT_ROOT / "models"
+
+
+class PipelineError(RuntimeError):
+    def __init__(self, message: str, status: int = 500):
+        super().__init__(message)
+        self.status = status
+
+
+async def effective_defaults() -> dict:
+    """Настройки из БД поверх переменных окружения."""
+    db = await get_all_settings()
+    base_url = db.get("llm_base_url") or settings.LLM_BASE_URL
+    model = db.get("llm_model") or settings.LLM_MODEL
+    llm_cfg = None
+    if base_url and model:
+        llm_cfg = LLMConfig(
+            base_url=base_url,
+            api_key=db.get("llm_api_key") or settings.LLM_API_KEY,
+            model=model,
+            timeout=settings.LLM_TIMEOUT,
+            max_tokens=settings.LLM_MAX_TOKENS,
+            temperature=settings.LLM_TEMPERATURE,
+        )
+    try:
+        max_sentences = int(db.get("summary_max_sentences") or 15)
+    except ValueError:
+        max_sentences = 15
+    return {
+        "llm_cfg": llm_cfg,
+        "summary_max_sentences": max_sentences,
+        "keep_video": db.get("keep_video", "1") != "0",
+        "model_path": db.get("vosk_model_path") or settings.DEFAULT_VOSK_MODEL,
+    }
+
+
+def resolve_model_path(raw: str) -> Path:
+    p = Path(raw)
+    if not p.is_absolute():
+        p = _PROJECT_ROOT / raw
+    p = p.resolve()
+    try:
+        p.relative_to(_MODELS_DIR.resolve())
+    except ValueError:
+        raise PipelineError(
+            "model_path must point inside the project's models directory", status=400
+        )
+    if not p.exists():
+        raise PipelineError(f"Model not found: {p}", status=400)
+    return p
+
+
+async def process_single_video(
+    url: str,
+    *,
+    model_path: str | None = None,
+    summary_max_sentences: int | None = None,
+    keep_video: bool | None = None,
+    section_id: int | None = None,
+    job_id: str | None = None,
+    llm_cfg: Optional[LLMConfig] = None,
+    system_prompt: str | None = None,
+    user_template: str | None = None,
+    progress: Optional[Callable[[str], None]] = None,
+) -> dict:
+    """Скачивает, расшифровывает, сокращает и сохраняет одно видео.
+
+    Параметры со значением None подтягиваются из настроек (БД/env).
+    progress(msg) — живой статус для UI задач.
+    """
+    notify = progress or (lambda msg: None)
+    defaults = await effective_defaults()
+    if llm_cfg is None:
+        llm_cfg = defaults["llm_cfg"]
+    if summary_max_sentences is None:
+        summary_max_sentences = defaults["summary_max_sentences"]
+    if keep_video is None:
+        keep_video = defaults["keep_video"]
+    raw_model_path = model_path or defaults["model_path"]
+
+    video_uuid = _uuid.uuid4().hex
+    _VIDEO_DIR.mkdir(parents=True, exist_ok=True)
+    _TEXT_DIR.mkdir(parents=True, exist_ok=True)
+    loop = asyncio.get_running_loop()
+
+    logger.info("[%s] downloading %s (keep_video=%s)", video_uuid, url, keep_video)
+    notify("скачивание…")
+    try:
+        info = await loop.run_in_executor(
+            None,
+            lambda: download_video_with_subs(
+                url,
+                video_uuid,
+                _VIDEO_DIR,
+                settings.subtitle_langs_list,
+                # видео не сохраняем — достаточно аудио (для лекций в разы быстрее)
+                audio_only=not keep_video,
+                on_progress=lambda pct: notify(f"скачивание {pct}%"),
+            ),
+        )
+    except Exception as e:
+        raise PipelineError(f"Download failed: {e}")
+
+    title: str = info["title"]
+    video_path: Path = info["video_path"]
+    sub_path: Path | None = info["subtitle_path"]
+
+    if not video_path.exists():
+        raise PipelineError("Video file missing after download")
+
+    full_text = ""
+    method = ""
+
+    if sub_path is not None and sub_path.exists():
+        try:
+            content = sub_path.read_text(encoding="utf-8", errors="ignore")
+            full_text = vtt_to_text(content)
+            if full_text.strip():
+                method = f"subtitles:{info.get('subtitle_kind') or 'auto'}:{info.get('subtitle_lang') or 'unknown'}"
+                logger.info("[%s] using subtitles (%s)", video_uuid, method)
+        except Exception as e:
+            logger.warning("[%s] subtitle parse failed: %s", video_uuid, e)
+            full_text = ""
+
+    if not full_text.strip():
+        logger.info("[%s] no usable subtitles, falling back to Vosk", video_uuid)
+        notify("распознавание речи (Vosk)…")
+        resolved_model = resolve_model_path(raw_model_path)
+        try:
+            full_text = await loop.run_in_executor(
+                None,
+                lambda: transcribe_via_ffmpeg(
+                    video_path,
+                    resolved_model,
+                    on_progress=lambda m: notify(f"распознано {m} мин аудио"),
+                ),
+            )
+            method = f"vosk:{resolved_model.name}"
+        except Exception as e:
+            raise PipelineError(f"Transcription failed: {e}")
+
+    if not full_text.strip():
+        raise PipelineError("No subtitles and transcription returned empty text")
+
+    text_full_path = _TEXT_DIR / f"{video_uuid}.txt"
+    text_summary_path = _TEXT_DIR / f"{video_uuid}_summary.txt"
+    text_full_path.write_text(full_text, encoding="utf-8")
+
+    notify("выжимка (LLM)…" if llm_cfg else "выжимка…")
+    try:
+        summary = await loop.run_in_executor(
+            None,
+            lambda: summarize(
+                full_text,
+                llm_cfg=llm_cfg,
+                system_prompt=system_prompt,
+                user_template=user_template,
+                max_sentences=summary_max_sentences,
+            ),
+        )
+    except Exception as e:
+        raise PipelineError(f"Summarization failed: {e}")
+    text_summary_path.write_text(summary, encoding="utf-8")
+
+    if keep_video:
+        rel_video = str(video_path.relative_to(_PROJECT_ROOT))
+    else:
+        # пользователь просил не хранить видео: текст уже извлечён, файлы не нужны
+        video_path.unlink(missing_ok=True)
+        for p in _VIDEO_DIR.glob(f"{video_uuid}*.vtt"):
+            p.unlink(missing_ok=True)
+        rel_video = ""
+    rel_full = str(text_full_path.relative_to(_PROJECT_ROOT))
+    rel_summary = str(text_summary_path.relative_to(_PROJECT_ROOT))
+
+    try:
+        await create_video(
+            uuid=video_uuid,
+            source_url=url,
+            title=title,
+            video_path=rel_video,
+            text_full_path=rel_full,
+            text_summary_path=rel_summary,
+            transcription_method=method,
+            section_id=section_id,
+            job_id=job_id,
+        )
+    except Exception as e:
+        raise PipelineError(f"DB insert failed: {e}")
+
+    logger.info("[%s] done (method=%s)", video_uuid, method)
+    return {
+        "uuid": video_uuid,
+        "title": title,
+        "source_url": url,
+        "video_path": rel_video,
+        "text_full_path": rel_full,
+        "text_summary_path": rel_summary,
+        "transcription_method": method,
+        "summary_preview": summary[:300],
+    }

api/lib/summarize.py

@@ -65,7 +65,7 @@ DEFAULT_SYSTEM_PROMPT = (
 
 DEFAULT_USER_TEMPLATE = (
     "Сделай связную краткую выжимку следующего текста. "
-    "Выдели основные тезисы и логику изложения. Объём — 5–12 предложений.\n\n"
+    "Выдели основные тезисы и логику изложения. Объём — примерно {n} предложений.\n\n"
     "---\n{text}\n---"
 )
 
@@ -154,6 +154,7 @@ def summarize_llm(
     user_template: Optional[str] = None,
     reduce_template: Optional[str] = None,
     chunk_chars: int = 10000,
+    max_sentences: int = 15,
 ) -> str:
     """Summarize via an external OpenAI-compatible API. Map-reduce for long texts."""
     text = (text or "").strip()
@@ -164,12 +165,19 @@ def summarize_llm(
     user_t = user_template or DEFAULT_USER_TEMPLATE
     reduce_t = reduce_template or DEFAULT_REDUCE_TEMPLATE
 
+    def _fmt(template: str, chunk: str) -> str:
+        try:
+            return template.format(text=chunk, n=max_sentences)
+        except (KeyError, IndexError):
+            # пользовательский шаблон без {n} / с лишними скобками
+            return template.replace("{text}", chunk)
+
     def _one(chunk: str, template: str) -> str:
         return chat_complete(
             cfg,
             [
                 {"role": "system", "content": sys_p},
-                {"role": "user", "content": template.format(text=chunk)},
+                {"role": "user", "content": _fmt(template, chunk)},
             ],
         )
 
@@ -194,6 +202,7 @@ def summarize_llm(
             user_template=reduce_t,
             reduce_template=reduce_t,
             chunk_chars=chunk_chars,
+            max_sentences=max_sentences,
         )
     logger.info("LLM reduce step over %d partials", len(partials))
     return _one(combined, reduce_t)
@@ -215,6 +224,7 @@ def summarize(
                 llm_cfg,
                 system_prompt=system_prompt,
                 user_template=user_template,
+                max_sentences=max_sentences,
             )
         except LLMError as e:
             logger.warning("LLM summarization failed, falling back to extractive: %s", e)

api/lib/transcribe.py

@@ -53,8 +53,16 @@ class VoskModelManager:
             t.join(timeout=2)
 
 
-def transcribe_via_ffmpeg(media_path: Path, model_path: Path, sample_rate: int = 16000) -> str:
+def transcribe_via_ffmpeg(
-    """Decode the source media to PCM s16le mono via ffmpeg and feed it to Vosk in chunks."""
+    media_path: Path,
+    model_path: Path,
+    sample_rate: int = 16000,
+    on_progress=None,
+) -> str:
+    """Decode the source media to PCM s16le mono via ffmpeg and feed it to Vosk in chunks.
+
+    on_progress: callback(minutes: int) — вызывается каждые ~5 минут распознанного аудио.
+    """
     if Model is None or KaldiRecognizer is None:
         raise RuntimeError("vosk is not installed in runtime")
     if shutil.which("ffmpeg") is None:
@@ -103,10 +111,16 @@ def transcribe_via_ffmpeg(media_path: Path, model_path: Path, sample_rate: int =
                 break
             recognizer.AcceptWaveform(chunk)
             bytes_read += len(chunk)
-            mb = bytes_read // (1024 * 1024)
+            # PCM s16le mono: sample_rate * 2 байта в секунду
-            if mb >= last_logged_mb + 5:
+            minutes = bytes_read // (sample_rate * 2 * 60)
-                last_logged_mb = mb
+            if minutes >= last_logged_mb + 5:
-                logger.info("Transcribed %d MB of audio so far", mb)
+                last_logged_mb = minutes
+                logger.info("Transcribed %d minutes of audio so far", minutes)
+                if on_progress is not None:
+                    try:
+                        on_progress(minutes)
+                    except Exception:
+                        pass
 
         result = json.loads(recognizer.FinalResult())
         text = result.get("text", "")

api/lib/youtube.py

@@ -12,7 +12,44 @@ import yt_dlp
 logger = logging.getLogger("youtube")
 
 
-_VIDEO_EXTS = {".mp4", ".mkv", ".webm", ".m4a", ".mp3", ".mov"}
+_VIDEO_EXTS = {".mp4", ".mkv", ".webm", ".m4a", ".mp3", ".mov", ".opus", ".ogg", ".aac"}
+
+
+def expand_url(url: str) -> dict:
+    """Определяет, видео это или плейлист, и возвращает список элементов.
+
+    Returns: {"kind": "video"|"playlist", "title": str,
+              "entries": [{"url": str, "title": str}, ...]}
+    """
+    opts = {
+        "extract_flat": "in_playlist",
+        "quiet": True,
+        "no_warnings": True,
+        "skip_download": True,
+    }
+    with yt_dlp.YoutubeDL(opts) as ydl:
+        info = ydl.extract_info(url, download=False)
+
+    if info.get("_type") == "playlist":
+        entries = []
+        for e in info.get("entries") or []:
+            if not e:
+                continue
+            u = e.get("url") or ""
+            if u and not u.startswith("http"):
+                u = f"https://www.youtube.com/watch?v={u}"
+            if not u and e.get("id"):
+                u = f"https://www.youtube.com/watch?v={e['id']}"
+            if u:
+                entries.append({"url": u, "title": e.get("title") or u})
+        return {
+            "kind": "playlist",
+            "title": info.get("title") or url,
+            "entries": entries,
+        }
+
+    title = info.get("title") or url
+    return {"kind": "video", "title": title, "entries": [{"url": url, "title": title}]}
 
 
 def download_video_with_subs(
@@ -20,9 +57,15 @@ def download_video_with_subs(
     video_id: str,
     video_dir: Path,
     langs: Optional[list[str]] = None,
+    audio_only: bool = False,
+    on_progress=None,
 ) -> dict:
     """Download a single video, also requesting subtitles for the given languages.
 
+    audio_only: качает только аудиодорожку (для длинных лекций, когда видео
+    не сохраняется — в разы быстрее и меньше по объёму).
+    on_progress: callback(percent: int), вызывается с шагом ~5%.
+
     Returns:
         {
             "title": str,
@@ -40,11 +83,34 @@ def download_video_with_subs(
 
     base_opts = {
         "outtmpl": outtmpl,
-        "format": "bv*+ba/b",
-        "merge_output_format": "mp4",
         "quiet": True,
         "no_warnings": True,
+        "noplaylist": True,  # ссылка с list= в задаче на одно видео не тянет весь плейлист
     }
+    if audio_only:
+        base_opts["format"] = "ba/b"
+    else:
+        base_opts["format"] = "bv*+ba/b"
+        base_opts["merge_output_format"] = "mp4"
+
+    if on_progress is not None:
+        state = {"last": -5}
+
+        def _hook(dd: dict):
+            if dd.get("status") != "downloading":
+                return
+            total = dd.get("total_bytes") or dd.get("total_bytes_estimate")
+            if not total:
+                return
+            pct = int(dd.get("downloaded_bytes", 0) * 100 / total)
+            if pct >= state["last"] + 5:
+                state["last"] = pct
+                try:
+                    on_progress(pct)
+                except Exception:
+                    pass
+
+        base_opts["progress_hooks"] = [_hook]
     sub_opts = {
         **base_opts,
         "writesubtitles": True,

api/main.py

@@ -1,10 +1,13 @@
+import asyncio
 import logging
 from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
 
 from api.db.connection import wait_for_postgres, init_db
-from api.route import default, llm, pipeline, sections, videos
+from api.db.job import mark_interrupted
+from api.lib import jobqueue
+from api.route import app_settings, default, jobs, llm, pipeline, sections, videos
 
 
 logging.basicConfig(
@@ -17,13 +20,18 @@ logging.basicConfig(
 async def lifespan(app: FastAPI):
     await wait_for_postgres()
     await init_db()
+    await mark_interrupted()
+    worker_task = asyncio.create_task(jobqueue.worker())
     yield
+    worker_task.cancel()
 
 
-app = FastAPI(title="LLM-infa API", version="0.3.0", lifespan=lifespan)
+app = FastAPI(title="LLM-infa API", version="0.4.0", lifespan=lifespan)
 
 app.include_router(default.router)
 app.include_router(pipeline.router)
+app.include_router(jobs.router)
 app.include_router(videos.router)
 app.include_router(sections.router)
+app.include_router(app_settings.router)
 app.include_router(llm.router)

api/route/app_settings.py

@@ -0,0 +1,59 @@
+"""Настройки приложения (LLM-ключи, дефолты пайплайна) — живут в Postgres,
+переживают перезапуск контейнеров и перезагрузку страницы.
+"""
+from __future__ import annotations
+
+from fastapi import APIRouter
+from pydantic import BaseModel
+
+from api.db.setting import get_all_settings, set_settings
+
+
+router = APIRouter(prefix="/settings", tags=["settings"])
+
+_KEYS = (
+    "llm_base_url",
+    "llm_api_key",
+    "llm_model",
+    "summary_max_sentences",
+    "keep_video",
+    "vosk_model_path",
+)
+
+
+class SettingsPayload(BaseModel):
+    llm_base_url: str | None = None
+    llm_api_key: str | None = None
+    llm_model: str | None = None
+    summary_max_sentences: int | None = None
+    keep_video: bool | None = None
+    vosk_model_path: str | None = None
+
+
+@router.get("/")
+async def get_settings():
+    db = await get_all_settings()
+    return {
+        "llm_base_url": db.get("llm_base_url", ""),
+        "llm_api_key": db.get("llm_api_key", ""),
+        "llm_model": db.get("llm_model", ""),
+        "summary_max_sentences": int(db.get("summary_max_sentences") or 15),
+        "keep_video": db.get("keep_video", "1") != "0",
+        "vosk_model_path": db.get("vosk_model_path", "models/vosk-model-small-ru-0.22"),
+    }
+
+
+@router.put("/")
+async def put_settings(body: SettingsPayload):
+    values: dict[str, str] = {}
+    for key in _KEYS:
+        v = getattr(body, key)
+        if v is None:
+            continue
+        if key == "keep_video":
+            values[key] = "1" if v else "0"
+        else:
+            values[key] = str(v)
+    if values:
+        await set_settings(values)
+    return await get_settings()

api/route/jobs.py

@@ -0,0 +1,112 @@
+"""Фоновые задачи: видео или плейлист любой длины.
+
+POST ставит задачу в очередь и сразу возвращает id; прогресс — в GET /jobs.
+"""
+from __future__ import annotations
+
+import uuid as _uuid
+from datetime import datetime
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from api.db.job import create_job, delete_job, get_job, list_jobs
+from api.lib import jobqueue
+
+
+router = APIRouter(prefix="/jobs", tags=["jobs"])
+
+
+class JobIn(BaseModel):
+    url: str
+    section_id: int | None = None
+    # None → берутся из сохранённых настроек
+    keep_video: bool | None = None
+    summary_max_sentences: int | None = None
+    model_path: str | None = None
+
+
+class JobOut(BaseModel):
+    id: str
+    url: str
+    kind: str
+    title: str
+    status: str
+    total_items: int
+    done_items: int
+    failed_items: int
+    current_item: str
+    live: str
+    error: str
+    created_at: datetime
+
+
+def _to_out(j) -> JobOut:
+    return JobOut(
+        id=j.id,
+        url=j.url,
+        kind=j.kind,
+        title=j.title,
+        status=j.status,
+        total_items=j.total_items,
+        done_items=j.done_items,
+        failed_items=j.failed_items,
+        current_item=j.current_item,
+        live=jobqueue.live_status(j.id) or j.current_item,
+        error=j.error,
+        created_at=j.created_at,
+    )
+
+
+@router.post("/", response_model=JobOut)
+async def create(body: JobIn):
+    url = body.url.strip()
+    if not url:
+        raise HTTPException(status_code=400, detail="URL is empty")
+    job_id = _uuid.uuid4().hex
+    j = await create_job(job_id, url)
+    jobqueue.submit(
+        job_id,
+        {
+            "section_id": body.section_id,
+            "keep_video": body.keep_video,
+            "summary_max_sentences": body.summary_max_sentences,
+            "model_path": body.model_path,
+        },
+    )
+    return _to_out(j)
+
+
+@router.get("/", response_model=list[JobOut])
+async def list_all(limit: int = 50):
+    return [_to_out(j) for j in await list_jobs(limit)]
+
+
+@router.get("/{job_id}", response_model=JobOut)
+async def get_one(job_id: str):
+    j = await get_job(job_id)
+    if j is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    return _to_out(j)
+
+
+@router.post("/{job_id}/cancel")
+async def cancel(job_id: str):
+    j = await get_job(job_id)
+    if j is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    if j.status not in ("queued", "running"):
+        raise HTTPException(status_code=400, detail=f"Job is {j.status}")
+    jobqueue.request_cancel(job_id)
+    return {"ok": True, "note": "остановится после текущего элемента"}
+
+
+@router.delete("/{job_id}")
+async def delete(job_id: str):
+    j = await get_job(job_id)
+    if j is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    if j.status in ("queued", "running"):
+        raise HTTPException(status_code=400, detail="Сначала отмените задачу")
+    await delete_job(job_id)
+    return {"ok": True}

api/route/pipeline.py

@@ -1,29 +1,16 @@
-"""End-to-end pipeline: download → subtitles-or-transcribe → summarize → persist."""
+"""Синхронный запуск пайплайна для одного видео (для длинных видео и
+плейлистов используйте /jobs — этот эндпоинт держит HTTP-соединение открытым).
+"""
 from __future__ import annotations
 
-import asyncio
-import logging
-import uuid as _uuid
-from pathlib import Path
-
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 
-from api.config import settings
+from api.lib.llm import LLMConfig
-from api.db.video import create_video
+from api.lib.processing import PipelineError, process_single_video
-from api.lib.llm import LLMConfig, from_settings as llm_from_settings
-from api.lib.summarize import summarize
-from api.lib.transcribe import transcribe_via_ffmpeg
-from api.lib.youtube import download_video_with_subs, vtt_to_text
 
 
 router = APIRouter(prefix="/pipeline", tags=["pipeline"])
-logger = logging.getLogger("pipeline")
-
-_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
-_VIDEO_DIR = _PROJECT_ROOT / "data" / "videos"
-_TEXT_DIR = _PROJECT_ROOT / "data" / "text"
-_MODELS_DIR = _PROJECT_ROOT / "models"
 
 
 class LLMOverride(BaseModel):
@@ -40,12 +27,12 @@ class LLMOverride(BaseModel):
 
 class ProcessRequest(BaseModel):
     url: str
-    model_path: str | None = None  # path inside models/, used when subtitles are unavailable
+    model_path: str | None = None  # None → из настроек
-    summary_max_sentences: int = 15
+    summary_max_sentences: int | None = None
-    keep_video: bool = True  # False: файл видео удаляется после расшифровки
+    keep_video: bool | None = None
     section_id: int | None = None
-    # If supplied, this LLM is used for the summary; otherwise env-configured LLM;
+    # Если задано — используется этот LLM; иначе сохранённые настройки/env;
-    # otherwise the built-in extractive fallback.
+    # иначе встроенная экстрактивная выжимка.
     llm: LLMOverride | None = None
 
 
@@ -60,86 +47,10 @@ class ProcessResponse(BaseModel):
     summary_preview: str
 
 
-def _resolve_model_path(req_model_path: str | None) -> Path:
-    raw = req_model_path or settings.DEFAULT_VOSK_MODEL
-    p = Path(raw)
-    if not p.is_absolute():
-        p = _PROJECT_ROOT / raw
-    p = p.resolve()
-    try:
-        p.relative_to(_MODELS_DIR.resolve())
-    except ValueError:
-        raise HTTPException(
-            status_code=400,
-            detail="model_path must point inside the project's models directory",
-        )
-    if not p.exists():
-        raise HTTPException(status_code=400, detail=f"Model not found: {p}")
-    return p
-
-
 @router.post("/process", response_model=ProcessResponse)
 async def process_video(req: ProcessRequest):
-    video_uuid = _uuid.uuid4().hex
+    llm_cfg = None
-    _VIDEO_DIR.mkdir(parents=True, exist_ok=True)
+    sys_p = user_t = None
-    _TEXT_DIR.mkdir(parents=True, exist_ok=True)
-
-    loop = asyncio.get_running_loop()
-
-    logger.info("[%s] downloading %s", video_uuid, req.url)
-    try:
-        info = await loop.run_in_executor(
-            None,
-            download_video_with_subs,
-            req.url,
-            video_uuid,
-            _VIDEO_DIR,
-            settings.subtitle_langs_list,
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Download failed: {e}")
-
-    title: str = info["title"]
-    video_path: Path = info["video_path"]
-    sub_path: Path | None = info["subtitle_path"]
-
-    if not video_path.exists():
-        raise HTTPException(status_code=500, detail="Video file missing after download")
-
-    full_text = ""
-    method = ""
-
-    if sub_path is not None and sub_path.exists():
-        try:
-            content = sub_path.read_text(encoding="utf-8", errors="ignore")
-            full_text = vtt_to_text(content)
-            if full_text.strip():
-                method = f"subtitles:{info.get('subtitle_kind') or 'auto'}:{info.get('subtitle_lang') or 'unknown'}"
-                logger.info("[%s] using subtitles (%s)", video_uuid, method)
-        except Exception as e:
-            logger.warning("[%s] subtitle parse failed: %s", video_uuid, e)
-            full_text = ""
-
-    if not full_text.strip():
-        logger.info("[%s] no usable subtitles, falling back to Vosk", video_uuid)
-        model_path = _resolve_model_path(req.model_path)
-        try:
-            full_text = await loop.run_in_executor(
-                None, transcribe_via_ffmpeg, video_path, model_path
-            )
-            method = f"vosk:{model_path.name}"
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Transcription failed: {e}")
-
-    if not full_text.strip():
-        raise HTTPException(
-            status_code=500, detail="No subtitles and transcription returned empty text"
-        )
-
-    text_full_path = _TEXT_DIR / f"{video_uuid}.txt"
-    text_summary_path = _TEXT_DIR / f"{video_uuid}_summary.txt"
-    text_full_path.write_text(full_text, encoding="utf-8")
-
     if req.llm is not None:
         llm_cfg = LLMConfig(
             base_url=req.llm.base_url,
@@ -152,60 +63,18 @@ async def process_video(req: ProcessRequest):
         )
         sys_p = req.llm.system_prompt
         user_t = req.llm.user_template
-    else:
-        llm_cfg = llm_from_settings()
-        sys_p = None
-        user_t = None
 
     try:
-        summary = await loop.run_in_executor(
+        result = await process_single_video(
-            None,
+            req.url,
-            lambda: summarize(
+            model_path=req.model_path,
-                full_text,
+            summary_max_sentences=req.summary_max_sentences,
+            keep_video=req.keep_video,
+            section_id=req.section_id,
             llm_cfg=llm_cfg,
             system_prompt=sys_p,
             user_template=user_t,
-                max_sentences=req.summary_max_sentences,
-            ),
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Summarization failed: {e}")
-    text_summary_path.write_text(summary, encoding="utf-8")
-
-    if req.keep_video:
-        rel_video = str(video_path.relative_to(_PROJECT_ROOT))
-    else:
-        # пользователь просил не хранить видео: текст уже извлечён, файлы не нужны
-        video_path.unlink(missing_ok=True)
-        for p in _VIDEO_DIR.glob(f"{video_uuid}*.vtt"):
-            p.unlink(missing_ok=True)
-        rel_video = ""
-    rel_full = str(text_full_path.relative_to(_PROJECT_ROOT))
-    rel_summary = str(text_summary_path.relative_to(_PROJECT_ROOT))
-
-    try:
-        await create_video(
-            uuid=video_uuid,
-            source_url=req.url,
-            title=title,
-            video_path=rel_video,
-            text_full_path=rel_full,
-            text_summary_path=rel_summary,
-            transcription_method=method,
-            section_id=req.section_id,
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"DB insert failed: {e}")
-
-    logger.info("[%s] done (method=%s)", video_uuid, method)
-
-    return ProcessResponse(
-        uuid=video_uuid,
-        title=title,
-        source_url=req.url,
-        video_path=rel_video,
-        text_full_path=rel_full,
-        text_summary_path=rel_summary,
-        transcription_method=method,
-        summary_preview=summary[:300],
         )
+    except PipelineError as e:
+        raise HTTPException(status_code=e.status, detail=str(e))
+    return ProcessResponse(**result)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "llm-infa"
-version = "0.3.0"
+version = "0.4.0"
 description = "YouTube -> транскрипция -> LLM-выжимка -> Postgres"
 readme = "README.md"
 requires-python = ">=3.14"

web/api_client.py

@@ -21,10 +21,15 @@ class ApiClient:
     def close(self) -> None:
         self._client.close()
 
-    # --- LLM ---
+    # --- Settings (persisted server-side in Postgres) ---
 
-    def llm_status(self) -> dict:
+    def get_settings(self) -> dict:
-        return self._get("/llm/config")
+        return self._get("/settings/")
+
+    def save_settings(self, values: dict) -> dict:
+        return self._request("PUT", "/settings/", values)
+
+    # --- LLM ---
 
     def llm_models(self, base_url: str, api_key: str) -> list[dict]:
         data = self._post(
@@ -39,10 +44,21 @@ class ApiClient:
             {"base_url": base_url, "api_key": api_key, "model": model},
         )
 
-    # --- Pipeline / videos ---
+    # --- Jobs (видео и плейлисты, в фоне) ---
 
-    def pipeline_process(self, payload: dict) -> dict:
+    def create_job(self, payload: dict) -> dict:
-        return self._post("/pipeline/process", payload)
+        return self._post("/jobs/", payload)
+
+    def list_jobs(self) -> list[dict]:
+        return self._get("/jobs/")
+
+    def cancel_job(self, job_id: str) -> dict:
+        return self._post(f"/jobs/{job_id}/cancel", None)
+
+    def delete_job(self, job_id: str) -> dict:
+        return self._request("DELETE", f"/jobs/{job_id}")
+
+    # --- Videos ---
 
     def list_videos(
         self,

web/main.py

@@ -19,20 +19,14 @@ def main(page: ft.Page) -> None:
     view = MainView(page, client)
     page.add(view.build())
 
-    # probe env-configured LLM and initial library load
+    # настройки живут на сервере (БД) — заполняем поля сохранёнными значениями
     try:
-        status = client.llm_status()
+        view.apply_settings(client.get_settings())
-        if status.get("configured"):
-            view._set_status(ok=True)
-            if status.get("base_url") and not view.base_url.value:
-                view.base_url.value = status["base_url"]
-            if status.get("model") and not view.selected_model:
-                view.selected_model = status["model"]
-                view.model_input.value = status["model"]
     except Exception:
         pass
 
     view._refresh_library()
+    view._start_jobs_poll()
     page.update()
 
 

web/views/main_view.py

@@ -1,7 +1,8 @@
-"""Main page of the LLM-infa Flet web app."""
+"""Main page of the LLM-infa Flet web app: вкладки «Главная» и «Настройки»."""
 from __future__ import annotations
 
 import threading
+import time
 
 import flet as ft
 
@@ -9,6 +10,16 @@ from web.api_client import ApiClient, ApiError
 import web.designer as d
 
 
+_JOB_STATUS = {
+    "queued": ("в очереди", None),
+    "running": ("выполняется", None),
+    "done": ("готово", "ok"),
+    "error": ("ошибка", "danger"),
+    "cancelled": ("отменена", None),
+    "interrupted": ("прервана", "danger"),
+}
+
+
 class MainView:
     def __init__(self, page: ft.Page, client: ApiClient):
         self.page = page
@@ -20,14 +31,15 @@ class MainView:
         self.sections: list[dict] = []
         self.current_section_id: int | None = None  # None = все видео
 
-        # ── LLM config fields ──────────────────────────────────────────────
+        self._polling = False
+
+        # ── Settings tab fields ────────────────────────────────────────────
         self.base_url = d.text_field("Base URL", hint="https://openrouter.ai/api/v1", expand=True)
         self.api_key = d.text_field("API Key", hint="sk-…", password=True, expand=True)
 
-        # Searchable model combobox
         self.model_input = d.text_field(
             "Модель",
-            hint="Сначала нажмите «Загрузить модели»",
+            hint="Нажмите «Загрузить модели» и выберите из списка",
             on_change=self._on_model_search,
             expand=True,
         )
@@ -44,7 +56,7 @@ class MainView:
 
         self.llm_msg = ft.Text("", color=d.MUTED, size=13, expand=True)
         self.llm_status_chip = ft.Container(
-            content=ft.Text("LLM не подключена", size=12, color=d.MUTED),
+            content=ft.Text("LLM не настроена", size=12, color=d.MUTED),
             padding=ft.padding.symmetric(horizontal=10, vertical=4),
             border=ft.border.all(1, d.BORDER),
             border_radius=99,
@@ -52,12 +64,6 @@ class MainView:
         self.btn_load = d.primary_button("Загрузить модели", self._load_models)
         self.btn_test = d.secondary_button("Проверить связь", self._test_conn)
 
-        # ── Pipeline fields ────────────────────────────────────────────────
-        self.yt_url = d.text_field(
-            "Ссылка на YouTube",
-            hint="https://youtu.be/…",
-            expand=True,
-        )
         self.vosk_path = d.text_field(
             "Vosk-модель (fallback, когда нет субтитров)",
             value="models/vosk-model-small-ru-0.22",
@@ -76,19 +82,28 @@ class MainView:
             width=220,
         )
         self.keep_video_sw = ft.Switch(
-            label="Сохранять видео на диске",
+            label="Сохранять видео на диске (иначе качается только аудио)",
             value=True,
             active_color=d.ACCENT,
             label_style=ft.TextStyle(color=d.TEXT, size=13),
         )
-        self.pipeline_section_dd = self._dropdown("Раздел для нового видео", width=280)
+        self.settings_msg = ft.Text("", color=d.MUTED, size=13, expand=True)
+        self.btn_save = d.primary_button("💾  Сохранить настройки", self._save_settings)
+
+        # ── Pipeline (main tab) ────────────────────────────────────────────
+        self.yt_url = d.text_field(
+            "Ссылка на YouTube: видео или плейлист",
+            hint="https://youtu.be/…  или  https://www.youtube.com/playlist?list=…",
+            expand=True,
+        )
+        self.pipeline_section_dd = self._dropdown("Раздел для новых видео", width=280)
         self.pipeline_msg = ft.Text("", color=d.MUTED, size=13, expand=True)
-        self.btn_run = d.primary_button("▶  Запустить", self._run_pipeline)
+        self.btn_run = d.primary_button("▶  В очередь", self._run_pipeline)
 
-        # Result block (initially hidden)
+        # ── Jobs ───────────────────────────────────────────────────────────
-        self._result_col = ft.Column(spacing=6, visible=False)
+        self._jobs_col = ft.Column(spacing=6)
 
-        # ── Library controls ───────────────────────────────────────────────
+        # ── Library ────────────────────────────────────────────────────────
         self.search_field = d.text_field(
             "Поиск по названию / URL", on_change=None, expand=True
         )
@@ -124,14 +139,11 @@ class MainView:
     # ─────────────────────── build ───────────────────────────────────────
 
     def build(self) -> ft.Control:
-        return ft.Column(
+        main_tab = ft.Container(
-            controls=[
-                self._build_topbar(),
-                ft.Container(
             content=ft.Column(
                 [
-                            self._build_llm_card(),
                     self._build_pipeline_card(),
+                    self._build_jobs_card(),
                     self._build_library_card(),
                 ],
                 spacing=20,
@@ -139,8 +151,34 @@ class MainView:
             ),
             padding=ft.padding.symmetric(horizontal=24, vertical=20),
             expand=True,
-                ),
+        )
+        settings_tab = ft.Container(
+            content=ft.Column(
+                [
+                    self._build_llm_card(),
+                    self._build_defaults_card(),
                 ],
+                spacing=20,
+                scroll=ft.ScrollMode.AUTO,
+            ),
+            padding=ft.padding.symmetric(horizontal=24, vertical=20),
+            expand=True,
+        )
+        tabs = ft.Tabs(
+            selected_index=0,
+            animation_duration=200,
+            label_color=d.ACCENT_2,
+            unselected_label_color=d.MUTED,
+            indicator_color=d.ACCENT,
+            divider_color=d.BORDER,
+            tabs=[
+                ft.Tab(text="Главная", content=main_tab),
+                ft.Tab(text="Настройки", content=settings_tab),
+            ],
+            expand=True,
+        )
+        return ft.Column(
+            controls=[self._build_topbar(), tabs],
             expand=True,
             spacing=0,
         )
@@ -174,13 +212,14 @@ class MainView:
             border=ft.border.only(bottom=ft.BorderSide(1, d.BORDER)),
         )
 
+    # ── Settings tab cards ────────────────────────────────────────────────
+
     def _build_llm_card(self) -> ft.Container:
         return d.card(
             d.section_title("Подключение к LLM"),
             d.hint(
-                "Укажите любой OpenAI-совместимый провайдер. "
+                "Любой OpenAI-совместимый провайдер (OpenRouter, OpenAI, локальная Ollama…). "
-                "Нейронка внутри Docker не разворачивается — "
+                "Ключ и модель хранятся на сервере в БД и переживают перезапуск."
-                "указываем URL внешнего сервиса."
             ),
             ft.Row([self.base_url, self.api_key], spacing=12),
             ft.Row([self.btn_load, self.btn_test, self.llm_msg], spacing=10),
@@ -189,22 +228,43 @@ class MainView:
             self._model_list_wrap,
         )
 
+    def _build_defaults_card(self) -> ft.Container:
+        return d.card(
+            d.section_title("Параметры обработки"),
+            d.hint("Применяются ко всем новым задачам."),
+            ft.Row([self.vosk_path, self.n_sentences], spacing=12),
+            self.keep_video_sw,
+            ft.Divider(color=d.BORDER, height=1),
+            ft.Row([self.btn_save, self.settings_msg], spacing=10),
+        )
+
+    # ── Main tab cards ────────────────────────────────────────────────────
+
     def _build_pipeline_card(self) -> ft.Container:
         return d.card(
-            d.section_title("Обработать видео"),
+            d.section_title("Обработать видео или плейлист"),
             d.hint(
-                "Pipeline: yt-dlp → субтитры (или Vosk fallback) "
+                "Задача уходит в фон: качаем, расшифровываем (субтитры или Vosk), "
-                "→ выжимка через выбранную LLM → запись в БД."
+                "сокращаем через LLM, пишем в БД. Плейлисты и лекции на 4–5 часов — ок."
             ),
             self.yt_url,
-            ft.Row([self.vosk_path, self.n_sentences], spacing=12),
             ft.Row(
-                [self.pipeline_section_dd, self.keep_video_sw],
+                [self.btn_run, self.pipeline_section_dd, self.pipeline_msg],
-                spacing=18,
+                spacing=12,
                 vertical_alignment=ft.CrossAxisAlignment.CENTER,
             ),
-            ft.Row([self.btn_run, self.pipeline_msg], spacing=10),
+        )
-            self._result_col,
+
+    def _build_jobs_card(self) -> ft.Container:
+        return d.card(
+            ft.Row(
+                [
+                    d.section_title("Задачи"),
+                    ft.Container(expand=True),
+                    d.secondary_button("Обновить", lambda _: self._start_jobs_poll()),
+                ]
+            ),
+            self._jobs_col,
         )
 
     def _build_library_card(self) -> ft.Container:
@@ -256,6 +316,39 @@ class MainView:
             ),
         )
 
+    # ─────────────────── settings load/save ──────────────────────────────
+
+    def apply_settings(self, s: dict) -> None:
+        """Заполняет поля значениями с сервера (вызывается при старте сессии)."""
+        self.base_url.value = s.get("llm_base_url", "")
+        self.api_key.value = s.get("llm_api_key", "")
+        self.selected_model = s.get("llm_model", "")
+        self.model_input.value = self.selected_model
+        self.n_sentences.value = str(s.get("summary_max_sentences", 15))
+        self.keep_video_sw.value = bool(s.get("keep_video", True))
+        self.vosk_path.value = s.get("vosk_model_path", "models/vosk-model-small-ru-0.22")
+        self._set_status(ok=bool(self.base_url.value and self.selected_model))
+
+    def _save_settings(self, _):
+        model = self.selected_model or (self.model_input.value or "").strip()
+        payload = {
+            "llm_base_url": self.base_url.value.strip(),
+            "llm_api_key": self.api_key.value.strip(),
+            "llm_model": model,
+            "summary_max_sentences": int(self.n_sentences.value or 15),
+            "keep_video": bool(self.keep_video_sw.value),
+            "vosk_model_path": self.vosk_path.value.strip(),
+        }
+        try:
+            self.client.save_settings(payload)
+            self.settings_msg.value = "Сохранено ✓ (хранится в БД, переживёт перезапуск)"
+            self.settings_msg.color = d.OK
+            self._set_status(ok=bool(payload["llm_base_url"] and model))
+        except ApiError as e:
+            self.settings_msg.value = str(e)
+            self.settings_msg.color = d.DANGER
+        self.page.update()
+
     # ─────────────────── model combobox ──────────────────────────────────
 
     def _on_model_search(self, e):
@@ -292,6 +385,8 @@ class MainView:
         self.selected_model = mid
         self.model_input.value = mid
         self._model_list_wrap.visible = False
+        self.llm_msg.value = "Не забудьте нажать «Сохранить настройки»"
+        self.llm_msg.color = d.MUTED
         self.page.update()
 
     # ─────────────────── LLM actions ─────────────────────────────────────
@@ -311,10 +406,8 @@ class MainView:
                 self.models = self.client.llm_models(base_url, self.api_key.value.strip())
                 self._render_model_list(self.models)
                 self._set_llm_msg(f"Загружено: {len(self.models)} моделей", ok=True)
-                self._set_status(ok=True)
             except ApiError as e:
                 self._set_llm_msg(str(e), error=True)
-                self._set_status(ok=False)
             finally:
                 self.btn_load.disabled = False
                 self.page.update()
@@ -322,7 +415,8 @@ class MainView:
         threading.Thread(target=_task, daemon=True).start()
 
     def _test_conn(self, _):
-        if not self.selected_model:
+        model = self.selected_model or (self.model_input.value or "").strip()
+        if not model:
             self._set_llm_msg("Сначала выберите модель", error=True)
             return
 
@@ -335,87 +429,164 @@ class MainView:
                 r = self.client.llm_test(
                     self.base_url.value.strip(),
                     self.api_key.value.strip(),
-                    self.selected_model,
+                    model,
                 )
                 preview = (r.get("reply") or "")[:80]
                 self._set_llm_msg(f"OK · {preview}", ok=True)
-                self._set_status(ok=True)
             except ApiError as e:
                 self._set_llm_msg(str(e), error=True)
-                self._set_status(ok=False)
             finally:
                 self.btn_test.disabled = False
                 self.page.update()
 
         threading.Thread(target=_task, daemon=True).start()
 
-    # ─────────────────── pipeline ─────────────────────────────────────────
+    # ─────────────────── pipeline → jobs ──────────────────────────────────
 
     def _run_pipeline(self, _):
         url = self.yt_url.value.strip()
         if not url:
-            self._set_pipeline_msg("Укажите ссылку на видео", error=True)
+            self._set_pipeline_msg("Укажите ссылку на видео или плейлист", error=True)
             return
 
-        payload: dict = {
+        payload: dict = {"url": url}
-            "url": url,
-            "model_path": self.vosk_path.value.strip() or None,
-            "summary_max_sentences": int(self.n_sentences.value or 15),
-            "keep_video": bool(self.keep_video_sw.value),
-        }
         if self.pipeline_section_dd.value:
             payload["section_id"] = int(self.pipeline_section_dd.value)
-        if self.base_url.value.strip() and self.selected_model:
-            payload["llm"] = {
-                "base_url": self.base_url.value.strip(),
-                "api_key": self.api_key.value.strip(),
-                "model": self.selected_model,
-            }
 
-        self._set_pipeline_msg("Обработка видео…  (может занять несколько минут)")
-        self._result_col.visible = False
-        self.btn_run.disabled = True
-        self.page.update()
-
-        def _task():
         try:
-                r = self.client.pipeline_process(payload)
+            self.client.create_job(payload)
-                self._render_result(r)
+            self.yt_url.value = ""
-                self._set_pipeline_msg("Готово ✓", ok=True)
+            self._set_pipeline_msg("Задача поставлена в очередь ✓", ok=True)
-                self._refresh_library()
         except ApiError as e:
             self._set_pipeline_msg(str(e), error=True)
-            finally:
-                self.btn_run.disabled = False
         self.page.update()
+        self._start_jobs_poll()
 
-        threading.Thread(target=_task, daemon=True).start()
+    # ─────────────────── jobs ─────────────────────────────────────────────
 
-    def _render_result(self, r: dict):
+    def _start_jobs_poll(self):
-        self._result_col.controls = [
+        if self._polling:
-            ft.Divider(color=d.BORDER, height=1),
+            return
-            ft.Text(r.get("title") or r.get("uuid", ""), color=d.ACCENT_2, size=15, weight=ft.FontWeight.W_600),
+        self._polling = True
-            *[
+        threading.Thread(target=self._poll_loop, daemon=True).start()
-                self._kv(k, v)
+
-                for k, v in [
+    def _poll_loop(self):
-                    ("UUID", r.get("uuid", "")),
+        prev: dict = {}
-                    ("Источник", r.get("source_url", "")),
+        try:
-                    ("Метод", r.get("transcription_method", "")),
+            while True:
-                    ("Видео", r.get("video_path") or "не сохранено"),
+                try:
-                    ("Полный текст", r.get("text_full_path", "")),
+                    jobs = self.client.list_jobs()
-                    ("Выжимка", r.get("text_summary_path", "")),
+                except (ApiError, Exception):
+                    break
+                self._render_jobs(jobs)
+                state = {j["id"]: (j["done_items"], j["status"]) for j in jobs}
+                if state != prev:
+                    prev = state
+                    self._refresh_library()  # там же page.update()
+                else:
+                    self.page.update()
+                if not any(j["status"] in ("queued", "running") for j in jobs):
+                    break
+                time.sleep(3)
+        finally:
+            self._polling = False
+
+    def _render_jobs(self, jobs: list[dict]):
+        if not jobs:
+            self._jobs_col.controls = [
+                ft.Text("Задач ещё не было", color=d.MUTED, size=13)
             ]
-            ],
+            return
-            ft.Text("Превью выжимки:", color=d.MUTED, size=12),
+        self._jobs_col.controls = [self._job_row(j) for j in jobs]
-            ft.Container(
+
-                content=ft.Text(r.get("summary_preview", ""), color=d.TEXT, size=13, selectable=True, no_wrap=False),
+    def _job_row(self, j: dict) -> ft.Container:
-                padding=12,
+        label, tone = _JOB_STATUS.get(j["status"], (j["status"], None))
-                bgcolor=d.SURFACE_3,
+        color = d.OK if tone == "ok" else (d.DANGER if tone == "danger" else d.MUTED)
-                border=ft.border.all(1, d.BORDER),
+        active = j["status"] in ("queued", "running")
-                border_radius=8,
+
+        chip = ft.Container(
+            content=ft.Text(label, size=11, color=color),
+            padding=ft.padding.symmetric(horizontal=8, vertical=2),
+            border=ft.border.all(1, color),
+            border_radius=99,
+        )
+        head = [
+            chip,
+            ft.Text(
+                (j["title"] or j["url"])[:90],
+                color=d.TEXT,
+                size=13,
+                weight=ft.FontWeight.W_600,
+                expand=True,
+                no_wrap=True,
             ),
         ]
-        self._result_col.visible = True
+        if j["kind"] == "playlist":
+            head.append(ft.Text("плейлист", color=d.ACCENT_2, size=11))
+        if j["total_items"]:
+            head.append(
+                ft.Text(f"{j['done_items']}/{j['total_items']}", color=d.MUTED, size=12)
+            )
+        if active:
+            head.append(
+                ft.IconButton(
+                    icon=ft.Icons.STOP_CIRCLE_OUTLINED,
+                    icon_color=d.DANGER,
+                    icon_size=17,
+                    tooltip="Отменить (после текущего элемента)",
+                    on_click=lambda e, jid=j["id"]: self._cancel_job(jid),
+                )
+            )
+        else:
+            head.append(
+                ft.IconButton(
+                    icon=ft.Icons.CLOSE,
+                    icon_color=d.MUTED,
+                    icon_size=15,
+                    tooltip="Убрать из списка",
+                    on_click=lambda e, jid=j["id"]: self._delete_job(jid),
+                )
+            )
+
+        rows: list[ft.Control] = [ft.Row(head, spacing=8)]
+        if active:
+            total = j["total_items"] or 0
+            value = (j["done_items"] / total) if total else None
+            rows.append(
+                ft.ProgressBar(value=value, color=d.ACCENT, bgcolor=d.SURFACE_3, height=4)
+            )
+            if j.get("live"):
+                rows.append(ft.Text(j["live"][:140], color=d.ACCENT_2, size=11))
+        if j.get("error"):
+            rows.append(
+                ft.Text(j["error"][:300], color=d.DANGER, size=11, no_wrap=False)
+            )
+        if j["failed_items"]:
+            rows.append(
+                ft.Text(f"с ошибкой: {j['failed_items']}", color=d.DANGER, size=11)
+            )
+
+        return ft.Container(
+            content=ft.Column(rows, spacing=5, tight=True),
+            padding=10,
+            bgcolor=d.SURFACE_2,
+            border=ft.border.all(1, d.BORDER),
+            border_radius=10,
+        )
+
+    def _cancel_job(self, job_id: str):
+        try:
+            self.client.cancel_job(job_id)
+        except ApiError as e:
+            self.lib_msg.value = str(e)
+        self._start_jobs_poll()
+
+    def _delete_job(self, job_id: str):
+        try:
+            self.client.delete_job(job_id)
+        except ApiError as e:
+            self.lib_msg.value = str(e)
+        self._start_jobs_poll()
 
     # ─────────────────── sections ─────────────────────────────────────────
 
@@ -688,6 +859,6 @@ class MainView:
 
     def _set_status(self, *, ok: bool):
         chip = self.llm_status_chip
-        chip.content.value = "LLM подключена" if ok else "Ошибка подключения"
+        chip.content.value = "LLM настроена" if ok else "LLM не настроена"
-        chip.content.color = d.OK if ok else d.DANGER
+        chip.content.color = d.OK if ok else d.MUTED
-        chip.border = ft.border.all(1, "rgba(109,213,140,.4)" if ok else "rgba(227,90,114,.4)")
+        chip.border = ft.border.all(1, "rgba(109,213,140,.4)" if ok else d.BORDER)