Рабочий пайплайн: YouTube -> транскрипция -> выжимка -> Postgres

- новый api/: /pipeline/process (yt-dlp, субтитры или Vosk, LLM/экстрактивная
  суммаризация), /videos, /llm; старый код перенесён в api_legacy/
- web/: Flet UI (flet 0.28.3 + flet-web, порт 8550)
- Dockerfile.api: ffmpeg слоем из mwader/static-ffmpeg, pip с кэш-маунтом
- requirements/pyproject: убраны moviepy, imageio-ffmpeg, битый asyncio
- README с инструкцией запуска и загрузкой Vosk-модели

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

.env

@@ -1,5 +1,27 @@
-# Настройки базы данных
+# Postgres
 DB_USER=postgres
 DB_PASS=secretpassword
 DB_NAME=test_db
+DB_HOST=db
 DB_PORT=5432
+
+# Pipeline defaults
+DEFAULT_VOSK_MODEL=models/vosk-model-small-ru-0.22
+SUBTITLE_LANGS=ru,en
+
+# External LLM (OpenAI-compatible API). The LLM is NOT hosted here.
+# Leave empty to fall back to the built-in extractive summarizer.
+# Examples:
+#   OpenAI:     LLM_BASE_URL=https://api.openai.com/v1
+#               LLM_MODEL=gpt-4o-mini
+#   OpenRouter: LLM_BASE_URL=https://openrouter.ai/api/v1
+#               LLM_MODEL=anthropic/claude-3.5-sonnet
+#   Ollama:     LLM_BASE_URL=http://host.docker.internal:11434/v1
+#               LLM_MODEL=llama3.1
+#   vLLM:       LLM_BASE_URL=http://vllm-host:8000/v1
+LLM_BASE_URL=
+LLM_API_KEY=
+LLM_MODEL=
+LLM_TIMEOUT=120
+LLM_MAX_TOKENS=1000
+LLM_TEMPERATURE=0.3

Dockerfile.api

@@ -1,51 +1,16 @@
 # syntax=docker/dockerfile:1.5
-
+FROM python:3.14-slim
-# Builder: install build deps and build wheels
-FROM python:3.14-slim AS builder
 WORKDIR /app
 
-#RUN apt-get update && apt-get install -y --no-install-recommends \
+# Static ffmpeg/ffprobe as a cached image layer — no apt, no slow mirror downloads
-#    build-essential \
+COPY --from=mwader/static-ffmpeg:7.1 /ffmpeg /ffprobe /usr/local/bin/
-#    ca-certificates \
+
-#    && rm -rf /var/lib/apt/lists/*
+# Deps first for layer caching; BuildKit cache mount speeds up rebuilds
-#
 COPY requirements.txt ./
-# Build wheels into /wheels (use cache for pip downloads)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip wheel --no-cache-dir -r requirements.txt -w /wheels
+    pip install -r requirements.txt
 
+# Application code (data/, models/, pg_data/ excluded via .dockerignore)
+COPY . .
 
-# Final image: small and without build tools
+CMD ["python", "-m", "uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000"]
-FROM python:3.14-slim AS final
-WORKDIR /app
-
-## minimal runtime deps (use static ffmpeg to avoid many apt deps)
-#RUN apt-get update && apt-get install -y --no-install-recommends \
-#    ca-certificates \
-#    curl \
-#    && rm -rf /var/lib/apt/lists/*
-
-# Copy wheels from builder and install
-COPY --from=builder /wheels /wheels
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install --no-cache-dir /wheels/* || pip install --no-cache-dir /wheels/* --no-deps
-
-# Download a static ffmpeg build (faster than installing via apt and avoids many deps)
-RUN set -eux; \
-        arch="$(dpkg --print-architecture)"; \
-        if [ "$arch" = "amd64" ]; then \
-            FFMPEG_URL="https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz"; \
-        else \
-            echo "Unsupported arch $arch"; exit 1; \
-        fi; \
-        curl -fsSL "$FFMPEG_URL" -o /tmp/ffmpeg.tar.xz; \
-        tar -xJf /tmp/ffmpeg.tar.xz -C /tmp; \
-        cp /tmp/ffmpeg-*-amd64-static/ffmpeg /usr/local/bin/; \
-        cp /tmp/ffmpeg-*-amd64-static/ffprobe /usr/local/bin/; \
-        chmod +x /usr/local/bin/ffmpeg /usr/local/bin/ffprobe; \
-        rm -rf /tmp/ffmpeg*
-
-# Copy application code (exclude models/data via .dockerignore)
-COPY . ./api
-
-CMD ["python", "-m", "uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000"]

Dockerfile.web

@@ -0,0 +1,14 @@
+# syntax=docker/dockerfile:1.5
+FROM python:3.12-slim
+WORKDIR /app
+
+# install deps separately for layer caching (flet is ~100 MB)
+COPY web/requirements.txt /tmp/web-req.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install --no-cache-dir -r /tmp/web-req.txt
+
+COPY web /app/web
+ENV PYTHONPATH=/app
+
+EXPOSE 8550
+CMD ["python", "-m", "web.main"]

README.md

@@ -0,0 +1,71 @@
+# LLM-infa
+
+Пайплайн: **YouTube → скачивание видео → текст речи → LLM-выжимка → Postgres**.
+
+Скачивает видео (yt-dlp), достаёт текст из субтитров (или распознаёт речь через Vosk,
+если субтитров нет), сокращает текст до краткой выжимки (через любой
+OpenAI-совместимый LLM API или встроенный экстрактивный алгоритм) и складывает всё
+в базу: пути к видео, полному тексту и выжимке.
+
+## Состав
+
+| Сервис | Порт | Что делает |
+|--------|------|------------|
+| `api`  | 8000 | FastAPI: пайплайн, каталог видео, проверка LLM |
+| `web`  | 8550 | Flet UI: запуск пайплайна и библиотека результатов |
+| `db`   | 5433→5432 | Postgres 15, таблица `videos` |
+
+Файлы складываются в `data/videos/` (mp4) и `data/text/` (`<uuid>.txt` — полный
+текст, `<uuid>_summary.txt` — выжимка); в БД хранятся пути и метаданные.
+
+## Запуск
+
+```bash
+# 1. Модель Vosk для распознавания без субтитров (однократно, ~90 МБ)
+mkdir -p models && cd models
+curl -LO https://alphacephei.com/vosk/models/vosk-model-small-ru-0.22.zip
+unzip vosk-model-small-ru-0.22.zip && rm vosk-model-small-ru-0.22.zip && cd ..
+
+# 2. Поднять стек
+docker compose up -d --build
+```
+
+UI: http://localhost:8550 · API-доки: http://localhost:8000/docs
+
+## LLM для выжимки
+
+LLM не разворачивается в контейнере — указывается любой внешний
+OpenAI-совместимый эндпоинт. Два способа:
+
+1. **В `.env`** (используется по умолчанию для всех запросов):
+   ```
+   LLM_BASE_URL=https://openrouter.ai/api/v1
+   LLM_API_KEY=sk-...
+   LLM_MODEL=anthropic/claude-haiku-4.5
+   ```
+2. **Через UI / поле `llm` в запросе** — на каждый запрос отдельно.
+
+Если LLM не настроена, работает встроенная экстрактивная суммаризация
+(выбор ключевых предложений, без нейросети).
+
+## API
+
+```bash
+# обработать видео целиком
+curl -X POST localhost:8000/pipeline/process \
+  -H 'Content-Type: application/json' \
+  -d '{"url": "https://youtu.be/...", "summary_max_sentences": 10}'
+
+# каталог обработанных видео
+curl localhost:8000/videos/
+
+# проверить связь с LLM
+curl -X POST localhost:8000/llm/test
+```
+
+## Заметки
+
+- Старый код (users/subscriptions/media-convert) лежит в `api_legacy/` и в
+  сборку не входит.
+- Если Docker Desktop не тянет образы (TLS timeout к registry), использовать
+  системный демон: `docker context use default`.

api/config.py

@@ -1,28 +1,38 @@
-from pydantic_settings import BaseSettings
 import os
+from pydantic_settings import BaseSettings
 
-def _env(*names, default=None):
-    for n in names:
-        v = os.getenv(n)
-        if v:
-            return v
-    return default
 
 class Settings(BaseSettings):
-    # support both custom DB_* names and common POSTGRES_* names from postgres images
+    DB_USER: str = os.getenv("DB_USER", "postgres")
-    DB_USER: str = _env("DB_USER", "POSTGRES_USER", default="postgres")
+    DB_PASS: str = os.getenv("DB_PASS", "postgres")
-    DB_PASS: str = _env("DB_PASS", "POSTGRES_PASSWORD", default="postgres")
+    DB_NAME: str = os.getenv("DB_NAME", "test_db")
-    DB_NAME: str = _env("DB_NAME", "POSTGRES_DB", default="test_db")
+    DB_HOST: str = os.getenv("DB_HOST", "db")
-    DB_HOST: str = _env("DB_HOST", "POSTGRES_HOST", "DB_HOST", default="db")
+    DB_PORT: str = os.getenv("DB_PORT", "5432")
-    DB_PORT: str = _env("DB_PORT", "POSTGRES_PORT", default="5432")
 
-    # If explicit DB_* variables are provided, build DATABASE_URL from them (priority).
+    DEFAULT_VOSK_MODEL: str = os.getenv(
-    # Otherwise fall back to explicit DATABASE_URL or DATABASE_URL_ASYNC env.
+        "DEFAULT_VOSK_MODEL", "models/vosk-model-small-ru-0.22"
-    _built_db_url: str | None = None
+    )
-    if DB_USER and DB_PASS and DB_NAME and DB_HOST and DB_PORT:
+    SUBTITLE_LANGS: str = os.getenv("SUBTITLE_LANGS", "ru,en")
-        _built_db_url = f"postgresql+asyncpg://{DB_USER}:{DB_PASS}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
 
-    DATABASE_URL: str = _built_db_url or _env("DATABASE_URL", "DATABASE_URL_ASYNC", default="")
+    # External LLM (OpenAI-compatible). The LLM itself is NOT hosted in this container —
+    # point at any provider/proxy/local server that speaks /v1/chat/completions.
+    LLM_BASE_URL: str = os.getenv("LLM_BASE_URL", "")
+    LLM_API_KEY: str = os.getenv("LLM_API_KEY", "")
+    LLM_MODEL: str = os.getenv("LLM_MODEL", "")
+    LLM_TIMEOUT: int = int(os.getenv("LLM_TIMEOUT", "120"))
+    LLM_MAX_TOKENS: int = int(os.getenv("LLM_MAX_TOKENS", "1000"))
+    LLM_TEMPERATURE: float = float(os.getenv("LLM_TEMPERATURE", "0.3"))
+
+    @property
+    def database_url(self) -> str:
+        return (
+            f"postgresql+asyncpg://{self.DB_USER}:{self.DB_PASS}"
+            f"@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
+        )
+
+    @property
+    def subtitle_langs_list(self) -> list[str]:
+        return [x.strip() for x in self.SUBTITLE_LANGS.split(",") if x.strip()]
 
 
 settings = Settings()

api/db/connection.py

@@ -1,27 +1,63 @@
-from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
-from sqlalchemy.orm import sessionmaker
-from sqlalchemy import text
 import asyncio
-from api.config import settings
+import logging
 
-DATABASE_URL = settings.DATABASE_URL
+import asyncpg
-engine = create_async_engine(DATABASE_URL, echo=False, future=True)
+from sqlalchemy import text
+from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
+from sqlalchemy.orm import sessionmaker
+
+from api.config import settings
+from api.models.base import Base
+import api.models.video  # noqa: F401  - register model with Base.metadata
+
+
+logger = logging.getLogger("db")
+
+engine = create_async_engine(settings.database_url, echo=False, future=True)
 SessionLocal = sessionmaker(engine, expire_on_commit=False, class_=AsyncSession)
 
 
-async def wait_for_db(retries: int = 30, delay: float = 1.0) -> None:
+async def wait_for_postgres(retries: int = 30, delay: float = 1.0) -> None:
-	"""Wait until the database is available. Retries with exponential backoff.
+    """Wait for the postgres server to accept connections (using the default 'postgres' db)."""
+    last_exc: Exception | None = None
+    for attempt in range(1, retries + 1):
+        try:
+            conn = await asyncpg.connect(
+                user=settings.DB_USER,
+                password=settings.DB_PASS,
+                database="postgres",
+                host=settings.DB_HOST,
+                port=int(settings.DB_PORT),
+            )
+            await conn.close()
+            return
+        except Exception as e:
+            last_exc = e
+            wait = min(delay * (2 ** (attempt - 1)), 5)
+            logger.info("Waiting for postgres (attempt %s/%s): %s", attempt, retries, e)
+            await asyncio.sleep(wait)
+    raise RuntimeError(f"Could not connect to postgres after {retries} attempts") from last_exc
 
-	Raises RuntimeError if DB is still unavailable after retries.
+
-	"""
+async def _create_database_if_missing() -> None:
-	last_exc = None
+    admin = await asyncpg.connect(
-	for attempt in range(1, retries + 1):
+        user=settings.DB_USER,
-		try:
+        password=settings.DB_PASS,
-			async with engine.connect() as conn:
+        database="postgres",
-				await conn.execute(text("SELECT 1"))
+        host=settings.DB_HOST,
-				return
+        port=int(settings.DB_PORT),
-		except Exception as e:
+    )
-			last_exc = e
+    try:
-			wait = min(delay * (2 ** (attempt - 1)), 5)
+        exists = await admin.fetchval(
-			await asyncio.sleep(wait)
+            "SELECT 1 FROM pg_database WHERE datname=$1", settings.DB_NAME
-	raise RuntimeError(f"Could not connect to DB after {retries} attempts") from last_exc
+        )
+        if not exists:
+            await admin.execute(f'CREATE DATABASE "{settings.DB_NAME}"')
+    finally:
+        await admin.close()
+
+
+async def init_db() -> None:
+    await _create_database_if_missing()
+    async with engine.begin() as conn:
+        await conn.run_sync(Base.metadata.create_all)

api/db/video.py

@@ -0,0 +1,53 @@
+from sqlalchemy import select
+
+from api.db.connection import SessionLocal
+from api.models.video import Video
+
+
+async def create_video(
+    *,
+    uuid: str,
+    source_url: str,
+    title: str,
+    video_path: str,
+    text_full_path: str,
+    text_summary_path: str,
+    transcription_method: str,
+) -> Video:
+    async with SessionLocal() as session:
+        v = Video(
+            uuid=uuid,
+            source_url=source_url,
+            title=title or "",
+            video_path=video_path or "",
+            text_full_path=text_full_path or "",
+            text_summary_path=text_summary_path or "",
+            transcription_method=transcription_method or "",
+        )
+        session.add(v)
+        await session.commit()
+        await session.refresh(v)
+        return v
+
+
+async def get_video(video_uuid: str) -> Video | None:
+    async with SessionLocal() as session:
+        res = await session.execute(select(Video).where(Video.uuid == video_uuid))
+        return res.scalars().first()
+
+
+async def list_videos() -> list[Video]:
+    async with SessionLocal() as session:
+        res = await session.execute(select(Video).order_by(Video.created_at.desc()))
+        return list(res.scalars().all())
+
+
+async def delete_video(video_uuid: str) -> bool:
+    async with SessionLocal() as session:
+        res = await session.execute(select(Video).where(Video.uuid == video_uuid))
+        v = res.scalars().first()
+        if not v:
+            return False
+        await session.delete(v)
+        await session.commit()
+        return True

api/lib/llm.py

@@ -0,0 +1,164 @@
+"""Generic OpenAI-compatible chat-completions client.
+
+The LLM is *not* deployed in this container — point this client at any external
+endpoint that speaks the OpenAI Chat Completions wire format:
+    - https://api.openai.com/v1
+    - https://openrouter.ai/api/v1
+    - https://api.anthropic.com (via OpenAI-compat proxies)
+    - http://host.docker.internal:11434/v1   (local Ollama)
+    - http://vllm-host:8000/v1                (vLLM / llama.cpp server / LM Studio)
+
+Configure once via environment (LLM_BASE_URL, LLM_API_KEY, LLM_MODEL),
+or override per-request through /pipeline/process by passing an `llm` block.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import urllib.error
+import urllib.request
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from api.config import settings
+
+
+logger = logging.getLogger("llm")
+
+
+class LLMConfig(BaseModel):
+    """Wire-level config for an OpenAI-compatible chat-completions endpoint."""
+
+    base_url: str = Field(..., description="Base URL ending in /v1 (or equivalent)")
+    api_key: str = ""
+    # `model` is optional so the same config can be used for /v1/models discovery
+    # before the user has picked one.
+    model: str = ""
+    timeout: int = 120
+    max_tokens: int = 1000
+    temperature: float = 0.3
+    extra_headers: dict[str, str] = Field(default_factory=dict)
+
+
+class LLMError(RuntimeError):
+    pass
+
+
+def from_settings() -> Optional[LLMConfig]:
+    """Build LLMConfig from environment variables, or return None if not configured."""
+    if not settings.LLM_BASE_URL or not settings.LLM_MODEL:
+        return None
+    return LLMConfig(
+        base_url=settings.LLM_BASE_URL,
+        api_key=settings.LLM_API_KEY,
+        model=settings.LLM_MODEL,
+        timeout=settings.LLM_TIMEOUT,
+        max_tokens=settings.LLM_MAX_TOKENS,
+        temperature=settings.LLM_TEMPERATURE,
+    )
+
+
+def chat_complete(cfg: LLMConfig, messages: list[dict]) -> str:
+    """Synchronous Chat Completions call. Returns assistant message content."""
+    url = cfg.base_url.rstrip("/") + "/chat/completions"
+    payload: dict = {
+        "model": cfg.model,
+        "messages": messages,
+        "max_tokens": cfg.max_tokens,
+        "temperature": cfg.temperature,
+    }
+    headers = {"Content-Type": "application/json", "Accept": "application/json"}
+    if cfg.api_key:
+        headers["Authorization"] = f"Bearer {cfg.api_key}"
+    headers.update(cfg.extra_headers or {})
+
+    body = json.dumps(payload).encode("utf-8")
+    req = urllib.request.Request(url, data=body, headers=headers, method="POST")
+
+    try:
+        with urllib.request.urlopen(req, timeout=cfg.timeout) as resp:
+            raw = resp.read()
+    except urllib.error.HTTPError as e:
+        try:
+            err_body = e.read().decode("utf-8", errors="ignore")
+        except Exception:
+            err_body = ""
+        raise LLMError(f"LLM HTTP {e.code} {e.reason}: {err_body[:500]}")
+    except Exception as e:
+        raise LLMError(f"LLM request failed: {e}")
+
+    try:
+        data = json.loads(raw.decode("utf-8"))
+        choices = data.get("choices") or []
+        if not choices:
+            raise LLMError(f"LLM returned no choices: {data}")
+        msg = choices[0].get("message") or {}
+        content = msg.get("content")
+        if content is None:
+            raise LLMError(f"LLM choice has no content: {choices[0]}")
+        return content.strip()
+    except LLMError:
+        raise
+    except Exception as e:
+        raise LLMError(f"Failed to parse LLM response: {e}")
+
+
+def ping(cfg: LLMConfig) -> str:
+    """Tiny round-trip to verify connectivity. Returns the assistant reply text."""
+    return chat_complete(
+        cfg,
+        [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Reply with the single word OK."},
+        ],
+    )
+
+
+def list_models(cfg: LLMConfig) -> list[dict]:
+    """GET {base_url}/models. Returns a normalized list of {id, name, owned_by?}.
+
+    Compatible with OpenAI, OpenRouter, Ollama OpenAI-compat, vLLM, LM Studio.
+    """
+    url = cfg.base_url.rstrip("/") + "/models"
+    headers = {"Accept": "application/json"}
+    if cfg.api_key:
+        headers["Authorization"] = f"Bearer {cfg.api_key}"
+    headers.update(cfg.extra_headers or {})
+
+    req = urllib.request.Request(url, headers=headers, method="GET")
+    try:
+        with urllib.request.urlopen(req, timeout=cfg.timeout) as resp:
+            raw = resp.read()
+    except urllib.error.HTTPError as e:
+        try:
+            err_body = e.read().decode("utf-8", errors="ignore")
+        except Exception:
+            err_body = ""
+        raise LLMError(f"LLM HTTP {e.code} {e.reason}: {err_body[:500]}")
+    except Exception as e:
+        raise LLMError(f"List models failed: {e}")
+
+    try:
+        data = json.loads(raw.decode("utf-8"))
+    except Exception as e:
+        raise LLMError(f"Failed to parse models response: {e}")
+
+    items = data.get("data") or data.get("models") or data.get("results") or []
+    out: list[dict] = []
+    for item in items:
+        if isinstance(item, str):
+            out.append({"id": item, "name": item})
+            continue
+        if not isinstance(item, dict):
+            continue
+        mid = item.get("id") or item.get("name") or item.get("model")
+        if not mid:
+            continue
+        entry = {"id": mid, "name": item.get("name") or mid}
+        owned = item.get("owned_by") or item.get("organization")
+        if owned:
+            entry["owned_by"] = owned
+        out.append(entry)
+    out.sort(key=lambda x: x["id"].lower())
+    return out

api/lib/summarize.py

@@ -0,0 +1,221 @@
+"""Summarization with two backends:
+
+* `summarize_llm` — uses any external OpenAI-compatible API (configured via
+  `LLMConfig`). Long transcripts are map-reduced: per-chunk summaries are
+  concatenated and re-summarized.
+* `summarize_extractive` — pure-Python TF-IDF-ish fallback (RU + EN aware).
+
+`summarize(text, llm_cfg=...)` picks the LLM path when a config is supplied,
+otherwise falls back to extractive. The pipeline calls this entry point.
+"""
+from __future__ import annotations
+
+import logging
+import re
+from collections import Counter
+from typing import Optional
+
+from api.lib.llm import LLMConfig, LLMError, chat_complete
+
+
+logger = logging.getLogger("summarize")
+
+
+_RU_STOPWORDS = {
+    "и", "в", "во", "не", "на", "я", "что", "тот", "быть", "с", "со", "а", "весь",
+    "как", "это", "но", "он", "она", "оно", "мы", "вы", "они", "к", "у", "из", "за",
+    "по", "до", "от", "для", "же", "или", "бы", "если", "так", "там", "тут", "когда",
+    "где", "кто", "какой", "этот", "эта", "эти", "мой", "твой", "наш", "ваш",
+    "свой", "его", "её", "их", "был", "была", "было", "были", "есть", "нет", "да",
+    "ну", "вот", "ещё", "еще", "уже", "только", "очень", "может", "можно", "надо",
+    "будет", "будут", "всё", "все", "этого", "этом", "этой", "тоже", "также", "чтобы",
+    "кого", "чего", "чем", "тем", "том", "нем", "ней", "себя", "себе", "мне", "тебе",
+    "нам", "вам", "нас", "вас", "про", "без", "над", "под", "через", "при", "об",
+    "о", "обо", "ли", "вон", "сюда", "туда", "оттуда", "потом", "теперь", "тогда",
+    "сейчас", "иногда", "всегда", "никогда", "просто", "именно", "ведь", "значит",
+    "хотя", "что-то", "кто-то",
+}
+
+_EN_STOPWORDS = {
+    "the", "a", "an", "and", "or", "but", "is", "are", "was", "were", "be", "been",
+    "being", "have", "has", "had", "do", "does", "did", "will", "would", "should",
+    "could", "may", "might", "must", "shall", "can", "i", "you", "he", "she", "it",
+    "we", "they", "them", "my", "your", "his", "her", "its", "our", "their", "this",
+    "that", "these", "those", "in", "on", "at", "by", "for", "with", "about", "as",
+    "of", "to", "from", "into", "so", "not", "no", "yes", "if", "then", "than",
+    "when", "where", "why", "how", "what", "which", "who", "whom", "whose", "me",
+    "us", "him", "just", "only", "also", "too", "very", "there", "here", "out",
+    "up", "down", "over", "under", "between", "through", "again", "more", "most",
+    "some", "any", "all", "each", "every", "such", "while", "because", "until",
+    "during", "above", "below", "now", "ever", "never",
+}
+
+_STOPWORDS = _RU_STOPWORDS | _EN_STOPWORDS
+
+_WORD_RE = re.compile(r"[\w']+", re.UNICODE)
+_SENT_SPLIT_RE = re.compile(r"(?<=[.!?…])\s+(?=[А-ЯA-Z\"«„])")
+
+
+DEFAULT_SYSTEM_PROMPT = (
+    "Ты ассистент, который делает краткую и точную выжимку текста. "
+    "Сохраняй ключевые факты, имена, цифры, причинно-следственные связи. "
+    "Если оригинал на русском — пиши по-русски, иначе — на языке оригинала. "
+    "Не добавляй информацию, которой нет в исходном тексте."
+)
+
+DEFAULT_USER_TEMPLATE = (
+    "Сделай связную краткую выжимку следующего текста. "
+    "Выдели основные тезисы и логику изложения. Объём — 5–12 предложений.\n\n"
+    "---\n{text}\n---"
+)
+
+DEFAULT_REDUCE_TEMPLATE = (
+    "Ниже — последовательность кратких выжимок частей одного длинного текста. "
+    "Объедини их в единую связную выжимку, сохранив главные факты и логику.\n\n"
+    "---\n{text}\n---"
+)
+
+
+def _split_sentences(text: str) -> list[str]:
+    text = re.sub(r"\s+", " ", text).strip()
+    if not text:
+        return []
+    parts = _SENT_SPLIT_RE.split(text)
+    if len(parts) <= 1:
+        parts = re.split(r"(?<=[.!?…])\s+", text)
+    return [p.strip() for p in parts if p.strip()]
+
+
+def _chunk_by_chars(text: str, max_chars: int) -> list[str]:
+    if len(text) <= max_chars:
+        return [text]
+    chunks: list[str] = []
+    cur: list[str] = []
+    cur_len = 0
+    for s in _split_sentences(text):
+        if cur_len + len(s) + 1 > max_chars and cur:
+            chunks.append(" ".join(cur))
+            cur, cur_len = [], 0
+        cur.append(s)
+        cur_len += len(s) + 1
+    if cur:
+        chunks.append(" ".join(cur))
+    return chunks
+
+
+def summarize_extractive(
+    text: str,
+    *,
+    max_sentences: int = 15,
+    min_sentences: int = 3,
+    ratio: float = 0.15,
+) -> str:
+    """Frequency-based extractive summary. Picks top sentences by mean word weight."""
+    text = (text or "").strip()
+    if not text:
+        return ""
+    sentences = _split_sentences(text)
+    if not sentences:
+        return ""
+
+    target = max(min_sentences, min(max_sentences, int(len(sentences) * ratio)))
+    target = max(1, min(target, len(sentences)))
+    if len(sentences) <= target:
+        return " ".join(sentences)
+
+    words = _WORD_RE.findall(text.lower())
+    freq = Counter(w for w in words if w not in _STOPWORDS and len(w) > 2)
+    if not freq:
+        return " ".join(sentences[:target])
+
+    max_f = max(freq.values())
+    norm = {w: c / max_f for w, c in freq.items()}
+
+    scored: list[tuple[int, float]] = []
+    for i, s in enumerate(sentences):
+        sw = _WORD_RE.findall(s.lower())
+        if not sw:
+            continue
+        score = sum(norm.get(w, 0.0) for w in sw) / len(sw)
+        scored.append((i, score))
+    if not scored:
+        return " ".join(sentences[:target])
+
+    top = sorted(scored, key=lambda x: -x[1])[:target]
+    top.sort(key=lambda x: x[0])
+    return " ".join(sentences[i] for i, _ in top)
+
+
+def summarize_llm(
+    text: str,
+    cfg: LLMConfig,
+    *,
+    system_prompt: Optional[str] = None,
+    user_template: Optional[str] = None,
+    reduce_template: Optional[str] = None,
+    chunk_chars: int = 10000,
+) -> str:
+    """Summarize via an external OpenAI-compatible API. Map-reduce for long texts."""
+    text = (text or "").strip()
+    if not text:
+        return ""
+
+    sys_p = system_prompt or DEFAULT_SYSTEM_PROMPT
+    user_t = user_template or DEFAULT_USER_TEMPLATE
+    reduce_t = reduce_template or DEFAULT_REDUCE_TEMPLATE
+
+    def _one(chunk: str, template: str) -> str:
+        return chat_complete(
+            cfg,
+            [
+                {"role": "system", "content": sys_p},
+                {"role": "user", "content": template.format(text=chunk)},
+            ],
+        )
+
+    chunks = _chunk_by_chars(text, chunk_chars)
+    logger.info("LLM summarize: %d chunk(s), total chars=%d", len(chunks), len(text))
+
+    if len(chunks) == 1:
+        return _one(chunks[0], user_t)
+
+    partials: list[str] = []
+    for i, ch in enumerate(chunks, start=1):
+        logger.info("LLM summarize chunk %d/%d", i, len(chunks))
+        partials.append(_one(ch, user_t))
+
+    combined = "\n\n".join(partials)
+    if len(combined) > chunk_chars:
+        # If even concatenated partials are too long, recurse: summarize partials in batches.
+        return summarize_llm(
+            combined,
+            cfg,
+            system_prompt=sys_p,
+            user_template=reduce_t,
+            reduce_template=reduce_t,
+            chunk_chars=chunk_chars,
+        )
+    logger.info("LLM reduce step over %d partials", len(partials))
+    return _one(combined, reduce_t)
+
+
+def summarize(
+    text: str,
+    *,
+    llm_cfg: Optional[LLMConfig] = None,
+    system_prompt: Optional[str] = None,
+    user_template: Optional[str] = None,
+    max_sentences: int = 15,
+) -> str:
+    """Primary entry point. Uses LLM if configured, else extractive fallback."""
+    if llm_cfg is not None:
+        try:
+            return summarize_llm(
+                text,
+                llm_cfg,
+                system_prompt=system_prompt,
+                user_template=user_template,
+            )
+        except LLMError as e:
+            logger.warning("LLM summarization failed, falling back to extractive: %s", e)
+    return summarize_extractive(text, max_sentences=max_sentences)

api/lib/transcribe.py

@@ -0,0 +1,119 @@
+"""Vosk-based fallback transcription: stream PCM from ffmpeg into a Vosk recognizer."""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import shutil
+import subprocess
+import sys
+import threading
+from pathlib import Path
+
+try:
+    from vosk import KaldiRecognizer, Model
+except Exception:
+    Model = None
+    KaldiRecognizer = None
+
+
+logger = logging.getLogger("transcribe")
+
+
+class VoskModelManager:
+    """Loads a Vosk model. Captures the model's stderr to Python logs while loading."""
+
+    def __init__(self, model_path: Path):
+        if Model is None or KaldiRecognizer is None:
+            raise RuntimeError("vosk is not installed in runtime")
+        self.model = self._load_with_stderr_capture(model_path)
+
+    @staticmethod
+    def _load_with_stderr_capture(path: Path):
+        log = logging.getLogger("vosk.loader")
+        r_fd, w_fd = os.pipe()
+        saved_stderr = os.dup(sys.stderr.fileno())
+        os.dup2(w_fd, sys.stderr.fileno())
+        os.close(w_fd)
+
+        def reader(fd: int) -> None:
+            with os.fdopen(fd, "rb") as fh:
+                for raw in iter(fh.readline, b""):
+                    log.info(raw.decode(errors="ignore").rstrip())
+
+        t = threading.Thread(target=reader, args=(r_fd,), daemon=True)
+        t.start()
+        try:
+            return Model(str(path))
+        finally:
+            try:
+                os.dup2(saved_stderr, sys.stderr.fileno())
+            finally:
+                os.close(saved_stderr)
+            t.join(timeout=2)
+
+
+def transcribe_via_ffmpeg(media_path: Path, model_path: Path, sample_rate: int = 16000) -> str:
+    """Decode the source media to PCM s16le mono via ffmpeg and feed it to Vosk in chunks."""
+    if Model is None or KaldiRecognizer is None:
+        raise RuntimeError("vosk is not installed in runtime")
+    if shutil.which("ffmpeg") is None:
+        raise RuntimeError("ffmpeg is not available in PATH")
+    if not media_path.exists():
+        raise FileNotFoundError(media_path)
+
+    mgr = VoskModelManager(model_path)
+    recognizer = KaldiRecognizer(mgr.model, sample_rate)
+    try:
+        recognizer.SetWords(True)
+    except Exception:
+        pass
+
+    cmd = [
+        "ffmpeg",
+        "-hide_banner",
+        "-loglevel",
+        "error",
+        "-i",
+        str(media_path),
+        "-f",
+        "s16le",
+        "-acodec",
+        "pcm_s16le",
+        "-ac",
+        "1",
+        "-ar",
+        str(sample_rate),
+        "-vn",
+        "-",
+    ]
+    logger.info("Spawning ffmpeg streaming decode for %s", media_path.name)
+
+    proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    if proc.stdout is None:
+        proc.kill()
+        raise RuntimeError("ffmpeg did not provide stdout")
+
+    try:
+        bytes_read = 0
+        last_logged_mb = 0
+        while True:
+            chunk = proc.stdout.read(4000)
+            if not chunk:
+                break
+            recognizer.AcceptWaveform(chunk)
+            bytes_read += len(chunk)
+            mb = bytes_read // (1024 * 1024)
+            if mb >= last_logged_mb + 5:
+                last_logged_mb = mb
+                logger.info("Transcribed %d MB of audio so far", mb)
+
+        result = json.loads(recognizer.FinalResult())
+        text = result.get("text", "")
+        logger.info("Transcription finished, length=%d", len(text))
+        return text
+    finally:
+        try:
+            proc.kill()
+        except Exception:
+            pass

api/lib/youtube.py

@@ -0,0 +1,172 @@
+"""yt-dlp wrapper: download a YouTube video together with available subtitles."""
+from __future__ import annotations
+
+import logging
+import re
+from pathlib import Path
+from typing import Optional
+
+import yt_dlp
+
+
+logger = logging.getLogger("youtube")
+
+
+_VIDEO_EXTS = {".mp4", ".mkv", ".webm", ".m4a", ".mp3", ".mov"}
+
+
+def download_video_with_subs(
+    url: str,
+    video_id: str,
+    video_dir: Path,
+    langs: Optional[list[str]] = None,
+) -> dict:
+    """Download a single video, also requesting subtitles for the given languages.
+
+    Returns:
+        {
+            "title": str,
+            "video_path": Path,
+            "subtitle_path": Path | None,
+            "subtitle_lang": str | None,
+            "subtitle_kind": "manual" | "auto" | None,
+        }
+    """
+    if langs is None:
+        langs = ["ru", "en"]
+    video_dir.mkdir(parents=True, exist_ok=True)
+
+    outtmpl = str(video_dir / f"{video_id}.%(ext)s")
+
+    ydl_opts = {
+        "outtmpl": outtmpl,
+        "format": "bv*+ba/b",
+        "merge_output_format": "mp4",
+        "writesubtitles": True,
+        "writeautomaticsub": True,
+        "subtitleslangs": langs,
+        "subtitlesformat": "vtt",
+        "quiet": True,
+        "no_warnings": True,
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info = ydl.extract_info(url, download=True)
+
+    title = info.get("title") or video_id
+
+    # Locate the merged video file
+    video_path = video_dir / f"{video_id}.mp4"
+    if not video_path.exists():
+        for p in video_dir.glob(f"{video_id}.*"):
+            if p.suffix.lower() in _VIDEO_EXTS:
+                video_path = p
+                break
+
+    sub_path, sub_lang, sub_kind = _find_subtitle(video_dir, video_id, langs, info)
+
+    return {
+        "title": title,
+        "video_path": video_path,
+        "subtitle_path": sub_path,
+        "subtitle_lang": sub_lang,
+        "subtitle_kind": sub_kind,
+    }
+
+
+def _find_subtitle(
+    video_dir: Path,
+    video_id: str,
+    langs: list[str],
+    info: dict,
+) -> tuple[Optional[Path], Optional[str], Optional[str]]:
+    """Pick the best subtitle file among downloaded ones, preferring manual subs."""
+    requested = info.get("requested_subtitles") or {}
+    manual_keys = set(info.get("subtitles", {}).keys())
+
+    # Build a list of (lang, kind, path) candidates from yt-dlp's reported requested_subtitles
+    candidates: list[tuple[str, str, Path]] = []
+    for lang_code, sub_info in requested.items():
+        filepath = sub_info.get("filepath")
+        if not filepath:
+            continue
+        p = Path(filepath)
+        if not p.exists():
+            continue
+        kind = "manual" if lang_code in manual_keys else "auto"
+        candidates.append((lang_code, kind, p))
+
+    # Fallback: glob the directory
+    if not candidates:
+        for p in video_dir.glob(f"{video_id}*.vtt"):
+            m = re.match(rf"^{re.escape(video_id)}\.([A-Za-z0-9_\-]+)\.vtt$", p.name)
+            lang_code = m.group(1) if m else "unknown"
+            candidates.append((lang_code, "auto", p))
+
+    if not candidates:
+        return None, None, None
+
+    def score(c: tuple[str, str, Path]) -> tuple[int, int]:
+        lang, kind, _ = c
+        try:
+            lang_rank = next(
+                i for i, l in enumerate(langs) if lang == l or lang.startswith(l + "-")
+            )
+        except StopIteration:
+            lang_rank = len(langs)
+        kind_rank = 0 if kind == "manual" else 1
+        return (lang_rank, kind_rank)
+
+    candidates.sort(key=score)
+    lang, kind, path = candidates[0]
+    return path, lang, kind
+
+
+def vtt_to_text(vtt_content: str) -> str:
+    """Convert a VTT subtitle file to a deduplicated plain-text string.
+
+    YouTube auto-captions ship as rolling cues — each new cue extends the previous.
+    Strategy: per cue block, keep only the last text line, then drop consecutive
+    duplicates and identical sentence fragments.
+    """
+    blocks = re.split(r"\r?\n\s*\r?\n", vtt_content)
+    out: list[str] = []
+    last = ""
+
+    for block in blocks:
+        block = block.strip()
+        if not block:
+            continue
+        text_lines: list[str] = []
+        for raw in block.splitlines():
+            line = raw.strip()
+            if not line:
+                continue
+            if line.startswith("WEBVTT"):
+                continue
+            if line.startswith(("NOTE", "STYLE", "Kind:", "Language:", "Region:")):
+                continue
+            if "-->" in line:
+                continue
+            if re.match(r"^\d+$", line):
+                continue
+            line = re.sub(r"<[^>]+>", "", line)
+            line = re.sub(r"\s+", " ", line).strip()
+            if line:
+                text_lines.append(line)
+        if not text_lines:
+            continue
+        candidate = text_lines[-1]
+        if candidate == last:
+            continue
+        # Avoid the rolling-caption case where a cue strictly extends the previous one
+        if last and candidate.startswith(last):
+            out[-1] = candidate
+            last = candidate
+            continue
+        if last and last.startswith(candidate):
+            continue
+        out.append(candidate)
+        last = candidate
+
+    return " ".join(out)

api/main.py

@@ -1,22 +1,28 @@
-import asyncio
+import logging
+from contextlib import asynccontextmanager
+
 from fastapi import FastAPI
-from api.route import default, subscription
-# renamed route modules for clarity
-from api.route import users, installing, media_convert, mp3_ffmpeg_stream, moviepy
-#from api.db.subscription import init_db
-#from api.db.connection import wait_for_db
 
-app = FastAPI(title="LLM-infa API")
+from api.db.connection import wait_for_postgres, init_db
+from api.route import default, llm, pipeline, videos
 
-#@app.on_event("startup")
+
-#async def on_startup():
+logging.basicConfig(
-#    await wait_for_db()
+    level=logging.INFO,
-#    await init_db()
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    await wait_for_postgres()
+    await init_db()
+    yield
+
+
+app = FastAPI(title="LLM-infa API", version="0.3.0", lifespan=lifespan)
 
 app.include_router(default.router)
-#app.include_router(subscription.router)
+app.include_router(pipeline.router)
-#app.include_router(users.router)
+app.include_router(videos.router)
-app.include_router(installing.router)
+app.include_router(llm.router)
-app.include_router(media_convert.router)
-app.include_router(mp3_ffmpeg_stream.router)
-app.include_router(moviepy.router)

api/route/default.py

@@ -1,16 +1,19 @@
-from fastapi import APIRouter
-import toml
 from pathlib import Path
 
+import toml
+from fastapi import APIRouter
+
+
 router = APIRouter()
 
+
 @router.get("/")
 async def root():
     version = "dev"
-    pyproject_path = Path(__file__).parent.parent.parent / "pyproject.toml"
+    pyproject = Path(__file__).resolve().parent.parent.parent / "pyproject.toml"
-    if pyproject_path.exists():
+    if pyproject.exists():
         try:
-            data = toml.load(pyproject_path)
+            data = toml.load(pyproject)
             version = data.get("project", {}).get("version", "dev")
         except Exception:
             pass

api/route/llm.py

@@ -0,0 +1,95 @@
+"""Endpoints for inspecting and testing the external LLM hookup.
+
+The LLM is not hosted here — the user supplies a base URL + model + key.
+"""
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from api.lib.llm import (
+    LLMConfig,
+    LLMError,
+    chat_complete,
+    from_settings,
+    list_models,
+    ping,
+)
+
+
+router = APIRouter(prefix="/llm", tags=["llm"])
+
+
+class LLMConfigIn(BaseModel):
+    base_url: str
+    api_key: str = ""
+    # model is optional here so /llm/models can be called before a model is picked
+    model: str = ""
+    timeout: int = 120
+    max_tokens: int = 1000
+    temperature: float = 0.3
+    extra_headers: dict[str, str] = {}
+
+
+class LLMStatus(BaseModel):
+    configured: bool
+    base_url: str | None = None
+    model: str | None = None
+
+
+class ChatIn(BaseModel):
+    config: LLMConfigIn | None = None
+    messages: list[dict]
+
+
+@router.get("/config", response_model=LLMStatus)
+async def get_status():
+    cfg = from_settings()
+    if cfg is None:
+        return LLMStatus(configured=False)
+    return LLMStatus(configured=True, base_url=cfg.base_url, model=cfg.model)
+
+
+@router.post("/test")
+async def test_connection(cfg: LLMConfigIn | None = None):
+    """Send a tiny ping to verify the endpoint accepts our requests."""
+    config = LLMConfig(**cfg.model_dump()) if cfg else from_settings()
+    if config is None:
+        raise HTTPException(
+            status_code=400,
+            detail="No LLM config in body and none configured in environment",
+        )
+    try:
+        reply = ping(config)
+    except LLMError as e:
+        raise HTTPException(status_code=502, detail=str(e))
+    return {"ok": True, "model": config.model, "reply": reply}
+
+
+@router.post("/models")
+async def models(cfg: LLMConfigIn | None = None):
+    """List available models from the configured (or supplied) endpoint."""
+    config = LLMConfig(**cfg.model_dump()) if cfg else from_settings()
+    if config is None:
+        raise HTTPException(
+            status_code=400,
+            detail="No LLM config in body and none configured in environment",
+        )
+    try:
+        items = list_models(config)
+    except LLMError as e:
+        raise HTTPException(status_code=502, detail=str(e))
+    return {"models": items, "count": len(items)}
+
+
+@router.post("/chat")
+async def chat(body: ChatIn):
+    """Pass-through chat endpoint — useful for sanity-testing prompts."""
+    config = LLMConfig(**body.config.model_dump()) if body.config else from_settings()
+    if config is None:
+        raise HTTPException(status_code=400, detail="No LLM config supplied")
+    try:
+        reply = chat_complete(config, body.messages)
+    except LLMError as e:
+        raise HTTPException(status_code=502, detail=str(e))
+    return {"reply": reply, "model": config.model}

api/route/pipeline.py

@@ -0,0 +1,201 @@
+"""End-to-end pipeline: download → subtitles-or-transcribe → summarize → persist."""
+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.db.video import create_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):
+    base_url: str
+    api_key: str = ""
+    model: str
+    timeout: int = 120
+    max_tokens: int = 1000
+    temperature: float = 0.3
+    extra_headers: dict[str, str] = {}
+    system_prompt: str | None = None
+    user_template: str | None = None
+
+
+class ProcessRequest(BaseModel):
+    url: str
+    model_path: str | None = None  # path inside models/, used when subtitles are unavailable
+    summary_max_sentences: int = 15
+    # If supplied, this LLM is used for the summary; otherwise env-configured LLM;
+    # otherwise the built-in extractive fallback.
+    llm: LLMOverride | None = None
+
+
+class ProcessResponse(BaseModel):
+    uuid: str
+    title: str
+    source_url: str
+    video_path: str
+    text_full_path: str
+    text_summary_path: str
+    transcription_method: str
+    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
+    _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", 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,
+            api_key=req.llm.api_key,
+            model=req.llm.model,
+            timeout=req.llm.timeout,
+            max_tokens=req.llm.max_tokens,
+            temperature=req.llm.temperature,
+            extra_headers=req.llm.extra_headers,
+        )
+        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(
+            None,
+            lambda: summarize(
+                full_text,
+                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")
+
+    rel_video = str(video_path.relative_to(_PROJECT_ROOT))
+    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,
+        )
+    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],
+    )

api/route/videos.py

@@ -0,0 +1,55 @@
+from datetime import datetime
+from typing import List
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from api.db.video import delete_video, get_video, list_videos
+
+
+router = APIRouter(prefix="/videos", tags=["videos"])
+
+
+class VideoOut(BaseModel):
+    uuid: str
+    source_url: str
+    title: str
+    video_path: str
+    text_full_path: str
+    text_summary_path: str
+    transcription_method: str
+    created_at: datetime
+
+
+def _to_out(v) -> VideoOut:
+    return VideoOut(
+        uuid=v.uuid,
+        source_url=v.source_url,
+        title=v.title,
+        video_path=v.video_path,
+        text_full_path=v.text_full_path,
+        text_summary_path=v.text_summary_path,
+        transcription_method=v.transcription_method,
+        created_at=v.created_at,
+    )
+
+
+@router.get("/", response_model=List[VideoOut])
+async def list_all():
+    return [_to_out(v) for v in await list_videos()]
+
+
+@router.get("/{video_uuid}", response_model=VideoOut)
+async def get_one(video_uuid: str):
+    v = await get_video(video_uuid)
+    if not v:
+        raise HTTPException(status_code=404, detail="Video not found")
+    return _to_out(v)
+
+
+@router.delete("/{video_uuid}")
+async def delete_one(video_uuid: str):
+    ok = await delete_video(video_uuid)
+    if not ok:
+        raise HTTPException(status_code=404, detail="Video not found")
+    return {"ok": True}

api_legacy/config.py

@@ -0,0 +1,28 @@
+from pydantic_settings import BaseSettings
+import os
+
+def _env(*names, default=None):
+    for n in names:
+        v = os.getenv(n)
+        if v:
+            return v
+    return default
+
+class Settings(BaseSettings):
+    # support both custom DB_* names and common POSTGRES_* names from postgres images
+    DB_USER: str = _env("DB_USER", "POSTGRES_USER", default="postgres")
+    DB_PASS: str = _env("DB_PASS", "POSTGRES_PASSWORD", default="postgres")
+    DB_NAME: str = _env("DB_NAME", "POSTGRES_DB", default="test_db")
+    DB_HOST: str = _env("DB_HOST", "POSTGRES_HOST", "DB_HOST", default="db")
+    DB_PORT: str = _env("DB_PORT", "POSTGRES_PORT", default="5432")
+
+    # If explicit DB_* variables are provided, build DATABASE_URL from them (priority).
+    # Otherwise fall back to explicit DATABASE_URL or DATABASE_URL_ASYNC env.
+    _built_db_url: str | None = None
+    if DB_USER and DB_PASS and DB_NAME and DB_HOST and DB_PORT:
+        _built_db_url = f"postgresql+asyncpg://{DB_USER}:{DB_PASS}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
+
+    DATABASE_URL: str = _built_db_url or _env("DATABASE_URL", "DATABASE_URL_ASYNC", default="")
+
+
+settings = Settings()

api_legacy/db/connection.py

@@ -0,0 +1,27 @@
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy import text
+import asyncio
+from api.config import settings
+
+DATABASE_URL = settings.DATABASE_URL
+engine = create_async_engine(DATABASE_URL, echo=False, future=True)
+SessionLocal = sessionmaker(engine, expire_on_commit=False, class_=AsyncSession)
+
+
+async def wait_for_db(retries: int = 30, delay: float = 1.0) -> None:
+	"""Wait until the database is available. Retries with exponential backoff.
+
+	Raises RuntimeError if DB is still unavailable after retries.
+	"""
+	last_exc = None
+	for attempt in range(1, retries + 1):
+		try:
+			async with engine.connect() as conn:
+				await conn.execute(text("SELECT 1"))
+				return
+		except Exception as e:
+			last_exc = e
+			wait = min(delay * (2 ** (attempt - 1)), 5)
+			await asyncio.sleep(wait)
+	raise RuntimeError(f"Could not connect to DB after {retries} attempts") from last_exc

api_legacy/main.py

@@ -0,0 +1,22 @@
+import asyncio
+from fastapi import FastAPI
+from api.route import default, subscription
+# renamed route modules for clarity
+from api.route import users, installing, media_convert, mp3_ffmpeg_stream, moviepy
+#from api.db.subscription import init_db
+#from api.db.connection import wait_for_db
+
+app = FastAPI(title="LLM-infa API")
+
+#@app.on_event("startup")
+#async def on_startup():
+#    await wait_for_db()
+#    await init_db()
+
+app.include_router(default.router)
+#app.include_router(subscription.router)
+#app.include_router(users.router)
+app.include_router(installing.router)
+app.include_router(media_convert.router)
+app.include_router(mp3_ffmpeg_stream.router)
+app.include_router(moviepy.router)

api_legacy/route/default.py

@@ -0,0 +1,17 @@
+from fastapi import APIRouter
+import toml
+from pathlib import Path
+
+router = APIRouter()
+
+@router.get("/")
+async def root():
+    version = "dev"
+    pyproject_path = Path(__file__).parent.parent.parent / "pyproject.toml"
+    if pyproject_path.exists():
+        try:
+            data = toml.load(pyproject_path)
+            version = data.get("project", {}).get("version", "dev")
+        except Exception:
+            pass
+    return {"status": "ok", "msg": "LLM-infa API", "version": version}

docker-compose.yml

@@ -1,20 +1,4 @@
-
 services:
-  #bot:
-  #  build:
-  #    context: .
-  #    dockerfile: Dockerfile.bot
-  #  container_name: telegram_bot
-  #  restart: no
-  #  env_file:
-  #    - .env
-  #  environment:
-  #    - BOT_TOKEN=${BOT_TOKEN}
-  #  command: python -m bot.main
-  #  depends_on:
-  #    - db
-  #  volumes:
-  #    - .:/app
 
   api:
     build:
@@ -31,28 +15,46 @@ services:
       - .:/app
       - ./data:/app/data
       - ./models:/app/models
+    depends_on:
+      db:
+        condition: service_healthy
     dns:
       - 8.8.8.8
       - 1.1.1.1
 
-  #db:
+  web:
-  #  image: postgres:15-alpine
+    build:
-  #  container_name: db
+      context: .
-  #  restart: always
+      dockerfile: Dockerfile.web
-  #  environment:
+    container_name: web
-  #    POSTGRES_USER: ${DB_USER}
+    restart: no
-  #    POSTGRES_PASSWORD: ${DB_PASS}
+    environment:
-  #    POSTGRES_DB: ${DB_NAME}
+      - API_BASE_URL=http://api:8000
-  #  volumes:
+      - PORT=8550
-  #    - ./pg_data:/var/lib/postgresql/data
+      - HTTP_TIMEOUT=1800
-  #  ports:
+    ports:
-  #    - "5433:5432"
+      - "8550:8550"
-  #  healthcheck:
+    depends_on:
-  #    test: ["CMD-SHELL", "pg_isready -U ${DB_USER} -d ${DB_NAME}"]
+      - api
-  #    interval: 5s
+    dns:
-  #    timeout: 5s
+      - 8.8.8.8
-  #    retries: 10
+      - 1.1.1.1
-  #    start_period: 10s
 
-#volumes:
+  db:
-#  pg_data:
+    image: postgres:15-alpine
+    container_name: db
+    restart: always
+    environment:
+      POSTGRES_USER: ${DB_USER}
+      POSTGRES_PASSWORD: ${DB_PASS}
+      POSTGRES_DB: ${DB_NAME}
+    volumes:
+      - ./pg_data:/var/lib/postgresql/data
+    ports:
+      - "5433:5432"
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${DB_USER} -d ${DB_NAME}"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+      start_period: 10s

pyproject.toml

@@ -1,14 +1,12 @@
 [project]
 name = "llm-infa"
-version = "0.2.0"
+version = "0.3.0"
-description = "Add your description here"
+description = "YouTube -> транскрипция -> LLM-выжимка -> Postgres"
 readme = "README.md"
 requires-python = ">=3.14"
 dependencies = [
-    "asyncio>=4.0.0",
     "asyncpg>=0.31.0",
     "fastapi>=0.129.0",
-    "moviepy>=2.2.1",
     "pydantic>=2.12.5",
     "pydantic-settings>=2.13.0",
     "sqlalchemy>=2.0.46",
@@ -17,3 +15,11 @@ dependencies = [
     "vosk>=0.3.45",
     "yt-dlp>=2026.2.4",
 ]
+
+[dependency-groups]
+# Flet UI (запускается в контейнере web на python 3.12; классический API flet)
+web = [
+    "flet==0.28.3",
+    "flet-web==0.28.3",
+    "httpx>=0.28.1",
+]

requirements.txt

@@ -6,6 +6,4 @@ toml
 pydantic
 pydantic-settings
 yt-dlp
-moviepy
+vosk
-imageio-ffmpeg
-vosk

uv.lock

@@ -32,15 +32,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
-[[package]]
-name = "asyncio"
-version = "4.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/71/ea/26c489a11f7ca862d5705db67683a7361ce11c23a7b98fc6c2deaeccede2/asyncio-4.0.0.tar.gz", hash = "sha256:570cd9e50db83bc1629152d4d0b7558d6451bb1bfd5dfc2e935d96fc2f40329b", size = 5371, upload-time = "2025-08-05T02:51:46.605Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/57/64/eff2564783bd650ca25e15938d1c5b459cda997574a510f7de69688cb0b4/asyncio-4.0.0-py3-none-any.whl", hash = "sha256:c1eddb0659231837046809e68103969b2bef8b0400d59cfa6363f6b5ed8cc88b", size = 5555, upload-time = "2025-08-05T02:51:45.767Z" },
-]
-
 [[package]]
 name = "asyncpg"
 version = "0.31.0"
@@ -153,15 +144,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
-[[package]]
-name = "decorator"
-version = "5.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" },
-]
-
 [[package]]
 name = "fastapi"
 version = "0.129.0"
@@ -178,6 +160,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9e/dd/d0ee25348ac58245ee9f90b6f3cbb666bf01f69be7e0911f9851bddbda16/fastapi-0.129.0-py3-none-any.whl", hash = "sha256:b4946880e48f462692b31c083be0432275cbfb6e2274566b1be91479cc1a84ec", size = 102950, upload-time = "2026-02-12T13:54:54.528Z" },
 ]
 
+[[package]]
+name = "flet"
+version = "0.28.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx", marker = "platform_system != 'Pyodide'" },
+    { name = "oauthlib", marker = "platform_system != 'Pyodide'" },
+    { name = "repath" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2f/d0/9ba4ee34972e9e0cf54b1f7d17c695491632421f81301993f2aec8d12105/flet-0.28.3-py3-none-any.whl", hash = "sha256:649bfc4af7933956ecf44963df6c0d997bff9ceeaf89d3c86d96803840cab83e", size = 463000, upload-time = "2025-05-20T19:44:58.651Z" },
+]
+
+[[package]]
+name = "flet-web"
+version = "0.28.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fastapi" },
+    { name = "flet" },
+    { name = "uvicorn", extra = ["standard"] },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/65/8a/01f73ae7123090b2974f0c5ba70d7d9fc463f47f6e12daeca59fdc40e1a9/flet_web-0.28.3-py3-none-any.whl", hash = "sha256:919c13f374e7cee539d29a8ccd6decb739528ef257c88e60af4df1ebab045bfb", size = 3137744, upload-time = "2025-05-20T19:27:32.443Z" },
+]
+
 [[package]]
 name = "greenlet"
 version = "3.3.1"
@@ -187,7 +195,6 @@ wheels = [
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 [[package]]
 name = "vosk"
 version = "0.3.45"
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 ]
 
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+
 [[package]]
 name = "websockets"
 version = "16.0"

web/api_client.py

@@ -0,0 +1,79 @@
+"""Thin httpx wrapper for the FastAPI backend.
+
+The browser never talks to FastAPI directly — Flet's Python process is the
+only client of the API. So no CORS, no auth surface exposed publicly.
+"""
+from __future__ import annotations
+
+import httpx
+
+from web.config import API_BASE_URL, HTTP_TIMEOUT
+
+
+class ApiError(RuntimeError):
+    pass
+
+
+class ApiClient:
+    def __init__(self, base_url: str = API_BASE_URL, timeout: float = HTTP_TIMEOUT):
+        self._client = httpx.Client(base_url=base_url, timeout=timeout)
+
+    def close(self) -> None:
+        self._client.close()
+
+    # --- LLM ---
+
+    def llm_status(self) -> dict:
+        return self._get("/llm/config")
+
+    def llm_models(self, base_url: str, api_key: str) -> list[dict]:
+        data = self._post(
+            "/llm/models",
+            {"base_url": base_url, "api_key": api_key},
+        )
+        return data.get("models", [])
+
+    def llm_test(self, base_url: str, api_key: str, model: str) -> dict:
+        return self._post(
+            "/llm/test",
+            {"base_url": base_url, "api_key": api_key, "model": model},
+        )
+
+    # --- Pipeline / videos ---
+
+    def pipeline_process(self, payload: dict) -> dict:
+        return self._post("/pipeline/process", payload)
+
+    def list_videos(self) -> list[dict]:
+        return self._get("/videos/")
+
+    # --- internals ---
+
+    def _get(self, path: str) -> dict | list:
+        try:
+            r = self._client.get(path)
+        except httpx.HTTPError as e:
+            raise ApiError(f"Сеть: {e}")
+        return self._unwrap(r)
+
+    def _post(self, path: str, json: dict | None) -> dict:
+        try:
+            r = self._client.post(path, json=json)
+        except httpx.HTTPError as e:
+            raise ApiError(f"Сеть: {e}")
+        return self._unwrap(r)
+
+    @staticmethod
+    def _unwrap(r: httpx.Response):
+        try:
+            data = r.json()
+        except Exception:
+            data = None
+        if r.is_success:
+            return data
+        detail = ""
+        if isinstance(data, dict):
+            detail = str(data.get("detail") or data.get("message") or "")
+        if not detail:
+            detail = r.text[:300] if r.text else r.reason_phrase
+        raise ApiError(f"HTTP {r.status_code}: {detail}")

web/config.py

@@ -0,0 +1,12 @@
+import os
+
+
+# URL of the FastAPI service inside the docker network.
+# In docker-compose this resolves to the `api` service container.
+API_BASE_URL = os.getenv("API_BASE_URL", "http://api:8000")
+
+# Port on which the Flet web server listens (exposed by docker-compose).
+PORT = int(os.getenv("PORT", "8550"))
+
+# Pipeline can take many minutes for long videos; keep the HTTP timeout generous.
+HTTP_TIMEOUT = float(os.getenv("HTTP_TIMEOUT", "1800"))

web/designer.py

@@ -0,0 +1,91 @@
+"""Centralized colors and reusable Flet widgets.
+
+Palette is inspired by the dark-purple theme of the related project
+(api-copp): #2F184B base surface, #9b72cf accent.
+"""
+from __future__ import annotations
+
+import flet as ft
+
+
+BG = "#14101e"
+SURFACE = "#2f184b"
+SURFACE_2 = "#3a2160"
+SURFACE_3 = "#1c1230"
+BORDER = "#4a2f6f"
+ACCENT = "#9b72cf"
+ACCENT_2 = "#c0a4e8"
+TEXT = "#ece5f5"
+MUTED = "#a89ec0"
+DANGER = "#e35a72"
+OK = "#6dd58c"
+
+
+def card(*controls: ft.Control, padding: int = 22) -> ft.Container:
+    return ft.Container(
+        content=ft.Column(controls=list(controls), spacing=12, tight=True),
+        bgcolor=SURFACE,
+        border=ft.border.all(1, BORDER),
+        border_radius=12,
+        padding=padding,
+    )
+
+
+def primary_button(text: str, on_click) -> ft.ElevatedButton:
+    return ft.ElevatedButton(
+        text=text,
+        on_click=on_click,
+        bgcolor=ACCENT,
+        color="#ffffff",
+        style=ft.ButtonStyle(
+            shape=ft.RoundedRectangleBorder(radius=8),
+            padding=ft.padding.symmetric(horizontal=18, vertical=12),
+        ),
+    )
+
+
+def secondary_button(text: str, on_click) -> ft.OutlinedButton:
+    return ft.OutlinedButton(
+        text=text,
+        on_click=on_click,
+        style=ft.ButtonStyle(
+            color=TEXT,
+            side=ft.BorderSide(1, BORDER),
+            shape=ft.RoundedRectangleBorder(radius=8),
+            padding=ft.padding.symmetric(horizontal=18, vertical=12),
+        ),
+    )
+
+
+def text_field(
+    label: str,
+    value: str = "",
+    hint: str | None = None,
+    password: bool = False,
+    on_change=None,
+    expand: bool | int | None = True,
+) -> ft.TextField:
+    return ft.TextField(
+        label=label,
+        value=value,
+        hint_text=hint or "",
+        password=password,
+        can_reveal_password=password,
+        on_change=on_change,
+        bgcolor=SURFACE_3,
+        color=TEXT,
+        border_color=BORDER,
+        focused_border_color=ACCENT,
+        cursor_color=ACCENT_2,
+        label_style=ft.TextStyle(color=MUTED, size=12),
+        text_size=14,
+        expand=expand,
+    )
+
+
+def section_title(text: str) -> ft.Text:
+    return ft.Text(text, color=TEXT, size=18, weight=ft.FontWeight.W_600)
+
+
+def hint(text: str) -> ft.Text:
+    return ft.Text(text, color=MUTED, size=12)

web/main.py

@@ -0,0 +1,45 @@
+import flet as ft
+
+from web.api_client import ApiClient
+from web.config import PORT
+import web.designer as d
+from web.views.main_view import MainView
+
+
+def main(page: ft.Page) -> None:
+    page.title = "LLM-infa"
+    page.theme_mode = ft.ThemeMode.DARK
+    page.bgcolor = d.BG
+    page.padding = 0
+    page.scroll = ft.ScrollMode.HIDDEN
+    page.window.width = 1100
+    page.fonts = {}
+
+    client = ApiClient()
+    view = MainView(page, client)
+    page.add(view.build())
+
+    # probe env-configured LLM and initial library load
+    try:
+        status = client.llm_status()
+        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()
+    page.update()
+
+
+if __name__ == "__main__":
+    ft.app(
+        target=main,
+        view=ft.AppView.WEB_BROWSER,
+        port=PORT,
+        host="0.0.0.0",
+    )

web/requirements.txt

@@ -0,0 +1,5 @@
+# classic flet API (ft.app/ft.Colors/page.window); 0.70+ is a breaking rewrite
+flet==0.28.3
+# web-server runtime for ft.app(view=WEB_BROWSER) — separate package since 0.24
+flet-web==0.28.3
+httpx>=0.27

web/views/main_view.py

@@ -0,0 +1,406 @@
+"""Main page of the LLM-infa Flet web app."""
+from __future__ import annotations
+
+import threading
+
+import flet as ft
+
+from web.api_client import ApiClient, ApiError
+import web.designer as d
+
+
+class MainView:
+    def __init__(self, page: ft.Page, client: ApiClient):
+        self.page = page
+        self.client = client
+
+        self.models: list[dict] = []
+        self.selected_model: str = ""
+
+        # ── LLM config fields ──────────────────────────────────────────────
+        self.base_url = d.text_field("Base URL", hint="https://api.openai.com/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»",
+            on_change=self._on_model_search,
+            expand=True,
+        )
+        self._model_list_col = ft.Column(spacing=2, scroll=ft.ScrollMode.AUTO)
+        self._model_list_wrap = ft.Container(
+            content=self._model_list_col,
+            height=260,
+            bgcolor=d.SURFACE_3,
+            border=ft.border.all(1, d.BORDER),
+            border_radius=8,
+            padding=4,
+            visible=False,
+        )
+
+        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),
+            padding=ft.padding.symmetric(horizontal=10, vertical=4),
+            border=ft.border.all(1, d.BORDER),
+            border_radius=99,
+        )
+        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",
+            expand=True,
+        )
+        self.n_sentences = ft.TextField(
+            label="Предложений в выжимке",
+            value="15",
+            input_filter=ft.NumbersOnlyInputFilter(),
+            bgcolor=d.SURFACE_3,
+            color=d.TEXT,
+            border_color=d.BORDER,
+            focused_border_color=d.ACCENT,
+            label_style=ft.TextStyle(color=d.MUTED, size=12),
+            text_size=14,
+            width=220,
+        )
+        self.pipeline_msg = ft.Text("", color=d.MUTED, size=13, expand=True)
+        self.btn_run = d.primary_button("▶  Запустить", self._run_pipeline)
+
+        # Result block (initially hidden)
+        self._result_col = ft.Column(spacing=6, visible=False)
+
+        # Library
+        self._library_col = ft.Column(spacing=8)
+
+    # ─────────────────────── build ───────────────────────────────────────
+
+    def build(self) -> ft.Control:
+        return ft.Column(
+            controls=[
+                self._build_topbar(),
+                ft.Container(
+                    content=ft.Column(
+                        [
+                            self._build_llm_card(),
+                            self._build_pipeline_card(),
+                            self._build_library_card(),
+                        ],
+                        spacing=20,
+                        scroll=ft.ScrollMode.AUTO,
+                    ),
+                    padding=ft.padding.symmetric(horizontal=24, vertical=20),
+                    expand=True,
+                ),
+            ],
+            expand=True,
+            spacing=0,
+        )
+
+    def _build_topbar(self) -> ft.Container:
+        return ft.Container(
+            content=ft.Row(
+                [
+                    ft.Row(
+                        [
+                            ft.Container(
+                                width=14,
+                                height=14,
+                                border_radius=4,
+                                gradient=ft.LinearGradient(
+                                    begin=ft.alignment.top_left,
+                                    end=ft.alignment.bottom_right,
+                                    colors=[d.ACCENT, "#5b8def"],
+                                ),
+                            ),
+                            ft.Text("LLM-infa", color=d.TEXT, size=17, weight=ft.FontWeight.W_600),
+                        ],
+                        spacing=10,
+                    ),
+                    ft.Container(expand=True),
+                    self.llm_status_chip,
+                ],
+            ),
+            padding=ft.padding.symmetric(horizontal=24, vertical=14),
+            bgcolor=ft.Colors.with_opacity(0.6, d.SURFACE),
+            border=ft.border.only(bottom=ft.BorderSide(1, d.BORDER)),
+        )
+
+    def _build_llm_card(self) -> ft.Container:
+        return d.card(
+            d.section_title("Подключение к LLM"),
+            d.hint(
+                "Укажите любой OpenAI-совместимый провайдер. "
+                "Нейронка внутри Docker не разворачивается — "
+                "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),
+            ft.Divider(color=d.BORDER, height=1),
+            self.model_input,
+            self._model_list_wrap,
+        )
+
+    def _build_pipeline_card(self) -> ft.Container:
+        return d.card(
+            d.section_title("Обработать видео"),
+            d.hint(
+                "Pipeline: yt-dlp → субтитры (или Vosk fallback) "
+                "→ выжимка через выбранную LLM → запись в БД."
+            ),
+            self.yt_url,
+            ft.Row([self.vosk_path, self.n_sentences], spacing=12),
+            ft.Row([self.btn_run, self.pipeline_msg], spacing=10),
+            self._result_col,
+        )
+
+    def _build_library_card(self) -> ft.Container:
+        return d.card(
+            ft.Row(
+                [
+                    d.section_title("Библиотека"),
+                    ft.Container(expand=True),
+                    d.secondary_button("Обновить", lambda _: self._refresh_library()),
+                ]
+            ),
+            d.hint("Все обработанные видео и пути к файлам."),
+            self._library_col,
+        )
+
+    # ─────────────────── model combobox ──────────────────────────────────
+
+    def _on_model_search(self, e):
+        q = (self.model_input.value or "").strip().lower()
+        filtered = [m for m in self.models if q in m["id"].lower()] if q else self.models
+        self._render_model_list(filtered)
+
+    def _render_model_list(self, items: list[dict]):
+        self._model_list_col.controls = [
+            ft.Container(
+                content=ft.Row(
+                    [
+                        ft.Text(m["id"], color=d.TEXT, size=13, expand=True, no_wrap=True),
+                        ft.Text(m.get("owned_by", ""), color=d.MUTED, size=11),
+                    ],
+                    alignment=ft.MainAxisAlignment.SPACE_BETWEEN,
+                ),
+                ink=True,
+                bgcolor=d.SURFACE_2,
+                padding=ft.padding.symmetric(horizontal=10, vertical=8),
+                border_radius=6,
+                on_click=lambda e, mid=m["id"]: self._select_model(mid),
+            )
+            for m in items
+        ]
+        if not items:
+            self._model_list_col.controls = [
+                ft.Text("Ничего не найдено", color=d.MUTED, size=13)
+            ]
+        self._model_list_wrap.visible = bool(self.models)
+        self.page.update()
+
+    def _select_model(self, mid: str):
+        self.selected_model = mid
+        self.model_input.value = mid
+        self._model_list_wrap.visible = False
+        self.page.update()
+
+    # ─────────────────── LLM actions ─────────────────────────────────────
+
+    def _load_models(self, _):
+        base_url = self.base_url.value.strip()
+        if not base_url:
+            self._set_llm_msg("Укажите Base URL", error=True)
+            return
+
+        self._set_llm_msg("Загружаю…")
+        self.btn_load.disabled = True
+        self.page.update()
+
+        def _task():
+            try:
+                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()
+
+        threading.Thread(target=_task, daemon=True).start()
+
+    def _test_conn(self, _):
+        if not self.selected_model:
+            self._set_llm_msg("Сначала выберите модель", error=True)
+            return
+
+        self._set_llm_msg("Проверяю…")
+        self.btn_test.disabled = True
+        self.page.update()
+
+        def _task():
+            try:
+                r = self.client.llm_test(
+                    self.base_url.value.strip(),
+                    self.api_key.value.strip(),
+                    self.selected_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 ─────────────────────────────────────────
+
+    def _run_pipeline(self, _):
+        url = self.yt_url.value.strip()
+        if not url:
+            self._set_pipeline_msg("Укажите ссылку на видео", error=True)
+            return
+
+        payload: dict = {
+            "url": url,
+            "model_path": self.vosk_path.value.strip() or None,
+            "summary_max_sentences": int(self.n_sentences.value or 15),
+        }
+        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._render_result(r)
+                self._set_pipeline_msg("Готово ✓", 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()
+
+        threading.Thread(target=_task, daemon=True).start()
+
+    def _render_result(self, r: dict):
+        self._result_col.controls = [
+            ft.Divider(color=d.BORDER, height=1),
+            ft.Text(r.get("title") or r.get("uuid", ""), color=d.ACCENT_2, size=15, weight=ft.FontWeight.W_600),
+            *[
+                self._kv(k, v)
+                for k, v in [
+                    ("UUID", r.get("uuid", "")),
+                    ("Источник", r.get("source_url", "")),
+                    ("Метод", r.get("transcription_method", "")),
+                    ("Видео", r.get("video_path", "")),
+                    ("Полный текст", r.get("text_full_path", "")),
+                    ("Выжимка", r.get("text_summary_path", "")),
+                ]
+            ],
+            ft.Text("Превью выжимки:", color=d.MUTED, size=12),
+            ft.Container(
+                content=ft.Text(r.get("summary_preview", ""), color=d.TEXT, size=13, selectable=True, no_wrap=False),
+                padding=12,
+                bgcolor=d.SURFACE_3,
+                border=ft.border.all(1, d.BORDER),
+                border_radius=8,
+            ),
+        ]
+        self._result_col.visible = True
+
+    # ─────────────────── library ─────────────────────────────────────────
+
+    def _refresh_library(self):
+        try:
+            items = self.client.list_videos()
+        except ApiError as e:
+            self._library_col.controls = [ft.Text(str(e), color=d.DANGER, size=13)]
+            self.page.update()
+            return
+
+        if not items:
+            self._library_col.controls = [ft.Text("Пока пусто", color=d.MUTED, size=13)]
+        else:
+            self._library_col.controls = [self._lib_card(v) for v in items]
+        self.page.update()
+
+    def _lib_card(self, v: dict) -> ft.Container:
+        created = v.get("created_at", "")[:19].replace("T", " ")
+        return ft.Container(
+            content=ft.Column(
+                [
+                    ft.Text(
+                        v.get("title") or v.get("uuid", ""),
+                        color=d.TEXT,
+                        size=14,
+                        weight=ft.FontWeight.W_600,
+                        no_wrap=True,
+                    ),
+                    ft.Text(v.get("source_url", ""), color=d.MUTED, size=11, selectable=True),
+                    ft.Row(
+                        [
+                            ft.Text(f"uuid: {v.get('uuid', '')}", color=d.MUTED, size=11, selectable=True, expand=True),
+                            ft.Text(created, color=d.MUTED, size=11),
+                        ]
+                    ),
+                    self._kv("Метод", v.get("transcription_method", "")),
+                    self._kv("Видео", v.get("video_path", "")),
+                    self._kv("Выжимка", v.get("text_summary_path", "")),
+                ],
+                spacing=3,
+                tight=True,
+            ),
+            padding=12,
+            bgcolor=d.SURFACE_2,
+            border=ft.border.all(1, d.BORDER),
+            border_radius=10,
+        )
+
+    # ─────────────────── helpers ──────────────────────────────────────────
+
+    def _kv(self, key: str, value: str) -> ft.Row:
+        return ft.Row(
+            [
+                ft.Text(key, color=d.MUTED, size=12, width=120),
+                ft.Text(value, color=d.TEXT, size=12, selectable=True, expand=True, no_wrap=False),
+            ],
+        )
+
+    def _set_llm_msg(self, text: str, *, ok: bool = False, error: bool = False):
+        self.llm_msg.value = text
+        self.llm_msg.color = d.OK if ok else (d.DANGER if error else d.MUTED)
+
+    def _set_pipeline_msg(self, text: str, *, ok: bool = False, error: bool = False):
+        self.pipeline_msg.value = text
+        self.pipeline_msg.color = d.OK if ok else (d.DANGER if error else d.MUTED)
+
+    def _set_status(self, *, ok: bool):
+        chip = self.llm_status_chip
+        chip.content.value = "LLM подключена" if ok else "Ошибка подключения"
+        chip.content.color = d.OK if ok else d.DANGER
+        chip.border = ft.border.all(1, "rgba(109,213,140,.4)" if ok else "rgba(227,90,114,.4)")