"""Конфигурация — pydantic models + YAML loader. Структура YAML: broker: host: localhost port: 1883 username_env: MQTT_USERNAME password_env: MQTT_PASSWORD instances: - name: livingroom_tv zmq_endpoint: tcp://127.0.0.1:5555 default_layout: quad ha_discovery: enabled: true prefix: homeassistant device_name: "CUDA Grid Composer" http: host: 0.0.0.0 port: 8080 log: level: INFO """ from __future__ import annotations import os from pathlib import Path from typing import Self import yaml from pydantic import BaseModel, Field, field_validator class BrokerCfg(BaseModel): host: str = "localhost" port: int = 1883 client_id: str = "cuda-grid-controller" username_env: str | None = None password_env: str | None = None keepalive_sec: int = 30 @property def username(self) -> str | None: return os.environ.get(self.username_env) if self.username_env else None @property def password(self) -> str | None: return os.environ.get(self.password_env) if self.password_env else None class InstanceCfg(BaseModel): """Один FFmpeg pipeline = одна cuda_grid filter instance.""" name: str = Field(description="уникальное имя — становится частью HA entity ID") zmq_endpoint: str = Field( description="ZMQ endpoint видео-pipeline (cuda_grid + overlay filters)" ) audio_zmq_endpoint: str | None = Field( default=None, description="ZMQ endpoint отдельного audio sidecar (Phase 5d split-process). " "None = audio_set/intercom вызывают video pipeline (Phase 5a single source)", ) default_layout: str = "quad" filter_target: str = Field( default="Parsed_cuda_grid_0", description="Filter target name в FFmpeg filter graph (для process_command)", ) output_rtsp_url: str | None = Field( default=None, description="URL куда pipeline push'ит composed stream — controller read'ит для snapshot/preview endpoints", ) audio_sources: list["AudioSourceCfg"] = Field( default_factory=list, description="Audio sources для astreamselect switching (Phase 5b)", ) audio_filter_target: str = Field( default="astreamselect@as", description="Target filter для ZMQ команд переключения audio (должен соответствовать pipeline filter_complex)", ) music_volume_target: str = Field( default="volume@music", description="Target filter для управления громкостью music chain (Phase 5c ducking)", ) intercom_volume_target: str = Field( default="volume@intercom", description="Target filter для управления громкостью intercom (Phase 5c)", ) music_ducked_volume: float = Field( default=0.2, ge=0.0, le=1.0, description="Громкость music когда intercom активен (0.2 = -14 dB)", ) class AudioSourceCfg(BaseModel): """Описание audio source в порядке как они добавлены в pipeline -i ...""" name: str = Field(description="Уникальное имя для API (e.g. 'europa_plus')") index: int = Field(ge=0, description="Index в astreamselect inputs (соответствует порядку -i в pipeline)") label: str | None = Field(default=None, description="UI-friendly label (default = name)") @field_validator("name") @classmethod def name_alnum(cls, v: str) -> str: if not v.replace("_", "").isalnum(): raise ValueError(f"instance name '{v}' must be alphanumeric + underscore") return v class HaDiscoveryCfg(BaseModel): enabled: bool = True prefix: str = "homeassistant" device_name: str = "CUDA Grid Composer" device_identifier: str = "cuda_grid_controller" class HttpCfg(BaseModel): host: str = "0.0.0.0" port: int = 8080 class LogCfg(BaseModel): level: str = "INFO" class Config(BaseModel): broker: BrokerCfg = BrokerCfg() instances: list[InstanceCfg] = [] ha_discovery: HaDiscoveryCfg = HaDiscoveryCfg() http: HttpCfg = HttpCfg() log: LogCfg = LogCfg() # Frigate bridge — late import чтобы избежать circular dep frigate: dict | None = None # parsed в FrigateBridgeCfg при runtime # Dynamic overlays (charts/chats) — late import тоже dynamic_overlays: dict | None = None # parsed в DynamicRenderer cfg icon_dir: str = Field( default="/var/lib/cuda-grid/icons", description="Shared volume куда controller пишет dynamic PNG; filter (`icon_dir=` option) читает оттуда", ) @classmethod def from_yaml(cls, path: Path | str) -> Self: with open(path) as f: data = yaml.safe_load(f) or {} return cls.model_validate(data)