mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-10-29 11:27:43 +08:00
598 lines
21 KiB
Python
598 lines
21 KiB
Python
"""完整媒体识别结果的分类执行与兼容回退服务。"""
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from __future__ import annotations
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from collections.abc import Callable, Mapping
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from copy import deepcopy
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from enum import Enum
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from typing import Final, Protocol, TypeAlias, cast
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from app.application.classification.legacy import (
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build_legacy_tmdb_extension_facts,
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resolve_legacy_tmdb_category,
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)
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from app.domain.classification.evaluator import ClassificationEvaluator
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from app.domain.classification.facts import build_classification_facts
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from app.domain.context import MediaInfo, MusicAlbumInfo, MusicArtistInfo, MusicInfo
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from app.schemas.category import (
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CategoryConfig,
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ClassificationEvaluation,
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ClassificationFacts,
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ClassificationFactValue,
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ClassificationPolicy,
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ClassificationResult,
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ClassificationSelection,
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)
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ClassificationSubject: TypeAlias = (
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MediaInfo | MusicInfo | MusicAlbumInfo | MusicArtistInfo
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)
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"""可以在完整识别出口执行自动分类的标准媒体对象。"""
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ClassificationExtensionFactsProvider: TypeAlias = Callable[
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[ClassificationSubject],
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Mapping[str, Mapping[str, ClassificationFactValue]],
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]
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"""按当前插件注册表校验并提供来源扩展分类事实的端口。"""
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_LEGACY_TMDB_SOURCE: Final[str] = "themoviedb"
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_LEGACY_RULE_PREFIX: Final[str] = "legacy."
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class ClassificationRuntimePort(Protocol):
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"""分类执行只需要的活动策略与 legacy 快照端口。"""
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def active_policy(self) -> ClassificationPolicy | None:
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"""返回活动策略；配置不可用时返回空值。"""
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...
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def legacy_config(self) -> CategoryConfig:
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"""返回隔离的 legacy 配置快照。"""
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...
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class ClassificationExecutionPort(Protocol):
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"""Chain、订阅和整理应用层共享的纯分类执行端口。"""
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async def async_build_facts(
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self,
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media: ClassificationSubject,
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*,
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policy: ClassificationPolicy | None = None,
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) -> ClassificationFacts | None:
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"""按指定或活动策略异步准备事实，供预览和新旧策略比较复用。"""
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...
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def build_facts(
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self,
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media: ClassificationSubject,
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*,
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policy: ClassificationPolicy | None = None,
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) -> ClassificationFacts | None:
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"""同步构造与实际分类一致的完整事实快照，不写入媒体或策略。"""
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...
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def finalize(
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self,
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media: ClassificationSubject,
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*,
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extensions: Mapping[
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str,
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Mapping[str, ClassificationFactValue],
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]
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| None = None,
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effective_override: ClassificationSelection | None = None,
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refresh: bool = False,
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allow_enrichment: bool = True,
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) -> ClassificationSubject:
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"""分类完整媒体；离线整理可禁止外部事实补充，仍执行当前用户分类策略。"""
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...
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async def async_finalize(
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self,
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media: ClassificationSubject,
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*,
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extensions: Mapping[
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str,
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Mapping[str, ClassificationFactValue],
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]
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| None = None,
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effective_override: ClassificationSelection | None = None,
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refresh: bool = False,
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allow_enrichment: bool = True,
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) -> ClassificationSubject:
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"""异步分类完整媒体对象，并允许有界补充缺失标准事实。"""
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...
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class ClassificationEnrichmentPort(Protocol):
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"""分类执行服务消费的同步与异步缺失事实补充端口。"""
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def enrich(
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self,
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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media: object,
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) -> ClassificationFacts:
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"""同步补充缺失标准事实。"""
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...
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async def async_enrich(
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self,
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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media: object,
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) -> ClassificationFacts:
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"""异步补充缺失标准事实。"""
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...
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class ClassificationExecutionService:
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"""按当前不可变策略分类完整媒体对象，并隔离来源缓存对象。"""
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def __init__(
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self,
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runtime: ClassificationRuntimePort,
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*,
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extension_facts_provider: ClassificationExtensionFactsProvider | None = None,
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enrichment: ClassificationEnrichmentPort | None = None,
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) -> None:
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"""保存策略运行时、扩展事实校验端口和可选跨来源补充服务。"""
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self._runtime = runtime
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self._extension_facts_provider = extension_facts_provider
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self._enrichment = enrichment
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async def async_build_facts(
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self,
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media: ClassificationSubject,
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*,
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policy: ClassificationPolicy | None = None,
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) -> ClassificationFacts | None:
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"""按指定或活动策略补充事实，隔离候选策略与原媒体对象。"""
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finalized, policy, facts, _ = self._prepare(
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media,
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extensions=None,
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effective_override=None,
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refresh=False,
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policy_override=policy,
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)
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if policy is None or facts is None:
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return None
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if self._enrichment is not None:
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try:
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facts = await self._enrichment.async_enrich(policy, facts, finalized)
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except Exception: # noqa: BLE001 详情补充失败时保留主来源事实
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pass
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return facts
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def build_facts(
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self,
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media: ClassificationSubject,
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*,
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policy: ClassificationPolicy | None = None,
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) -> ClassificationFacts | None:
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"""同步构造并按当前策略补充缺失事实，不修改媒体对象。"""
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finalized, active_policy, facts, _ = self._prepare(
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media,
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extensions=None,
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effective_override=None,
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refresh=True,
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policy_override=policy,
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)
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if active_policy is None or facts is None:
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return None
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policy = policy or active_policy
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if self._enrichment is not None:
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try:
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facts = self._enrichment.enrich(policy, facts, finalized)
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except Exception: # noqa: BLE001 详情补充失败时保留主来源事实
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pass
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return facts
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def finalize(
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self,
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media: ClassificationSubject,
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*,
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extensions: Mapping[
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str,
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Mapping[str, ClassificationFactValue],
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]
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| None = None,
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effective_override: ClassificationSelection | None = None,
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refresh: bool = False,
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allow_enrichment: bool = True,
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) -> ClassificationSubject:
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"""复制并分类完整结果；离线调用可禁用外部补充，但仍应用当前策略。"""
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finalized, policy, facts, effective_override = self._prepare(
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media,
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extensions=extensions,
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effective_override=effective_override,
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refresh=refresh,
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allow_local_music=not allow_enrichment,
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)
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if policy is None or facts is None:
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return finalized
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if allow_enrichment and self._enrichment is not None:
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try:
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facts = self._enrichment.enrich(policy, facts, finalized)
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except Exception: # noqa: BLE001 分类补充不得成为识别硬依赖
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pass
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return self._apply_evaluation(
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finalized,
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policy,
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facts,
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effective_override=effective_override,
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)
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async def async_finalize(
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self,
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media: ClassificationSubject,
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*,
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extensions: Mapping[
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str,
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Mapping[str, ClassificationFactValue],
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]
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| None = None,
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effective_override: ClassificationSelection | None = None,
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refresh: bool = False,
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allow_enrichment: bool = True,
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) -> ClassificationSubject:
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"""异步分类完整结果，支持只使用本地事实，补充失败时保留原始事实。"""
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finalized, policy, facts, effective_override = self._prepare(
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media,
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extensions=extensions,
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effective_override=effective_override,
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refresh=refresh,
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allow_local_music=not allow_enrichment,
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)
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if policy is None or facts is None:
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return finalized
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if allow_enrichment and self._enrichment is not None:
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try:
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facts = await self._enrichment.async_enrich(
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policy,
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facts,
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finalized,
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)
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except Exception: # noqa: BLE001 分类补充不得成为识别硬依赖
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pass
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return self._apply_evaluation(
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finalized,
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policy,
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facts,
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effective_override=effective_override,
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)
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def _prepare(
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self,
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media: ClassificationSubject,
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*,
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extensions: Mapping[str, Mapping[str, ClassificationFactValue]] | None,
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effective_override: ClassificationSelection | None,
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refresh: bool,
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policy_override: ClassificationPolicy | None = None,
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allow_local_music: bool = False,
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) -> tuple[
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ClassificationSubject,
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ClassificationPolicy | None,
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ClassificationFacts | None,
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ClassificationSelection | None,
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]:
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"""复制媒体、读取一次策略并构造同步异步共用的初始事实。"""
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del refresh
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effective_override = effective_override or _explicit_effective_override(media)
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finalized = deepcopy(media)
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policy = policy_override or self._runtime.active_policy()
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if policy is None:
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self._apply_invalid_policy_fallback(
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finalized,
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effective_override=effective_override,
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)
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return finalized, None, None, effective_override
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registered_extensions = self._registered_extensions(finalized)
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merged_extensions = _classification_extensions(
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policy,
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finalized,
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_merge_supplied_extensions(registered_extensions, extensions),
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)
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try:
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facts = build_classification_facts(
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finalized,
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extensions=merged_extensions,
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allow_local_music=allow_local_music,
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)
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except ValueError:
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finalized.set_library_category("")
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finalized.classification = ClassificationResult(
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effective=(
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effective_override.model_copy(deep=True)
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if effective_override
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else None
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),
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policy_revision=policy.revision,
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state="not_evaluated",
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)
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if effective_override:
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finalized.set_library_category(
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_category_path_snapshot(effective_override)
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)
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return finalized, policy, None, effective_override
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return finalized, policy, facts, effective_override
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@staticmethod
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def _apply_evaluation(
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finalized: ClassificationSubject,
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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*,
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effective_override: ClassificationSelection | None,
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) -> ClassificationSubject:
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"""应用纯求值结果、降级保护和人工覆盖，并更新兼容目录分类。"""
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evaluation = evaluate_classification_facts(policy, facts)
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result = evaluation.result.model_copy(deep=True)
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carried = _degraded_automatic_selection(finalized, policy, result)
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if carried is not None:
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result.effective = carried
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if effective_override:
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result.effective = effective_override.model_copy(deep=True)
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finalized.classification = result
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selection = result.effective or result.recommended
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finalized.set_library_category(
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_category_path_snapshot(selection) if selection else ""
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)
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return finalized
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def _registered_extensions(
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self,
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media: ClassificationSubject,
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) -> Mapping[str, Mapping[str, ClassificationFactValue]]:
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"""隔离可选插件事实端口故障，媒体识别结果仍按缺失字段继续分类。"""
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if self._extension_facts_provider is None:
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return {}
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try:
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return self._extension_facts_provider(media)
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except Exception: # noqa: BLE001 扩展事实不得成为识别硬依赖
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return {}
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def finalize_many(
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self,
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media_items: Mapping[str, ClassificationSubject],
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) -> dict[str, ClassificationSubject]:
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"""逐项分类路径映射，保持键顺序并隔离每个来源结果。"""
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return {
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key: self.finalize(media)
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for key, media in media_items.items()
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}
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def _apply_invalid_policy_fallback(
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self,
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media: ClassificationSubject,
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*,
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effective_override: ClassificationSelection | None,
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) -> None:
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"""策略不可用时仅恢复 legacy TMDB 目录分类并标记失效状态。"""
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media.set_library_category("")
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legacy_category = resolve_legacy_tmdb_category(
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self._runtime.legacy_config(),
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media_type=_enum_text(getattr(media, "type", None)),
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media_source=_enum_text(getattr(media, "media_source", None)),
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tmdb_info=getattr(media, "tmdb_info", None),
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)
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selection = (
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ClassificationSelection(
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category_path=[legacy_category],
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source="legacy",
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)
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if legacy_category
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else None
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)
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media.classification = ClassificationResult(
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recommended=selection,
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effective=(
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effective_override.model_copy(deep=True)
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if effective_override
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else selection
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),
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state="invalid_policy",
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)
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effective = effective_override or selection
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if effective:
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media.set_library_category(_category_path_snapshot(effective))
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def _evaluate_legacy_tmdb_compatibility(
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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*,
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trace: bool = False,
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) -> ClassificationEvaluation | None:
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"""让旧 TMDB 分类规则消费非 TMDB 来源已经拥有的标准事实。"""
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if facts.identity.media_source == _LEGACY_TMDB_SOURCE:
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return None
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legacy_rules = [
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rule.model_copy(deep=True, update={"sources": []})
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for rule in policy.rules
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if rule.id.startswith(_LEGACY_RULE_PREFIX)
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]
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if not legacy_rules:
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return None
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compatibility_policy = policy.model_copy(
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deep=True,
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update={"rules": legacy_rules},
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)
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compatibility_facts = _legacy_tmdb_compatibility_facts(policy, facts)
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return ClassificationEvaluator.evaluate(
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compatibility_policy,
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compatibility_facts,
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trace=trace,
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)
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def _legacy_tmdb_compatibility_facts(
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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) -> ClassificationFacts:
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"""把跨来源标准字段投影到旧规则的 TMDB 扩展命名空间。"""
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extensions = {
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str(source): {str(key): value for key, value in values.items()}
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for source, values in facts.extensions.items()
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}
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legacy_info = _legacy_tmdb_info_from_standard_facts(facts)
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for source, values in build_legacy_tmdb_extension_facts(
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policy,
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legacy_info,
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).items():
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target = extensions.setdefault(source, {})
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for field, value in values.items():
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target.setdefault(field, value)
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return cast(
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ClassificationFacts,
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facts.model_copy(
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deep=True,
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update={"extensions": extensions},
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),
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)
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def evaluate_classification_facts(
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policy: ClassificationPolicy,
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facts: ClassificationFacts,
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*,
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trace: bool = False,
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) -> ClassificationEvaluation:
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"""按真实执行路径求值事实，并兼容非 TMDB 来源的旧 TMDB 规则。"""
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evaluation = ClassificationEvaluator.evaluate(policy, facts, trace=trace)
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legacy_evaluation = _evaluate_legacy_tmdb_compatibility(policy, facts, trace=trace)
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if legacy_evaluation is not None and _uses_fallback(evaluation.result):
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return legacy_evaluation
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return evaluation
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def _legacy_tmdb_info_from_standard_facts(
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facts: ClassificationFacts,
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) -> dict[str, object]:
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"""构造旧规则投影所需的有限 TMDB 字段，不伪造缺失事实。"""
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media = facts.media
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info: dict[str, object] = {}
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if media.language:
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info["original_language"] = media.language
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if media.countries:
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countries = [str(country) for country in media.countries if country]
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if countries:
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info["origin_country"] = countries
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info["production_countries"] = [
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{"iso_3166_1": country} for country in countries
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]
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if media.year is not None:
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info["release_date"] = str(media.year)
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for field in (
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"adult",
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"runtime",
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"content_rating",
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"companies",
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"networks",
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):
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value = getattr(media, field, None)
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if value not in (None, "", []):
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info[field] = value
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return info
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def _uses_fallback(result: ClassificationResult) -> bool:
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"""判断主来源求值是否没有命中具体分类规则。"""
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selection = result.effective or result.recommended
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return selection is None or selection.source in {None, "fallback", "source_fallback"}
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def _classification_extensions(
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policy: ClassificationPolicy,
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media: ClassificationSubject,
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supplied: Mapping[str, Mapping[str, ClassificationFactValue]] | None,
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) -> dict[str, dict[str, ClassificationFactValue]]:
|
|
"""合并受控调用方扩展事实和策略实际需要的 legacy TMDB 事实。"""
|
|
merged = {
|
|
str(source): {str(key): value for key, value in values.items()}
|
|
for source, values in (supplied or {}).items()
|
|
}
|
|
tmdb_info = getattr(media, "tmdb_info", None)
|
|
if _enum_text(getattr(media, "media_source", None)) == _LEGACY_TMDB_SOURCE and isinstance(tmdb_info, Mapping):
|
|
for source, values in build_legacy_tmdb_extension_facts(
|
|
policy,
|
|
tmdb_info,
|
|
).items():
|
|
merged.setdefault(source, {}).update(values)
|
|
return merged
|
|
|
|
|
|
def _merge_supplied_extensions(
|
|
registered: Mapping[str, Mapping[str, ClassificationFactValue]],
|
|
supplied: Mapping[str, Mapping[str, ClassificationFactValue]] | None,
|
|
) -> dict[str, dict[str, ClassificationFactValue]]:
|
|
"""合并已校验插件事实和受信调用方事实，显式调用方值具有最终优先级。"""
|
|
merged = {
|
|
str(source): {str(key): value for key, value in values.items()}
|
|
for source, values in registered.items()
|
|
}
|
|
for source, values in (supplied or {}).items():
|
|
merged.setdefault(str(source), {}).update(
|
|
{str(key): value for key, value in values.items()}
|
|
)
|
|
return merged
|
|
|
|
|
|
def _explicit_effective_override(
|
|
media: ClassificationSubject,
|
|
) -> ClassificationSelection | None:
|
|
"""保留已装配的人工覆盖，避免补充元数据时被自动推荐覆盖。"""
|
|
classification = getattr(media, "classification", None)
|
|
if not isinstance(classification, ClassificationResult):
|
|
return None
|
|
effective = classification.effective
|
|
if effective is None or effective.source not in {"manual", "subscription"}:
|
|
return None
|
|
return deepcopy(effective)
|
|
|
|
|
|
def _degraded_automatic_selection(
|
|
media: ClassificationSubject,
|
|
policy: ClassificationPolicy,
|
|
result: ClassificationResult,
|
|
) -> ClassificationSelection | None:
|
|
"""事实缺失把同策略结果打成兜底时，返回可复用的既有自动分类选择。
|
|
|
|
补充元数据的入口会先裁剪媒体字段再重新分类，此时缺失事实只会让结果更差，
|
|
不会带来新的判断依据。因此仅在既有快照来自同一策略修订且命中过规则，而本次
|
|
求值同时出现事实缺失和兜底化时才复用；事实读齐后给出的兜底结果仍然生效，
|
|
策略调整和真正的重新分类不会被掩盖。
|
|
"""
|
|
if result.state != "partial":
|
|
return None
|
|
proposed = result.effective or result.recommended
|
|
if proposed is not None and proposed.source != "fallback":
|
|
return None
|
|
carried = getattr(media, "classification", None)
|
|
if not isinstance(carried, ClassificationResult):
|
|
return None
|
|
if carried.policy_revision != policy.revision:
|
|
return None
|
|
if carried.state not in {"complete", "partial"}:
|
|
return None
|
|
selection = carried.effective or carried.recommended
|
|
if selection is None or selection.source != "automatic":
|
|
return None
|
|
if not str(selection.category_id or "").strip():
|
|
return None
|
|
return cast(ClassificationSelection, selection.model_copy(deep=True))
|
|
|
|
|
|
def _enum_text(value: object) -> str:
|
|
"""把字符串枚举或普通值转换为去空白文本。"""
|
|
if isinstance(value, Enum):
|
|
value = value.value
|
|
return str(value or "").strip()
|
|
|
|
|
|
def _category_path_snapshot(selection: ClassificationSelection) -> str:
|
|
"""把已校验路径投影为过渡期 library_category 兼容字符串。"""
|
|
return "/".join(selection.category_path)
|