mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-08-14 02:05:13 +08:00
254 lines
8.9 KiB
Python
254 lines
8.9 KiB
Python
import asyncio
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from typing import Generator
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from unittest.mock import AsyncMock, patch
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import pytest
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from app.chain.recommend import RecommendChain
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from app.core.cache import TTLCache
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from app.core.context import MusicInfo
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from app.schemas.types import MUSIC_ENTITY_ALBUM
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SYNC_EMPTY_CACHE_CASES = [
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("tmdb_movies", "app.chain.recommend.TmdbChain", "tmdb_discover"),
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("tmdb_tvs", "app.chain.recommend.TmdbChain", "tmdb_discover"),
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("tmdb_trending", "app.chain.recommend.TmdbChain", "tmdb_trending"),
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("bangumi_calendar", "app.chain.recommend.BangumiChain", "calendar"),
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("douban_movie_showing", "app.chain.recommend.DoubanChain", "movie_showing"),
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("douban_movies", "app.chain.recommend.DoubanChain", "douban_discover"),
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("douban_tvs", "app.chain.recommend.DoubanChain", "douban_discover"),
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("douban_movie_top250", "app.chain.recommend.DoubanChain", "movie_top250"),
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("douban_tv_weekly_chinese", "app.chain.recommend.DoubanChain", "tv_weekly_chinese"),
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("douban_tv_weekly_global", "app.chain.recommend.DoubanChain", "tv_weekly_global"),
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("douban_tv_animation", "app.chain.recommend.DoubanChain", "tv_animation"),
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("douban_movie_hot", "app.chain.recommend.DoubanChain", "movie_hot"),
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("douban_tv_hot", "app.chain.recommend.DoubanChain", "tv_hot"),
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]
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ASYNC_EMPTY_CACHE_CASES = [
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("async_tmdb_movies", "app.chain.recommend.TmdbChain"),
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("async_tmdb_tvs", "app.chain.recommend.TmdbChain"),
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("async_tmdb_trending", "app.chain.recommend.TmdbChain"),
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("async_bangumi_calendar", "app.chain.recommend.BangumiChain"),
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("async_douban_movie_showing", "app.chain.recommend.DoubanChain"),
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("async_douban_movies", "app.chain.recommend.DoubanChain"),
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("async_douban_tvs", "app.chain.recommend.DoubanChain"),
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("async_douban_movie_top250", "app.chain.recommend.DoubanChain"),
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("async_douban_tv_weekly_chinese", "app.chain.recommend.DoubanChain"),
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("async_douban_tv_weekly_global", "app.chain.recommend.DoubanChain"),
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("async_douban_tv_animation", "app.chain.recommend.DoubanChain"),
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("async_douban_movie_hot", "app.chain.recommend.DoubanChain"),
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("async_douban_tv_hot", "app.chain.recommend.DoubanChain"),
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]
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def clear_recommend_cache() -> None:
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"""清理推荐缓存，避免缓存装饰器状态影响用例。"""
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TTLCache(region=RecommendChain.recommend_cache_region).clear()
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@pytest.fixture(autouse=True)
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def isolated_recommend_cache() -> Generator[None, None, None]:
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"""每个用例前后都清空推荐缓存。"""
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clear_recommend_cache()
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yield
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clear_recommend_cache()
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@pytest.mark.parametrize(
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("method_name", "chain_target", "backend_method"),
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SYNC_EMPTY_CACHE_CASES,
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)
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def test_sync_recommend_methods_do_not_cache_empty_result(
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method_name: str,
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chain_target: str,
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backend_method: str,
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) -> None:
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"""同步推荐来源返回空列表时不应缓存。"""
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chain = RecommendChain()
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recommend_method = getattr(chain, method_name)
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with patch(chain_target) as backend_chain:
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backend_call = getattr(backend_chain.return_value, backend_method)
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backend_call.side_effect = [[], []]
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assert recommend_method(page=1) == []
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assert recommend_method(page=1) == []
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assert backend_call.call_count == 2
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@pytest.mark.parametrize(("method_name", "chain_target"), ASYNC_EMPTY_CACHE_CASES)
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def test_async_recommend_methods_do_not_cache_empty_result(
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method_name: str,
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chain_target: str,
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) -> None:
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"""异步推荐来源返回空列表时不应缓存。"""
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chain = RecommendChain()
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recommend_method = getattr(chain, method_name)
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with patch(chain_target) as backend_chain:
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backend_chain.return_value.async_run_module = AsyncMock(side_effect=[[], []])
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assert asyncio.run(recommend_method(page=1)) == []
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assert asyncio.run(recommend_method(page=1)) == []
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assert backend_chain.return_value.async_run_module.call_count == 2
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def test_music_weekly_uses_music_chart():
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"""同步推荐缓存应从本周音乐榜单生成通用媒体字典。"""
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chain = RecommendChain()
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with patch("app.chain.recommend.ListenBrainzChain") as source_chain:
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source_chain.return_value.music_chart.return_value = [
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MusicInfo(media_source="musicbrainz", media_id="recording-1", title="晴天")
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]
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result = chain.music_weekly(page=2, count=10)
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assert result[0]["media_id"] == "recording-1"
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source_chain.return_value.music_chart.assert_called_once_with(
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range_name="this_week",
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page=2,
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count=10,
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entity="recording",
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)
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def test_async_music_weekly_uses_music_chart():
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"""异步推荐接口应从本周音乐榜单返回统一媒体字典。"""
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chain = RecommendChain()
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with patch("app.chain.recommend.ListenBrainzChain") as source_chain:
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source_chain.return_value.async_music_chart = AsyncMock(
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return_value=[
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MusicInfo(media_source="musicbrainz", media_id="recording-1", title="晴天")
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]
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)
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result = asyncio.run(chain.async_music_weekly(page=1, count=30))
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assert result[0]["type"] == "音乐"
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source_chain.return_value.async_music_chart.assert_awaited_once_with(
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range_name="this_week",
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page=1,
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count=30,
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entity="recording",
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)
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def test_music_douban_recommendations_use_discover():
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"""豆瓣音乐推荐入口应保留来源与实体，并输出统一媒体字典。"""
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chain = RecommendChain()
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with patch("app.chain.recommend.DoubanChain") as source_chain:
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source_chain.return_value.music_discover.return_value = [
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MusicInfo(
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media_source="doubanmusic",
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media_id="music-1",
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music_type=MUSIC_ENTITY_ALBUM,
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title="Music",
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)
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]
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result = chain.music_douban(page=2, count=10)
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assert result[0]["media_source"] == "doubanmusic"
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source_chain.return_value.music_discover.assert_called_once_with(
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page=2,
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count=10,
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entity=MUSIC_ENTITY_ALBUM,
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mode="chart",
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tags="",
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sort="U",
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)
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def test_async_music_douban_recommendations_use_discover():
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"""异步豆瓣音乐推荐入口应调用统一发现链并保留来源。"""
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chain = RecommendChain()
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with patch("app.chain.recommend.DoubanChain") as source_chain:
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source_chain.return_value.async_music_discover = AsyncMock(
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return_value=[
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MusicInfo(media_source="doubanmusic", media_id="music-1", title="Music")
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]
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)
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result = asyncio.run(chain.async_music_douban(page=1, count=30))
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assert result[0]["media_source"] == "doubanmusic"
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source_chain.return_value.async_music_discover.assert_awaited_once_with(
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page=1,
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count=30,
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entity=MUSIC_ENTITY_ALBUM,
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mode="chart",
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tags="",
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sort="U",
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)
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def test_music_chart_applies_filter_and_sort() -> None:
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"""音乐榜单应在 RecommendChain 统一执行热度、封面和排序约束。"""
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chain = RecommendChain()
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candidates = [
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MusicInfo(
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media_source="musicbrainz",
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media_id="low",
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title="Low",
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listen_count=10,
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cover_url="cover-low",
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),
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MusicInfo(
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media_source="musicbrainz",
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media_id="high",
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title="High",
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listen_count=30,
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cover_url="cover-high",
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),
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MusicInfo(
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media_source="musicbrainz",
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media_id="no-cover",
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title="No Cover",
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listen_count=40,
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),
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]
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with patch("app.chain.recommend.ListenBrainzChain") as source_chain:
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source_chain.return_value.music_chart.return_value = candidates
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result = chain.music_chart(
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range_name="this_month",
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page=1,
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count=10,
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sort_by="listen_count.desc",
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min_listen_count=20,
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with_cover=True,
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)
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assert [item.media_id for item in result] == ["high"]
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def test_async_music_fresh_releases_uses_listenbrainz_source() -> None:
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"""新发行推荐应委派 ListenBrainz 来源链并保留分页参数。"""
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chain = RecommendChain()
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with patch("app.chain.recommend.ListenBrainzChain") as source_chain:
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source_chain.return_value.async_music_fresh_releases = AsyncMock(
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return_value=[
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MusicInfo(
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media_source="musicbrainz",
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media_id="album-1",
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music_type=MUSIC_ENTITY_ALBUM,
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title="Album",
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)
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]
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)
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result = asyncio.run(chain.async_music_fresh_releases(page=2, count=12))
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assert result[0].media_id == "album-1"
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source_chain.return_value.async_music_fresh_releases.assert_awaited_once_with(
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days=14,
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sort="release_date",
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past=True,
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future=True,
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page=2,
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count=12,
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)
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