"""Refreshes Aurora's mirror of TMDb's own trending lists (section 17, additive) -- see app/models/trending.py for why this mirrors TMDb's ranking rather than Aurora computing its own from popularity deltas. Unlike recommendations' never-fabricate-a-stub rule, this actually ingests/refreshes each trending item's own metadata as a side effect -- there are only ~20 items per (media_type, time_window) window (TMDb's first page), cheap enough to fully ingest rather than silently drop anything Aurora hasn't seen yet. """ from __future__ import annotations from app.core.database import session_scope from app.core.logging import get_logger from app.providers.base import MetadataProvider from app.repositories.trending_repository import replace_trending from app.services.ingestion.movie_ingestion import ingest_movie from app.services.ingestion.series_ingestion import ingest_series logger = get_logger(__name__) # TMDb's own media_type values -> Aurora's entity_type values. _MEDIA_TYPE_TO_ENTITY_TYPE = {"movie": "movie", "tv": "series"} async def refresh_trending(provider: MetadataProvider, media_type: str, time_window: str) -> int: """Fetch TMDb's current trending list for (media_type, time_window), ingest/refresh each item, and replace Aurora's own ranked snapshot for that window. Returns how many entries were written.""" entity_type = _MEDIA_TYPE_TO_ENTITY_TYPE[media_type] raw_results = await provider.get_trending(media_type, time_window) entity_ids = [] for item in raw_results: external_id = str(item["id"]) async with session_scope() as session: if media_type == "movie": entity_id = await ingest_movie(session, provider, external_id) else: entity_id = await ingest_series(session, provider, external_id) entity_ids.append(entity_id) async with session_scope() as session: await replace_trending( session, entity_type=entity_type, time_window=time_window, entity_ids=entity_ids ) logger.info( "trending_refreshed", media_type=media_type, time_window=time_window, count=len(entity_ids) ) return len(entity_ids)