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"""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)