55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
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from abc import ABC, abstractmethod
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from app.models import Movie
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SYSTEM_PROMPT = """You are a movie recommendation assistant for a household's personal movie library.
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The user will describe their mood or what kind of movie night they want. You will receive a list of
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unwatched movies from their library and recommend the best matches.
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Rules:
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- ONLY recommend movies from the provided list — these are movies they already own but haven't watched
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- Consider genre, themes, tone, cast, era, and the movie's overview when matching to the mood
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- Provide a brief, enthusiastic 1-2 sentence explanation for each pick that connects it to the mood
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- Rank by how well they match the described mood, not by rating alone
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- If the mood mentions kids, children, or family, only recommend age-appropriate content (G, PG, or PG-13)
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- Return exactly {max_results} recommendations, or fewer only if the library has very few matches
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Respond with ONLY valid JSON in this exact format, no other text:
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{{
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"recommendations": [
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{{
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"jellyfin_id": "the-exact-id-from-the-list",
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"title": "Movie Title",
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"reasoning": "Why this fits the mood",
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"match_score": 0.95
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}}
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]
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}}"""
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def build_user_message(mood: str, candidates: list[Movie]) -> str:
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movie_list = []
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for m in candidates:
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entry = {
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"id": m.jellyfin_id,
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"title": m.title,
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"year": m.year,
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"genres": m.genres,
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"rating": m.community_rating,
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"runtime_min": m.runtime_minutes,
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"content_rating": m.content_rating,
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"overview": (m.overview or "")[:200],
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}
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movie_list.append(entry)
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import json
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movies_json = json.dumps(movie_list, indent=None)
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return f'Mood: "{mood}"\n\nAvailable unwatched movies ({len(candidates)} total):\n{movies_json}'
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class LLMProvider(ABC):
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@abstractmethod
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async def get_recommendations(self, mood: str, candidates: list[Movie], max_results: int = 6) -> list[dict]:
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"""Send mood + candidates to the LLM and return parsed recommendations."""
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...
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