from abc import ABC, abstractmethod

from app.models import Movie

SYSTEM_PROMPT = """You are a movie recommendation assistant for a household's personal movie library.
The user will describe their mood or what kind of movie night they want. You will receive a list of
unwatched movies from their library and recommend the best matches.

Rules:
- ONLY recommend movies from the provided list — these are movies they already own but haven't watched
- Consider genre, themes, tone, cast, era, and the movie's overview when matching to the mood
- Provide a brief, enthusiastic 1-2 sentence explanation for each pick that connects it to the mood
- Rank by how well they match the described mood, not by rating alone
- If the mood mentions kids, children, or family, only recommend age-appropriate content (G, PG, or PG-13)
- Return exactly {max_results} recommendations, or fewer only if the library has very few matches

Respond with ONLY valid JSON in this exact format, no other text:
{{
  "recommendations": [
    {{
      "jellyfin_id": "the-exact-id-from-the-list",
      "title": "Movie Title",
      "reasoning": "Why this fits the mood",
      "match_score": 0.95
    }}
  ]
}}"""


def build_user_message(mood: str, candidates: list[Movie]) -> str:
    movie_list = []
    for m in candidates:
        entry = {
            "id": m.jellyfin_id,
            "title": m.title,
            "year": m.year,
            "genres": m.genres,
            "rating": m.community_rating,
            "runtime_min": m.runtime_minutes,
            "content_rating": m.content_rating,
            "overview": (m.overview or "")[:200],
        }
        movie_list.append(entry)

    import json
    movies_json = json.dumps(movie_list, indent=None)
    return f'Mood: "{mood}"\n\nAvailable unwatched movies ({len(candidates)} total):\n{movies_json}'


class LLMProvider(ABC):
    @abstractmethod
    async def get_recommendations(self, mood: str, candidates: list[Movie], max_results: int = 6) -> list[dict]:
        """Send mood + candidates to the LLM and return parsed recommendations."""
        ...