import json
import logging

from openai import AsyncOpenAI

from app.config import settings
from app.models import Movie
from app.services.llm.base import SYSTEM_PROMPT, LLMProvider, build_user_message

logger = logging.getLogger("movie-night.llm.openai")


class OpenAIProvider(LLMProvider):
    def __init__(self):
        kwargs = {"api_key": settings.llm_api_key}
        if settings.llm_base_url:
            kwargs["base_url"] = settings.llm_base_url
        self.client = AsyncOpenAI(**kwargs)
        self.model = settings.llm_model or "gpt-4o"

    async def get_recommendations(self, mood: str, candidates: list[Movie], max_results: int = 6) -> list[dict]:
        system = SYSTEM_PROMPT.format(max_results=max_results)
        user_msg = build_user_message(mood, candidates)

        logger.info(f"Calling OpenAI ({self.model}) with {len(candidates)} candidates")

        response = await self.client.chat.completions.create(
            model=self.model,
            max_tokens=2048,
            messages=[
                {"role": "system", "content": system},
                {"role": "user", "content": user_msg},
            ],
            response_format={"type": "json_object"},
        )

        text = response.choices[0].message.content.strip()

        try:
            data = json.loads(text)
            return data.get("recommendations", [])
        except json.JSONDecodeError:
            logger.error(f"Failed to parse LLM response: {text[:200]}")
            return []