import json
import logging

import anthropic

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.anthropic")


class AnthropicProvider(LLMProvider):
    def __init__(self):
        self.client = anthropic.AsyncAnthropic(api_key=settings.llm_api_key)
        self.model = settings.llm_model or "claude-sonnet-4-6"

    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 Anthropic ({self.model}) with {len(candidates)} candidates")

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

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

        # Parse JSON response
        try:
            data = json.loads(text)
            return data.get("recommendations", [])
        except json.JSONDecodeError:
            # Try to extract JSON from the response
            start = text.find("{")
            end = text.rfind("}") + 1
            if start >= 0 and end > start:
                try:
                    data = json.loads(text[start:end])
                    return data.get("recommendations", [])
                except json.JSONDecodeError:
                    pass
            logger.error(f"Failed to parse LLM response: {text[:200]}")
            return []