from datetime import datetime
from loguru import logger
from openai import AsyncOpenAI
from pydantic import ConfigDict
from typing import Any, Collection, Self

from ctp_slack_bot.core import ApplicationComponentBase, Settings
from ctp_slack_bot.models import Chunk


class LanguageModelService(ApplicationComponentBase):
    """
    Service for language model operations.
    """

    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)

    settings: Settings
    open_ai_client: AsyncOpenAI

    async def answer_question(self, asker: str, question: str, context: Collection[Chunk]) -> str: # TODO: generify into just another agent. 
        """Generate a response using OpenAI’s API with retrieved context.

        Args:
            question (str): The user’s question
            context (List[RetreivedContext]): The context retreived for answering the question

        Returns:
            str: Generated answer
        """
        logger.debug("Generating response for question “{}” using {} context chunks…", question, len(context))
        messages = [
            {"role": "system", "content": self.settings.system_prompt},
            {"role": "user", "content": (
                f"""Inquirer Name: {asker}

                Current Time: {datetime.now().isoformat(" ", "seconds")}

                Question:
                {question}

                Context from class materials and transcripts:
                {'\n\n'.join(chunk.text for chunk in context)}""")}
        ]
        response = await self.open_ai_client.chat.completions.create(
            model=self.settings.chat_model,
            messages=messages,
            max_tokens=self.settings.max_tokens,
            temperature=self.settings.temperature
        )
        return response.choices[0].message.content

    @property
    def name(self: Self) -> str:
        return "language_model_service"