from typing import Optional

from app.configs.constants import GPTModels, TemperatureSettings
from app.helpers import ExercisesHelper
from app.services.abc import ILLMService, IReadingService


class PassageUtas:

    def __init__(self, llm: ILLMService, reading_service: IReadingService, mc_variants: dict):
        self._llm = llm
        self._reading_service = reading_service
        self._mc_variants = mc_variants

    async def gen_reading_passage_utas(
            self, start_id, mc_quantity: int, topic: Optional[str] # sa_quantity: int,
    ):

        passage = await self._reading_service.generate_reading_passage(1, topic)
        mc_exercises = await self._gen_text_multiple_choice_utas(passage["text"], start_id, mc_quantity)

        #short_answer = await self._gen_short_answer_utas(passage["text"], start_id, sa_quantity)
        # + sa_quantity, mc_quantity)

        """
            exercises: {
                "shortAnswer": short_answer,
                "multipleChoice": mc_exercises,
            },
        """
        return {
            "exercises": mc_exercises,
            "text": {
                "content": passage["text"],
                "title": passage["title"]
            }
        }

    async def _gen_short_answer_utas(self, text: str, start_id: int, sa_quantity: int):
        json_format = {"questions": [{"id": 1, "question": "question", "possible_answers": ["answer_1", "answer_2"]}]}

        messages = [
            {
                "role": "system",
                "content": f'You are a helpful assistant designed to output JSON on this format: {json_format}'
            },
            {
                "role": "user",
                "content": (
                    f'Generate {sa_quantity} short answer questions, and the possible answers, must have '
                    f'maximum 3 words per answer, about this text:\n"{text}"'
                )
            },
            {
                "role": "user",
                "content": f'The id starts at {start_id}.'
            }
        ]

        question = await self._llm.prediction(
            GPTModels.GPT_4_O, messages, ["questions"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
        )

        return question["questions"]

    async def _gen_text_multiple_choice_utas(self, text: str, start_id: int, mc_quantity: int):
        json_template = self._mc_variants["text_mc_utas"]

        messages = [
            {
                "role": "system",
                "content": f'You are a helpful assistant designed to output JSON on this format: {json_template}'
            },
            {
                "role": "user",
                "content": f'Generate {mc_quantity} multiple choice questions of 4 options for this text:\n{text}'
            },
            {
                "role": "user",
                "content": 'Make sure every question only has 1 correct answer.'
            }
        ]

        question = await self._llm.prediction(
            GPTModels.GPT_4_O, messages, ["questions"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
        )

        if len(question["questions"]) != mc_quantity:
            return await self._gen_text_multiple_choice_utas(text, mc_quantity, start_id)
        else:
            response = ExercisesHelper.fix_exercise_ids(question, start_id)
            response["questions"] = ExercisesHelper.randomize_mc_options_order(response["questions"])
            return response