import uuid

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


class TrueFalse:

    def __init__(self, llm: ILLMService):
        self._llm = llm

    async def gen_true_false_not_given_exercise(self, text: str, quantity: int, start_id: int, difficulty: str):
        messages = [
            {
                "role": "system",
                "content": (
                    'You are a helpful assistant designed to output JSON on this format: '
                    '{"prompts":[{"prompt": "statement_1", "solution": "true/false/not_given"}, '
                    '{"prompt": "statement_2", "solution": "true/false/not_given"}]}')
            },
            {
                "role": "user",
                "content": (
                    f'Generate {str(quantity)} {difficulty} difficulty statements based on the provided text. '
                    'Ensure that your statements accurately represent information or inferences from the text, and '
                    'provide a variety of responses, including, at least one of each True, False, and Not Given, '
                    f'as appropriate.\n\nReference text:\n\n {text}'
                )
            }
        ]

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

        if len(questions) > quantity:
            questions = ExercisesHelper.remove_excess_questions(questions, len(questions) - quantity)

        for i, question in enumerate(questions, start=start_id):
            question["id"] = str(i)

        return {
            "id": str(uuid.uuid4()),
            "prompt": "Do the following statements agree with the information given in the Reading Passage?",
            "questions": questions,
            "type": "trueFalse"
        }