132 lines
5.5 KiB
Python
132 lines
5.5 KiB
Python
from logging import getLogger
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from fastapi import UploadFile
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from app.configs.constants import GPTModels, FieldsAndExercises, TemperatureSettings
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from app.dtos.reading import ReadingDTO
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from app.helpers import ExercisesHelper
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from app.services.abc import IReadingService, ILLMService
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from .fill_blanks import FillBlanks
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from .idea_match import IdeaMatch
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from .paragraph_match import ParagraphMatch
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from .true_false import TrueFalse
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from .import_reading import ImportReadingModule
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from .write_blanks import WriteBlanks
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class ReadingService(IReadingService):
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def __init__(self, llm: ILLMService):
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self._llm = llm
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self._fill_blanks = FillBlanks(llm)
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self._idea_match = IdeaMatch(llm)
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self._paragraph_match = ParagraphMatch(llm)
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self._true_false = TrueFalse(llm)
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self._write_blanks = WriteBlanks(llm)
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self._logger = getLogger(__name__)
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self._import = ImportReadingModule(llm)
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async def import_exam(self, exercises: UploadFile, solutions: UploadFile = None):
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return await self._import.import_from_file(exercises, solutions)
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async def generate_reading_passage(self, part: int, topic: str, word_count: int = 800):
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part_system_message = {
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"1": 'The generated text should be fairly easy to understand and have multiple paragraphs.',
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"2": 'The generated text should be fairly hard to understand and have multiple paragraphs.',
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"3": (
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'The generated text should be very hard to understand and include different points, theories, '
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'subtle differences of opinions from people, correctly sourced to the person who said it, '
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'over the specified topic and have multiple paragraphs.'
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)
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}
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messages = [
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{
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"role": "system",
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"content": (
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'You are a helpful assistant designed to output JSON on this format: '
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'{"title": "title of the text", "text": "generated text"}')
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},
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{
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"role": "user",
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"content": (
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f'Generate an extensive text for IELTS Reading Passage {part}, of at least {word_count} words, '
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f'on the topic of "{topic}". The passage should offer a substantial amount of '
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'information, analysis, or narrative relevant to the chosen subject matter. This text '
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'passage aims to serve as the primary reading section of an IELTS test, providing an '
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'in-depth and comprehensive exploration of the topic. Make sure that the generated text '
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'does not contain forbidden subjects in muslim countries.'
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)
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},
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{
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"role": "system",
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"content": part_system_message[str(part)]
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}
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]
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if part == 3:
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messages.append({
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"role": "user",
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"content": "Use real text excerpts on your generated passage and cite the sources."
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})
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return await self._llm.prediction(
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GPTModels.GPT_4_O,
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messages,
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FieldsAndExercises.GEN_TEXT_FIELDS,
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TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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async def generate_reading_exercises(self, dto: ReadingDTO):
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exercises = []
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start_id = 1
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for req_exercise in dto.exercises:
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if req_exercise.type == "fillBlanks":
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question = await self._fill_blanks.gen_summary_fill_blanks_exercise(
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dto.text, req_exercise.quantity, start_id, dto.difficulty, req_exercise.num_random_words
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)
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exercises.append(question)
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self._logger.info(f"Added fill blanks: {question}")
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elif req_exercise.type == "trueFalse":
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question = await self._true_false.gen_true_false_not_given_exercise(
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dto.text, req_exercise.quantity, start_id, dto.difficulty
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)
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exercises.append(question)
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self._logger.info(f"Added trueFalse: {question}")
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elif req_exercise.type == "writeBlanks":
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question = await self._write_blanks.gen_write_blanks_exercise(
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dto.text, req_exercise.quantity, start_id, dto.difficulty, req_exercise.max_words
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)
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if ExercisesHelper.answer_word_limit_ok(question):
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exercises.append(question)
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self._logger.info(f"Added write blanks: {question}")
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else:
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exercises.append({})
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self._logger.info("Did not add write blanks because it did not respect word limit")
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elif req_exercise.type == "paragraphMatch":
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question = await self._paragraph_match.gen_paragraph_match_exercise(
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dto.text, req_exercise.quantity, start_id
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)
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exercises.append(question)
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self._logger.info(f"Added paragraph match: {question}")
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elif req_exercise.type == "ideaMatch":
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question = await self._idea_match.gen_idea_match_exercise(
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dto.text, req_exercise.quantity, start_id
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)
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question["variant"] = "ideaMatch"
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exercises.append(question)
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self._logger.info(f"Added idea match: {question}")
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start_id = start_id + req_exercise.quantity
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return {
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"exercises": exercises
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}
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