Fastapi refactor update
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Carlos-Mesquita
349
app/services/impl/exam/reading.py
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349
app/services/impl/exam/reading.py
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import random
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import uuid
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from queue import Queue
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from typing import List
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from app.services.abc import IReadingService, ILLMService
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from app.configs.constants import QuestionType, TemperatureSettings, FieldsAndExercises, GPTModels
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from app.helpers import ExercisesHelper
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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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async def gen_reading_passage(
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self,
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part: int,
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topic: str,
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req_exercises: List[str],
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number_of_exercises_q: Queue,
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difficulty: str,
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start_id: int
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):
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passage = await self.generate_reading_passage(part, topic)
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exercises = await self._generate_reading_exercises(
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passage["text"], req_exercises, number_of_exercises_q, start_id, difficulty
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)
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if ExercisesHelper.contains_empty_dict(exercises):
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return await self.gen_reading_passage(
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part, topic, req_exercises, number_of_exercises_q, difficulty, start_id
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)
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return {
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"exercises": exercises,
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"text": {
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"content": passage["text"],
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"title": passage["title"]
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},
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"difficulty": difficulty
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}
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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 you 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(
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self, passage: str, req_exercises: list, number_of_exercises_q, start_id, difficulty
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):
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exercises = []
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for req_exercise in req_exercises:
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number_of_exercises = number_of_exercises_q.get()
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if req_exercise == "fillBlanks":
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question = await self._gen_summary_fill_blanks_exercise(
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passage, number_of_exercises, start_id, difficulty
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)
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exercises.append(question)
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print("Added fill blanks: " + str(question))
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elif req_exercise == "trueFalse":
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question = await self._gen_true_false_not_given_exercise(
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passage, number_of_exercises, start_id, difficulty
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)
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exercises.append(question)
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print("Added trueFalse: " + str(question))
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elif req_exercise == "writeBlanks":
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question = await self._gen_write_blanks_exercise(passage, number_of_exercises, start_id, difficulty)
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if ExercisesHelper.answer_word_limit_ok(question):
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exercises.append(question)
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print("Added write blanks: " + str(question))
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else:
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exercises.append({})
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print("Did not add write blanks because it did not respect word limit")
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elif req_exercise == "paragraphMatch":
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question = await self._gen_paragraph_match_exercise(passage, number_of_exercises, start_id)
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exercises.append(question)
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print("Added paragraph match: " + str(question))
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elif req_exercise == "ideaMatch":
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question = await self._gen_idea_match_exercise(passage, number_of_exercises, start_id)
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exercises.append(question)
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print("Added idea match: " + str(question))
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start_id = start_id + number_of_exercises
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return exercises
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async def _gen_summary_fill_blanks_exercise(
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self, text: str, quantity: int, start_id, difficulty, num_random_words: int = 1
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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: { "summary": "summary" }'
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)
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},
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{
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"role": "user",
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"content": f'Summarize this text: "{text}"'
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["summary"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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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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'{"words": ["word_1", "word_2"] }'
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)
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},
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{
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"role": "user",
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"content": (
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f'Select {quantity} {difficulty} difficulty words, it must be words and not expressions, '
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f'from this:\n{response["summary"]}'
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)
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}
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]
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words_response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["words"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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response["words"] = words_response["words"]
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replaced_summary = ExercisesHelper.replace_first_occurrences_with_placeholders(
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response["summary"], response["words"], start_id
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)
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options_words = ExercisesHelper.add_random_words_and_shuffle(response["words"], num_random_words)
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solutions = ExercisesHelper.fillblanks_build_solutions_array(response["words"], start_id)
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return {
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"allowRepetition": True,
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"id": str(uuid.uuid4()),
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"prompt": (
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"Complete the summary below. Write the letter of the corresponding word(s) for it.\\nThere are "
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"more words than spaces so you will not use them all. You may use any of the words more than once."
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),
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"solutions": solutions,
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"text": replaced_summary,
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"type": "fillBlanks",
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"words": options_words
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}
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async def _gen_true_false_not_given_exercise(self, text: str, quantity: int, start_id, difficulty):
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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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'{"prompts":[{"prompt": "statement_1", "solution": "true/false/not_given"}, '
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'{"prompt": "statement_2", "solution": "true/false/not_given"}]}')
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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 {str(quantity)} {difficulty} difficulty statements based on the provided text. '
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'Ensure that your statements accurately represent information or inferences from the text, and '
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'provide a variety of responses, including, at least one of each True, False, and Not Given, '
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f'as appropriate.\n\nReference text:\n\n {text}'
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)
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["prompts"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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questions = response["prompts"]
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if len(questions) > quantity:
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questions = ExercisesHelper.remove_excess_questions(questions, len(questions) - quantity)
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for i, question in enumerate(questions, start=start_id):
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question["id"] = str(i)
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return {
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"id": str(uuid.uuid4()),
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"prompt": "Do the following statements agree with the information given in the Reading Passage?",
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"questions": questions,
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"type": "trueFalse"
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}
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async def _gen_write_blanks_exercise(self, text: str, quantity: int, start_id, difficulty):
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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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'{"questions": [{"question": question, "possible_answers": ["answer_1", "answer_2"]}]}'
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)
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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 {str(quantity)} {difficulty} difficulty short answer questions, and the '
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f'possible answers, must have maximum 3 words per answer, about this text:\n"{text}"'
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)
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["questions"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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questions = response["questions"][:quantity]
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return {
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"id": str(uuid.uuid4()),
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"maxWords": 3,
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"prompt": "Choose no more than three words and/or a number from the passage for each answer.",
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"solutions": ExercisesHelper.build_write_blanks_solutions(questions, start_id),
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"text": ExercisesHelper.build_write_blanks_text(questions, start_id),
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"type": "writeBlanks"
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}
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async def _gen_paragraph_match_exercise(self, text: str, quantity: int, start_id):
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paragraphs = ExercisesHelper.assign_letters_to_paragraphs(text)
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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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'{"headings": [ {"heading": "first paragraph heading"}, {"heading": "second paragraph heading"}]}'
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)
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},
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{
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"role": "user",
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"content": (
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'For every paragraph of the list generate a minimum 5 word heading for it. '
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f'The paragraphs are these: {str(paragraphs)}'
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)
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["headings"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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headings = response["headings"]
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options = []
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for i, paragraph in enumerate(paragraphs, start=0):
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paragraph["heading"] = headings[i]["heading"]
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options.append({
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"id": paragraph["letter"],
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"sentence": paragraph["paragraph"]
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})
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random.shuffle(paragraphs)
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sentences = []
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for i, paragraph in enumerate(paragraphs, start=start_id):
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sentences.append({
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"id": i,
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"sentence": paragraph["heading"],
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"solution": paragraph["letter"]
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})
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return {
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"id": str(uuid.uuid4()),
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"allowRepetition": False,
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"options": options,
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"prompt": "Choose the correct heading for paragraphs from the list of headings below.",
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"sentences": sentences[:quantity],
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"type": "matchSentences"
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}
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async def _gen_idea_match_exercise(self, text: str, quantity: int, start_id):
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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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'{"ideas": [ '
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'{"idea": "some idea or opinion", "from": "person, institution whose idea or opinion this is"}, '
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'{"idea": "some other idea or opinion", "from": "person, institution whose idea or opinion this is"}'
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']}'
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)
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},
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{
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"role": "user",
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"content": (
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f'From the text extract {quantity} ideas, theories, opinions and who they are from. '
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f'The text: {text}'
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)
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_4_O, messages, ["ideas"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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ideas = response["ideas"]
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return {
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"id": str(uuid.uuid4()),
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"allowRepetition": False,
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"options": ExercisesHelper.build_options(ideas),
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"prompt": "Choose the correct author for the ideas/opinions from the list of authors below.",
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"sentences": ExercisesHelper.build_sentences(ideas, start_id),
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"type": "matchSentences"
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}
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