Add exercises for utas level.
This commit is contained in:
Cristiano Ferreira
@@ -1153,3 +1153,241 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
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"questions": fix_exercise_ids(question, start_id)["questions"],
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"questions": fix_exercise_ids(question, start_id)["questions"],
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"type": "multipleChoice",
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"type": "multipleChoice",
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}
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}
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def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
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json_format = {
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"questions": [
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{
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"id": "9",
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"options": [
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{
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"id": "A",
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"text": "a"
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},
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{
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"id": "B",
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"text": "b"
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},
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{
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"id": "C",
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"text": "c"
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},
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{
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"id": "D",
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"text": "d"
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}
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],
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"prompt": "prompt",
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"solution": "A",
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"variant": "text"
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}
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]
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}
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gen_multiple_choice_for_text = 'Generate ' + str(quantity) + (' multiple choice questions of 4 options for an english '
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'level exam, some easy questions, some intermediate '
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'questions and some advanced questions.Ensure that '
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'the questions cover a range of topics such as verb '
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'tense, subject-verb agreement, pronoun usage, '
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'sentence structure, and punctuation. Make sure '
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'every question only has 1 correct answer.')
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messages = [
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{
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"role": "system",
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"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
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},
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{
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"role": "user",
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"content": gen_multiple_choice_for_text
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},
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{
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"role": "user",
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"content": (
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'The type of multiple choice is the prompt has wrong words or group of words and the options are to '
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'find the wrong word or group of words that are underlined in the prompt. \nExample:\n'
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'Prompt: "I <u>complain</u> about my boss <u>all the time</u>, but my colleagues <u>thinks</u> the boss <u>is</u> nice."\n'
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'Options:\na: "complain"\nb: "all the time"\nc: "thinks"\nd: "is"')
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}
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]
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token_count = count_total_tokens(messages)
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question = make_openai_call(GPT_4_O, messages, token_count,
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["questions"],
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GEN_QUESTION_TEMPERATURE)
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if len(question["questions"]) != quantity:
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return gen_multiple_choice_level(quantity, start_id)
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else:
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return {
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"id": str(uuid.uuid4()),
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"prompt": "Select the appropriate option.",
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"questions": fix_exercise_ids(question, start_id)["questions"],
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"type": "multipleChoice",
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}
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def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
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json_format = {
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"question": {
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"words": [
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{
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"id": "1",
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"text": "a"
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},
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{
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"id": "2",
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"text": "b"
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},
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{
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"id": "3",
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"text": "c"
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},
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{
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"id": "4",
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"text": "d"
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}
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],
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"text": "text"
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}
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}
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gen_text = 'Generate a text of at least ' + str(size) + ' words about the topic ' + topic + '.'
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messages = [
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{
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"role": "system",
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"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
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},
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{
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"role": "user",
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"content": gen_text
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},
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{
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"role": "user",
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"content": (
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'From the generated text choose ' + str(quantity) + ' words (cannot be sequential words) to replace '
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'once with {{id}} where id starts on ' + str(start_id) + ' and is '
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'incremented for each word. The ids must be ordered throughout the text and the words must be '
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'replaced only once. Put the removed words and respective ids on the words array of the json in the correct order.')
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}
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]
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token_count = count_total_tokens(messages)
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question = make_openai_call(GPT_4_O, messages, token_count,
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["question"],
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GEN_QUESTION_TEMPERATURE)
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return {
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"id": str(uuid.uuid4()),
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"prompt": "Select the appropriate option.",
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"questions": question["question"],
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"type": "blankSpace"
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}
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def gen_reading_passage_utas(start_id, sa_quantity: int, mc_quantity: int, topic=random.choice(mti_topics)):
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passage = generate_reading_passage(QuestionType.READING_PASSAGE_1, topic)
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exercises = gen_reading_exercises_utas(passage["text"], start_id, sa_quantity, mc_quantity)
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if contains_empty_dict(exercises):
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return gen_reading_passage_utas(start_id, sa_quantity, mc_quantity, topic)
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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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}
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def gen_reading_exercises_utas(passage: str, start_id: int, sa_quantity: int, mc_quantity: int):
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exercises = []
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sa_questions = gen_short_answer_utas(passage, start_id, sa_quantity)
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exercises.append(sa_questions)
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mc_questions = gen_text_multiple_choice_utas(passage, start_id+sa_quantity, mc_quantity)
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exercises.append(mc_questions)
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return exercises
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def gen_short_answer_utas(text: str, start_id: int, sa_quantity: int):
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json_format = {"questions": [{"id": 1, "question": "question", "possible_answers": ["answer_1", "answer_2"]}]}
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messages = [
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{
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"role": "system",
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"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
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},
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{
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"role": "user",
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"content": (
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'Generate ' + str(sa_quantity) + ' short answer questions, and the possible answers, must have '
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'maximum 3 words per answer, about this text:\n"' + text + '"')
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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token_count = count_total_tokens(messages)
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return make_openai_call(GPT_4_O, messages, token_count,
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["questions"],
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GEN_QUESTION_TEMPERATURE)
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def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int):
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json_format = {
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"questions": [
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{
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"id": "9",
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"options": [
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{
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"id": "A",
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"text": "a"
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},
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{
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"id": "B",
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"text": "b"
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},
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{
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"id": "C",
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"text": "c"
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},
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{
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"id": "D",
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"text": "d"
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}
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],
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"prompt": "prompt",
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"solution": "A",
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"variant": "text"
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}
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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": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
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},
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{
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"role": "user",
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"content": 'Generate ' + str(mc_quantity) + ' multiple choice questions of 4 options for this text:\n' + text
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},
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{
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"role": "user",
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"content": 'Make sure every question only has 1 correct answer.'
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}
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]
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token_count = count_total_tokens(messages)
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question = make_openai_call(GPT_4_O, messages, token_count,
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["questions"],
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GEN_QUESTION_TEMPERATURE)
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if len(question["questions"]) != mc_quantity:
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return gen_multiple_choice_level(mc_quantity, start_id)
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else:
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return {
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"id": str(uuid.uuid4()),
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"prompt": "Select the appropriate option.",
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"questions": fix_exercise_ids(question, start_id)["questions"],
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"type": "multipleChoice",
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}
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