devops/encoach_backend
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Added new ideaMatch exercise type.

Browse Source
This commit is contained in:
Cristiano Ferreira
2024-07-18 23:20:06 +01:00
parent 358f240d16
commit bef606fe14
2 changed files with 195 additions and 98 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
helper/constants.py
View File

@@ -18,6 +18,7 @@ GEN_FIELDS = ['topic']
GEN_TEXT_FIELDS = ['title'] GEN_TEXT_FIELDS = ['title']
LISTENING_GEN_FIELDS = ['transcript', 'exercise'] LISTENING_GEN_FIELDS = ['transcript', 'exercise']
READING_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch'] READING_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch']
READING_3_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch', 'ideaMatch']
LISTENING_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm'] LISTENING_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm']
LISTENING_1_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksFill', LISTENING_1_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksFill',
'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm'] 'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm']

290
helper/exercises.py
View File

@@ -7,7 +7,6 @@ import uuid
import nltk import nltk
from wonderwords import RandomWord from wonderwords import RandomWord
from helper.api_messages import QuestionType
from helper.constants import * from helper.constants import *
from helper.firebase_helper import get_all from helper.firebase_helper import get_all
from helper.openai_interface import make_openai_call, count_total_tokens from helper.openai_interface import make_openai_call, count_total_tokens
@@ -243,6 +242,7 @@ def build_write_blanks_solutions_listening(words: [], start_id):
) )
return solutions return solutions
def get_perfect_answer(question: str, size: int): def get_perfect_answer(question: str, size: int):
messages = [ messages = [
{ {
@@ -278,20 +278,20 @@ def generate_reading_passage_1_text(topic: str):
"role": "user", "role": "user",
"content": ( "content": (
'Generate an extensive text for IELTS Reading Passage 1, of at least 800 words, on the topic ' 'Generate an extensive text for IELTS Reading Passage 1, of at least 800 words, on the topic '
'of "' + topic + '". The passage should offer ' 'of "' + topic + '". The passage should offer '
'a substantial amount of information, ' 'a substantial amount of information, '
'analysis, or narrative relevant to the chosen ' 'analysis, or narrative relevant to the chosen '
'subject matter. This text passage aims to ' 'subject matter. This text passage aims to '
'serve as the primary reading section of an ' 'serve as the primary reading section of an '
'IELTS test, providing an in-depth and ' 'IELTS test, providing an in-depth and '
'comprehensive exploration of the topic. ' 'comprehensive exploration of the topic. '
'Make sure that the generated text does not ' 'Make sure that the generated text does not '
'contain forbidden subjects in muslim countries.') 'contain forbidden subjects in muslim countries.')
}, },
{ {
"role": "system", "role": "system",
"content": ('The generated text should be fairly easy to understand.') "content": ('The generated text should be fairly easy to understand and have multiple paragraphs.')
}, },
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
@@ -310,25 +310,26 @@ def generate_reading_passage_2_text(topic: str):
"role": "user", "role": "user",
"content": ( "content": (
'Generate an extensive text for IELTS Reading Passage 2, of at least 800 words, on the topic ' 'Generate an extensive text for IELTS Reading Passage 2, of at least 800 words, on the topic '
'of "' + topic + '". The passage should offer ' 'of "' + topic + '". The passage should offer '
'a substantial amount of information, ' 'a substantial amount of information, '
'analysis, or narrative relevant to the chosen ' 'analysis, or narrative relevant to the chosen '
'subject matter. This text passage aims to ' 'subject matter. This text passage aims to '
'serve as the primary reading section of an ' 'serve as the primary reading section of an '
'IELTS test, providing an in-depth and ' 'IELTS test, providing an in-depth and '
'comprehensive exploration of the topic. ' 'comprehensive exploration of the topic. '
'Make sure that the generated text does not ' 'Make sure that the generated text does not '
'contain forbidden subjects in muslim countries.') 'contain forbidden subjects in muslim countries.')
}, },
{ {
"role": "system", "role": "system",
"content": ('The generated text should be fairly hard to understand.') "content": ('The generated text should be fairly hard to understand and have multiple paragraphs.')
}, },
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
return make_openai_call(GPT_4_O, messages, token_count, GEN_TEXT_FIELDS, GEN_QUESTION_TEMPERATURE) return make_openai_call(GPT_4_O, messages, token_count, GEN_TEXT_FIELDS, GEN_QUESTION_TEMPERATURE)
def generate_reading_passage_3_text(topic: str): def generate_reading_passage_3_text(topic: str):
messages = [ messages = [
{ {
@@ -341,21 +342,22 @@ def generate_reading_passage_3_text(topic: str):
"role": "user", "role": "user",
"content": ( "content": (
'Generate an extensive text for IELTS Reading Passage 3, of at least 800 words, on the topic ' 'Generate an extensive text for IELTS Reading Passage 3, of at least 800 words, on the topic '
'of "' + topic + '". The passage should offer ' 'of "' + topic + '". The passage should offer '
'a substantial amount of information, ' 'a substantial amount of information, '
'analysis, or narrative relevant to the chosen ' 'analysis, or narrative relevant to the chosen '
'subject matter. This text passage aims to ' 'subject matter. This text passage aims to '
'serve as the primary reading section of an ' 'serve as the primary reading section of an '
'IELTS test, providing an in-depth and ' 'IELTS test, providing an in-depth and '
'comprehensive exploration of the topic. ' 'comprehensive exploration of the topic. '
'Make sure that the generated text does not ' 'Make sure that the generated text does not '
'contain forbidden subjects in muslim countries.') 'contain forbidden subjects in muslim countries.')
}, },
{ {
"role": "system", "role": "system",
"content": ('The generated text should be very hard to understand and include different points, theories, ' "content": ('The generated text should be very hard to understand and include different points, theories, '
'subtle differences of opinions from people over the specified topic .') 'subtle differences of opinions from people, correctly sourced to the person who said it, '
'over the specified topic and have multiple paragraphs.')
}, },
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
@@ -464,8 +466,8 @@ def generate_listening_3_conversation(topic: str):
"content": ( "content": (
'Compose an authentic and elaborate conversation between up to four individuals in the everyday ' 'Compose an authentic and elaborate conversation between up to four individuals in the everyday '
'social context of "' + topic + '". Please include random names and genders for the characters in your dialogue. ' 'social context of "' + topic + '". Please include random names and genders for the characters in your dialogue. '
'Make sure that the generated conversation does not contain forbidden subjects in ' 'Make sure that the generated conversation does not contain forbidden subjects in '
'muslim countries.') 'muslim countries.')
} }
] ]
@@ -507,7 +509,7 @@ def generate_listening_4_monologue(topic: str):
"content": ( "content": (
'Generate a comprehensive and complex monologue on the academic subject ' 'Generate a comprehensive and complex monologue on the academic subject '
'of: "' + topic + '". Make sure that the generated monologue does not contain forbidden subjects in ' 'of: "' + topic + '". Make sure that the generated monologue does not contain forbidden subjects in '
'muslim countries.') 'muslim countries.')
} }
] ]
@@ -547,6 +549,10 @@ def generate_reading_exercises(passage: str, req_exercises: list, number_of_exer
question = gen_paragraph_match_exercise(passage, number_of_exercises, start_id) question = gen_paragraph_match_exercise(passage, number_of_exercises, start_id)
exercises.append(question) exercises.append(question)
print("Added paragraph match: " + str(question)) print("Added paragraph match: " + str(question))
elif req_exercise == "ideaMatch":
question = gen_idea_match_exercise(passage, number_of_exercises, start_id)
exercises.append(question)
print("Added idea match: " + str(question))
start_id = start_id + number_of_exercises start_id = start_id + number_of_exercises
@@ -673,15 +679,15 @@ def gen_summary_fill_blanks_exercise(text: str, quantity: int, start_id, difficu
}, },
{ {
"role": "user", "role": "user",
"content": ('Summarize this text: "'+ text + '"') "content": ('Summarize this text: "' + text + '"')
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
response = make_openai_call(GPT_4_O, messages, token_count, response = make_openai_call(GPT_4_O, messages, token_count,
["summary"], ["summary"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
messages = [ messages = [
{ {
@@ -693,15 +699,16 @@ def gen_summary_fill_blanks_exercise(text: str, quantity: int, start_id, difficu
{ {
"role": "user", "role": "user",
"content": ('Select ' + str(quantity) + ' ' + difficulty + ' difficulty words, it must be words and not ' "content": ('Select ' + str(quantity) + ' ' + difficulty + ' difficulty words, it must be words and not '
'expressions, from this:\n' + response["summary"]) 'expressions, from this:\n' + response[
"summary"])
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
words_response = make_openai_call(GPT_4_O, messages, token_count, words_response = make_openai_call(GPT_4_O, messages, token_count,
["summary"], ["summary"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
response["words"] = words_response["words"] response["words"] = words_response["words"]
replaced_summary = replace_first_occurrences_with_placeholders(response["summary"], response["words"], start_id) replaced_summary = replace_first_occurrences_with_placeholders(response["summary"], response["words"], start_id)
options_words = add_random_words_and_shuffle(response["words"], 1) options_words = add_random_words_and_shuffle(response["words"], 1)
@@ -732,18 +739,19 @@ def gen_true_false_not_given_exercise(text: str, quantity: int, start_id, diffic
{ {
"role": "user", "role": "user",
"content": ( "content": (
'Generate ' + str(quantity) + ' ' + difficulty + ' difficulty statements based on the provided text. ' 'Generate ' + str(
'Ensure that your statements accurately represent ' quantity) + ' ' + difficulty + ' difficulty statements based on the provided text. '
'information or inferences from the text, and ' 'Ensure that your statements accurately represent '
'provide a variety of responses, including, at ' 'information or inferences from the text, and '
'least one of each True, False, and Not Given, ' 'provide a variety of responses, including, at '
'as appropriate.\n\nReference text:\n\n ' + text) 'least one of each True, False, and Not Given, '
'as appropriate.\n\nReference text:\n\n ' + text)
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
questions = make_openai_call(GPT_4_O, messages, token_count,["prompts"], questions = make_openai_call(GPT_4_O, messages, token_count, ["prompts"],
GEN_QUESTION_TEMPERATURE)["prompts"] GEN_QUESTION_TEMPERATURE)["prompts"]
if len(questions) > quantity: if len(questions) > quantity:
questions = remove_excess_questions(questions, len(questions) - quantity) questions = remove_excess_questions(questions, len(questions) - quantity)
@@ -777,7 +785,7 @@ def gen_write_blanks_exercise(text: str, quantity: int, start_id, difficulty):
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
questions = make_openai_call(GPT_4_O, messages, token_count,["questions"], questions = make_openai_call(GPT_4_O, messages, token_count, ["questions"],
GEN_QUESTION_TEMPERATURE)["questions"][:quantity] GEN_QUESTION_TEMPERATURE)["questions"][:quantity]
return { return {
@@ -802,13 +810,14 @@ def gen_paragraph_match_exercise(text: str, quantity: int, start_id):
{ {
"role": "user", "role": "user",
"content": ( "content": (
'For every paragraph of the list generate a minimum 5 word heading for it. The paragraphs are these: ' + str(paragraphs)) 'For every paragraph of the list generate a minimum 5 word heading for it. The paragraphs are these: ' + str(
paragraphs))
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
headings = make_openai_call(GPT_4_O, messages, token_count,["headings"], headings = make_openai_call(GPT_4_O, messages, token_count, ["headings"],
GEN_QUESTION_TEMPERATURE)["headings"] GEN_QUESTION_TEMPERATURE)["headings"]
options = [] options = []
@@ -838,6 +847,83 @@ def gen_paragraph_match_exercise(text: str, quantity: int, start_id):
} }
def gen_idea_match_exercise(text: str, quantity: int, start_id):
messages = [
{
"role": "system",
"content": (
'You are a helpful assistant designed to output JSON on this format: '
'{"ideas": [ '
'{"idea": "some idea or opinion", "from": "person, institution whose idea or opinion this is"}, '
'{"idea": "some other idea or opinion", "from": "person, institution whose idea or opinion this is"}'
']}')
},
{
"role": "user",
"content": (
'From the text extract ' + str(quantity) + ' ideas, theories, opinions and who they are from. The text: ' + str(text))
}
]
token_count = count_total_tokens(messages)
ideas = make_openai_call(GPT_4_O, messages, token_count, ["ideas"], GEN_QUESTION_TEMPERATURE)["ideas"]
# options = [
# {
# "id": "A",
# "sentence": "Cultural appropriation is a term that has gained significant traction in contemporary"
# },
# {
# "id": "B",
# "sentence": "Historically, cultural appropriation can be traced back to the era of colonialism"
# }
# ]
# sentences = [
# {
# "id": 21,
# "sentence": "Concluding Thoughts on Cultural Appropriation",
# "solution": "I"
# },
# {
# "id": 22,
# "sentence": "Understanding the Concept of Cultural Appropriation",
# "solution": "A"
# }
# ]
return {
"id": str(uuid.uuid4()),
"allowRepetition": False,
"options": build_options(ideas),
"prompt": "Choose the correct heading for paragraphs from the list of headings below.",
"sentences": build_sentences(ideas, start_id),
"type": "matchSentences"
}
def build_options(ideas):
options = []
letters = iter(string.ascii_uppercase)
for idea in ideas:
options.append({
"id": next(letters),
"sentence": idea["from"]
})
return options
def build_sentences(ideas, start_id):
sentences = []
letters = iter(string.ascii_uppercase)
for idea in ideas:
sentences.append({
"solution": next(letters),
"sentence": idea["idea"]
})
random.shuffle(sentences)
for i, sentence in enumerate(sentences, start=start_id):
sentence["id"] = i
return sentences
def assign_letters_to_paragraphs(paragraphs): def assign_letters_to_paragraphs(paragraphs):
result = [] result = []
letters = iter(string.ascii_uppercase) letters = iter(string.ascii_uppercase)
@@ -861,14 +947,15 @@ def gen_multiple_choice_exercise_listening_conversation(text: str, quantity: int
{ {
"role": "user", "role": "user",
"content": ( "content": (
'Generate ' + str(quantity) + ' ' + difficulty + ' difficulty multiple choice questions of ' + str(n_options) + ' options ' 'Generate ' + str(quantity) + ' ' + difficulty + ' difficulty multiple choice questions of ' + str(
'of for this conversation:\n"' + text + '"') n_options) + ' options '
'of for this conversation:\n"' + text + '"')
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,["questions"], GEN_QUESTION_TEMPERATURE) question = make_openai_call(GPT_4_O, messages, token_count, ["questions"], GEN_QUESTION_TEMPERATURE)
return { return {
"id": str(uuid.uuid4()), "id": str(uuid.uuid4()),
"prompt": "Select the appropriate option.", "prompt": "Select the appropriate option.",
@@ -892,14 +979,15 @@ def gen_multiple_choice_exercise_listening_monologue(text: str, quantity: int, s
"role": "user", "role": "user",
"content": ( "content": (
'Generate ' + str( 'Generate ' + str(
quantity) + ' ' + difficulty + ' difficulty multiple choice questions of ' + str(n_options) + ' options ' quantity) + ' ' + difficulty + ' difficulty multiple choice questions of ' + str(
'of for this monologue:\n"' + text + '"') n_options) + ' options '
'of for this monologue:\n"' + text + '"')
} }
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,["questions"], GEN_QUESTION_TEMPERATURE) question = make_openai_call(GPT_4_O, messages, token_count, ["questions"], GEN_QUESTION_TEMPERATURE)
return { return {
"id": str(uuid.uuid4()), "id": str(uuid.uuid4()),
"prompt": "Select the appropriate option.", "prompt": "Select the appropriate option.",
@@ -927,7 +1015,7 @@ def gen_write_blanks_questions_exercise_listening_conversation(text: str, quanti
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
questions = make_openai_call(GPT_4_O, messages, token_count,["questions"], questions = make_openai_call(GPT_4_O, messages, token_count, ["questions"],
GEN_QUESTION_TEMPERATURE)["questions"][:quantity] GEN_QUESTION_TEMPERATURE)["questions"][:quantity]
return { return {
@@ -993,7 +1081,6 @@ def gen_write_blanks_notes_exercise_listening_conversation(text: str, quantity:
questions = make_openai_call(GPT_4_O, messages, token_count, ["notes"], questions = make_openai_call(GPT_4_O, messages, token_count, ["notes"],
GEN_QUESTION_TEMPERATURE)["notes"][:quantity] GEN_QUESTION_TEMPERATURE)["notes"][:quantity]
formatted_phrases = "\n".join([f"{i + 1}. {phrase}" for i, phrase in enumerate(questions)]) formatted_phrases = "\n".join([f"{i + 1}. {phrase}" for i, phrase in enumerate(questions)])
word_messages = [ word_messages = [
@@ -1008,7 +1095,7 @@ def gen_write_blanks_notes_exercise_listening_conversation(text: str, quantity:
} }
] ]
words = make_openai_call(GPT_4_O, word_messages, token_count,["words"], words = make_openai_call(GPT_4_O, word_messages, token_count, ["words"],
GEN_QUESTION_TEMPERATURE)["words"][:quantity] GEN_QUESTION_TEMPERATURE)["words"][:quantity]
replaced_notes = replace_first_occurrences_with_placeholders_notes(questions, words, start_id) replaced_notes = replace_first_occurrences_with_placeholders_notes(questions, words, start_id)
return { return {
@@ -1149,11 +1236,11 @@ def gen_multiple_choice_level(quantity: int, start_id=1):
"role": "system", "role": "system",
"content": ( "content": (
'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": ' 'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": '
'[{"id": "A", "text": ' '[{"id": "A", "text": '
'"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": ' '"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": '
'"Happy"}, {"id": "D", "text": "Jump"}], ' '"Happy"}, {"id": "D", "text": "Jump"}], '
'"prompt": "Which of the following is a conjunction?", ' '"prompt": "Which of the following is a conjunction?", '
'"solution": "A", "variant": "text"}]}') '"solution": "A", "variant": "text"}]}')
}, },
{ {
"role": "user", "role": "user",
@@ -1163,8 +1250,8 @@ def gen_multiple_choice_level(quantity: int, start_id=1):
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count, question = make_openai_call(GPT_4_O, messages, token_count,
["questions"], ["questions"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != quantity: if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id) return gen_multiple_choice_level(quantity, start_id)
@@ -1204,6 +1291,7 @@ def replace_exercise_if_exists(all_exams, current_exercise, current_exam, seen_k
return replace_exercise_if_exists(all_exams, generate_single_mc_level_question(), current_exam, seen_keys) return replace_exercise_if_exists(all_exams, generate_single_mc_level_question(), current_exam, seen_keys)
return current_exercise, seen_keys return current_exercise, seen_keys
def replace_exercise_if_exists_utas(all_exams, current_exercise, current_exam, seen_keys): def replace_exercise_if_exists_utas(all_exams, current_exercise, current_exam, seen_keys):
# Extracting relevant fields for comparison # Extracting relevant fields for comparison
key = (current_exercise['prompt'], tuple(sorted(option['text'] for option in current_exercise['options']))) key = (current_exercise['prompt'], tuple(sorted(option['text'] for option in current_exercise['options'])))
@@ -1220,7 +1308,8 @@ def replace_exercise_if_exists_utas(all_exams, current_exercise, current_exam, s
current_exercise["options"]) current_exercise["options"])
for exercise in exam.get("questions", []) for exercise in exam.get("questions", [])
): ):
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_level_question(), current_exam, seen_keys) return replace_exercise_if_exists_utas(all_exams, generate_single_mc_level_question(), current_exam,
seen_keys)
return current_exercise, seen_keys return current_exercise, seen_keys
@@ -1243,8 +1332,8 @@ def generate_single_mc_level_question():
] ]
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,["options"], question = make_openai_call(GPT_4_O, messages, token_count, ["options"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
return question return question
@@ -1273,11 +1362,11 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
"role": "system", "role": "system",
"content": ( "content": (
'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": ' 'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": '
'[{"id": "A", "text": ' '[{"id": "A", "text": '
'"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": ' '"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": '
'"Happy"}, {"id": "D", "text": "Jump"}], ' '"Happy"}, {"id": "D", "text": "Jump"}], '
'"prompt": "Which of the following is a conjunction?", ' '"prompt": "Which of the following is a conjunction?", '
'"solution": "A", "variant": "text"}]}') '"solution": "A", "variant": "text"}]}')
}, },
{ {
"role": "user", "role": "user",
@@ -1287,8 +1376,8 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count, question = make_openai_call(GPT_4_O, messages, token_count,
["questions"], ["questions"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != quantity: if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id) return gen_multiple_choice_level(quantity, start_id)
@@ -1296,8 +1385,8 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
seen_keys = set() seen_keys = set()
for i in range(len(question["questions"])): for i in range(len(question["questions"])):
question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_exams, question["questions"][i], question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_exams, question["questions"][i],
question, question,
seen_keys) seen_keys)
return fix_exercise_ids(question, start_id) return fix_exercise_ids(question, start_id)
@@ -1331,13 +1420,14 @@ def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
] ]
} }
gen_multiple_choice_for_text = 'Generate ' + str(quantity) + (' multiple choice questions of 4 options for an english ' gen_multiple_choice_for_text = 'Generate ' + str(quantity) + (
'level exam, some easy questions, some intermediate ' ' multiple choice questions of 4 options for an english '
'questions and some advanced questions.Ensure that ' 'level exam, some easy questions, some intermediate '
'the questions cover a range of topics such as verb ' 'questions and some advanced questions.Ensure that '
'tense, subject-verb agreement, pronoun usage, ' 'the questions cover a range of topics such as verb '
'sentence structure, and punctuation. Make sure ' 'tense, subject-verb agreement, pronoun usage, '
'every question only has 1 correct answer.') 'sentence structure, and punctuation. Make sure '
'every question only has 1 correct answer.')
messages = [ messages = [
{ {
@@ -1360,14 +1450,15 @@ def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count, question = make_openai_call(GPT_4_O, messages, token_count,
["questions"], ["questions"],
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != quantity: if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id) return gen_multiple_choice_level(quantity, start_id)
else: else:
return fix_exercise_ids(question, start_id)["questions"] return fix_exercise_ids(question, start_id)["questions"]
def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)): def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
json_format = { json_format = {
"question": { "question": {
@@ -1406,10 +1497,11 @@ def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=ran
{ {
"role": "user", "role": "user",
"content": ( "content": (
'From the generated text choose ' + str(quantity) + ' words (cannot be sequential words) to replace ' 'From the generated text choose ' + str(
'once with {{id}} where id starts on ' + str(start_id) + ' and is ' quantity) + ' words (cannot be sequential words) to replace '
'incremented for each word. The ids must be ordered throughout the text and the words must be ' 'once with {{id}} where id starts on ' + str(start_id) + ' and is '
'replaced only once. Put the removed words and respective ids on the words array of the json in the correct order.') 'incremented for each word. The ids must be ordered throughout the text and the words must be '
'replaced only once. Put the removed words and respective ids on the words array of the json in the correct order.')
} }
] ]
@@ -1420,14 +1512,14 @@ def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=ran
return question["question"] return question["question"]
def gen_reading_passage_utas(start_id, sa_quantity: int, mc_quantity: int, topic=random.choice(mti_topics)):
def gen_reading_passage_utas(start_id, sa_quantity: int, mc_quantity: int, topic=random.choice(mti_topics)):
passage = generate_reading_passage_1_text(topic) passage = generate_reading_passage_1_text(topic)
short_answer = gen_short_answer_utas(passage["text"], start_id, sa_quantity) short_answer = gen_short_answer_utas(passage["text"], start_id, sa_quantity)
mc_exercises = gen_text_multiple_choice_utas(passage["text"], start_id+sa_quantity, mc_quantity) mc_exercises = gen_text_multiple_choice_utas(passage["text"], start_id + sa_quantity, mc_quantity)
return { return {
"exercises": { "exercises": {
"shortAnswer":short_answer, "shortAnswer": short_answer,
"multipleChoice": mc_exercises, "multipleChoice": mc_exercises,
}, },
"text": { "text": {
@@ -1436,6 +1528,7 @@ def gen_reading_passage_utas(start_id, sa_quantity: int, mc_quantity: int, topic
} }
} }
def gen_short_answer_utas(text: str, start_id: int, sa_quantity: int): def gen_short_answer_utas(text: str, start_id: int, sa_quantity: int):
json_format = {"questions": [{"id": 1, "question": "question", "possible_answers": ["answer_1", "answer_2"]}]} json_format = {"questions": [{"id": 1, "question": "question", "possible_answers": ["answer_1", "answer_2"]}]}
@@ -1458,8 +1551,10 @@ def gen_short_answer_utas(text: str, start_id: int, sa_quantity: int):
token_count = count_total_tokens(messages) token_count = count_total_tokens(messages)
return make_openai_call(GPT_4_O, messages, token_count, return make_openai_call(GPT_4_O, messages, token_count,
["questions"], ["questions"],
GEN_QUESTION_TEMPERATURE)["questions"] GEN_QUESTION_TEMPERATURE)["questions"]
def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int): def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int):
json_format = { json_format = {
"questions": [ "questions": [
@@ -1497,7 +1592,8 @@ def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int):
}, },
{ {
"role": "user", "role": "user",
"content": 'Generate ' + str(mc_quantity) + ' multiple choice questions of 4 options for this text:\n' + text "content": 'Generate ' + str(
mc_quantity) + ' multiple choice questions of 4 options for this text:\n' + text
}, },
{ {
"role": "user", "role": "user",