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Merged in utas-stuff (pull request #8)

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Utas stuff
This commit is contained in:
Cristiano Ferreira
2024-06-13 17:32:48 +00:00
parent 4ff3b02a1d 20dfd5be78
commit a4caecdb4f
3 changed files with 376 additions and 3 deletions
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101
app.py
View File

@@ -1013,6 +1013,107 @@ def get_level_exam():
except Exception as e:
return str(e)
@app.route('/level_utas', methods=['GET'])
@jwt_required()
def get_level_utas():
try:
# Formats
mc = {
"id": str(uuid.uuid4()),
"prompt": "Choose the correct word or group of words that completes the sentences.",
"questions": None,
"type": "multipleChoice",
"part": 1
}
umc = {
"id": str(uuid.uuid4()),
"prompt": "Choose the underlined word or group of words that is not correct.",
"questions": None,
"type": "multipleChoice",
"part": 2
}
bs_1 = {
"id": str(uuid.uuid4()),
"prompt": "Read the text and write the correct word for each space.",
"questions": None,
"type": "blankSpaceText",
"part": 3
}
bs_2 = {
"id": str(uuid.uuid4()),
"prompt": "Read the text and write the correct word for each space.",
"questions": None,
"type": "blankSpaceText",
"part": 4
}
reading = {
"id": str(uuid.uuid4()),
"prompt": "Read the text and answer the questions below.",
"questions": None,
"type": "readingExercises",
"part": 5
}
all_mc_questions = []
# PART 1
mc_exercises1 = gen_multiple_choice_blank_space_utas(15, 1, all_mc_questions)
print(json.dumps(mc_exercises1, indent=4))
all_mc_questions.append(mc_exercises1)
# PART 2
mc_exercises2 = gen_multiple_choice_blank_space_utas(15, 16, all_mc_questions)
print(json.dumps(mc_exercises2, indent=4))
all_mc_questions.append(mc_exercises2)
# PART 3
mc_exercises3 = gen_multiple_choice_blank_space_utas(15, 31, all_mc_questions)
print(json.dumps(mc_exercises3, indent=4))
all_mc_questions.append(mc_exercises3)
mc_exercises = mc_exercises1['questions'] + mc_exercises2['questions'] + mc_exercises3['questions']
print(json.dumps(mc_exercises, indent=4))
mc["questions"] = mc_exercises
# Underlined mc
underlined_mc = gen_multiple_choice_underlined_utas(15, 46)
print(json.dumps(underlined_mc, indent=4))
umc["questions"] = underlined_mc
# Blank Space text 1
blank_space_text_1 = gen_blank_space_text_utas(12, 61, 250)
print(json.dumps(blank_space_text_1, indent=4))
bs_1["questions"] = blank_space_text_1
# Blank Space text 2
blank_space_text_2 = gen_blank_space_text_utas(14, 73, 350)
print(json.dumps(blank_space_text_2, indent=4))
bs_2["questions"] = blank_space_text_2
# Reading text
reading_text = gen_reading_passage_utas(87, 10, 4)
print(json.dumps(reading_text, indent=4))
reading["questions"] = reading_text
return {
"exercises": {
"blankSpaceMultipleChoice": mc,
"underlinedMultipleChoice": umc,
"blankSpaceText1": bs_1,
"blankSpaceText2": bs_2,
"readingExercises": reading,
},
"isDiagnostic": False,
"minTimer": 25,
"module": "level"
}
except Exception as e:
return str(e)
@app.route('/fetch_tips', methods=['POST'])
@jwt_required()

2
helper/constants.py
View File

@@ -141,7 +141,6 @@ mti_topics = [
"Poverty Alleviation",
"Cybersecurity and Privacy",
"Human Rights",
"Social Justice",
"Food and Agriculture",
"Cyberbullying and Online Safety",
"Linguistic Diversity",
@@ -232,7 +231,6 @@ topics = [
"Meditation Practices",
"Literary Symbolism",
"Marine Conservation",
"Social Justice Movements",
"Sustainable Tourism",
"Ancient Philosophy",
"Cold War Era",

276
helper/exercises.py
View File

@@ -1036,7 +1036,7 @@ def gen_multiple_choice_level(quantity: int, start_id=1):
["questions"],
GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != 25:
if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id)
else:
all_exams = get_all("level")
@@ -1073,6 +1073,25 @@ 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 current_exercise, seen_keys
def replace_exercise_if_exists_utas(all_exams, current_exercise, current_exam, seen_keys):
# Extracting relevant fields for comparison
key = (current_exercise['prompt'], tuple(sorted(option['text'] for option in current_exercise['options'])))
# Check if the key is in the set
if key in seen_keys:
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_level_question(), current_exam, seen_keys)
else:
seen_keys.add(key)
for exam in all_exams:
if any(
exercise["prompt"] == current_exercise["prompt"] and
any(exercise["options"][0]["text"] == current_option["text"] for current_option in
current_exercise["options"])
for exercise in exam.get("questions", [])
):
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_level_question(), current_exam, seen_keys)
return current_exercise, seen_keys
def generate_single_mc_level_question():
messages = [
@@ -1109,3 +1128,258 @@ def parse_conversation(conversation_data):
readable_text.append(f"{name}: {text}")
return "\n".join(readable_text)
def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams):
gen_multiple_choice_for_text = "Generate " + str(
quantity) + " multiple choice blank space questions of 4 options for an english level exam, some easy questions, some intermediate " \
"questions and some advanced questions. Ensure that the questions cover a range of topics such as " \
"verb tense, subject-verb agreement, pronoun usage, sentence structure, and punctuation. Make sure " \
"every question only has 1 correct answer."
messages = [
{
"role": "system",
"content": (
'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": '
'[{"id": "A", "text": '
'"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": '
'"Happy"}, {"id": "D", "text": "Jump"}], '
'"prompt": "Which of the following is a conjunction?", '
'"solution": "A", "variant": "text"}]}')
},
{
"role": "user",
"content": gen_multiple_choice_for_text
}
]
token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,
["questions"],
GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id)
else:
seen_keys = set()
for i in range(len(question["questions"])):
question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_exams, question["questions"][i],
question,
seen_keys)
return fix_exercise_ids(question, start_id)
def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
json_format = {
"questions": [
{
"id": "9",
"options": [
{
"id": "A",
"text": "a"
},
{
"id": "B",
"text": "b"
},
{
"id": "C",
"text": "c"
},
{
"id": "D",
"text": "d"
}
],
"prompt": "prompt",
"solution": "A",
"variant": "text"
}
]
}
gen_multiple_choice_for_text = 'Generate ' + str(quantity) + (' multiple choice questions of 4 options for an english '
'level exam, some easy questions, some intermediate '
'questions and some advanced questions.Ensure that '
'the questions cover a range of topics such as verb '
'tense, subject-verb agreement, pronoun usage, '
'sentence structure, and punctuation. Make sure '
'every question only has 1 correct answer.')
messages = [
{
"role": "system",
"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
},
{
"role": "user",
"content": gen_multiple_choice_for_text
},
{
"role": "user",
"content": (
'The type of multiple choice is the prompt has wrong words or group of words and the options are to '
'find the wrong word or group of words that are underlined in the prompt. \nExample:\n'
'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'
'Options:\na: "complain"\nb: "all the time"\nc: "thinks"\nd: "is"')
}
]
token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,
["questions"],
GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != quantity:
return gen_multiple_choice_level(quantity, start_id)
else:
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)):
json_format = {
"question": {
"words": [
{
"id": "1",
"text": "a"
},
{
"id": "2",
"text": "b"
},
{
"id": "3",
"text": "c"
},
{
"id": "4",
"text": "d"
}
],
"text": "text"
}
}
gen_text = 'Generate a text of at least ' + str(size) + ' words about the topic ' + topic + '.'
messages = [
{
"role": "system",
"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
},
{
"role": "user",
"content": gen_text
},
{
"role": "user",
"content": (
'From the generated text choose ' + str(quantity) + ' words (cannot be sequential words) to replace '
'once with {{id}} where id starts on ' + str(start_id) + ' and is '
'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.')
}
]
token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,
["question"],
GEN_QUESTION_TEMPERATURE)
return question["question"]
def gen_reading_passage_utas(start_id, sa_quantity: int, mc_quantity: int, topic=random.choice(mti_topics)):
passage = generate_reading_passage(QuestionType.READING_PASSAGE_1, topic)
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)
return {
"exercises": {
"shortAnswer":short_answer,
"multipleChoice": mc_exercises,
},
"text": {
"content": passage["text"],
"title": passage["title"]
}
}
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"]}]}
messages = [
{
"role": "system",
"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
},
{
"role": "user",
"content": (
'Generate ' + str(sa_quantity) + ' short answer questions, and the possible answers, must have '
'maximum 3 words per answer, about this text:\n"' + text + '"')
},
{
"role": "user",
"content": 'The id starts at ' + str(start_id) + '.'
}
]
token_count = count_total_tokens(messages)
return make_openai_call(GPT_4_O, messages, token_count,
["questions"],
GEN_QUESTION_TEMPERATURE)["questions"]
def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int):
json_format = {
"questions": [
{
"id": "9",
"options": [
{
"id": "A",
"text": "a"
},
{
"id": "B",
"text": "b"
},
{
"id": "C",
"text": "c"
},
{
"id": "D",
"text": "d"
}
],
"prompt": "prompt",
"solution": "A",
"variant": "text"
}
]
}
messages = [
{
"role": "system",
"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
},
{
"role": "user",
"content": 'Generate ' + str(mc_quantity) + ' multiple choice questions of 4 options for this text:\n' + text
},
{
"role": "user",
"content": 'Make sure every question only has 1 correct answer.'
}
]
token_count = count_total_tokens(messages)
question = make_openai_call(GPT_4_O, messages, token_count,
["questions"],
GEN_QUESTION_TEMPERATURE)
if len(question["questions"]) != mc_quantity:
return gen_multiple_choice_level(mc_quantity, start_id)
else:
return fix_exercise_ids(question, start_id)["questions"]