quizgeneration / quiz_generation.py
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import re
import os
from apiclient import discovery
from oauth2client import client, file, tools
import bardapi
from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
from transformers import T5ForConditionalGeneration, T5Tokenizer
SCOPES = "https://www.googleapis.com/auth/forms.body"
DISCOVERY_DOC = "https://forms.googleapis.com/$discovery/rest?version=v1"
NEW_FORM = {
"info": {
"title": "Quiz"
}
}
model_name = "t5-base"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
def generate_quiz_questions(prompt):
# Set your Bard API key as an environment variable
os.environ['_BARD_API_KEY'] = "WwgqSrcbBC71HsiWpTlqnbDC9TQ3-9N1YyY6CHxOEfFp_qeCe0laziZoOT_dkTEjhJmOcw."
prompt_suffix = ". Each generated question has to begin with '๐Ÿ”น', each choice has to begin with '๐Ÿ”ธ', and each correct answer has to begin with 'โœ”๏ธ'."
# Send API requests and get responses
response = bardapi.core.Bard().get_answer(prompt + prompt_suffix)
quiz = response["content"]
return quiz
'''
def generate_quiz_url(prompt_text, form_service):
# Generate quiz questions based on the transcribed text
text = generate_quiz_questions(prompt_text)
# Questions, choices, and correct answers
questions = re.findall(r"๐Ÿ”น (.*?)\n", text)
choices = re.findall(r"๐Ÿ”ธ (.*?)\n", text)
answers = re.findall(r"โœ”๏ธ (.*?)\n", text)
# Remove the '**' from the questions list (they are not part of the question), choices, and correct answers
questions = [question.replace('**', '') for question in questions]
answers = [answer.replace('**', '') for answer in answers]
questions_list = []
# Fill the questions_list variable
for i, question in enumerate(questions):
choices_for_question = choices[i * 4:(i + 1) * 4]
correct_answer = answers[i] if i < len(answers) else ""
question_dict = {
"question": question,
"choices": choices_for_question,
"correct_answer": correct_answer
}
questions_list.append(question_dict)
# Create the initial form
result = form_service.forms().create(body=NEW_FORM).execute()
# Add the questions to the form
question_requests = []
for index, question in enumerate(questions_list):
new_question = {
"createItem": {
"item": {
"title": question["question"],
"questionItem": {
"question": {
"required": True,
"choiceQuestion": {
"type": "RADIO",
"options": [
{"value": choice} for choice in question["choices"]
],
"shuffle": True
}
}
}
},
"location": {
"index": index
}
}
}
question_requests.append(new_question)
NEW_QUESTIONS = {
"requests": question_requests
}
question_setting = form_service.forms().batchUpdate(formId=result["formId"], body=NEW_QUESTIONS).execute()
# Retrieve the updated form result
get_result = form_service.forms().get(formId=result["formId"]).execute()
# Get the form ID
form_id = get_result["formId"]
# Construct the quiz link using the form ID
form_url = result["responderUri"]
return form_url
'''
def explain_quiz_answers(questions_list):
explanations = []
for question in questions_list:
context = question["question"]
choices = question["choices"]
correct_answer = question["correct_answer"]
explanation = f"Question: {context}\n"
for choice in choices:
# Construct a query with each choice as a question
query = f"What is the reason for choosing '{choice}' in {context}?"
# Tokenize the query and context
inputs = tokenizer.encode_plus(query, context, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
# Generate the explanation using the T5 model
outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_length=256)
# Decode the explanation
explanation_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Add the explanation to the overall explanation
explanation += f"\nChoice: {choice}\nExplanation: {explanation_text}"
# Add an indicator if the choice is the correct answer
if choice == correct_answer:
explanation += " (Correct Answer)"
explanation += "\n"
explanations.append(explanation)
return explanations
def generate_quiz_url(prompt_text, form_service):
# Generate quiz questions based on the transcribed text
text = generate_quiz_questions(prompt_text)
# Questions, choices, and correct answers
questions = re.findall(r"๐Ÿ”น (.*?)\n", text)
choices = re.findall(r"๐Ÿ”ธ (.*?)\n", text)
answers = re.findall(r"โœ”๏ธ (.*?)\n", text)
# Remove the '**' from the questions list (they are not part of the question), choices, and correct answers
questions = [question.replace('**', '') for question in questions]
answers = [answer.replace('**', '') for answer in answers]
questions_list = []
# Fill the questions_list variable
for i, question in enumerate(questions):
choices_for_question = choices[i * 4:(i + 1) * 4]
correct_answer = answers[i] if i < len(answers) else ""
question_dict = {
"question": question,
"choices": choices_for_question,
"correct_answer": correct_answer
}
questions_list.append(question_dict)
# Create the initial form
result = form_service.forms().create(body=NEW_FORM).execute()
# Add the questions to the form
question_requests = []
for index, question in enumerate(questions_list):
new_question = {
"createItem": {
"item": {
"title": question["question"],
"questionItem": {
"question": {
"required": True,
"choiceQuestion": {
"type": "RADIO",
"options": [
{"value": choice} for choice in question["choices"]
],
"shuffle": True
}
}
}
},
"location": {
"index": index
}
}
}
question_requests.append(new_question)
NEW_QUESTIONS = {
"requests": question_requests
}
question_setting = form_service.forms().batchUpdate(formId=result["formId"], body=NEW_QUESTIONS).execute()
# Retrieve the updated form result
get_result = form_service.forms().get(formId=result["formId"]).execute()
# Get the form ID
form_id = get_result["formId"]
# Construct the quiz link using the form ID
form_url = result["responderUri"]
# Get the explanations for the quiz
explanations = explain_quiz_answers(questions_list)
return form_url, explanations