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import torch
import random
from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
class QuestionGenerator:
def __init__(self, model_name='deepset/roberta-base-squad2'):
"""
Initialize question generation system
"""
self.device = 'cuda' if torch.cuda.is_available() else 'cpu' # Detect and set device
self.model = AutoModelForQuestionAnswering.from_pretrained(model_name) # Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
# Create QA pipeline
self.qa_pipeline = pipeline('question-answering', model=self.model, tokenizer=self.tokenizer,device=0 if self.device == 'cuda' else -1)
# Question templates
self.question_templates = ["What is the main idea of","Who is responsible for","When did this occur","Where does this take place","Why is this important","How does this work","What are the key features of","Explain the significance of","What is the purpose of","Describe the process of"]
def generate_questions(self, context, num_questions=3, difficulty='medium'):
"""
Generate multiple questions from context
"""
generated_questions = []
attempts = 0
max_attempts = num_questions * 10
while len(generated_questions) < num_questions and attempts < max_attempts:
try:
template = random.choice(self.question_templates) # Select random template
words = context.split() # Create question
start_index = random.randint(0, max(0, len(words) - 5))
full_question = f"{template} {' '.join(words[start_index:start_index+5])}?"
result = self.qa_pipeline(question=full_question, context=context) # Get answer
# Validate result
if (result['answer'] and len(result['answer']) > 3 and result['score'] > 0.5 and not any(q['answer'] == result['answer'] for q in generated_questions)):
generated_questions.append({'question': full_question,'answer': result['answer'],'confidence': result['score']})
attempts += 1
except Exception as e:
print(f"Question generation error: {e}")
attempts += 1
return generated_questions
def display_questions(self, questions):
"""
Display generated questions
"""
print("\n--- Generated Questions ---")
for idx, q in enumerate(questions, 1):
print(f"Q{idx}: {q['question']}")
print(f"Expected keyword: {q['answer']} \n")
def get_user_input():
"""
Get user input for question generation
"""
print("\n--- Interactive Question Generator ---")
print("\n>> Enter the context for question generation: ") # Context input
context = input().strip()
# Number of questions
while True:
try:
num_questions = int(input("\n>> How many questions do you want? (1-10): "))
if 1 <= num_questions <= 10:
break
else:
print("Please enter a number between 1 and 10.")
except ValueError:
print("Invalid input. Please enter a number.")
return context, num_questions
def main():
# Initialize generator
generator = QuestionGenerator()
while True:
try:
context, num_questions = get_user_input() # Get user input
questions = generator.generate_questions(context, num_questions=num_questions) # Generate questions
if questions: # Display questions
generator.display_questions(questions)
else:
print("Could not generate questions. Please try a different context.")
# Continue option
continue_choice = input("Generate more questions? (yes/no): ").lower()
if continue_choice not in ['yes', 'y']:
break
except Exception as e:
print(f"An error occurred: {e}")
print("Thank you for using the Question Generator!")
if __name__ == "__main__":
main()
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