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() import torch import random from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer class QuestionGenerator: def __init__(self, model_name='distilbert-base-uncased-distilled-squad'): """ Initialize question generation system using a stable QA model """ self.device = 'cuda' if torch.cuda.is_available() else 'cpu' self.model = AutoModelForQuestionAnswering.from_pretrained(model_name) 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 ) # Sample templates to simulate natural QA generation 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 short answer questions based on provided 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) words = context.split() start_index = random.randint(0, max(0, len(words) - 5)) snippet = ' '.join(words[start_index:start_index + 5]) full_question = f"{template} {snippet}?" result = self.qa_pipeline(question=full_question, context=context) # Validate and deduplicate if ( result['answer'] and len(result['answer']) > 3 and result['score'] > 0.5 and not any(q['answer'].lower() == result['answer'].lower() 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): print("\n--- Generated Questions ---") for idx, q in enumerate(questions, 1): print(f"Q{idx}: {q['question']}") print(f"Expected keyword: {q['answer']} \n") # Run this if testing standalone if __name__ == "__main__": print("\n>> Enter the context for question generation: ") context = input().strip() while True: try: num_q = int(input("\n>> How many questions do you want? (1-10): ")) if 1 <= num_q <= 10: break print("Please enter a number between 1 and 10.") except ValueError: print("Invalid input. Please enter a number.") generator = QuestionGenerator() questions = generator.generate_questions(context, num_questions=num_q) if questions: generator.display_questions(questions) else: print("❌ Could not generate any questions.")