resolved model issue

short_answer_generator.py

@@ -95,3 +95,103 @@ def main():
 
 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.")