Update utils/quiz_offline.py

utils/quiz_offline.py

@@ -1,18 +1,17 @@
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+def generate_mcqs(text, num_questions=1):
+    input_text = f"generate question: {text}"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
 
-model_name = "valhalla/t5-small-qg-hl"
-tokenizer = T5Tokenizer.from_pretrained(model_name)
-model = T5ForConditionalGeneration.from_pretrained(model_name)
+    questions = []
+    for _ in range(num_questions):
+        output = model.generate(
+            input_ids=input_ids,
+            max_length=128,
+            num_return_sequences=1,
+            temperature=0.7
+        )
+        decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+        questions.append(decoded.strip())
 
-def generate_question(context, answer):
-    highlighted = context.replace(answer, f"<hl> {answer} <hl>")
-    prompt = f"generate question: {highlighted}"
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(input_ids, max_length=128)
-    question = tokenizer.decode(output[0], skip_special_tokens=True)
-    return question
-
-# Example
-context = "The mitochondria is responsible for producing energy in the cell."
-answer = "mitochondria"
-print(generate_question(context, answer))
+    # Join all questions into one string for Gradio output
+    return "\n".join(f"{i+1}. {q}" for i, q in enumerate(questions))