Update app.py

app.py

@@ -14,20 +14,23 @@ model_plag = SentenceTransformer('all-MiniLM-L6-v2')
 # ------------------------------
 # Offline Quiz Generator
 # ------------------------------
+model_qg = T5ForConditionalGeneration.from_pretrained("valhalla/t5-small-qg-hl")
+tokenizer_qg = T5Tokenizer.from_pretrained("valhalla/t5-small-qg-hl")
+
 def generate_mcqs(text, num_questions=3):
-    input_text = f"generate question: {text}"
-    print(input_text)
+    input_text = f"generate questions: {text}"
     input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
 
-    questions = []
-    for _ in range(num_questions):
-        outputs = model_qg.generate(input_ids=input_ids, max_length=100, num_return_sequences=1, temperature=0.7)
-        decoded = tokenizer_qg.decode(outputs[0], skip_special_tokens=True)
-        print("decoded")
-        print(decoded)
-        questions.append(decoded.strip())
+    outputs = model_qg.generate(
+        input_ids=input_ids,
+        max_length=128,
+        num_return_sequences=num_questions,
+        num_beams=num_questions,
+        early_stopping=True
+    )
 
-    return "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
+    decoded = [tokenizer_qg.decode(output, skip_special_tokens=True).strip() for output in outputs]
+    return "\n".join([f"{i+1}. {q}" for i, q in enumerate(decoded)])
 
 # ------------------------------
 # Weakness Analyzer