Update app.py

app.py

@@ -5,16 +5,28 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration
 from sentence_transformers import SentenceTransformer, util
 
 # ------------------------------
-# Offline Quiz Generator
+# Load Models
 # ------------------------------
 model_qg = T5ForConditionalGeneration.from_pretrained("t5-base")
 tokenizer_qg = T5Tokenizer.from_pretrained("t5-base")
+model_plag = SentenceTransformer('all-MiniLM-L6-v2')
 
+# ------------------------------
+# Offline Quiz Generator
+# ------------------------------
 def generate_mcqs(text, num_questions=3):
-    input_text = f"generate questions: {text}"
+    input_text = f"generate question: {text}"
+    print(input_text)
     input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model_qg.generate(input_ids=input_ids, max_length=256, num_return_sequences=1)
-    return tokenizer_qg.decode(outputs[0], skip_special_tokens=True).strip()
+
+    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)
+        questions.append(decoded.strip())
+
+    return "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
 
 # ------------------------------
 # Weakness Analyzer
@@ -25,7 +37,7 @@ def analyze_weakness(csv_file):
     return summary.to_string()
 
 # ------------------------------
-# Teaching Assistant (Placeholder)
+# Teaching Assistant
 # ------------------------------
 def chatbot_response(message, history):
     return "This is a placeholder response for now. (LLM not integrated)"
@@ -75,8 +87,6 @@ def translate_text(text, target_lang):
 # ------------------------------
 # Plagiarism Checker
 # ------------------------------
-model_plag = SentenceTransformer('all-MiniLM-L6-v2')
-
 def check_plagiarism(text1, text2):
     emb1 = model_plag.encode(text1, convert_to_tensor=True)
     emb2 = model_plag.encode(text2, convert_to_tensor=True)
@@ -93,7 +103,7 @@ with gr.Blocks() as demo:
         quiz_text = gr.Textbox(label="Content", lines=5)
         quiz_slider = gr.Slider(1, 10, value=3, label="Number of Questions")
         quiz_btn = gr.Button("Generate Quiz")
-        quiz_out = gr.Textbox(label="Generated Quiz")
+        quiz_out = gr.Textbox(label="Generated Quiz", lines=10)
         quiz_btn.click(fn=generate_mcqs, inputs=[quiz_text, quiz_slider], outputs=quiz_out)
 
     with gr.Tab("📉 Weakness Analyzer"):