Update app.py

app.py

@@ -1,24 +1,22 @@
 import gradio as gr
 from transformers import pipeline
+
+# Load the question generation model
 question_gen = pipeline("text2text-generation", model="iarfmoose/t5-base-question-generator")
 
+# Function to generate questions
 def generate_questions(text, num_questions, question_type):
-    # Insert highlight tags around the first sentence for question generation
-    sentences = text.split('.')
-    if len(sentences) > 1:
-        highlighted = f"<hl> {sentences[0].strip()} <hl>." + '.'.join(sentences[1:])
-    else:
-        highlighted = f"<hl> {text.strip()} <hl>"
-
-    prompt = f"generate questions: {highlighted}"
+    prompt = f"generate questions: {text}"
     results = question_gen(prompt, max_length=128, num_return_sequences=num_questions)
     return "\n\n".join([f"{i+1}. {r['generated_text']}" for i, r in enumerate(results)])
+
+# Gradio app
 with gr.Blocks() as demo:
     gr.Markdown("# AI Mock Test Generator")
-    input_text = gr.Textbox(lines=10, label="Paste text or content here")
-    num_questions = gr.Slider(minimum=1, maximum=10, value=5, label="Number of Questions")
-    question_type = gr.Radio(choices=["mcq", "subjective", "mixed"], value="mixed", label="Question Type")
-    output = gr.Textbox(label="Generated Questions", lines=10)
+    input_text = gr.Textbox(lines=10, label="Paste your study material here")
+    num_questions = gr.Slider(minimum=1, maximum=5, value=3, label="Number of Questions")
+    question_type = gr.Radio(["subjective"], value="subjective", label="Question Type (only subjective supported now)")
+    output = gr.Textbox(label="Generated Questions")
     btn = gr.Button("Generate")
     btn.click(fn=generate_questions, inputs=[input_text, num_questions, question_type], outputs=output)