app.py

import gradio as gr
from transformers import pipeline
question_gen = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl")

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}"
    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)])
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)
    btn = gr.Button("Generate")
    btn.click(fn=generate_questions, inputs=[input_text, num_questions, question_type], outputs=output)

demo.launch()