app.py

import gradio as gr
from transformers import pipeline

# ✅ Use the correct pipeline task
question_generator = pipeline("text2text-generation", model="valhalla/t5-base-e2e-qg")

def generate_questions(text):
    # Split text into chunks (optional)
    chunks = text.split(". ")
    questions = []
    for chunk in chunks:
        if len(chunk.strip()) > 0:
            result = question_generator(chunk, max_length=64)[0]['generated_text']
            questions.append(f"❓ {result}")
    return "\n".join(questions)

# Gradio interface
interface = gr.Interface(
    fn=generate_questions,
    inputs=gr.Textbox(lines=15, placeholder="Paste your long text here..."),
    outputs="text",
    title="🔍 Generate Questions from Text",
    description="Uses a T5 model to generate questions from a multi-sentence paragraph."
)

interface.launch()