app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the model and tokenizer with use_fast=False to avoid SentencePiece issues
model_name = "valhalla/t5-base-e2e-qg"
tokenizer = T5Tokenizer.from_pretrained(model_name, use_fast=False)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def generate_questions(text):
    input_text = f"generate questions: {text}"
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    outputs = model.generate(input_ids, max_length=256, num_beams=4, do_sample=False)
    questions = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return questions

gr.Interface(
    fn=generate_questions,
    inputs=gr.Textbox(label="Enter a paragraph", lines=8),
    outputs="text",
    title="📘 Question Generator (T5)"
).launch()