app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
import re

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

def generate_qas(paragraph):
    sentences = re.split(r'(?<=[.!?]) +', paragraph.strip())
    qas = []

    for sent in sentences:
        if not sent.strip():
            continue
        input_text = f"generate questions: {sent.strip()}"
        input_ids = tokenizer.encode(input_text, return_tensors="pt")
        outputs = model.generate(input_ids, max_length=128, num_beams=4, do_sample=False)
        raw_output = tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Split on <sep> and remove duplicates and empties
        questions = [q.strip() for q in raw_output.split("<sep>") if q.strip()]
        seen = set()
        unique_questions = []
        for q in questions:
            if q not in seen:
                unique_questions.append(q)
                seen.add(q)

        for q in unique_questions:
            qas.append(f"Q: {q}\nA: {sent.strip()}")

    return "\n\n".join(qas)

gr.Interface(
    fn=generate_qas,
    inputs=gr.Textbox(label="Enter a paragraph", lines=8),
    outputs=gr.Textbox(label="Generated Questions and Answers"),
    title="🧠 Multi Q&A Generator",
    description="Generates diverse question-answer pairs for each sentence using T5."
).launch()