app.py

import gradio as gr
import requests
import os
import numpy as np
import pandas as pd
import io
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
from questiongenerator import QuestionGenerator

qg = QuestionGenerator()
# num_que = 5

def generate_questions(article,num_que):
    result = ''
    if num_que == None or num_que == '':
        num_que = 5
    else:
        num_que = num_que
    generated_questions_list = qg.generate(article, num_questions=int(num_que))
    summarized_data = {
        "generated_questions" : generated_questions_list
    }
    generated_questions = summarized_data.get("generated_questions",'')
    for q in generated_questions:
        result = result + q + '\n'
    return result

## design 1
inputs=gr.Textbox(lines=5, label="Article/Text",elem_id="inp_div")
total_que = gr.Textbox(label="Number of Question want to generate",elem_id="inp_div")
outputs=gr.Textbox(lines=5, label="Generated Questions",elem_id="inp_div")

demo = gr.Interface(
    generate_questions,
    [inputs,total_que],
    outputs,
    title="Question Generation using T5",
    description="Feel free to give your feedback", 
    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;"
)
demo.launch(enable_queue = False)