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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)
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