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import gradio as gr | |
import random | |
import torch | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
from question_generation import question_generation_sampling | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
g1_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") | |
g1_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") | |
g2_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-Distractor") | |
g2_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-Distractor") | |
g1_model.eval() | |
g2_model.eval() | |
g1_model.to(device) | |
g2_model.to(device) | |
def generate_multiple_choice_question( | |
context | |
): | |
num_questions = 5 | |
question_item = question_generation_sampling( | |
g1_model, g1_tokenizer, | |
g2_model, g2_tokenizer, | |
context, num_questions, device | |
)[0] | |
question = question_item['question'] | |
options = question_item['options'] | |
options[0] = f"{options[0]} [ANSWER]" | |
random.shuffle(options) | |
output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}" | |
return output_string | |
demo = gr.Interface( | |
fn=generate_multiple_choice_question, | |
inputs=gr.Textbox(lines=8, placeholder="Context Here..."), | |
outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "), | |
title="Multiple-choice Question Generator", | |
description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.", | |
allow_flagging='never' | |
) | |
demo.launch() |