from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
import gradio as grad
import ast

# 1. The RoBERTa base model is used, fine-tuned using the SQuAD 2.0 dataset. 
# It’s been trained on question-answer pairs, including unanswerable questions, for the task of question and answering.
# mdl_name = "deepset/roberta-base-squad2"
# my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)

# 2. Different model.
mdl_name = "distilbert-base-cased-distilled-squad"
my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)

def answer_question(question,context):
    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
    di=ast.literal_eval(text)
    response = my_pipeline(di)
    return response

grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()