app.py

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()

# 3. Different task: language translation.
# First model translates English to German.
mdl_name = "Helsinki-NLP/opus-mt-en-de"
opus_translator = pipeline("translation", model=mdl_name)

def translate(text):
    response = opus_translator(text)
    return response

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