#from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
#import gradio as grad
#import ast
#mdl_name = "deepset/roberta-base-squad2"
#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()

from transformers import pipeline
import gradio as grad
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()