app.py

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

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()