from transformers import AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, 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)

model_translate_name = 'danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR'
model_translate = AutoModelForSeq2SeqLM.from_pretrained(model_translate_name)
model_translate_token = AutoTokenizer.from_pretrained(model_translate_name)
translate_pipeline = ('translation', model=model_translate_name)

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


def translate(text):
    inputs = model_translate_token(text, return_tensor='pt')
    translate_output = model_translate.generate(**inputs)
    response = model_translate_token(translate_output[0], skip_special_tokens=True)
    #response = translate_pipeline(text)
    return response

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