app.py

import gradio as grad
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "Helsini-NLP/opus-mt-en-de"

model = AutoModelForSeq2SeqLM(model_name)
model_tkn = AutoTokenizer(model_name)

#opus_translation = pipeline("translation", model=model_name)

def translate(text):
    inputs = model_tkn(text, return_tensors="pt")
    trans_ouput = model.generate(**inputs) #"generate": This method includes the logic for autoregressive generation
    response = model_tkn.decode(trans_output[0], skip_special_tokens=True)
    #response = opus_translator(text)
    return response
grad.Interface(translate, inputs="text", output="text").launch()