app.py

import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline



def text(input):
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    input_ids = tokenizer.encode(input, return_tensors="pt")
    outputs = model.generate(input_ids)
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)

    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
    input_ids = tokenizer.encode(decoded, return_tensors="pt")
    outputs = model.generate(input_ids)
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return decoded


iface = gr.Interface(fn=text,
                     inputs=[
                         gr.inputs.Textbox(
                             lines=2, placeholder=None, label='Sentence'),
                     ],
                     outputs=[gr.outputs.JSON(label=None)])
iface.launch()