app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
from langs import LANGS

TASK = "translation"
CKPT = "facebook/nllb-200-distilled-600M"

model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
tokenizer = AutoTokenizer.from_pretrained(CKPT)

device = 0 if torch.cuda.is_available() else -1


def translate(text):
    """
    Translate the text from source lang to target lang
    """
    src_lang = "zho-Hans"
    tgt_lang = "eng_Latn"
    max_length = 400
    
    translation_pipeline = pipeline(TASK,
                                    model=model,
                                    tokenizer=tokenizer,
                                    src_lang=src_lang,
                                    tgt_lang=tgt_lang,
                                    max_length=max_length,
                                    device=device)

    result = translation_pipeline(text)
    return result[0]['translation_text']


gr.Interface(
    translate,
    [
        gr.components.Textbox(label="Text"),
    ],
    ["text"],
).launch()