app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
from ui import title, description, examples
from langs import LANGS
#from langs_all import LANGS  ##for 200+ languages

TASK = "translation"
# CKPT = "facebook/nllb-200-distilled-1.3B"
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, src_lang, tgt_lang, max_length=512):
    """
    Translate the text from source lang to target lang
    """
    translation_pipeline = pipeline(TASK,
                                    model=model,
                                    tokenizer=tokenizer,
                                    src_lang=src_lang,
                                    tgt_lang=tgt_lang,
                                    max_length=max_length)
    
    # 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"),
        gr.components.Dropdown(label="Source Language", choices=LANGS),
        gr.components.Dropdown(label="Target Language", choices=LANGS),
        gr.components.Slider(8, 512, value=512, step=8, label="Max Length")
    ],
    ["text"],
    examples=examples,
    # article=article,
    cache_examples=False,
    title=title,
    description=description
).launch()