app.py

from random import choices
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from ui import title, description
from langs import LANGS

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

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




def translate(text, src_lang, tgt_lang, max_length=400):
    translation_pipeline = pipeline(TASK,
                                    model=model,
                                    tokenizer=tokenizer,
                                    src_lang=src_lang,
                                    tgt_lang=tgt_lang,
                                    max_length=max_length)

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


translate_interface = gr.Interface(fn=translate,
                                   inputs=outputs="text",
                                   title=title,
                                   description=description)


gr.Interface(
    translate,
    [
        gr.inputs.Textbox(label="Text"),
        gr.inputs.Dropdown(label="Source Language", choices=LANGS),
        gr.inputs.Dropdown(label="Target Language", choices=LANGS),
        gr.inputs.slider(label="Max Length", min=8,
                         max=512, step=8, default=400)
    ],
    ["text"],
    # examples=examples,
    # article=article,
    cache_examples=False,
    title=title,
    description=description
).launch()