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

model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")

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

src_lang = 'som_Latn'
tgt_lang = "eng_Latn"

def translate(text):
    translation_pipeline = pipeline("translation",
                                    model=model,
                                    tokenizer=tokenizer,
                                    src_lang=src_lang,
                                    tgt_lang=tgt_lang,
                                    device=device)

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


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