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import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import pipeline
REPO_ID_NLLB = "facebook/nllb-200-distilled-600M"
REPO_ID_MARIANNMT_en = "mbarnig/MarianNMT-tatoeba-en-lb"
REPO_ID_MARIANNMT_lb = "mbarnig/MarianNMT-tatoeba-lb-en"
REPO_ID_T5MT5 = "mbarnig/T5-mt5-tatoeba-en-lb"
translator = pipeline("translation", model=REPO_ID_NLLB)
my_title = "🇫🇷 Mir iwwersetzen vun an op Lëtzebuergesch ! 🇬🇧"
my_description = "English-Luxembourgish machine translation (MT) demo based on 3 transformer models: NLLB, MarianNMT & T5/mt5."
my_article = "abc"
TRANSLATION_MODELS = [
"NLLB",
"MarianNMT",
"T5/mt5"
]
TRANSLATION_DIRECTION = [
"en -> lb",
"lb -> en"
]
EXAMPLE = "..."
my_input = gr.Textbox(label="Input")
my_output = gr.Textbox(label="Translation")
def iwwersetz(source_text):
translation = translator(source_text)
return translation
demo=gr.Interface(
fn=iwwersetz,
inputs=my_input,
outputs=my_output,
title=my_title,
description=my_description,
article=my_article,
examples=EXAMPLE,
allow_flagging=False)
demo.launch() |