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# Use a pipeline as a high-level helper
from transformers import pipeline
import torch
import json
import gradio as gr
text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M", torch_dtype=torch.bfloat16)
# Load the JSON table
with open('language.json') as f:
language_data = json.load(f)
def get_flores_200_code(language):
for code in language_data:
if code['Language'] == language:
return code['FLORES-200 code']
return None
def translate_text(text, destination_language):
# text = "Hello friends how are you?"
dest_code = get_flores_200_code(destination_language)
translation = text_translator(text,
src_lang="eng_Latn",
tgt_lang=dest_code)
return translation[0]["translation_text"]
gr.close_all()
demo = gr.Interface(fn=translate_text,
inputs=[gr.Textbox(label="Input text to translate",lines=6), gr.Dropdown(["English", "Bulgarian",
"Croatian",
"Czech",
"Danish",
"Dutch",
"English",
"Estonian",
"Finnish",
"French",
"German",
"Greek",
"Hungarian",
"Irish",
"Italian",
"Latvian",
"Lithuanian",
"Maltese",
"Polish",
"Portuguese",
"Romanian",
"Slovak",
"Slovenian",
"Spanish",
"Swedish"],label="Select destination language")],
outputs=[gr.Textbox(label="Translated text", lines=4)],
title="Multi Language translator - From European languages to English",
description="This application uses NLLB model from Facebook trained on 200 languages for machine translation research",
concurrency_limit=16)
demo.launch() |