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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")
# 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", "German", "Eastern Panjabi", "Sanskrit", "Urdu", "Tamil", "Telugu", "Yue Chinese", "Chinese (Simplified)", "Chinese (Traditional)", "Hindi", "French", "Spanish"],label="Select destination language")],
outputs=[gr.Textbox(label="Translated text", lines=4)],
title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Project 4: Multi Language translator",
description="THIS APPLICATION WILL BE USED TO TRANSLATE ANY ENGLISH TO MULTIPLE LANGUAGES",
theme=gr.themes.Soft(),
concurrency_limit=16)
demo.launch()
# Hello Friends, Welcome to my channel. I hope this video helps you understand AI.
# "Hello friends how are you?" |