app.py

import gradio as gr
from transformers import pipeline
import spaces
import torch

# Check if CUDA is available and set device accordingly
device = 0 if torch.cuda.is_available() else -1

# Initialize the translation pipeline with the device argument
translator = pipeline("translation", model="facebook/m2m100_418M", device=device)

LANGS = ["hi", "zh", "fr", "en"]

@spaces.GPU
def translate(text, src_lang, tgt_lang):
    """
    Translate the text from source lang to target lang
    """
    translation = translator(text, src_lang=src_lang, tgt_lang=tgt_lang)
    return translation[0]['translation_text']

demo = gr.Interface(
    fn=translate,
    inputs=[
        gr.components.Textbox(label="Text"),
        gr.components.Dropdown(label="Source Language", choices=LANGS),
        gr.components.Dropdown(label="Target Language", choices=LANGS),
    ],
    outputs=["text"],
    examples=[["Building a translation demo with Gradio is so easy!", "en", "fr"]],
    cache_examples=False,
    title="Translation Demo",
    description="This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space"
)

demo.queue(default_concurrency_limit=8)

demo.launch()