app.py

import gradio as gr
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration, pipeline

model_name = "dsfsi/nr-en-m2m100-gov"
tokenizer = M2M100Tokenizer.from_pretrained(model_name)
model = M2M100ForConditionalGeneration.from_pretrained(model_name)

tokenizer.src_lang = "nr"

model.config.forced_bos_token_id = tokenizer.get_lang_id("en")

translater_nr_en = pipeline(
    "translation",
    model=model,
    tokenizer=tokenizer,
)

def translate(inp):
    res = translater_nr_en(inp, max_length=512, early_stopping=True)[0]['translation_text']
    return res

# Update description and article as needed
description = """
<p>
<center>
One-way Translation from isiNdebele to English
</center>
</p>
"""
article = "<p style='text-align: center'><a href='https://huggingface.co/dsfsi/nr-en-m2m100-gov' target='_blank'>by dsfsi</a></p></center></p>"

examples = [
    ["Ngiyabonga kakhulu ngesipho osinike sona."],
    ["Ukuthula kuhlale kuyindlela ephilayo yempilo yethu."]
]

iface = gr.Interface(
    fn=translate,
    title="isiNdebele to English Translation",
    description=description,
    article=article,
    examples=examples,
    inputs=gr.components.Textbox(lines=5, placeholder="Enter isiNdebele text (maximum 5 lines)", label="Input"),
    outputs="text"
)

iface.launch(enable_queue=True)