app.py

import gradio as gr
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

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")

def translate(inp):
    inputs = tokenizer(inp, return_tensors="pt")
    
    translated_tokens = model.generate(**inputs, max_length=512, forced_bos_token_id=tokenizer.get_lang_id("en"))
    
    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
    return translated_text

description = """
<p>
<center>
isiNdebele to English Translation
</center>
</p>
"""
article = "<p style='text-align: center'><a href='https://huggingface.co/dsfsi/nr-en-m2m100-gov' target='_blank'>by dsfsi</a></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)