import gradio as gr
from transformers import pipeline, M2M100Tokenizer

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

print(tokenizer.lang_code_to_token)

translater_nr_en = pipeline("translation", model=model_name, src_lang="nr", tgt_lang="en")

def translate(inp):
    # Translate from isiNdebele to English
    res = translater_nr_en(inp, max_length=512, early_stopping=True)[0]['translation_text']
    return res

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)