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import os
import torch
import gradio as gr
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-1.3B")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-1.3B")
def translation(source, target, text) -> str:
translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
output = translator(text, max_length=400)
output = output[0]['translation_text']
return output
if __name__ == '__main__':
# define gradio demo
lang_codes = ["eng_Latn", "fuv_Latn", "fra_Latn", "arb_Arab"]
#inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
inputs = [gr.inputs.Dropdown(lang_codes, default='fra_Latn', label='Source'),
gr.inputs.Dropdown(lang_codes, default='fuv_Latn', label='Target'),
gr.inputs.Textbox(lines=5, label="Input text"),
]
title = "Fulfulde translator"
demo_status = "Demo is running on CPU"
description = "Fulfulde to French, English or Arabic and vice-versa translation demo using NLLB."
examples = [
['fra_Latn', 'fuv_Latn', 'La traduction est une tâche facile.']
]
gr.Interface(
translation,
inputs,
["text"],
examples=examples,
cache_examples=False,
title=title,
description=description
).launch()
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