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import os
import torch
import gradio as gr
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from flores200_codes import flores_codes
def load_models():
# build model and tokenizer
model_name_dict = {
"nllb-distilled-600M": "facebook/nllb-200-distilled-600M",
#'nllb-1.3B': 'facebook/nllb-200-1.3B',
#'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
#'nllb-3.3B': 'facebook/nllb-200-3.3B',
}
model_dict = {}
for call_name, real_name in model_name_dict.items():
print("\tLoading model: %s" % call_name)
model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
tokenizer = AutoTokenizer.from_pretrained(real_name)
model_dict[call_name + "_model"] = model
model_dict[call_name + "_tokenizer"] = tokenizer
return model_dict
def translation(source, target, text):
if len(model_dict) == 2:
model_name = "nllb-distilled-600M"
start_time = time.time()
source = flores_codes[source]
target = flores_codes[target]
model = model_dict[model_name + "_model"]
tokenizer = model_dict[model_name + "_tokenizer"]
translator = pipeline(
"translation",
model=model,
tokenizer=tokenizer,
src_lang=source,
tgt_lang=target,
)
output = translator(text, max_length=400)
end_time = time.time()
output = output[0]["translation_text"]
result = {
"inference_time": end_time - start_time,
"source": source,
"target": target,
"result": output,
}
return result
if __name__ == "__main__":
print("\tinit models")
global model_dict
model_dict = load_models()
# define gradio demo
lang_codes = list(flores_codes.keys())
# inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
inputs = [
gr.inputs.Dropdown(lang_codes, default="English", label="Source"),
gr.inputs.Dropdown(lang_codes, default="Korean", label="Target"),
gr.inputs.Textbox(lines=5, label="Input text"),
]
outputs = gr.outputs.JSON()
title = "NLLB distilled 600M demo"
demo_status = "Demo is running on CPU"
description = (
f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
)
examples = [["English", "Korean", "Hi. nice to meet you"]]
gr.Interface(
translation,
inputs,
outputs,
title=title,
description=description,
examples=examples,
examples_per_page=50,
).launch()
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