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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-distilled-1.3B": "facebook/nllb-200-distilled-1.3B",
"nllb-1.3B": "facebook/nllb-200-1.3B",
# "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(model_name, source, target, text):
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()
full_output = output
output = output[0]["translation_text"]
result = {
"inference_time": end_time - start_time,
"source": source,
"target": target,
"result": output,
"full_output": full_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-distilled-1.3B",
"nllb-1.3B",
# "nllb-3.3B"
],
label="NLLB Model",
default="nllb-distilled-600M",
),
gr.inputs.Dropdown(lang_codes, default="Najdi Arabic", label="Source"),
gr.inputs.Dropdown(lang_codes, default="English", label="Target"),
gr.inputs.Textbox(lines=5, label="Input text"),
]
outputs = gr.outputs.JSON()
title = "NLLB (No Language Left Behind) demo"
demo_status = "Demo is running on CPU"
description = f"Using NLLB model, details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
examples = [["nllb-1.3B", "Najdi Arabic", "English", "جلست اطفال"]]
gr.Interface(
translation,
inputs,
outputs,
title=title,
description=description,
examples=examples,
examples_per_page=50,
).launch()
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