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import time
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from flores200_codes import flores_codes
# Load models and tokenizers once during initialization
def load_models():
model_name_dict = {
"nllb-distilled-600M": "facebook/nllb-200-distilled-600M",
}
model_dict = {}
for call_name, real_name in model_name_dict.items():
print("\tLoading model:", call_name)
model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
tokenizer = AutoTokenizer.from_pretrained(real_name)
model_dict[call_name] = {
"model": model,
"tokenizer": tokenizer,
}
return model_dict
# Translate text using preloaded models and tokenizers
def translate_text(source, target, text, model_dict):
model_name = "nllb-distilled-600M"
if model_name in model_dict:
model_info = model_dict[model_name]
model = model_info["model"]
tokenizer = model_info["tokenizer"]
start_time = time.time()
source_code = flores_codes[source]
target_code = flores_codes[target]
translator = pipeline(
"translation",
model=model,
tokenizer=tokenizer,
src_lang=source_code,
tgt_lang=target_code,
)
output = translator(text, max_length=400)
end_time = time.time()
output_text = output[0]["translation_text"]
result = {
"inference_time": end_time - start_time,
"source": source_code,
"target": target_code,
"result": output_text,
}
return result
else:
raise KeyError(f"Model '{model_name}' not found in model_dict")
if __name__ == "__main__":
print("\tInitializing models")
model_dict = load_models()
lang_codes = list(flores_codes.keys())
inputs = [
gr.inputs.Dropdown(lang_codes, default="English", label="Source"),
gr.inputs.Dropdown(lang_codes, default="Nepali", label="Target"),
gr.inputs.Textbox(lines=5, label="Input text"),
]
outputs = gr.outputs.JSON()
title = "The Master Betters Translator"
description = (
"This is a beta version of The Master Betters Translator that utilizes pre-trained language models for translation. To use this app you need to have chosen the source and target language with your input text to get the output."
)
examples = [["English", "Nepali", "Hello, how are you"]]
gr.Interface(
translate_text,
inputs,
outputs,
title=title,
description=description,
examples=examples,
examples_per_page=50,
).launch()
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