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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 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 1.3B 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, | |
| ).launch() | |