app.py

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-1.3B'

    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}
    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.Dropdown(label='Source | འདི་ནས།', choices=lang_codes, value='English'),
              gr.Dropdown( label='Target | འདི་ལ།', choices=lang_codes, value='Standard Tibetan'),
              gr.Textbox(lines=5, label="Input text | ཡིག་གེ་འབྲི་ཡུལ།"),
              ]

    # outputs = gr.outputs.JSON()
    outputs = gr.Textbox(lines=5, label="Translated Result | རྒྱུར་ཟིན་པ།")

    title = "Dhumra AI Translator"

    demo_status = "[སྐད་ཡིག་གཅིག་ཀྱང་མ་ལུས་པ།]"
    description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
    examples = [
    [ 'English', 'Standard Tibetan', 'Weather is very good today!', 'དེ་རིང་གནམ་གཤིས་ཧ་ཅང་ལེགས་འདུག།']
    ]

    gr.Interface(translation,
                 inputs,
                 outputs,
                 title=title,
                 description=description,
                 examples=examples,
                 examples_per_page=50,
                 ).launch()