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

LANGS = ["pes_Arab", "ckb_Arab", "eng_Latn"]
langs_dict = {
    "فارسی": "pes_Arab",
    "کردی": "ckb_Arab",
    "انگلیسی": "eng_Latn"
    }

def translate(text, src_lang, tgt_lang):
    """
    Translate the text from source lang to target lang
    """

    if len(model_dict) == 2:
        model_name = 'nllb-3.3B'
    model = model_dict[model_name + '_model']
    tokenizer = model_dict[model_name + '_tokenizer']

    translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=langs_dict[src_lang], tgt_lang=langs_dict[tgt_lang], max_length=400, device="cpu")
    result = translation_pipeline(text)
    return result[0]['translation_text']

def file_translate(sorce_file_path, pred_file_path):
    sorce_list = []
    with open(sorce_file_path, "r", encoding="utf-8") as sorce_file:
        for line in sorce_file:
            sorce_list.append(line.strip())

    pred_list = []
    for line in sorce_list:
        pred_list.append(translate(line, list(langs_dict.keys())[0], list(langs_dict.keys())[1]))

    with open(pred_file_path, "w", encoding="utf-8") as output_file:
        for translation in pred_list:
            output_file.write(translation + "\n")
    return pred_file_path

if __name__ == '__main__':
    print('\tinit models')

    global model_dict

    model_dict = load_models()
    
    interface = gr.Interface(
    fn=file_translate,
    inputs=[
        gr.components.File(label="Input File"),
        gr.components.Textbox(label="Output File Name (optional)"),
    ],
    outputs=[
        gr.components.File(label="Modified File"),
    ],
    title="Add 'Hello' Line to Text File",
    description="This Gradio demo adds the line 'Hello' to the end of a text file.",
    )
    interface.launch()