app.py

import os
import spaces
import gradio as gr #gr.load("models/NgalNgal/mT5-new").launch()
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import sentencepiece as spm
import ctranslate2
from nltk import sent_tokenize


ct_model_path = "mbart25enmy_ct2/"
sp_model_path = "mbart25enmy_ct2/sentence.bpe.model"
translator = ctranslate2.Translator(ct_model_path)
sp_model = spm.SentencePieceProcessor(sp_model_path)



   
def translate(source, translator, sp_model):
    """Use CTranslate model to translate a sentence

    Args:
        source (str): Source sentences to translate
        translator (object): Object of Translator, with the CTranslate2 model
        sp_model (object): Object of SentencePieceProcessor, with the SentencePiece source model

    Returns:
        Translation of the source text
    """

#    source_sentences = sent_tokenize(source)
    source_tokenized = sp_model.encode(source, out_type=str)
#    print("print 1" , source_tokenized)	
    source_tokenized = ["[en_XX]"] + source_tokenized
#    print("print " + source_tokenized)
    target_prefix = ["[my_MM]"]
    translations = translator.translate_batch([source_tokenized],target_prefix= [target_prefix])
#    translations = [translation[0]["tokens"] for translation in translations]
    translations = sp_model.decode(translations[0].hypotheses[0][1:])
#    translation = " ".join(translations_detokenized)

    return translations

def call_model(source):
  translation = translate(source, translator, sp_model)
  return translation

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            english = gr.Textbox(label="English text")
            translate_btn = gr.Button(value="Translate")
        with gr.Column():
            myanmar = gr.Textbox(label="Myanmar Text")

    translate_btn.click(call_model, inputs=english, outputs=myanmar, api_name="English-to-Myanmar")
    examples = gr.Examples(examples=["I went to the supermarket yesterday.", "Helen is a good swimmer."],
                           inputs=[english])

demo.launch()