main.py

import tensorflow as tf
from translator import Translator
from utils import tokenizer_utils
from utils.preprocessing import input_processing, output_processing
from models.transformer import Transformer
from models.encoder import Encoder
from models.decoder import Decoder
from models.layers import EncoderLayer, DecoderLayer, MultiHeadAttention, point_wise_feed_forward_network
from models.utils import masked_loss, masked_accuracy
import argparse

def main(sentences: list, model: tf.keras.Model, en_tokenizer, vi_tokenizer) -> None:
    """
    Translates input English sentences to Vietnamese using a pre-trained model.

    Args:
        sentences (list): List of English sentences to translate.
        model (tf.keras.Model): The pre-trained translation model.
        en_tokenizer: English tokenizer.
        vi_tokenizer: Vietnamese tokenizer.
    """
    # Initialize the translator with tokenizers and the model
    translator = Translator(en_tokenizer, vi_tokenizer, model)

    # Process and translate each sentence
    for sentence in sentences:
        processed_sentence = input_processing(sentence)
        translated_text = translator(processed_sentence)
        translated_text = output_processing(translated_text)

        # Display the input and translated text
        print("Input:", processed_sentence)
        print("Translated:", translated_text)
        print("-" * 50)

if __name__ == "__main__":
    # Set up argument parser
    parser = argparse.ArgumentParser(
        description="Translate English sentences to Vietnamese using a pre-trained transformer model.",
        epilog="Example: python translate.py --sentence 'Hello, world!' --sentence 'The sun is shining.'"
    )
    parser.add_argument(
        "--sentence",
        type=str,
        nargs="*",
        default=[
            (
                "For at least six centuries, residents along a lake in the mountains of central Japan "
                "have marked the depth of winter by celebrating the return of a natural phenomenon "
                "once revered as the trail of a wandering god."
            )
        ],
        help="One or more English sentences to translate (default: provided example sentence)"
    )
    parser.add_argument(
        "--model_path",
        type=str,
        default="ckpts/en_vi_translation.keras",
        help="Path to the pre-trained model file (default: ckpts/en_vi_translation.keras)"
    )

    # Parse arguments
    args = parser.parse_args()

    # Define custom objects required for loading the model
    custom_objects = {
        "Transformer": Transformer,
        "Encoder": Encoder,
        "Decoder": Decoder,
        "EncoderLayer": EncoderLayer,
        "DecoderLayer": DecoderLayer,
        "MultiHeadAttention": MultiHeadAttention,
        "point_wise_feed_forward_network": point_wise_feed_forward_network,
        "masked_loss": masked_loss,
        "masked_accuracy": masked_accuracy,
    }

    # Load the pre-trained model once
    print("Loading model from:", args.model_path)
    loaded_model = tf.keras.models.load_model(
        args.model_path, custom_objects=custom_objects
    )
    print("Model loaded successfully.")

    # Load English and Vietnamese tokenizers once
    en_tokenizer, vi_tokenizer = tokenizer_utils.load_tokenizers()

    # Run the translation for all provided sentences
    main(sentences=args.sentence, model=loaded_model, en_tokenizer=en_tokenizer, vi_tokenizer=vi_tokenizer)

    # Interactive loop for additional translations
    while True:
        choice = input("Would you like to translate another sentence? (Y/n): ").strip().lower()
        if choice in ['no', 'n', 'quit', 'q']:
            print("Exiting the program.")
            break
        elif choice in ['yes', 'y']:
            new_sentence = input("Enter an English sentence to translate: ").strip()
            if new_sentence:
                main(sentences=[new_sentence], model=loaded_model, en_tokenizer=en_tokenizer, vi_tokenizer=vi_tokenizer)
            else:
                print("No sentence provided. Please try again.")
        else:
            print("Invalid input. Please enter 'y' or 'n'.")