Create app.py

app.py

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+import gradio as gr
+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
+
+def load_model_and_tokenizers(model_path="saved_models/en_vi_translation.keras"):
+    """
+    Load the pre-trained model and tokenizers.
+    
+    Args:
+        model_path (str): Path to the pre-trained model file.
+    
+    Returns:
+        model: Loaded TensorFlow model.
+        en_tokenizer: English tokenizer.
+        vi_tokenizer: Vietnamese tokenizer.
+    """
+    # Define custom objects for 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 model
+    try:
+        model = tf.keras.models.load_model(model_path, custom_objects=custom_objects)
+        print("Model loaded successfully.")
+    except Exception as e:
+        raise Exception(f"Failed to load model: {str(e)}")
+    
+    # Load tokenizers
+    try:
+        en_tokenizer, vi_tokenizer = tokenizer_utils.load_tokenizers()
+        print("Tokenizers loaded successfully.")
+    except Exception as e:
+        raise Exception(f"Failed to load tokenizers: {str(e)}")
+    
+    return model, en_tokenizer, vi_tokenizer
+
+def translate_sentence(sentence, model, en_tokenizer, vi_tokenizer):
+    """
+    Translate a single English sentence to Vietnamese.
+    
+    Args:
+        sentence (str): English sentence to translate.
+        model: Pre-trained translation model.
+        en_tokenizer: English tokenizer.
+        vi_tokenizer: Vietnamese tokenizer.
+    
+    Returns:
+        str: Translated Vietnamese sentence.
+    """
+    if not sentence.strip():
+        return "Please provide a valid sentence."
+    
+    # Initialize translator
+    translator = Translator(en_tokenizer, vi_tokenizer, model)
+    
+    # Process and translate
+    processed_sentence = input_processing(sentence)
+    translated_text = translator(processed_sentence)
+    translated_text = output_processing(translated_text)
+    
+    return translated_text
+
+# Load model and tokenizers once at startup
+try:
+    model, en_tokenizer, vi_tokenizer = load_model_and_tokenizers()
+except Exception as e:
+    raise Exception(f"Initialization failed: {str(e)}")
+
+# Define Gradio interface
+def gradio_translate(sentence):
+    """
+    Gradio-compatible translation function.
+    
+    Args:
+        sentence (str): Input English sentence.
+    
+    Returns:
+        str: Translated Vietnamese sentence.
+    """
+    return translate_sentence(sentence, model, en_tokenizer, vi_tokenizer)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=gradio_translate,
+    inputs=gr.Textbox(
+        label="Enter English Sentence",
+        placeholder="Type an English sentence to translate to Vietnamese...",
+        lines=2
+    ),
+    outputs=gr.Textbox(
+        label="Translated Vietnamese Sentence"
+    ),
+    title="English to Vietnamese Translator",
+    description=(
+        "Enter an English sentence to translate it to Vietnamese using a pre-trained Transformer model. "
+        "Example: 'Hello, world!'"
+    ),
+    examples=[
+        [
+            "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."
+        ],
+        ["Hello, world!"],
+        ["The sun is shining."]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()