app.py

import numpy as np
import tensorflow as tf
import json
import gradio as gr
from tensorflow.keras.applications.efficientnet import preprocess_input

# Load the inference model (without augmentation layers)
model = tf.keras.models.load_model('./indo_fashion_classification_model.keras')

# Load class labels
with open('class_labels.json', 'r') as f:
    labels = json.load(f)

def predict_image(image):
    if image is None:
        return None
    
    # Convert image to RGB if it's grayscale or has alpha channel
    if len(image.shape) == 2:  # Grayscale image
        image = np.stack((image,) * 3, axis=-1)
    elif image.shape[2] == 4:  # RGBA image
        image = image[:, :, :3]  # Remove alpha channel
    elif image.shape[2] == 1:  # Single channel
        image = np.concatenate([image] * 3, axis=-1)
    
    # Resize to match model input shape
    image = tf.image.resize(image, (224, 224))
    
    # Preprocess for EfficientNet
    image = preprocess_input(image)
    
    # Add batch dimension and make prediction
    image_batch = tf.expand_dims(image, 0)
    predictions = model.predict(image_batch, verbose=0)
    
    # Get top prediction
    class_idx = np.argmax(predictions[0])
    confidence = predictions[0][class_idx]
    class_name = labels[str(class_idx)]
    
    # Get top 3 predictions
    top_3_indices = np.argsort(predictions[0])[-3:][::-1]
    top_3_predictions = []
    
    for idx in top_3_indices:
        top_3_predictions.append({
            'class': labels[str(idx)],
            'confidence': f"{predictions[0][idx]:.2%}"
        })
    
    # Format output
    result = f"**Predicted Class:** {class_name}\n**Confidence:** {confidence:.2%}\n\n"
    result += "**Top 3 Predictions:**\n"
    for i, pred in enumerate(top_3_predictions, 1):
        result += f"{i}. {pred['class']}: {pred['confidence']}\n"
    
    return result

# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
    gr.Markdown(
        """
        # 🪷 Indian Ethnic Wear Classifier
        
        Upload an image of Indian fashion attire to classify it using our EfficientNetB0 model trained on the Indo Fashion Dataset.
        
        **Available Classes:** 
        """ + ", ".join(sorted(labels.values())) + """
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(
                type="numpy",
                label="Upload Fashion Image",
                height=400,
                width=400,
                sources=["upload", "webcam", "clipboard"],
                show_download_button=True
            )
            
            gr.Examples(
                examples=[
                    ["example1.jpg"],  # You can add example images here
                    ["example2.jpg"],
                    ["example3.jpg"]
                ],
                inputs=input_image,
                label="Try these examples (if available)"
            )
            
        with gr.Column(scale=1):
            output_text = gr.Markdown(
                label="Classification Results",
                show_label=True
            )
    
    with gr.Row():
        predict_btn = gr.Button("🎯 Classify Image", variant="primary", size="lg")
        clear_btn = gr.Button("🗑️ Clear", variant="secondary")
    
    predict_btn.click(
        fn=predict_image,
        inputs=input_image,
        outputs=output_text
    )
    
    input_image.change(
        fn=predict_image,
        inputs=input_image,
        outputs=output_text
    )
    
    clear_btn.click(
        fn=lambda: (None, ""),
        inputs=[],
        outputs=[input_image, output_text]
    )
    
    gr.Markdown(
        """
        ---
        **Note:** 
        - The model classifies images into 15 categories of Indian ethnic wear
        - For best results, use clear, well-lit images focused on the clothing
        - Supported formats: JPG, PNG, WebP
        - Model: EfficientNetB0 trained on Indo Fashion Dataset
        """
    )

if __name__ == "__main__":
    demo.launch(
        share=True,
        show_error=True
    )