Update app.py

app.py

@@ -3,86 +3,103 @@ import tensorflow as tf
 import json
 import gradio as gr
 from tensorflow.keras.applications.efficientnet import preprocess_input
-from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Input, GlobalAveragePooling2D
-from tensorflow.keras import Model
 
-old_model = tf.keras.models.load_model('indo_fashion_classification_model.keras')
+# Load the inference model (without augmentation layers)
+model = tf.keras.models.load_model('./indo_fashion_classification_model.keras')
 
-with open('class_labels_new_keras.json', 'r') as f:
+# Load class labels
+with open('class_labels.json', 'r') as f:
     labels = json.load(f)
 
-pretrained_model = tf.keras.applications.efficientnet.EfficientNetB0(
-    input_shape=(224, 224, 3),
-    include_top=False,
-    weights='imagenet'
-)
-pretrained_model.trainable = False
-
-inputs = Input(shape=(224, 224, 3))
-x = pretrained_model(inputs, training=False)
-x = GlobalAveragePooling2D()(x)
-x = Dense(128, activation='relu')(x)
-x = BatchNormalization()(x)
-x = Dropout(0.45)(x)
-x = Dense(256, activation='relu')(x)
-x = BatchNormalization()(x)
-x = Dropout(0.45)(x)
-outputs = Dense(len(labels), activation='softmax', dtype='float32')(x)
-
-new_model = Model(inputs=inputs, outputs=outputs)
-
-new_model.set_weights(old_model.get_weights())
-
 def predict_image(image):
     if image is None:
         return None
     
-    # Convert PIL image to numpy array and ensure RGB
-    image = np.array(image)
-    if image.ndim == 2:  # Grayscale image
-        image = np.stack([image] * 3, axis=-1)  # Convert to RGB by stacking
-    elif image.shape[-1] == 4:  # RGBA image
-        image = image[..., :3]  # Remove alpha channel
+    # 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 image to (224, 224)
+    # Resize to match model input shape
     image = tf.image.resize(image, (224, 224))
-    image = tf.expand_dims(image, 0)  # Add batch dimension
-    image = preprocess_input(image)  # Apply EfficientNet preprocessing
     
-    # Make prediction
-    predictions = model.predict(image, verbose=0)
+    # 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)]
     
-    return f"**Predicted Class:** {class_name}\n**Confidence:** {confidence:.2%}"
+    # 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 the EfficientNetB0 model trained on the Indo Fashion Dataset.
+        # 🪷 Indian Ethnic Wear Classifier
         
-        **Classes:** """ + ", ".join(labels.values()) + """
+        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="pil",
-                label="Upload Image",
+                type="numpy",
+                label="Upload Fashion Image",
                 height=400,
                 width=400,
-                sources=["upload", "webcam"]
+                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="Prediction",
+                label="Classification Results",
                 show_label=True
             )
     
-    predict_btn = gr.Button("Classify Image", variant="primary", size="lg")
+    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,
@@ -93,4 +110,27 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) a
         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
     )