Update app.py

app.py

@@ -4,14 +4,14 @@ from PIL import Image
 import numpy as np
 
 # Load the dog breed classifier model
-model_path = "dog_breed_classifier_trained.keras"
+model_path = "best_model.keras"
 model = tf.keras.models.load_model(model_path)
 labels = ['Beagle', 'French Bulldog', 'German Shepherd', 'Golden Retriever', 'Labrador Retriever']
 
 # Define function for dog breed classification with data augmentation
 def preprocess_image(image):
     image = Image.fromarray(image.astype('uint8'), 'RGB')
-    image = image.resize((128, 128))
+    image = image.resize((150, 150))
     image = np.array(image)
     image = image / 255.0  # Normalization of pixel values
     return image
@@ -28,7 +28,9 @@ def predict_dog_breed(image):
 # Create Gradio interface
 input_image = gr.Image()
 output_text = gr.Textbox(label="Dog Breed")
-interface = gr.Interface(fn=predict_dog_breed, inputs=input_image, outputs=output_text, description="A dog breed classifier using transfer learning and fine-tuning with EfficientNetB0.", theme="default")
+interface = gr.Interface(fn=predict_dog_breed, inputs=input_image, outputs=output_text, 
+                         description="A dog breed classifier using a custom CNN model with data augmentation and regularization techniques.", 
+                         theme="default")
 
 if __name__ == "__main__":
     interface.launch()