Update app.py

app.py

@@ -6,14 +6,14 @@ import numpy as np
 # Load the dog breed classifier model
 model_path = "dog_breed_classifier_trained.keras"
 model = tf.keras.models.load_model(model_path)
-labels = ['Rasse 1', 'Rasse 2', 'Rasse 3', 'Rasse 4', 'Rasse 5']  # Ersetzen Sie dies durch Ihre tatsächlichen Rassennamen
+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))  # Entspricht der Größe aus Ihrem Notebook
+    image = image.resize((128, 128))
     image = np.array(image)
-    image = image / 255.0  # Normalisierung der Pixelwerte
+    image = image / 255.0  # Normalization of pixel values
     return image
 
 # Prediction function
@@ -22,7 +22,7 @@ def predict_dog_breed(image):
     prediction = model.predict(np.expand_dims(image, axis=0))
     predicted_class = labels[np.argmax(prediction)]
     confidence = np.round(np.max(prediction) * 100, 2)
-    result = f"Breed: {predicted_class}, Confidence: {confidence}%"
+    result = f"{predicted_class}, Confidence: {confidence}%"
     return result
 
 # Create Gradio interface