Update app.py

app.py

@@ -2,40 +2,33 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
-# Load the car brand classifier model
-model_path = "car_brand_classifier_finetuned.keras"
+# 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 = ['Hyundai', 'Lexus', 'Mazda', 'Mercedes', 'Opel', 'Skoda', 'Toyota', 'Volkswagen']
-
-# Define function for car brand classification with data augmentation
+# Define function for dog breed classification with data augmentation
 def preprocess_image(image):
     image = Image.fromarray(image.astype('uint8'), 'RGB')
-    image = image.resize((224, 224))
+    image = image.resize((128, 128))  # Entspricht der Größe aus Ihrem Notebook
     image = np.array(image)
-    image = image / 255.0  # Normalize pixel values
+    image = image / 255.0  # Normalisierung der Pixelwerte
     return image
 
-
 # Prediction function
-def predict_car_brand(image):
+def predict_dog_breed(image):
     image = preprocess_image(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"Label: {predicted_class}, Confidence: {confidence}%"
+    result = f"Breed: {predicted_class}, Confidence: {confidence}%"
     return result
 
 # Create Gradio interface
 input_image = gr.Image()
-output_text = gr.Textbox(label="Car Brand")
-interface = gr.Interface(fn=predict_car_brand,
-                         inputs=input_image,
-                         outputs=output_text,
-                         description="A car brand classifier using transfer learning and fine-tuning with EfficientNetB0.",
-                         theme="default")
+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")
 
 if __name__ == "__main__":
     interface.launch()