Update app.py

app.py

@@ -7,7 +7,7 @@ model_path = "DogClassifier2.4.keras"
 model = tf.keras.models.load_model(model_path)
 
 # Define the core prediction function
-def predict_bmwX(image):
+def predict_breed(image):
     # Preprocess image
     image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
     image = image.convert("RGB")  # Ensure the image is in RGB format
@@ -35,10 +35,6 @@ def predict_bmwX(image):
                    'Poodle', 'Pug', 'Rhodesian', 'Rottweiler', 'Saint Bernard', 'Schnauzer', 'Scotch Terrier', 'Shar_Pei', 
                    'Shiba Inu', 'Shih-Tzu', 'Siberian Husky', 'Vizsla', 'Yorkie']
     
-    # Check if the number of predictions matches the number of class names
-    if len(prediction[0]) != len(class_names):
-        return f"Error: Number of model outputs ({len(prediction[0])}) does not match number of class names ({len(class_names)})."
-    
     # Apply threshold and set probabilities lower than 0.015 to 0.0
     threshold = 0.01395
     prediction = np.array(prediction)
@@ -59,8 +55,8 @@ def predict_bmwX(image):
 
 input_image = gr.Image()
 iface = gr.Interface(
-    fn=predict_bmwX,
+    fn=predict_breed,
     inputs=input_image, 
     outputs=gr.Label(),
-    description="A simple MLP classification model for image classification using the MNIST dataset.")
+    description="A simple classification model for determining a dog breed.")
 iface.launch(share=True)