Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from PIL import Image
 
 
-model_path = "DogClassifierComplex.keras"
+model_path = "DogClassifier2.1.keras"
 model = tf.keras.models.load_model(model_path)
 
 # Define the core prediction function
@@ -12,7 +12,7 @@ def predict_bmwX(image):
     # Preprocess image
     print(type(image))
     image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((224, 224)) #resize the image to 224x224
+    image = image.resize((150, 150)) #resize the image to 150x150
     image = np.array(image)
     image = np.expand_dims(image, axis=0) # same as image[None, ...]
     
@@ -23,10 +23,15 @@ def predict_bmwX(image):
     prediction = tf.nn.softmax(prediction)
     
     # Define class names
-    class_names = ['Afghan', 'African Wild Dog', 'Beagle', 'Blenheim', 'Border Collie', 'Boston Terrier', 'Chinese Crested', 
-                   'Cocker', 'Corgi', 'Dingo', 'French Bulldog', 'German Shepard', 'Golden Retriever', 'Pit Bull', 
-                   'Rottweiler', 'Irish Spaniel', 'Labrador', 'Maltese', 'Newfoundland', 'Pomeranian', 'Poodle', 
-                   'Rhodesian', 'Saint Bernard', 'Schnauzer', 'Scotch Terrier', 'Shar Pei', 'Shiba Inu', 'Siberian Husky', 'Yorkie']
+    class_names = ['Afghan', 'African Wild Dog', 'Airedale', 'American Hairless', 'American Spaniel', 'Basenji', 'Basset', 'Beagle', 
+                   'Bearded Collie', 'Bermaise', 'Bichon Frise', 'Blenheim', 'Bloodhound', 'Bluetick', 'Border Collie', 'Borzoi',
+                   'Boston Terrier', 'Boxer', 'Bull Mastiff', 'Bull Terrier', 'Bulldog', 'Cairn', 'Chihuahua', 'Chinese Crested',
+                   'Chow', 'Clumber','Cockapoo', 'Cocker', 'Collie', 'Corgi', 'Coyote', 'Dalmation', 'Dhole', 'Dingo', 'Doberman',
+                   'Elk Hound', 'French Bulldog', 'German Sheperd', 'Golden Retriever', 'Great Dane', 'Great Perenees', 'Greyhound',
+                   'Groenendael', 'Irish Spaniel', 'Irish Wolfhound', 'Japanese Spaniel', 'Komondor', 'Labradoodle', 'Labrador',
+                   'Lhasa', 'Malinois', 'Maltese', 'Maltese', 'Mex Hairless', 'Newfoundland', 'Pekinese', 'Pit Bull', 'Pomeranian',
+                   'Poodle', 'Pug', 'Rhodesian', 'Rottweiler', 'Saint Bernard', 'Schnauzer', 'Scotch Terrier', 'Shar_Pei', 
+                   'Shiba Inu', 'Shih-Tzu', 'Siberian Husky', 'Vizsla', 'Yorkie']
     
     # Create a dictionary with the probabilities for each dog breed
     prediction_dict = {class_names[i]: np.round(float(prediction[0][i]), 2) for i in range(len(class_names))}