Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from PIL import Image
 
 
-model_path = "DogClassifier.keras"
+model_path = "DogClassifierComplex.keras"
 model = tf.keras.models.load_model(model_path)
 
 # Define the core prediction function
@@ -23,14 +23,45 @@ def predict_bmwX(image):
     prediction = tf.nn.softmax(prediction)
     
     # Create a dictionary with the probabilities for each Pokemon
-    borderCollie = np.round(float(prediction[0][0]), 2)
-    corgi = np.round(float(prediction[0][1]), 2)
-    germanShepard = np.round(float(prediction[0][2]), 2)
-    goldenRetriever = np.round(float(prediction[0][3]), 2)
-    pitBull = np.round(float(prediction[0][4]), 2)
-    rottweiler = np.round(float(prediction[0][4]), 2)
+    afghan = np.round(float(prediction[0][0]), 2)
+    africanWildDog = np.round(float(prediction[0][1]), 2)
+    beagle = np.round(float(prediction[0][2]), 2)
+    blenheim = np.round(float(prediction[0][3]), 2)
+    borderColie = np.round(float(prediction[0][4]), 2)
+    bostonTerrier = np.round(float(prediction[0][5]), 2)
+    chineseCrested = np.round(float(prediction[0][6]), 2)
+    cocker = np.round(float(prediction[0][7]), 2)
+    corgi = np.round(float(prediction[0][8]), 2)
+    dingo = np.round(float(prediction[0][9]), 2)
+    frenchBulldog = np.round(float(prediction[0][10]), 2)
+    germanShepard = np.round(float(prediction[0][11]), 2)
+    goldenRetriever = np.round(float(prediction[0][12]), 2)
+    pitBull = np.round(float(prediction[0][13]), 2)
+    rottweiler = np.round(float(prediction[0][14]), 2)
+    irishSpaniel = np.round(float(prediction[0][15]), 2)
+    labrador = np.round(float(prediction[0][16]), 2)
+    maltese = np.round(float(prediction[0][17]), 2)
+    newfoundland = np.round(float(prediction[0][18]), 2)
+    pomeranian = np.round(float(prediction[0][19]), 2)
+    poodle = np.round(float(prediction[0][20]), 2)
+    rhodesian = np.round(float(prediction[0][21]), 2)
+    saintBernard = np.round(float(prediction[0][22]), 2)
+    schnauzer = np.round(float(prediction[0][23]), 2)
+    scotchTerrier = np.round(float(prediction[0][24]), 2)
+    sharPei = np.round(float(prediction[0][25]), 2)
+    shibaInu = np.round(float(prediction[0][26]), 2)
+    siberianHusky = np.round(float(prediction[0][27]), 2)
+    yorkie = np.round(float(prediction[0][28]), 2)   
+    
         
-    return {'Border Collie': borderCollie, 'Corgi': corgi, 'German Shepard': germanShepard, 'Golder Retriver': goldenRetriever, 'Pit Bull': pitBull, 'Rottweiler': rottweiler}
+    return {'Afghan': afghan, 'African Wild Dog': africanWildDog, 'Beagle': beagle, 'Blenheim': blenheim,
+            'Border Collie': borderColie, 'Boston Terrier': bostonTerrier, 'Chinese Crested': chineseCrested, 'Cocker': cocker,
+           'Corgi': corgi, 'Dingo': dingo, 'French Bulldog': frenchBulldog, 'German Shepard': germanShepard,
+           'GoldenRetriever': goldenRetriever, 'Pit Bull': pitBull, 'Rottweiler': rottweiler, 'Irish Spaniel': irishSpaniel,
+           'Labrador': labrador, 'Maltese': maltese, 'Newfoundland': newfoundland, 'Pomeranian': pomeranian,
+           'Poodle': poodle, 'Rhodesian': rhodesian, 'Saint Bernard': saintBernard, 'Schnauzer': schnauzer,
+           'Scotch Terrier': scotchTerrier, 'Shar Pei': sharPei, 'Shiba Inu': shibaInu, 'Siberian Husky': siberianHusky,
+           'Yorkie': yorkie}
 
 
 input_image = gr.Image()