Update app.py

app.py

@@ -6,7 +6,6 @@ import numpy as np
 # Load your custom regression model
 model_path = "pokemon_transferlearning.keras"
 
-#model.load_weights(model_path)
 model = tf.keras.models.load_model(model_path)
 
 labels = ['Porygon', 'Seel', 'Vaporeon']
@@ -15,8 +14,9 @@ labels = ['Porygon', 'Seel', 'Vaporeon']
 def predict_regression(image):
     # Preprocess image
     image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
+    image = image.resize((150, 150)).convert('RGB') # Resize the image to 150x150 and convert it to RGB
     image = np.array(image)
+    image = image / 255.0  # Normalize the image to [0, 1]
     print(image.shape)
     # Predict
     prediction = model.predict(image[None, ...])  # Assuming single regression value