Update app.py

app.py

@@ -5,7 +5,6 @@ import numpy as np
 
 # Load your custom regression model
 model_path = "pokemon_transferlearning.keras"
-
 model = tf.keras.models.load_model(model_path)
 
 labels = ['Porygon', 'Seel', 'Vaporeon']
@@ -14,11 +13,13 @@ 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((150, 150)).convert('L') # Resize the image to 150x150 and convert it to RGB
+    image = image.resize((150, 150)).convert('RGB')  # Resize the image to 150x150 and convert it to RGB
     image = np.array(image)
-    print(image.shape)
+    image = image / 255.0  # Normalize image to [0, 1] range
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
     # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
+    prediction = model.predict(image)  # Assuming single regression value
     confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
     return confidences
 
@@ -29,5 +30,5 @@ interface = gr.Interface(fn=predict_regression,
                          inputs=input_image, 
                          outputs=gr.Label(),
                          examples=["images/porygon.png", "images/seel.jpg", "images/vaporeon.png"],   
-                         description="A simple mlp classification model for pokemon classification.")
-interface.launch()
+                         description="A simple MLP classification model for Pokemon classification.")
+interface.launch()