Update app.py

app.py

@@ -4,7 +4,7 @@ from PIL import Image
 import numpy as np
 
 # Load your custom regression model
-model_path = "pokemon_model_transferlearning.weights.h5"
+model_path = "trainpokemon_model_transferlearning.weights.h5"
 model_path = "pokemon_model_transferlearning.keras"
 
 #model.load_weights(model_path)
@@ -16,7 +16,7 @@ labels = ['Ditto','Venomoth','Venusaur']
 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('L') #resize the image to 28x28 and converts it to gray scale
     image = np.array(image)
     print(image.shape)
     # Predict
@@ -30,6 +30,6 @@ output_text = gr.Textbox(label="Predicted Pokemon")
 interface = gr.Interface(fn=predict_regression, 
                          inputs=input_image, 
                          outputs=gr.Label(),
-                         examples=["images/Ditto.jpeg", "images/Venomoth.jpeg", "images/Venusaur.jpeg"],   
+                         examples=["pokemons/train/Ditto/00000008.jpg", "images/Venomoth.jpeg", "images/Venusaur.jpeg"],   
                          description="A simple mlp classification model for image classification using the mnist dataset.")
 interface.launch()