Update app.py

app.py

@@ -5,12 +5,12 @@ import numpy as np
 
 # Load your custom regression model
 model_path = "kia_mnist_keras_model.weights.h5"
-model_path = "kia_mnist_keras_model.keras"
+model_path = "Ditto-premiumdelux-model_transferlearning.keras"
 
 #model.load_weights(model_path)
 model = tf.keras.models.load_model(model_path)
 
-labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+labels = ['Ditto','Golbat','Koffing']
 
 # Define regression function
 def predict_regression(image):
@@ -26,10 +26,10 @@ def predict_regression(image):
 
 # Create Gradio interface
 input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
+output_text = gr.Textbox(label="Predicted Pokemon")
 interface = gr.Interface(fn=predict_regression, 
                          inputs=input_image, 
                          outputs=gr.Label(),
-                         examples=["images/0.jpeg", "images/1.jpeg", "images/2.jpeg", "images/5.jpeg"],   
+                         examples=["images/Ditto.jpeg", "images/Golbat.jpeg", "images/Koffing.jpeg"],   
                          description="A simple mlp classification model for image classification using the mnist dataset.")
 interface.launch()