Update app.py

app.py

@@ -9,7 +9,7 @@ def predict_pokemon_type(uploaded_file):
     if uploaded_file is None:
         return "No file uploaded.", None, "No prediction"
 
-    model = tf.keras.models.load_model('pokemon-model_transferlearning.keras')
+    model = tf.keras.models.load_model('pokemon-model.keras')
 
     # Load the image from the file path
     with Image.open(uploaded_file) as img:
@@ -17,20 +17,18 @@ def predict_pokemon_type(uploaded_file):
         img_array = np.array(img)
 
         prediction = model.predict(np.expand_dims(img_array, axis=0))
-        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
         
-        # Identify the most confident prediction
         confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
 
         return img, confidences
 
 # Define the Gradio interface
 iface = gr.Interface(
-    fn=predict_pokemon_type,  # Function to process the input
-    inputs=gr.File(label="Upload File"),  # File upload widget
-    outputs=["image", "text"],  # Output types for image and text
-    title="Pokemon Classifier",  # Title of the interface
-    description="Upload a picture of a Pokemon (preferably Cubone, Ditto, Psyduck, Snorlax, or Weedle) to see its type and confidence level. The trained model has an accuracy of 96%!"  # Description of the interface
+    fn=predict_pokemon_type,  
+    inputs=gr.File(label="Upload File"),
+    outputs=["image", "text"],
+    title="Pokemon Classifier",
+    description="Upload a picture of a Pokemon (preferably Cubone, Ditto, Psyduck, Snorlax, or Weedle) to see its type and confidence level. The trained model has a test accuracy of 99.17%!"
 )
 
 # Launch the interface