Update app.py

app.py

@@ -3,10 +3,8 @@ import numpy as np
 from keras.models import load_model
 from PIL import Image
 
-# Load your Keras model
 model = load_model('pokemon-model_2_transferlearning.keras')
 
-# Define function to preprocess and predict on images
 def predict_pokemon(image):
     # Resize and preprocess the image
     image = Image.fromarray((image * 255).astype(np.uint8))
@@ -14,21 +12,20 @@ def predict_pokemon(image):
     image_array = np.asarray(image)
     image_array = image_array / 255.0
 
-    # Make prediction
+
     prediction = model.predict(np.expand_dims(image_array, axis=0))
     predicted_class = np.argmax(prediction)
 
-    # Example: Assuming you have a list of Pokémon names
+
     pokemon_names = ['Pikachu', 'Charmander', 'Bulbasaur', ...]
     predicted_pokemon = pokemon_names[predicted_class]
 
     return predicted_pokemon
 
-# Define input component for Gradio
+
 input_component = gr.inputs.Image(shape=(224, 224))
 
-# Define output component for Gradio
+
 output_component = gr.outputs.Label(num_top_classes=1)
 
-# Create the Gradio interface
 gr.Interface(fn=predict_pokemon, inputs=input_component, outputs=output_component, title='Pokémon Classifier').launch()