Update app.py

app.py

@@ -6,6 +6,7 @@ import numpy as np
 # Lade dein Modell
 model_path = "pokemon-model.keras"
 model = tf.keras.models.load_model(model_path)
+model.summary()  # Check if the model architecture loaded matches the expected one
 
 # Klassen Labels für deine vier Pokémon
 labels = ['Squirtle', 'Pikachu', 'Charizard', 'Butterfree']
@@ -15,14 +16,14 @@ def predict_pokemon(image):
     # Bildvorverarbeitung
     image = Image.fromarray(image.astype('uint8'), 'RGB')
     image = image.resize((150, 150))  # Anpassen der Bildgröße an das Modell
-    image = np.array(image) / 255.0  # Normalisieren der Pixelwerte
-
+    image = np.array(image)  # Normalisieren der Pixelwerte
+    print(image.shape)
+    
     # Bild in das Modell einspeisen und Vorhersage treffen
-    prediction = model.predict(np.expand_dims(image, axis=0))
-    confidences = {labels[i]: float(np.round(prediction[0][i], 2)) for i in range(len(labels))}
+    prediction = model.predict(image[None, ...])
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}    return confidences
     return confidences
 
-
 # Gradio Interface definieren
 input_image = gr.Image()
 output_text = gr.Textbox(label="Predicted Pokemon")