Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import tensorflow as tf
-
+import numpy as np
 
 # Lade dein benutzerdefiniertes Regressionsmodell
 model = tf.keras.models.load_model('Task_Pokemon.keras')
@@ -9,16 +9,17 @@ model = tf.keras.models.load_model('Task_Pokemon.keras')
 class_names = ['Aerodactyl', 'Charizard', 'Victreebel']
 
 def classify_image(image):
-    img = image.resize((160, 160))  # Hier definieren wir die Größe der Bilder
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    # Konvertiere das Bild in ein PIL-Bildobjekt
+    img_pil = image.resize((160, 160))  # Hier definieren wir die Größe der Bilder
+    img_array = tf.keras.preprocessing.image.img_to_array(img_pil)
     img_array = tf.expand_dims(img_array, 0)  # Erstelle einen Batch
     predictions = model.predict(img_array)
     predicted_class = class_names[np.argmax(predictions[0])]
     confidence = np.max(predictions[0])
     return predicted_class, confidence
 
-image_input = gr.Image()
-label = gr.Label(num_top_classes=3)
+image_input = gr.inputs.Image()
+label = gr.outputs.Label(num_top_classes=3)
 
 iface = gr.Interface(
     fn=classify_image,