Update app.py

app.py

@@ -1,35 +1,35 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-
-# Modell laden
-model = tf.keras.models.load_model('pokemon_classifier.keras')
-
-def classify_image(image):
-    # Bild vorverarbeiten
-    image = Image.fromarray(image.astype('uint8')).convert('RGB')
-    image = image.resize((150, 150))  # Anpassung der Größe an das Modell
-    image = np.array(image) / 255.0  # Normalisieren
-    image = np.expand_dims(image, axis=0)  # Hinzufügen der Batch-Dimension
-
-    # Vorhersage machen
-    prediction = model.predict(image).flatten()
-    classes = ['Abra', 'Ditto', 'Gengar']  # Namen der Klassen
-
-    # Wahrscheinlichkeiten mit Klassen verbinden und formatieren
-    return {classes[i]: float(prediction[i]) for i in range(len(classes))}
-
-# Gradio-Interface erstellen
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=input_image,
-    outputs=gr.Label(num_top_classes=3),
-    examples=["pokemon/Abra/00000000.png", "pokemon/Ditto/00000000.jpg", "pokemon/Gengar/00000000.png"],  # Beispiele hinzufügen
-    description="Upload an image of a Pokémon to classify it as Pikachu, Charmander, or Bulbasaur."
-)
-
-# Interface starten
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+
+# Modell laden
+model = tf.keras.models.load_model('pokemon_classifier.keras')
+
+def classify_image(image):
+    # Bild vorverarbeiten
+    image = Image.fromarray(image.astype('uint8')).convert('RGB')
+    image = image.resize((150, 150))  # Anpassung der Größe an das Modell
+    image = np.array(image) / 255.0  # Normalisieren
+    image = np.expand_dims(image, axis=0)  # Hinzufügen der Batch-Dimension
+
+    # Vorhersage machen
+    prediction = model.predict(image).flatten()
+    classes = ['Abra', 'Ditto', 'Gengar']  # Namen der Klassen
+
+    # Wahrscheinlichkeiten mit Klassen verbinden und formatieren
+    return {classes[i]: float(prediction[i]) for i in range(len(classes))}
+
+# Gradio-Interface erstellen
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=input_image,
+    outputs=gr.Label(num_top_classes=3),
+    examples=["pokemon/Abra/00000000.png", "pokemon/Ditto/00000000.jpg", "pokemon/Gengar/00000000.png"],  # Beispiele hinzufügen
+    description="Upload an image of a Pokémon to classify it as Pikachu, Charmander, or Bulbasaur."
+)
+
+# Interface starten
 iface.launch()