Update app.py

app.py

@@ -1,45 +1,45 @@
-import gradio as gr
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-
-# Modellpfad relativ zum aktuellen Arbeitsverzeichnis
-model_path = 'models/chess_piece_classifier.keras'
-
-# Modell laden
-model = tf.keras.models.load_model(model_path)
-
-# Klassenlabels (Passe diese entsprechend deinem Modell an)
-labels = ['Black bishop', 'Black king', 'Black knight', 'Black pawn', 'Black queen', 'Black rook', 'White bishop', 'White king', 'White knight', 'White pawn', 'White queen', 'White rook']
-
-# Vorhersagefunktion
-def predict(image):
-    try:
-        # Bildvorverarbeitung
-        image = image.resize((224, 224))  # Bildgröße auf 224x224 ändern
-        image = np.array(image) / 255.0
-        image = np.expand_dims(image, axis=0)
-        
-        # Vorhersage
-        predictions = model.predict(image)
-        confidences = {labels[i]: float(predictions[0][i]) for i in range(len(labels))}
-        
-        return confidences
-    except Exception as e:
-        return str(e)  # Fehlernachricht zurückgeben
-
-# Gradio-Interface erstellen
-iface = gr.Interface(
-    fn=predict, 
-    inputs=gr.Image(type="pil"),  # Bild als PIL-Objekt
-    outputs=gr.Label(),
-    description="Chess Piece Classifier",
-    examples=[
-        ['data/example1.png'],
-        ['data/example2.png'],
-        ['data/example3.png']
-    ]
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Modellpfad relativ zum aktuellen Arbeitsverzeichnis
+model_path = 'chess_piece_classifier.keras'
+
+# Modell laden
+model = tf.keras.models.load_model(model_path)
+
+# Klassenlabels (Passe diese entsprechend deinem Modell an)
+labels = ['Black bishop', 'Black king', 'Black knight', 'Black pawn', 'Black queen', 'Black rook', 'White bishop', 'White king', 'White knight', 'White pawn', 'White queen', 'White rook']
+
+# Vorhersagefunktion
+def predict(image):
+    try:
+        # Bildvorverarbeitung
+        image = image.resize((224, 224))  # Bildgröße auf 224x224 ändern
+        image = np.array(image) / 255.0
+        image = np.expand_dims(image, axis=0)
+        
+        # Vorhersage
+        predictions = model.predict(image)
+        confidences = {labels[i]: float(predictions[0][i]) for i in range(len(labels))}
+        
+        return confidences
+    except Exception as e:
+        return str(e)  # Fehlernachricht zurückgeben
+
+# Gradio-Interface erstellen
+iface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"),  # Bild als PIL-Objekt
+    outputs=gr.Label(),
+    description="Chess Piece Classifier",
+    examples=[
+        ['data/example1.png'],
+        ['data/example2.png'],
+        ['data/example3.png']
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()