Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+
+# 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((150, 150))
+        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/example1.png'],
+        ['data/example1.png']
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()