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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_mobilenet.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.jpg'],
['data/example2.jpg'],
['data/example3.jpg'],
['data/example4.jpg'],
['data/example5.jpg'],
['data/example6.jpg'],
['data/example7.jpg'],
['data/example8.jpg'],
['data/example9.jpg'],
['data/example10.jpg'],
['data/example11.jpg'],
['data/example12.jpg']
]
)
if __name__ == "__main__":
iface.launch()
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