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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pokemon_classifier_small_effnet.keras filter=lfs diff=lfs merge=lfs -text
app.py ADDED
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+ import gradio as gr
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+ import tensorflow as tf
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+ from PIL import Image
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+ import numpy as np
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+
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+ # Laden Sie Ihr angepasstes EfficientNetB0-Modell
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+ model_path = "pokemon_classifier_small_effnet.keras"
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+ model = tf.keras.models.load_model(model_path)
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+
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+ labels = ['Arbok', 'Nidoking', 'Tauros']
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+
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+ # Vorverarbeitungsfunktion für das Bild
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+ def preprocess_image(image):
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+ image = Image.fromarray(image.astype('uint8'))
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+ image = image.resize((224, 224))
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+ image = np.array(image)
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+ image = image / 255.0 # Normalisierung
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+ return image
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+
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+ # Vorhersagefunktion mit postprocess
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+ def predict_class(image):
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+ image = preprocess_image(image)
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+ prediction = model.predict(image[None, ...])
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+ predicted_class = labels[np.argmax(prediction)]
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+ confidence = np.round(np.max(prediction) * 100, 2)
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+ result = f"Label: {predicted_class}, Confidence: {confidence}%"
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+ return result
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+
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+ # Gradio-Schnittstelle erstellen
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+ input_image = gr.Image()
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+ output_text = gr.Textbox(label="Predicted Class and Confidence")
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+ interface = gr.Interface(fn=predict_class,
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+ inputs=input_image,
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+ outputs=output_text,
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+ examples=[
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+ ["images/imagesexample_pokemon1.jpeg"],
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+ ["images/imagesexample_pokemon2.jpeg"],
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+ ["images/imagesexample_pokemon3.jpeg"]
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+ ],
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+ description="A simple classification model for Pokemon images.")
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+
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+ if __name__ == "__main__":
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+ interface.launch()
pokemon_classifier_small_effnet.keras ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:6d9fa18357912e506f43ef7355774fd52c1c1d512b0644984cbab3f35dd44d59
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+ size 19171544