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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Pfad zum gespeicherten Modell
+model_path = "Pokemon_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Definieren der Klassennamen
+labels = ['Abra', 'Blastoise', 'Zubat']
+
+# Funktion zur Klassifizierung
+def classify_pokemon(image):
+    if image is None:
+        return {"Error": "No image uploaded"}
+    
+    # Bildvorverarbeitung
+    image = Image.fromarray(image).resize((150, 150))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    
+    # Vorhersage
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction[0])
+    confidence = np.max(prediction[0])
+    
+    # Konfidenzwerte
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    
+    return confidences, f"Predicted: {labels[predicted_class]}, Confidence: {confidence:.2f}"
+
+# Erstellen einer Gradio-Schnittstelle
+iface = gr.Interface(
+    fn=classify_pokemon,
+    inputs=gr.Image(),
+    outputs=["label", "text"],
+    live=True
+)
+
+# Starten der Schnittstelle
+iface.launch()