Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Modell laden
+model = tf.keras.models.load_model('pokemon_classifier_model.keras')
+
+# Klassenlabels
+class_names = ['Bisasam', 'Schiggy', 'Glumanda']
+
+# Vorhersagefunktion
+def predict(img):
+    img = img.resize((224, 224))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = tf.keras.applications.vgg16.preprocess_input(img_array)
+
+    predictions = model.predict(img_array)
+    score = tf.nn.softmax(predictions[0])
+
+    return {class_names[i]: float(score[i]) for i in range(3)}
+
+# Gradio Interface erstellen
+image_input = gr.inputs.Image(type='pil')
+label_output = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=predict, inputs=image_input, outputs=label_output, 
+             title="Pokémon Classifier", 
+             description="Laden Sie ein Bild hoch, um das Pokémon zu klassifizieren.").launch()