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app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Laden des vortrainierten Pokémon-Modells
+model_path = "pokemon_classifier_model.h5"
+model = tf.keras.models.load_model(model_path)
+
+# Labels für den Pokémon Classifier 
+labels = [
+    'Balastoise','Charizard','Venusaur'
+]
+
+def predict_pokemon(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((299, 299)) 
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # same as image[None, ...]
+    
+    # Predict
+    predictions = model.predict(image)
+    prediction = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions)
+    
+    # Vorbereiten der Ausgabe
+    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}"
+    return result
+
+# Erstellen der Gradio-Oberfläche
+input_image = gr.Image() 
+output_label = gr.Label()
+interface = gr.Interface(fn=predict_pokemon,
+                         inputs=input_image,
+                         outputs=output_label,
+                         examples=["Blastoise.jpg", "Charizard.png", "Venusaur.png"], 
+                         title="Pokémon Classifier",
+                         description="Drag and drop an image or select an example below to predict the Pokémon.")
+
+# Interface starten
+interface.launch()

requirements.txt

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+tensorflow==2.15.0
+gradio
+pillow
+numpy