Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+from tensorflow.keras.preprocessing import image as keras_image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.models import load_model
+
+# Load your trained model
+model = load_model('/Users/stud/Downloads/Mandatory Sauber/mein_modell.h5')  # Ensure this path is correct
+
+def predict_pokemon(img):
+    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Ensure the image is in RGB
+    img = img.resize((224, 224))  # Resize the image properly using PIL
+    img_array = keras_image.img_to_array(img)  # Convert the image to an array
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to fit model input
+    img_array = preprocess_input(img_array)  # Preprocess the input as expected by ResNet50
+    
+    prediction = model.predict(img_array)  # Predict using the model
+    classes = ['Caterpie', 'Charizard', 'Dragonair' ]  # Specific Pokémon names
+    return {classes[i]: float(prediction[0][i]) for i in range(3)}  # Return the prediction
+
+# Define Gradio interface
+interface = gr.Interface(fn=predict_pokemon, 
+                         inputs="image",  # Simplified input type
+                         outputs="label",  # Simplified output type
+                         title="Pokémon Classifier",
+                         description="Upload an image of a Pokémon and the classifier will predict its species.")
+
+# Launch the interface
+interface.launch()