/Users/thomen/Desktop/Thomas/ZHAW/4.Semester/KIA/pokemon_classifier/hugginfaceGradio/imageclassification/app.py

app.py

@@ -1,7 +1,34 @@
 import gradio as gr
+import numpy as np
+from keras.models import load_model
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your Keras model
+model = load_model('pokemon-model_2_transferlearning.keras.keras')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Define function to preprocess and predict on images
+def predict_pokemon(image):
+    # Resize and preprocess the image
+    image = Image.fromarray((image * 255).astype(np.uint8))
+    image = image.resize((224, 224))
+    image_array = np.asarray(image)
+    image_array = image_array / 255.0
+
+    # Make prediction
+    prediction = model.predict(np.expand_dims(image_array, axis=0))
+    predicted_class = np.argmax(prediction)
+
+    # Example: Assuming you have a list of Pokémon names
+    pokemon_names = ['Pikachu', 'Charmander', 'Bulbasaur', ...]
+    predicted_pokemon = pokemon_names[predicted_class]
+
+    return predicted_pokemon
+
+# Define input component for Gradio
+input_component = gr.inputs.Image(shape=(224, 224))
+
+# Define output component for Gradio
+output_component = gr.outputs.Label(num_top_classes=1)
+
+# Create the Gradio interface
+gr.Interface(fn=predict_pokemon, inputs=input_component, outputs=output_component, title='Pokémon Classifier').launch()