Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+model_path = "mabel_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the core prediction function
+def predict_pokemons(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+    
+    # Predict
+    prediction = model.predict(image)
+    
+    # Apply sigmoid to get probabilities
+    prediction_prob = tf.sigmoid(prediction).numpy()
+
+    p_Abra = round(prediction_prob[0][0], 2)
+    p_Pikachu = round(prediction_prob[0][1], 2)
+    p_Beedrill = round(prediction_prob[0][2], 2)
+
+    return{'Abra': p_Abra, 'Pikachu': p_Pikachu, 'Beedrill': p_Beedrill}
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemons,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["Abra1.png", "Abra2.png", "Abra3.jpg", "Beedrill1.jpg", "Beedrill2.jpg", "Beedrill3.png", "Pikachu1.png", "Pikachu2.jpg", "Pikachu3.png"], 
+    description="Pokemon Classifier")
+iface.launch()