Upload 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 = "transferlearning_pokemon.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the core prediction function
+def predict_pokemon(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 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Expand dimensions to match the model input shape
+    
+    # Predict
+    prediction = model.predict(image)
+    
+    # Print the shape of the prediction to debug
+    print(f"Prediction shape: {prediction.shape}")
+    
+    # Assuming the output is already softmax probabilities
+    probabilities = prediction[0]
+    
+    # Print the probabilities array to debug
+    print(f"Probabilities: {probabilities}")
+    
+    # Assuming your model was trained with these class names
+    class_names = ['charmander', 'eevee', 'pikachuu']  # Replace 'another_pokemon' with your third class name
+    
+    # Create a dictionary of class probabilities
+    result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
+    
+    return result
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["pokemon_examples/charmander.png", "pokemon_examples/charmander1.jpg", "pokemon_examples/eevee.png", "pokemon_examples/eevee1.jpg", "pokemon_examples/pika.png", "pokemon_examples/pika1.jpg"],   
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch()