import gradio as gr import tensorflow as tf import numpy as np from PIL import Image model_path = "Dog_transfer_learning_NASNetLarge.keras" model = tf.keras.models.load_model(model_path) # Define the core prediction function def predict_dog(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) # No need to apply sigmoid, as the output layer already uses softmax # Convert the probabilities to rounded values prediction = np.round(prediction, 3) # Separate the probabilities for each class p_husky = prediction[0][0] # Probability for class 'articuno' p_pomeranian = prediction[0][1] # Probability for class 'moltres' p_rottwiler = prediction[0][2] # Probability for class 'zapdos' p_shiba = prediction[0][3] # Probability for class 'zapdos' return {'husky': p_husky, 'pomeranian': p_pomeranian, 'rottwiler': p_rottwiler, 'shiba': p_shiba} # Create the Gradio interface input_image = gr.Image() iface = gr.Interface( fn=predict_dog, inputs=input_image, outputs=gr.Label(), examples=["images/husky_1.jpg", "images/husky_2.jpg", "images/husky_3.jpg", "images/pomeranian_1.jpg", "images/pomeranian_2.jpg", "images/pomeranian_3.jpg", "images/rottwiler_1.jpg", "images/rottwiler_2.jpg", "images/rottwiler_3.jpg", "images/shiba_1.jpg", "images/shiba_2.jpg", "images/shiba_3.jpg"], description="TEST.") iface.launch()