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import gradio as gr
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import tensorflow as tf
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import numpy as np
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from PIL import Image
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model_path = "Dog_transfer_learning_NASNetLarge.keras"
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model = tf.keras.models.load_model(model_path)
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def predict_dog(image):
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print(type(image))
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image = Image.fromarray(image.astype('uint8'))
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image = image.resize((150, 150))
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image = np.array(image)
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image = np.expand_dims(image, axis=0)
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prediction = model.predict(image)
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prediction = np.round(prediction, 3)
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p_husky = prediction[0][0]
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p_pomeranian = prediction[0][1]
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p_rottwiler = prediction[0][2]
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p_shiba = prediction[0][3]
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return {'husky': p_husky, 'pomeranian': p_pomeranian, 'rottwiler': p_rottwiler, 'shiba': p_shiba}
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input_image = gr.Image()
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iface = gr.Interface(
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fn=predict_dog,
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inputs=input_image,
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outputs=gr.Label(),
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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"],
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description="TEST.")
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iface.launch()
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