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import keras.backend as K |
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import gradio as gr |
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def psnr(y_true, y_pred): |
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return -10*K.log(K.mean(K.flatten((y_true - y_pred))**2)) / np.log(10) |
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from keras.models import load_model |
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model = load_model("./MyNet.h5", custom_objects={'psnr': psnr, 'val_psnr': psnr}) |
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def predict_input_image(img): |
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img_4d=img.reshape(256,256,3) |
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prediction=model.predict(img_4d)[0] |
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return {flower_classes[i]: float(prediction[i]) for i in range(5)} |
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image = gr.inputs.Image(shape=(256,256)) |
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gr.Interface(fn=predict_input_image, inputs=image, outputs=image,interpretation='default').launch(debug='True') |
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