import keras.backend as K
def psnr(y_true, y_pred):
    return -10*K.log(K.mean(K.flatten((y_true - y_pred))**2)) / np.log(10)

from keras.models import load_model
model = load_model("C:/Users/owner/Desktop/MyNet.h5", custom_objects={'psnr': psnr, 'val_psnr': psnr})

def predict_input_image(img):
  #img_4d=img.reshape(-1,180,180,3)
  img_4d=img.reshape(256,256,3)
  prediction=model.predict(img_4d)[0]
  return {flower_classes[i]: float(prediction[i]) for i in range(5)}

image = gr.inputs.Image(shape=(256,256))

gr.Interface(fn=predict_input_image, inputs=image, outputs=image,interpretation='default').launch(debug='True')