For decision tree classification, the variable to predict is most often ordinal-encoded (0, 1, 2 and so on) The numeric predictors do not need to be normalized to all the same range -- typically 0.0 ...

Because the decision tree model was trained using normalized and encoded data, the x-input must be normalized and encoded in the same way. Notice the double square brackets on the x-input. The predict ...