I would recommend to read a few tutorials such as this one. At a general level, GINI metric that the algorithm computes to decide whether to split. The variable name and comparison (
>, etc) and value in a node represent on what information you should travel on the left side of the node or right side. The class is the most frequently occurring class in your tree.
In regards to your encoding you should check your code.
To measure the quality of your model you can compute a confusion matrix that will tell you how many true positives you have and true negatives. Moreover, you can compute the receiver operator curve (ROC) and the Area under the curve (AUC), both metrics are implemented in sklearn.