How to predict on new data row using trained XGB classification model?

I trained a model with and got a decent auc. Now, I want to predict on completely new data but I am not sure how to. Can someone help?

# fit model no training data
model = XGBClassifier()
model.fit(X_train, y_train)

# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]

#evaluate predictions train vs test data
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

Now, I have a brand new data I want to score with this model. How would I do this? Something predict.proba()?

Solution

Just fitting new data

NEW_DTA = pd.read_csv(data)
New_y_test = NEW_DTA.iloc[:,-1]
New_x_test = NEW_DTA.drop(colums='Target')
New_pred = model.predict(New_x_test)