how to get prediction from trained random forest model? i have a dataset with two columns user posts (posts) and personality type (type) , i need personality type according to posts using this dataset so i used random forest regression for prediction 
here is my code:-

df = pd.read_csv('personality_types.csv')

count_vectorizer = CountVectorizer(decode_error='ignore')
X = count_vectorizer.fit_transform(df['posts'])
y = df['type'].values

Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y, test_size=0.33)

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(Xtrain, Ytrain)
Y_prediction = random_forest.predict(Xtest)


accuracy:

random_forest.score(Xtrain, Ytrain)
acc_random_forest = round(random_forest.score(Xtrain, Ytrain) * 100, 2)
print(round(acc_random_forest,2,), "%")

100%


now i want to get prediction from a custom text how can i achive that ?
how can i get personality type of a post separately using this model.
Use the CountVectorizer you have fitted to preprocess your custom input then feed it to your model for prediction.

custom_input = ['insert text here']
custom_input = count_vectorizer.transform(custom_input)
custom_prediction = random_forest.predict(custom_input)