how to get the labelencoder for new data in Decision Tree

Question

I'm performing the Decision Tree with the help of below sample data.

So I've converted the above data to LabelEncoder to perform Decision Tree and successfully created a DT model.

So now my requirement is I would like to predict on the below values, So how to pass these values in the python code.

In order to predict the existing value I can use the below Predict Code.

model.predict([[2,1,1]])

COMPLETE CODE

import pandas as pd

df = pd.read_csv(r"salaries.csv")
df.head()

inputs = df.drop('salary_more_then_100k',axis='columns')
target = df['salary_more_then_100k']

from sklearn.preprocessing import LabelEncoder
le_company = LabelEncoder()
le_job = LabelEncoder()
le_degree = LabelEncoder()

inputs['company_n'] = le_company.fit_transform(inputs['company'])
inputs['job_n'] = le_job.fit_transform(inputs['job'])
inputs['degree_n'] = le_degree.fit_transform(inputs['degree'])

inputs_n = inputs.drop(['company','job','degree'],axis='columns')

from sklearn import tree
model = tree.DecisionTreeClassifier()
model.fit(inputs_n, target)

model.score(inputs_n,target)