content: 
  # coding: utf-8
  
  # In[ ]:
  
  import h2o
  import pandas as pd
  
  # initialize the model scoring server
  h2o.init(nthreads=1,max_mem_size=1, start_h2o=True, strict_version_check = False)
  
  def approve_loan(Loan_Amount,Term,Interest_Rate,Employment_Years,Home_Ownership,Annual_Income,Verification_Status,Loan_Purpose,State,
                   Debt_to_Income,Delinquent_2yr,Revolving_Cr_Util,Total_Accounts,Longest_Credit_Length):
      # connect to the model scoring service
      h2o.connect()
  
      # open the downloaded model
      ChurnPredictor = h2o.load_model(path='DRF_model_1496459915419_4') 
  
      # define a feature vector to evaluate with the model
      newData = pd.DataFrame({'Loan_Amount' : Loan_Amount,
                              'Term' : Term,
                              'Interest_Rate' : Interest_Rate,
                              'Employment_Years' : Employment_Years,
                              'Home_Ownership' : Home_Ownership,
                              'Annual_Income' : Annual_Income,
                              'Verification_Status' : Verification_Status,
                              'Loan_Purpose' : Loan_Purpose,
                              'State' : State,
                              'Debt_to_Income' : Debt_to_Income,
                              'Delinquent_2yr' : Delinquent_2yr,
                              'Revolving_Cr_Util' : Revolving_Cr_Util,
                              'Total_Accounts' : Total_Accounts,
                              'Longest_Credit_Length' : Longest_Credit_Length}, index=[0])
      
      # evaluate the feature vector using the model
      predictions = ChurnPredictor.predict(h2o.H2OFrame(newData))
      predictionsOut = h2o.as_list(predictions, use_pandas=False)
      prediction = predictionsOut[1][0]
      probabilityBad = predictionsOut[1][1]
      probabilityGood = predictionsOut[1][2]
      return "Prediction: " + str(prediction) + " |Probability of Bad Loan: " + str(probabilityBad) + " |Probability of Good Loan: " + str(probabilityGood)
  
  