#!/usr/bin/env python

#importing tabpy_client library and connect to the location (client) for subsequent operations  
import tabpy_client
client=tabpy_client.Client('http://localhost:9004/') 


#training of the model and predicting the 1 or 2 based on the arguments given (_arg1 to _arg20) 
def germancreditcheck(_arg1,_arg2,_arg3,_arg4,_arg5,_arg6,_arg7,_arg8,_arg9,_arg10,_arg11,_arg12,_arg13,_arg14,_arg15,_arg16,_arg17,_arg18,_arg19,_arg20): 

  import numpy as np
  from sklearn.tree import DecisionTreeClassifier
  import pandas as pd
            
  data=pd.DataFrame.from_csv(r'C:\Users\ElianaLambrou\Documents\Projects\PythonInTableau\GermanCreditData.csv')
  X=data.drop('Class',axis=1)
  y=data['Class']

  clf = DecisionTreeClassifier(random_state=2)
  clf.fit(X,y)
                  
  X_pred=np.transpose(np.array([_arg1,_arg2,_arg3,_arg4,_arg5,_arg6,_arg7,_arg8,_arg9,_arg10,_arg11,_arg12,_arg13,_arg14,_arg15,_arg16,_arg17,_arg18,_arg19,_arg20]))

  pred = clf.predict(X_pred)
  return pred.tolist()


#deploying the function 
client_deploy('GermanCreditCheck', germancreditcheck, 'Classifies bad or good according to the model trained by relevant dataset', override=True)