test.py

from wsgiref import simple_server
from flask import Flask, request
from flask import Response
import os
import pandas as pd
from flask_cors import CORS, cross_origin
from prediction_Validation_Insertion import pred_validation
from trainingModel import trainModel
from training_Validation_Insertion import train_validation
import flask_monitoringdashboard as dashboard
from predictFromModel import prediction

os.putenv('LANG', 'en_US.UTF-8')
os.putenv('LC_ALL', 'en_US.UTF-8')




#
try:
  path = 'Prediction_Batch_Files'

     #pred_val = pred_validation(path) #object initialization
     #
     #pred_val.prediction_validation() #calling the prediction_validation function

  pred = prediction(path) #object initialization

    # predicting for dataset present in database
  path = pred.predictionFromModel()
  print("Prediction File created at %s!!!" % path)
#
except ValueError:
    print("Error Occurred! %s" %ValueError)
except KeyError:
    print("Error Occurred! %s" %KeyError)
except Exception as e:
    print("Error Occurred! %s" %e)




# try:
#
#     path = 'Training_Batch_Files'
#     train_valObj = train_validation(path) #object initialization
#     train_valObj.train_validation()
#     print("validation successfull!!")
#     #train_valObj.train_validation()#calling the training_validation function
#
#
#     trainModelObj = trainModel() #object initialization
#     trainModelObj.trainingModel() #training the model for the files in the table
#
#
# except ValueError:
#
#     print("Error Occurred! %s" % ValueError)
#
# except KeyError:
#
#     print("Error Occurred! %s" % KeyError)
#
# except Exception as e:
#
#     print("Error Occurred! %s" % e)
# print("Training successfull!!")
# path = 'Prediction_Batch_Files'
# newpath = 'Prediction_Batch_Files/new'
#
# onlyfiles = [f for f in os.listdir(path)]
# for file in onlyfiles:
#     data = pd.read_csv(path + "/" + file)
#     data= data.drop(['Income'],axis=1)
#     data.to_csv(newpath + "/" + file, index=None, header=True)