test.py

from wsgiref import simple_server
from flask import Flask, request
from flask import Response
import os
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()#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!!")