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test.py
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test.py
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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!!")