def trainRouteClient():
try:
if request.json['folderPath'] is not None:
path = request.json['folderPath']
train_valObj = train_validation(path)
train_valObj.train_validation()
trainModelObj = trainModel()
trainModelObj.trainingModel()
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
try:
a = Azure_Functions(
connection_string=
"DefaultEndpointsProtocol=https;AccountName=trainingbatchfiles;AccountKey=JPHQiUP+0kPN4UlfW+jXZm9EaPg0nsSUd9MZMLnhpjmJZnO7OXiemYqM+vosRjXA8MLOTqV2fsDEAmz6tIjGFw==;EndpointSuffix=core.windows.net"
)
b = a.gettingcsvfile("predictionbatchfiles")
print(b)
trainobj = train_validation(b)
trainobj.train_validation()
trainModelObj = trainModel()
trainModelObj.trainingModel()
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
try:
#if request.json['folderPath'] is not None:
#path = request.json['C:/Machine learning project/Visibility/visibility/code/7visibility_climate/Training_Batch_Files/visibility_08012008_120010.csv']
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:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
try:
if request.json['folderPath'] is not None:
path = request.json['folderPath']
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:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
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:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainingTest():
try:
az_blb_mgt = AzureBlobManagement()
execution_id = str(uuid.uuid4())
path = 'training-batch-files'
train_valObj = train_validation(path,
execution_id) # object initialization
train_valObj.train_validation(
) # calling the training_validation function
trainModelObj = trainModel(execution_id) # object initialization
trainModelObj.trainingModel(
) # training the model for the files in the table
bad_data_archived = "lat-" + execution_id
directory = [
container_name.name for container_name in
az_blb_mgt.blob_service_client.list_containers()
]
for dir in directory:
if re.search('^' + bad_data_archived, dir):
bad_data_archived = dir
file_names = az_blb_mgt.getAllFileNameFromDirectory(bad_data_archived)
message = "Hi Team,\n\n We have listed file name which was failed to process due to validation"
i = 0
for file in file_names:
i = i + 1
message = message + "\n" + str(i) + ") " + file
message = message + "\n Thanks & regards\n Avnish Yadav"
emailSender = EmailSender()
emailSender.sendEmail(message, "Trainning failed file")
print("Traing Completed")
except Exception as e:
print(str(e))
def trainRouteClient():
try:
# if request.json['folderPath'] is not None:
# path = request.json['folderPath']
# s3_client = boto3.client("s3", aws_access_key_id=Access_keys.access_key, aws_secret_access_key=Access_keys.secret_key)
# path = 'Training_Batch_Files'
path = Buckets.GetFilesFromBuckte("waferproject20210427")
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:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
try:
if request.json is not None: # 'folderPath' is nothing but path of folder we provide where trainign data set file is placed
path = request.json['filepath']
train_valObj = train_validation(
path
) # object initialization for class train_Validation(we r creating a instance of class train_validation
# as train_valObj
train_valObj.train_validation(
) # calling the training_validation function(from hat object calling taining_Validation function)
trainModelObj = trainModel() # object initialization
summary_of_training = trainModelObj.trainingModel(
) # training the model for the files in the table
print(summary_of_training)
return Response("result of training %s!!!" %
summary_of_training.to_html())
elif request.form is not None:
path = request.form['filepath']
train_valObj = train_validation(
path
) # object initialization for class train_Validation(we r creating a instance of class train_validation
# as train_valObj
train_valObj.train_validation(
) # calling the training_validation function(from hat object calling taining_Validation function)
trainModelObj = trainModel() # object initialization
summary_of_training = trainModelObj.trainingModel(
) # training the model for the files in the table
print(summary_of_training)
return Response("result of training %s!!!" %
summary_of_training.to_html())
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
def trainRouteClient():
try:
path = "Training_Batch_Files1"
#path = request.form['folderPath']
print(type(path))
if request.method == 'POST':
path = request.form['folderPath']
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
elif type(path) is str:
path = "Training_Batch_Files1"
print(path)
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
else:
print("Nothing")
except ValueError as v:
return Response("Error Occurred1! %s" % v)
except KeyError as k:
return Response("Error Occurred2! %s" % k)
except Exception as e:
return Response("Error Occurred3! %s" % e)
return Response("Training successfull!!")
def trainRouteClient():
try:
if request.json is not None:
path = request.json['filepath']
train_valObj = train_validation(path)
train_valObj.train_validation()
trainModelObj = trainModel()
trainModelObj.trainingModel()
return Response('Successful End of Training')
else:
return Response("Invalid filepath")
except ValueError:
return Response("Error occured {}".format(ValueError))
except KeyError:
return Response("Error occured {}".format(KeyError))
except Exception as e:
return Response("Error occured {}".format(e))
def trainRouteClient():
try:
user_input = st.text_input("Enter the name of folder containing files for training", "default")
if user_input!="default":
path=user_input
# print(path)
train_valObj = train_validation(path)
df=train_valObj.train_validation()
if len(df)>0:
st.write("The processed data used for training")
st.write(df)
trainModelObj = trainModel() # object initialization
training_msg=trainModelObj.trainingModel() # training the model for the files in the table
if len(training_msg)>0:
st.write(training_msg)
except Exception as e:
st.write("Error Occurred!:",e)
def trainRouteClient():
try:
execution_id = str(uuid.uuid4())
#if request.json['folderPath'] is not None:
#path = request.json['folderPath']
path = 'training-batch-files'
execution_id = str(uuid.uuid4())
train_valObj = train_validation(
path, execution_id) #object initialization of class
train_valObj.train_validation(
) #calling the training_validation function
trainModelObj = trainModel(execution_id) #object initialization
trainModelObj.trainingModel(
) #training the model for the files in the table
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
def retrain():
file_object = open("log_file/FlaskApi_log.txt", 'a+')
try:
logger.log(file_object,
'============= Retraining Model Started =============')
if request.method == "POST":
file = request.files['retrain_file']
if file:
file.save(secure_filename(file.filename))
a = trainModel()
a.trainingModel(file.filename, file_object)
logger.log(
file_object,
'============= Model Retraining Done =============')
os.remove(file.filename)
file_object.close()
return render_template(
'home.html', text=".... Model Retrained Successfully ....")
except Exception as e:
logger.log(file_object,
'Model Retraining Failed . ERROR message : ' + str(e))
file_object.close()
return 'Something went wrong , check your file extension .(should be .csv )'
def trainRouteClient():
try:
if request.json[
'filepath'] is not None: # prection file access from Postman server (Main folder file:- RawData)
path = request.json['filepath'] # Obtained file path
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:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
return Response("Training successfull!!")
# Run in order to generate default pickle file .. from trainingModel import trainModel start_model_training = trainModel() start_model_training.trainingModel()
# 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!!")
from wsgiref import simple_server