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