def predictRouteClient():
try:
if request.json is not None:
path = request.json['filepath']
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()
return Response("Prediction File created at %s!!!" % path)
elif request.form is not None:
path = request.form['filepath']
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()
return Response("Prediction File created at %s!!!" % path)
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 predictRouteClient():
try:
if request.json is not None:
path = request.json['C:/Machine learning project/Visibility/visibility/code/7visibility_climate/Prediction_Batch_files/visibility_082019_120021.csv']
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()
return Response("Prediction File created at %s!!!" % path)
elif request.form is not None:
path = request.form['filepath']
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()
return Response("Prediction File created at %s!!!" % path)
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 predictRouteClient():
try:
if request.json is not None:
path = request.json['filepath']
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,json_predictions = pred.predictionFromModel()
return Response("Prediction File created at !!!" +str(path) +'and few of the predictions are '+str(json.loads(json_predictions) ))
elif request.form is not None:
path = request.form['filepath']
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,json_predictions = pred.predictionFromModel()
return Response("Prediction File created at !!!" +str(path) +'and few of the predictions are '+str(json.loads(json_predictions) ))
else:
print('Nothing Matched')
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 predictRouteClient():
try:
if request.json is not None:
path = request.json['filepath']
pred_val = pred_validation(path) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path) # object initialization
pred_input_data = pd.read_csv(
"Prediction_FileFromDB/inputFile.Csv")
# predicting for dataset present in database
path, result = pred.predictionFromModel()
X = pd.concat(
[pred_input_data, pd.DataFrame(result)], axis=1, sort=False)
print(X)
return Response("Prediction File created at %s!!!" % path + " " +
"prediction results are given below %s" %
X.to_html())
elif request.form is not None:
path = request.form['filepath']
pred_val = pred_validation(path) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path) # object initialization
pred_data = pd.read_csv("Prediction_FileFromDB/inputFile.Csv")
# predicting for dataset present in database
path, result = pred.predictionFromModel()
X = pd.concat([pred_data, pd.DataFrame(result)],
axis=1,
sort=False)
return Response("Prediction File created at %s!!!" % path + " " +
"prediction results are given below %s" %
X.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 predictRouteClient():
try:
path = request.form['Default_File_Predict']
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, json_predictions = pred.predictionFromModel()
return render_template(
'results.html',
prediction=
'Prediction has been saved at {} and few of the predictions are '.
format(path) + ' ' + str(json.loads(json_predictions)))
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 predictionTest(path=None):
try:
az_blb_mgt = AzureBlobManagement()
execution_id = str(uuid.uuid4())
if path is None:
path = 'prediction-batch-files'
else:
path = path
pred_val = pred_validation(path, execution_id) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path, execution_id) # object initialization
# predicting for dataset present in database
path, json_predictions = pred.predictionFromModel()
prediction_location = "prediction-output-file"
file_list = "prediction-output-file"
#selecting all failed file name
return Response("Prediction File created at !!!" + str(path) +
'and few of the predictions are ' +
str(json.loads(json_predictions)))
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 predictRouteClient():
try:
user_input = st.text_input("Enter the name of folder containing files for prediction", "default")
if user_input!="default":
path=user_input
pred_val = pred_validation(path) #object initialization
df=pred_val.prediction_validation() #calling the prediction_validation function
if len(df)>0:
st.subheader("The Predicted cement strength for each input is")
# st.write(df)
pred = prediction(path) #object initialization
# predicting for dataset present in database
path,json_data,result = pred.predictionFromModel()
if len(result)>0:
df1=pd.concat([df,result],axis=1,ignore_index=True)
df1.columns = list(df.columns.values) + list(result.columns.values)
st.write(df1)
except Exception as e:
st.write("Error Occurred!:", e)
def predictRouteClient():
try:
if request.json is not None: # prection file access from Postman server
path = request.json['filepath'] # Obtained file path
pred_val = pred_validation(path) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path) # object initialization
path = pred.predictionFromModel(
) # predicting for dataset using saved model
return Response("Prediction File created at %s!!!" %
path) # Predicted file path
elif request.form is not None: # prection file access from Web API
path = request.form['filepath'] # Obtained file path
pred_val = pred_validation(path) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path) # object initialization
path = pred.predictionFromModel(
) # predicting for dataset using saved model
return Response("Prediction File created at %s!!!" %
path) # Predicted file path
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 predictRouteClient():
try:
global glbPath
print('Within predictRouteClient, glbpath:'+glbPath)
if request.json is not None:
path = request.json['filepath']
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()
glbPath=path
return Response("Prediction File created at %s!!!" % path)
elif request.form is not None:
path = request.form['filepath']
print(path)
pred_val = pred_validation(path) # object initialization
print('Post Object initialization '+path)
pred_val.prediction_validation() # calling the prediction_validation function
print('Post prediction_validation function ' + path)
pred = prediction(path) # object initialization
print('Post prediction object initialization ' + path)
# predicting for dataset present in database
path = pred.predictionFromModel()
glbPath = path
print("Inside prediction code:"+path)
return Response("Prediction File created at %s!!!" % path)
except ValueError:
print("Error Occurred! " + str(ValueError))
return Response("Error Occurred! %s" % str(ValueError))
except KeyError:
print("Error Occurred! " + str(KeyError))
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
print("Error Occurred! " + str(e))
return Response("Error Occurred! %s" % e)
def common_operations(path):
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, json_predictions = pred.predictionFromModel()
return Response("Prediction File created at !!!" + str(path) +
'and few of the predictions are ' +
str(json.loads(json_predictions)))
def predictRouteClient():
try:
if request.json is not None:
path = request.json['filepath']
pred_val = pred_validation(path)
pred_val.prediction_validation()
pred = prediction(path)
path = pred.predictionFromModel()
return Response("Prediction File created at %s!!!" % path)
else:
return Response("invalid file path")
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 predictRouteClient():
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 = pred_validation(b)
trainobj.prediction_validation()
pred = prediction(b)
json_predictions = pred.predictionFromModel()
return Response('and few of the predictions are ' +
str(json.loads(json_predictions)))
def predictionTest(path=None):
try:
az_blb_mgt = AzureBlobManagement()
execution_id = str(uuid.uuid4())
if path is None:
path = 'prediction-batch-files'
else:
path = path
pred_val = pred_validation(path, execution_id) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path, execution_id) # object initialization
# predicting for dataset present in database
path, json_predictions = pred.predictionFromModel()
prediction_location = "prediction-output-file"
file_list = "prediction-output-file"
#selecting all failed file name
bad_data_archived = "lap-" + 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, "Prediction failed file")
print(path, json_predictions)
except Exception as e:
print(str(e))
def predictRouteClient():
try:
if request.json['folderPath'] is not None:
path = request.json['folderPath']
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()
s3.upload_file(Filename=path, Bucket='wafer-123', Key=f'Prediction_Output_File/Predictions-{datetime.now()}.csv')
return Response("Prediction File created at %s!!!" % path)
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)
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'
from wsgiref import simple_server
