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 = 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,json_predictions = pred.predictionFromModel() return Response("Prediction File created at %s!!!" +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(): print(request) 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() return Response("Prediction File created at %s!!!" % path) elif request.form is not None: path = request.form['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() 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 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: 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 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: 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 predictRouteClient(): try: execution_id = str(uuid.uuid4()) if request.json is not None: path = request.json['filepath'] path = 'prediction-batch-files' 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() return Response("Prediction File created at azure container !!!" + str(path) + 'and few of the predictionss are ' + str(json.loads(json_predictions))) elif request.form is not None: path = request.form['filepath'] path = 'prediction-batch-files' 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() 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: 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 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 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, 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) pred_val.prediction_validation() pred = prediction(path) 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 predict_for_multiple(): try: if request.json is not None: path = request.json['filepath'] pred_val = pred_validation(path) pred_val.prediction_validation() pred = Prediction(path) path, json_predictions = pred.predictFromModel() 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: 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 predictClientRoute(): try: if request.json['folderPath'] is not None: path = request.json['folderPath'] predict_valObj = pred_validation(path) predict_valObj.prediction_validation() pred = Prediction(path) 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(): 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 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_data, result = pred.predictionFromModel() return Response(json_data) 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['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 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:
from wsgiref import simple_server