def main(): if request.method == 'GET': result = request.form print(result) return 'This is a get request from python' elif request.method == 'POST': print("Starting Classification on Python side...") encoder = HTMLEncoder() encoder.clear() parser = MyHTMLParser() parser.clear() response = Response(request.form) handler = DataHandler() clf = RFC() # The Data expected_output = handler.convert_to_matrix('three_outputs.csv') login_files = handler.convert_to_matrix('login_pages.csv') reg_files = handler.convert_to_matrix('registration_pages.csv') payment_files = handler.convert_to_matrix('payment_pages.csv') # Directories cur_dir = os.getcwd() login_dir = cur_dir + "/Login_Pages/" reg_dir = cur_dir + "/Registration_Pages/" payment_dir = cur_dir + "/Payment_Pages/" # Load Data handler.load_data(login_dir, login_files, expected_output) handler.load_data(reg_dir, reg_files, expected_output) handler.load_data(payment_dir, payment_files, expected_output) # Transfer Data training_input, testing_input = handler.getinputs() training_output, testing_output = handler.getoutputs() predictions, classifier = clf.findbestestimator( training_input, training_output, testing_input, testing_output) return clf.test_raw_html(response.retrieve_html(), parser, encoder) else: return 'None'
from data_handler import DataHandler from html_encoder import * from randomforestclassifier import RFC handler = DataHandler() clf = RFC() # The Data expected_output = handler.convert_to_matrix('three_outputs.csv') login_files = handler.convert_to_matrix('login_pages.csv') reg_files = handler.convert_to_matrix('registration_pages.csv') payment_files = handler.convert_to_matrix('payment_pages.csv') # Directories cur_dir = os.getcwd() login_dir = cur_dir + "/Login_Pages/" reg_dir = cur_dir + "/Registration_Pages/" payment_dir = cur_dir + "/Payment_Pages/" # Load Data handler.load_data(login_dir, login_files, expected_output) handler.load_data(reg_dir, reg_files, expected_output) handler.load_data(payment_dir, payment_files, expected_output) # Transfer Data training_input, testing_input = handler.getinputs() training_output, testing_output = handler.getoutputs() predictions, classifer = clf.findbestestimator(training_input, training_output, testing_input, testing_output)