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)
