#!/usr/bin/env python

import sys

import PySimpleGUI as sg

def MachineLearningGUI():

sg.set_options(text_justification='right')

flags = [[sg.CB('Normalize', size=(12, 1), default=True), sg.CB('Verbose', size=(20, 1))],

[sg.CB('Cluster', size=(12, 1)), sg.CB(

'Flush Output', size=(20, 1), default=True)],

[sg.CB('Write Results', size=(12, 1)), sg.CB(

'Keep Intermediate Data', size=(20, 1))],

[sg.CB('Normalize', size=(12, 1), default=True),

sg.CB('Verbose', size=(20, 1))],

[sg.CB('Cluster', size=(12, 1)), sg.CB(

'Flush Output', size=(20, 1), default=True)],

[sg.CB('Write Results', size=(12, 1)), sg.CB('Keep Intermediate Data', size=(20, 1))], ]

loss_functions = [[sg.Rad('Cross-Entropy', 'loss', size=(12, 1)), sg.Rad('Logistic', 'loss', default=True, size=(12, 1))],

[sg.Rad('Hinge', 'loss', size=(12, 1)),

sg.Rad('Huber', 'loss', size=(12, 1))],

[sg.Rad('Kullerback', 'loss', size=(12, 1)),

sg.Rad('MAE(L1)', 'loss', size=(12, 1))],

[sg.Rad('MSE(L2)', 'loss', size=(12, 1)), sg.Rad('MB(L0)', 'loss', size=(12, 1))], ]

command_line_parms = [[sg.Text('Passes', size=(8, 1)), sg.Spin(values=[i for i in range(1, 1000)], initial_value=20, size=(6, 1)),

sg.Text('Steps', size=(8, 1), pad=((7, 3))), sg.Spin(values=[i for i in range(1, 1000)], initial_value=20, size=(6, 1))],

[sg.Text('ooa', size=(8, 1)), sg.Input(default_text='6', size=(8, 1)), sg.Text('nn', size=(8, 1)),

sg.Input(default_text='10', size=(10, 1))],

[sg.Text('q', size=(8, 1)), sg.Input(default_text='ff', size=(8, 1)), sg.Text('ngram', size=(8, 1)),

sg.Input(default_text='5', size=(10, 1))],

[sg.Text('l', size=(8, 1)), sg.Input(default_text='0.4', size=(8, 1)), sg.Text('Layers', size=(8, 1)),

sg.Drop(values=('BatchNorm', 'other'))], ]

layout = [[sg.Frame('Command Line Parameteres', command_line_parms, title_color='green', font='Any 12')],

[sg.Frame('Flags', flags, font='Any 12', title_color='blue')],

[sg.Frame('Loss Functions', loss_functions,

font='Any 12', title_color='red')],

[sg.Submit(), sg.Cancel()]]

sg.set_options(text_justification='left')

window = sg.Window('Machine Learning Front End',

layout, font=("Helvetica", 12))

button, values = window.read()

window.close()

print(button, values)

def CustomMeter():

# layout the form

layout = [[sg.Text('A custom progress meter')],

[sg.ProgressBar(1000, orientation='h',

size=(20, 20), key='progress')],

[sg.Cancel()]]

# create the form`

window = sg.Window('Custom Progress Meter', layout)

progress_bar = window['progress']

# loop that would normally do something useful

for i in range(1000):

# check to see if the cancel button was clicked and exit loop if clicked

event, values = window.read(timeout=0, timeout_key='timeout')

if event == 'Cancel' or event == None:

break

# update bar with loop value +1 so that bar eventually reaches the maximum

progress_bar.update_bar(i+1)

# done with loop... need to destroy the window as it's still open

window.CloseNonBlocking()

if __name__ == '__main__':

CustomMeter()

MachineLearningGUI()