PySimpleGUI/Demo_Machine_Learning.py

#!/usr/bin/env python
import sys
if sys.version_info[0] >= 3:
    import PySimpleGUI as sg
else:
    import PySimpleGUI27 as sg

def MachineLearningGUI():
    sg.SetOptions(text_justification='right')

    flags = [[sg.Checkbox('Normalize', size=(12, 1), default=True), sg.Checkbox('Verbose', size=(20, 1))],
    [sg.Checkbox('Cluster', size=(12, 1)), sg.Checkbox('Flush Output', size=(20, 1), default=True)],
    [sg.Checkbox('Write Results', size=(12, 1)), sg.Checkbox('Keep Intermediate Data', size=(20, 1))],
    [sg.Checkbox('Normalize', size=(12, 1), default=True), sg.Checkbox('Verbose', size=(20, 1))],
    [sg.Checkbox('Cluster', size=(12, 1)), sg.Checkbox('Flush Output', size=(20, 1), default=True)],
    [sg.Checkbox('Write Results', size=(12, 1)), sg.Checkbox('Keep Intermediate Data', size=(20, 1))],]

    loss_functions = [[sg.Radio('Cross-Entropy', 'loss', size=(12, 1)), sg.Radio('Logistic', 'loss', default=True, size=(12, 1))],
        [sg.Radio('Hinge', 'loss', size=(12, 1)), sg.Radio('Huber', 'loss', size=(12, 1))],
        [sg.Radio('Kullerback', 'loss', size=(12, 1)), sg.Radio('MAE(L1)', 'loss', size=(12, 1))],
        [sg.Radio('MSE(L2)', 'loss', size=(12, 1)), sg.Radio('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.In(default_text='6', size=(8, 1)), sg.Text('nn', size=(8, 1)),
         sg.In(default_text='10', size=(10, 1))],
        [sg.Text('q', size=(8, 1)), sg.In(default_text='ff', size=(8, 1)), sg.Text('ngram', size=(8, 1)),
         sg.In(default_text='5', size=(10, 1))],
        [sg.Text('l', size=(8, 1)), sg.In(default_text='0.4', size=(8, 1)), sg.Text('Layers', size=(8, 1)),
         sg.Drop(values=('BatchNorm', 'other'), auto_size_text=True)],]

    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()]]

    window = sg.Window('Machine Learning Front End', font=("Helvetica", 12)).Layout(layout)
    button, values = window.Read()
    sg.SetOptions(text_justification='left')

    print(button, values)

def CustomMeter():
    # layout the form
    layout = [[sg.Text('A custom progress meter')],
              [sg.ProgressBar(10000, orientation='h', size=(20,20), key='progress')],
              [sg.Cancel()]]

    # create the form`
    window = sg.Window('Custom Progress Meter').Layout(layout)
    progress_bar = window.FindElement('progress')
    # loop that would normally do something useful
    for i in range(10000):
        # check to see if the cancel button was clicked and exit loop if clicked
        event, values = window.ReadNonBlocking()
        if event == 'Cancel' or values == None:
            break
        # update bar with loop value +1 so that bar eventually reaches the maximum
        progress_bar.UpdateBar(i+1)
    # done with loop... need to destroy the window as it's still open
    window.CloseNonBlocking()

if __name__ == '__main__':
    CustomMeter()
    MachineLearningGUI()