# tk_pattern_function_indexify.py

# tk_pattern_function_indexify.py

import math
from tkinter import *

fn_code = """
def create_fn(index):
    # split the pattern into a list of strings
    pattern_list = pattern.split(" ")

    # determine the length of the pattern
    pattern_length = len(pattern_list)

    # create a function that will determine the value at a given index
    def pattern_index_fn(index): # rename this function
"""

# create a flag variable to indicate whether the neural network should continue running or not
go = True

# create a function to stop the neural network
def stop_neural_network():
    global go
    go = False

def neural_network(pattern, index):
    # split the pattern into a list of strings
    pattern_list = pattern.split(" ")

    # determine the length of the pattern
    pattern_length = len(pattern_list)

    # create a function that will determine the value at a given index
    def fn(index):
        # determine the remainder when the index is divided by the pattern length
        remainder = index % pattern_length

        # return the value at the remainder index in the pattern list
        return pattern_list[remainder]

    return fn

# create a function to train the neural network
def train(pattern, index, expected_result):
    # run the neural network with the given pattern and index
    result = neural_network(pattern, index)

    # compare the result to the expected result
    if result == expected_result:
        # if the result is correct, return True
        return True
    else:
        # if the result is incorrect, update the pattern and return False
        pattern_list = pattern.split(" ")
        pattern_list[index % len(pattern_list)] = expected_result
        pattern = " ".join(pattern_list)
        return False

# create a tkinter button that will run the neural network and train it if necessary when clicked
def button_clicked():
    global go
    go = True

    # get the user input for the pattern
    pattern = pattern_input.get()

    # get the user input for the index
    index = index_input.get()

    # get the user input for the expected result
    expected_result = expected_result_input.get()

    # run the neural network
    while go:

        # run the neural network and get the result
        result = neural_network(pattern, index)

        # check if the result is correct
        if result == expected_result:
            # if the result is correct, display it to the user
            result_label.config(text=result)
            go = False

            # print the fn_code string followed by the string representation of the fn function
            print(fn_code + str(result))
        else:
            # if the result is incorrect, train the neural network and try again
            training_success = train(pattern, index, expected_result)
            if training_success:
                # if training was successful, display the correct result to the user
                result_label.config(text=expected_result)

# create the tkinter window and widgets
window = Tk()
window.title("tk_pattern_function_indexify")

pattern_label = Label(window, text="Enter a pattern:")
pattern_input = Entry(window)
index_label = Label(window, text="Enter an index:")
index_input = Entry(window)
expected_result_label = Label(window, text="Enter expected result:")
expected_result_input = Entry(window)
button = Button(window, text="Run Neural Network", command=button_clicked)
result_label = Label(window, text="")
cancel_button = Button(window, text="Cancel", command=stop_neural_network)

# add the widgets to the window
pattern_label.pack()
pattern_input.pack()
index_label.pack()
index_input.pack()
expected_result_label.pack()
expected_result_input.pack()
button.pack()
result_label.pack()
cancel_button.pack()

window.mainloop()