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import socket
import pickle
import threading
import time
import numpy

import nn
import gann

import kivy.app

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import socket
import pickle
import time
import numpy

import nn
import gann

class SocketThread(threading.Thread):

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import threading

class ListenThread(threading.Thread):

    def __init__(self, kivy_app):
        threading.Thread.__init__(self)
        self.kivy_app = kivy_app

    def run(self):
        while True:

ahmedfgad

import kivy.app
import kivy.uix.button
import kivy.uix.label
import kivy.uix.textinput
import kivy.uix.boxlayout

class ServerApp(kivy.app.App):
    
    def __init__(self):
        super().__init__()

ahmedfgad

import kivy.app
import kivy.uix.button
import kivy.uix.label
import kivy.uix.textinput
import kivy.uix.boxlayout

class ServerApp(kivy.app.App):

    def build(self):
        self.create_socket_btn = kivy.uix.button.Button(text="Create Socket", disabled=False)

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import socket
import pickle
import threading
import time

import pygad
import pygad.nn
import pygad.gann
import numpy

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def model_averaging(self, model, other_model):
    model_weights = pygad.nn.layers_weights(last_layer=model, initial=False)
    other_model_weights = pygad.nn.layers_weights(last_layer=other_model, initial=False)
        
    new_weights = numpy.array(model_weights + other_model_weights)/2

    pygad.nn.update_layers_trained_weights(last_layer=model, final_weights=new_weights)

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# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[1, 1],
                           [1, 0],
                           [0, 1],
                           [0, 0]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([0, 
                            1, 
                            1, 

ahmedfgad

import socket
import pickle
import numpy

import pygad
import pygad.nn
import pygad.gann

def fitness_func(solution, sol_idx):
    global GANN_instance, data_inputs, data_outputs

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import pygad
import pygad.nn
import pygad.gann

# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[0, 1],
                           [0, 0]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([1,