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TOKEN = "YOUR TOKEN HERE"

def install_ngrok():
    import os
    from zipfile import ZipFile
    from urllib.request import urlretrieve
    
    url = 'https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip'
    urlretrieve(url, 'ngrok-amd64.zip')
    

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import threading
import time
import requests
import json
import subprocess

def torrent(port):
    command = subprocess.Popen(['qbittorrent-nox', f'--webui-port={port}'])

def ngrok(port):

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[program:jupyter]

directory=/home/ubuntu/jupyter-notebooks/
command=sudo jupyter notebook --allow-root --config=/home/ubuntu/.jupyter/jupyter_notebook_config.py
autostart=true
autorestart=true
stderr_logfile=/var/log/jupyter.err.log
stdout_logfile=/var/log/jupyter.out.log

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server {
listen 1222;
listen [::]:1222;
location / {
    proxy_pass http://0.0.0.0:8888;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header Host $http_host;
    proxy_http_version 1.1;
    proxy_redirect off;

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DATASET_DIR = 'your-dataset-directory'

BASE_DIR = os.getcwd() + '\\trainset'
os.mkdir(BASE_DIR)

TRAIN_DIR = os.path.join(BASE_DIR, 'train')
TRAIN_DOGS_DIR = os.path.join(TRAIN_DIR, 'dogs')
TRAIN_CATS_DIR = os.path.join(TRAIN_DIR, 'cats')

os.mkdir(TRAIN_DIR)

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from keras import models
from keras import layers

model = models.Sequential()

model.add(layers.Conv2D(
    filters=32,
    kernel_size=(3, 3),
    strides=(1, 1),
    padding='valid',

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from keras.preprocessing.image import ImageDataGenerator

# Rescaling all images by 1/255
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    directory=TRAIN_DIR,
    target_size=(150, 150), # Resizing all images to 150 x 150
    batch_size=20,