work/cat_and_dog/fetching_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import requests, json, io, time, os, tqdm\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dog_api = 'https://dog.ceo/api/breeds/image/random'\n",
    "cat_api = 'https://api.thecatapi.com/v1/images/search'\n",
    "\n",
    "img_size = (64, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cat Images: 2475\n",
      "Total Dog Images: 3002\n"
     ]
    }
   ],
   "source": [
    "total_dog_images = max([int(i[8:-4]) for i in os.listdir(f\"fetched_data_3/dogs\")]) + 1\n",
    "total_cat_images = max([int(i[8:-4]) for i in os.listdir(f\"fetched_data_3/cats\")]) + 1\n",
    "print(\"Total Cat Images:\", total_cat_images)\n",
    "print(\"Total Dog Images:\", total_dog_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|████▌     | 365/800 [25:31<30:24,  4.19s/it]  \n"
     ]
    },
    {
     "ename": "ConnectionError",
     "evalue": "('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    702\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[0;32m    704\u001b[0m     conn,\n\u001b[0;32m    705\u001b[0m     method,\n\u001b[0;32m    706\u001b[0m     url,\n\u001b[0;32m    707\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[0;32m    708\u001b[0m     body\u001b[39m=\u001b[39;49mbody,\n\u001b[0;32m    709\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m    710\u001b[0m     chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[0;32m    711\u001b[0m )\n\u001b[0;32m    713\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    714\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    715\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    716\u001b[0m \u001b[39m# mess.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:386\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 386\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_conn(conn)\n\u001b[0;32m    387\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    388\u001b[0m     \u001b[39m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:1042\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m   1041\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mgetattr\u001b[39m(conn, \u001b[39m\"\u001b[39m\u001b[39msock\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m):  \u001b[39m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[1;32m-> 1042\u001b[0m     conn\u001b[39m.\u001b[39;49mconnect()\n\u001b[0;32m   1044\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m conn\u001b[39m.\u001b[39mis_verified:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connection.py:414\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    412\u001b[0m     context\u001b[39m.\u001b[39mload_default_certs()\n\u001b[1;32m--> 414\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m ssl_wrap_socket(\n\u001b[0;32m    415\u001b[0m     sock\u001b[39m=\u001b[39;49mconn,\n\u001b[0;32m    416\u001b[0m     keyfile\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkey_file,\n\u001b[0;32m    417\u001b[0m     certfile\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcert_file,\n\u001b[0;32m    418\u001b[0m     key_password\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkey_password,\n\u001b[0;32m    419\u001b[0m     ca_certs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_certs,\n\u001b[0;32m    420\u001b[0m     ca_cert_dir\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_cert_dir,\n\u001b[0;32m    421\u001b[0m     ca_cert_data\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_cert_data,\n\u001b[0;32m    422\u001b[0m     server_hostname\u001b[39m=\u001b[39;49mserver_hostname,\n\u001b[0;32m    423\u001b[0m     ssl_context\u001b[39m=\u001b[39;49mcontext,\n\u001b[0;32m    424\u001b[0m     tls_in_tls\u001b[39m=\u001b[39;49mtls_in_tls,\n\u001b[0;32m    425\u001b[0m )\n\u001b[0;32m    427\u001b[0m \u001b[39m# If we're using all defaults and the connection\u001b[39;00m\n\u001b[0;32m    428\u001b[0m \u001b[39m# is TLSv1 or TLSv1.1 we throw a DeprecationWarning\u001b[39;00m\n\u001b[0;32m    429\u001b[0m \u001b[39m# for the host.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\util\\ssl_.py:449\u001b[0m, in \u001b[0;36mssl_wrap_socket\u001b[1;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[39mif\u001b[39;00m send_sni:\n\u001b[1;32m--> 449\u001b[0m     ssl_sock \u001b[39m=\u001b[39m _ssl_wrap_socket_impl(\n\u001b[0;32m    450\u001b[0m         sock, context, tls_in_tls, server_hostname\u001b[39m=\u001b[39;49mserver_hostname\n\u001b[0;32m    451\u001b[0m     )\n\u001b[0;32m    452\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\util\\ssl_.py:493\u001b[0m, in \u001b[0;36m_ssl_wrap_socket_impl\u001b[1;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[0;32m    492\u001b[0m \u001b[39mif\u001b[39;00m server_hostname:\n\u001b[1;32m--> 493\u001b[0m     \u001b[39mreturn\u001b[39;00m ssl_context\u001b[39m.\u001b[39;49mwrap_socket(sock, server_hostname\u001b[39m=\u001b[39;49mserver_hostname)\n\u001b[0;32m    494\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:513\u001b[0m, in \u001b[0;36mSSLContext.wrap_socket\u001b[1;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrap_socket\u001b[39m(\u001b[39mself\u001b[39m, sock, server_side\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[0;32m    508\u001b[0m                 do_handshake_on_connect\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    509\u001b[0m                 suppress_ragged_eofs\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    510\u001b[0m                 server_hostname\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, session\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    511\u001b[0m     \u001b[39m# SSLSocket class handles server_hostname encoding before it calls\u001b[39;00m\n\u001b[0;32m    512\u001b[0m     \u001b[39m# ctx._wrap_socket()\u001b[39;00m\n\u001b[1;32m--> 513\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msslsocket_class\u001b[39m.\u001b[39;49m_create(\n\u001b[0;32m    514\u001b[0m         sock\u001b[39m=\u001b[39;49msock,\n\u001b[0;32m    515\u001b[0m         server_side\u001b[39m=\u001b[39;49mserver_side,\n\u001b[0;32m    516\u001b[0m         do_handshake_on_connect\u001b[39m=\u001b[39;49mdo_handshake_on_connect,\n\u001b[0;32m    517\u001b[0m         suppress_ragged_eofs\u001b[39m=\u001b[39;49msuppress_ragged_eofs,\n\u001b[0;32m    518\u001b[0m         server_hostname\u001b[39m=\u001b[39;49mserver_hostname,\n\u001b[0;32m    519\u001b[0m         context\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[0;32m    520\u001b[0m         session\u001b[39m=\u001b[39;49msession\n\u001b[0;32m    521\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:1071\u001b[0m, in \u001b[0;36mSSLSocket._create\u001b[1;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mdo_handshake_on_connect should not be specified for non-blocking sockets\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m-> 1071\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo_handshake()\n\u001b[0;32m   1072\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mOSError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m):\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:1342\u001b[0m, in \u001b[0;36mSSLSocket.do_handshake\u001b[1;34m(self, block)\u001b[0m\n\u001b[0;32m   1341\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msettimeout(\u001b[39mNone\u001b[39;00m)\n\u001b[1;32m-> 1342\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mdo_handshake()\n\u001b[0;32m   1343\u001b[0m \u001b[39mfinally\u001b[39;00m:\n",
      "\u001b[1;31mConnectionResetError\u001b[0m: [WinError 10054] An existing connection was forcibly closed by the remote host",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProtocolError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\adapters.py:489\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    488\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m chunked:\n\u001b[1;32m--> 489\u001b[0m     resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[0;32m    490\u001b[0m         method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[0;32m    491\u001b[0m         url\u001b[39m=\u001b[39;49murl,\n\u001b[0;32m    492\u001b[0m         body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[0;32m    493\u001b[0m         headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[0;32m    494\u001b[0m         redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    495\u001b[0m         assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    496\u001b[0m         preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    497\u001b[0m         decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    498\u001b[0m         retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[0;32m    499\u001b[0m         timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[0;32m    500\u001b[0m     )\n\u001b[0;32m    502\u001b[0m \u001b[39m# Send the request.\u001b[39;00m\n\u001b[0;32m    503\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    785\u001b[0m     e \u001b[39m=\u001b[39m ProtocolError(\u001b[39m\"\u001b[39m\u001b[39mConnection aborted.\u001b[39m\u001b[39m\"\u001b[39m, e)\n\u001b[1;32m--> 787\u001b[0m retries \u001b[39m=\u001b[39m retries\u001b[39m.\u001b[39;49mincrement(\n\u001b[0;32m    788\u001b[0m     method, url, error\u001b[39m=\u001b[39;49me, _pool\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m, _stacktrace\u001b[39m=\u001b[39;49msys\u001b[39m.\u001b[39;49mexc_info()[\u001b[39m2\u001b[39;49m]\n\u001b[0;32m    789\u001b[0m )\n\u001b[0;32m    790\u001b[0m retries\u001b[39m.\u001b[39msleep()\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\util\\retry.py:550\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    549\u001b[0m \u001b[39mif\u001b[39;00m read \u001b[39mis\u001b[39;00m \u001b[39mFalse\u001b[39;00m \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_method_retryable(method):\n\u001b[1;32m--> 550\u001b[0m     \u001b[39mraise\u001b[39;00m six\u001b[39m.\u001b[39;49mreraise(\u001b[39mtype\u001b[39;49m(error), error, _stacktrace)\n\u001b[0;32m    551\u001b[0m \u001b[39melif\u001b[39;00m read \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\packages\\six.py:769\u001b[0m, in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    768\u001b[0m \u001b[39mif\u001b[39;00m value\u001b[39m.\u001b[39m__traceback__ \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m tb:\n\u001b[1;32m--> 769\u001b[0m     \u001b[39mraise\u001b[39;00m value\u001b[39m.\u001b[39mwith_traceback(tb)\n\u001b[0;32m    770\u001b[0m \u001b[39mraise\u001b[39;00m value\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    702\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[0;32m    704\u001b[0m     conn,\n\u001b[0;32m    705\u001b[0m     method,\n\u001b[0;32m    706\u001b[0m     url,\n\u001b[0;32m    707\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[0;32m    708\u001b[0m     body\u001b[39m=\u001b[39;49mbody,\n\u001b[0;32m    709\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m    710\u001b[0m     chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[0;32m    711\u001b[0m )\n\u001b[0;32m    713\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    714\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    715\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    716\u001b[0m \u001b[39m# mess.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:386\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 386\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_conn(conn)\n\u001b[0;32m    387\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    388\u001b[0m     \u001b[39m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connectionpool.py:1042\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m   1041\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mgetattr\u001b[39m(conn, \u001b[39m\"\u001b[39m\u001b[39msock\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m):  \u001b[39m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[1;32m-> 1042\u001b[0m     conn\u001b[39m.\u001b[39;49mconnect()\n\u001b[0;32m   1044\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m conn\u001b[39m.\u001b[39mis_verified:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\connection.py:414\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    412\u001b[0m     context\u001b[39m.\u001b[39mload_default_certs()\n\u001b[1;32m--> 414\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m ssl_wrap_socket(\n\u001b[0;32m    415\u001b[0m     sock\u001b[39m=\u001b[39;49mconn,\n\u001b[0;32m    416\u001b[0m     keyfile\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkey_file,\n\u001b[0;32m    417\u001b[0m     certfile\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcert_file,\n\u001b[0;32m    418\u001b[0m     key_password\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkey_password,\n\u001b[0;32m    419\u001b[0m     ca_certs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_certs,\n\u001b[0;32m    420\u001b[0m     ca_cert_dir\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_cert_dir,\n\u001b[0;32m    421\u001b[0m     ca_cert_data\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mca_cert_data,\n\u001b[0;32m    422\u001b[0m     server_hostname\u001b[39m=\u001b[39;49mserver_hostname,\n\u001b[0;32m    423\u001b[0m     ssl_context\u001b[39m=\u001b[39;49mcontext,\n\u001b[0;32m    424\u001b[0m     tls_in_tls\u001b[39m=\u001b[39;49mtls_in_tls,\n\u001b[0;32m    425\u001b[0m )\n\u001b[0;32m    427\u001b[0m \u001b[39m# If we're using all defaults and the connection\u001b[39;00m\n\u001b[0;32m    428\u001b[0m \u001b[39m# is TLSv1 or TLSv1.1 we throw a DeprecationWarning\u001b[39;00m\n\u001b[0;32m    429\u001b[0m \u001b[39m# for the host.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\util\\ssl_.py:449\u001b[0m, in \u001b[0;36mssl_wrap_socket\u001b[1;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[39mif\u001b[39;00m send_sni:\n\u001b[1;32m--> 449\u001b[0m     ssl_sock \u001b[39m=\u001b[39m _ssl_wrap_socket_impl(\n\u001b[0;32m    450\u001b[0m         sock, context, tls_in_tls, server_hostname\u001b[39m=\u001b[39;49mserver_hostname\n\u001b[0;32m    451\u001b[0m     )\n\u001b[0;32m    452\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\urllib3\\util\\ssl_.py:493\u001b[0m, in \u001b[0;36m_ssl_wrap_socket_impl\u001b[1;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[0;32m    492\u001b[0m \u001b[39mif\u001b[39;00m server_hostname:\n\u001b[1;32m--> 493\u001b[0m     \u001b[39mreturn\u001b[39;00m ssl_context\u001b[39m.\u001b[39;49mwrap_socket(sock, server_hostname\u001b[39m=\u001b[39;49mserver_hostname)\n\u001b[0;32m    494\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:513\u001b[0m, in \u001b[0;36mSSLContext.wrap_socket\u001b[1;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrap_socket\u001b[39m(\u001b[39mself\u001b[39m, sock, server_side\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[0;32m    508\u001b[0m                 do_handshake_on_connect\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    509\u001b[0m                 suppress_ragged_eofs\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    510\u001b[0m                 server_hostname\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, session\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    511\u001b[0m     \u001b[39m# SSLSocket class handles server_hostname encoding before it calls\u001b[39;00m\n\u001b[0;32m    512\u001b[0m     \u001b[39m# ctx._wrap_socket()\u001b[39;00m\n\u001b[1;32m--> 513\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msslsocket_class\u001b[39m.\u001b[39;49m_create(\n\u001b[0;32m    514\u001b[0m         sock\u001b[39m=\u001b[39;49msock,\n\u001b[0;32m    515\u001b[0m         server_side\u001b[39m=\u001b[39;49mserver_side,\n\u001b[0;32m    516\u001b[0m         do_handshake_on_connect\u001b[39m=\u001b[39;49mdo_handshake_on_connect,\n\u001b[0;32m    517\u001b[0m         suppress_ragged_eofs\u001b[39m=\u001b[39;49msuppress_ragged_eofs,\n\u001b[0;32m    518\u001b[0m         server_hostname\u001b[39m=\u001b[39;49mserver_hostname,\n\u001b[0;32m    519\u001b[0m         context\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[0;32m    520\u001b[0m         session\u001b[39m=\u001b[39;49msession\n\u001b[0;32m    521\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:1071\u001b[0m, in \u001b[0;36mSSLSocket._create\u001b[1;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mdo_handshake_on_connect should not be specified for non-blocking sockets\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m-> 1071\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo_handshake()\n\u001b[0;32m   1072\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mOSError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m):\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\ssl.py:1342\u001b[0m, in \u001b[0;36mSSLSocket.do_handshake\u001b[1;34m(self, block)\u001b[0m\n\u001b[0;32m   1341\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msettimeout(\u001b[39mNone\u001b[39;00m)\n\u001b[1;32m-> 1342\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mdo_handshake()\n\u001b[0;32m   1343\u001b[0m \u001b[39mfinally\u001b[39;00m:\n",
      "\u001b[1;31mProtocolError\u001b[0m: ('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32mz:\\Documents\\Coding\\Projects\\React ML - Models\\dev\\fetching_data.ipynb Cell 4\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m \t\u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39m# with Image.open(io.BytesIO(requests.get(img_url).content)) as im:\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m \u001b[39m# \tim.save('dog'+str(i)+\".png\")\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m img \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mpreprocessing\u001b[39m.\u001b[39mimage\u001b[39m.\u001b[39mload_img(\n\u001b[1;32m---> <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m \tio\u001b[39m.\u001b[39mBytesIO(requests\u001b[39m.\u001b[39;49mget(img_url)\u001b[39m.\u001b[39mcontent),\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m \ttarget_size\u001b[39m=\u001b[39mimg_size,\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m )\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mpreprocessing\u001b[39m.\u001b[39mimage\u001b[39m.\u001b[39msave_img(\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m \t\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mparent_dir \u001b[39m+\u001b[39m anim\u001b[39m}\u001b[39;00m\u001b[39ms/\u001b[39m\u001b[39m{\u001b[39;00manim\u001b[39m}\u001b[39;00m\u001b[39m_fet_\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m.jpg\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m \timg,\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m )\n\u001b[0;32m     <a href='vscode-notebook-cell:/z%3A/Documents/Coding/Projects/React%20ML%20-%20Models/dev/fetching_data.ipynb#W3sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m \u001b[39m# print(f\"fetched image {i} in %.2f seconds\"%(time.time()-time_i))\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\api.py:73\u001b[0m, in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget\u001b[39m(url, params\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m     63\u001b[0m     \u001b[39mr\u001b[39m\u001b[39m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \n\u001b[0;32m     65\u001b[0m \u001b[39m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[39m    :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 73\u001b[0m     \u001b[39mreturn\u001b[39;00m request(\u001b[39m\"\u001b[39m\u001b[39mget\u001b[39m\u001b[39m\"\u001b[39m, url, params\u001b[39m=\u001b[39mparams, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[39m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[39m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \u001b[39m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m     58\u001b[0m \u001b[39mwith\u001b[39;00m sessions\u001b[39m.\u001b[39mSession() \u001b[39mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[39mreturn\u001b[39;00m session\u001b[39m.\u001b[39mrequest(method\u001b[39m=\u001b[39mmethod, url\u001b[39m=\u001b[39murl, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\sessions.py:587\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    582\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[0;32m    583\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[0;32m    584\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[0;32m    585\u001b[0m }\n\u001b[0;32m    586\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 587\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msend(prep, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39msend_kwargs)\n\u001b[0;32m    589\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\sessions.py:701\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    698\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[0;32m    700\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[1;32m--> 701\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39msend(request, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    703\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m    704\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n",
      "File \u001b[1;32mc:\\Users\\Sacrum\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\requests\\adapters.py:547\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    544\u001b[0m             \u001b[39mraise\u001b[39;00m\n\u001b[0;32m    546\u001b[0m \u001b[39mexcept\u001b[39;00m (ProtocolError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m--> 547\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m(err, request\u001b[39m=\u001b[39mrequest)\n\u001b[0;32m    549\u001b[0m \u001b[39mexcept\u001b[39;00m MaxRetryError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    550\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(e\u001b[39m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[0;32m    551\u001b[0m         \u001b[39m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n",
      "\u001b[1;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))"
     ]
    }
   ],
   "source": [
    "# FETCHING IMAGES\n",
    "\n",
    "parent_dir = \"fetched_data_3/\"\n",
    "anim = \"cat\"\n",
    "anim = \"dog\"\n",
    "n_of_images = 100\n",
    "for anim, n_of_images in (('cat', 500), ('dog', 0)):\n",
    "\t\n",
    "\tstart_with_index = max([int(i[8:-4]) for i in os.listdir(parent_dir + anim + \"s\")]) + 1\n",
    "\n",
    "\tfor i in tqdm.tqdm(range(start_with_index, start_with_index+n_of_images)):\n",
    "\t\ttime_i = time.time()\n",
    "\n",
    "\t\ttry:\n",
    "\t\t\tif anim == \"dog\":\n",
    "\t\t\t\timg_url = json.loads(requests.get(dog_api).content)['message']\n",
    "\t\t\telse:\n",
    "\t\t\t\timg_url = json.loads(requests.get(cat_api).content)[0]['url']\n",
    "\t\texcept:\n",
    "\t\t\t# print(\"Could not Complete\")\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\t# with Image.open(io.BytesIO(requests.get(img_url).content)) as im:\n",
    "\t\t# \tim.save('dog'+str(i)+\".png\")\n",
    "\t\timg = tf.keras.preprocessing.image.load_img(\n",
    "\t\t\tio.BytesIO(requests.get(img_url).content),\n",
    "\t\t\ttarget_size=img_size,\n",
    "\t\t)\n",
    "\t\ttf.keras.preprocessing.image.save_img(\n",
    "\t\t\tf\"{parent_dir + anim}s/{anim}_fet_{i}.jpg\",\n",
    "\t\t\timg,\n",
    "\t\t)\n",
    "\n",
    "\t\t# print(f\"fetched image {i} in %.2f seconds\"%(time.time()-time_i))\n",
    "\n",
    "total_dog_images = max([int(i[8:-4]) for i in os.listdir(f\"{parent_dir}dogs\")])\n",
    "total_cat_images = max([int(i[8:-4]) for i in os.listdir(f\"{parent_dir}cats\")])\n",
    "print(\"Total Cat Images:\", total_cat_images)\n",
    "print(\"Total Dog Images:\", total_dog_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3011"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1680522f100>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 334.365x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_url = json.loads(requests.get(dog_api).content)['message']\n",
    "img = tf.keras.preprocessing.image.load_img(\n",
    "\tio.BytesIO(requests.get(img_url).content),\n",
    "\t# target_size=(200, 200),\n",
    ")\n",
    "plt.matshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_url = json.loads(requests.get(dog_api).content)['message']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dog_993_500x357.jpg'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = tf.keras.preprocessing.image.load_img(\n",
    "\tio.BytesIO(requests.get(img_url).content),\n",
    "\t# target_size=(72, 72),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.resize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = tf.keras.preprocessing.image.load_img(\n",
    "\tio.BytesIO(requests.get(img_url).content),\n",
    "\ttarget_size = model.input_shape[1:-1],\n",
    ")\n",
    "tf.keras.preprocessing.image.save_img(\n",
    "\tf\"loaded_images/dog/dog_{np.random.randint(0, 1000)}_{img.size[0]}x{img.size[1]}.jpg\",\n",
    "\timg,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"model_85.9.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 227ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 66ms/step\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 71ms/step\n",
      "1/1 [==============================] - 0s 101ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n"
     ]
    }
   ],
   "source": [
    "# GETTING CORRECT DOG IMAGES\n",
    " \n",
    "i = 0\n",
    "while i<20:\n",
    "\timg_url = json.loads(requests.get(dog_api).content)['message']\n",
    "\n",
    "\timg = tf.keras.preprocessing.image.load_img(\n",
    "\t\tio.BytesIO(requests.get(img_url).content),\n",
    "\t)\n",
    "\timg_resized = img.resize(model.input_shape[1:-1])\n",
    "\timg_resized = np.array(img_resized)\n",
    "\timg_resized = np.expand_dims(img_resized, axis=0)\n",
    "\timg_resized = img_resized / 255.\n",
    "\tpred = model.predict(img_resized)[0, 0]\n",
    "\tif pred > 0.5:\n",
    "\t\ttf.keras.preprocessing.image.save_img(\n",
    "\t\t\tf\"loaded_images/dog/dog_true/dog_{i}.jpg\",\n",
    "\t\t\timg,\n",
    "\t\t)\n",
    "\t\ti += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 66ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 68ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
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      "1/1 [==============================] - 0s 60ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
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      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 43ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 85ms/step\n",
      "1/1 [==============================] - 0s 91ms/step\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n"
     ]
    }
   ],
   "source": [
    "# GETTING FALSE DOG IMAGES\n",
    " \n",
    "i = 0\n",
    "while i<20:\n",
    "\timg_url = json.loads(requests.get(dog_api).content)['message']\n",
    "\n",
    "\timg = tf.keras.preprocessing.image.load_img(\n",
    "\t\tio.BytesIO(requests.get(img_url).content),\n",
    "\t)\n",
    "\timg_resized = img.resize(model.input_shape[1:-1])\n",
    "\timg_resized = np.array(img_resized)\n",
    "\timg_resized = np.expand_dims(img_resized, axis=0)\n",
    "\timg_resized = img_resized / 255.\n",
    "\tpred = model.predict(img_resized)[0, 0]\n",
    "\tif pred < 0.5:\n",
    "\t\ttf.keras.preprocessing.image.save_img(\n",
    "\t\t\tf\"loaded_images/dog/dog_false/dog_{i}.jpg\",\n",
    "\t\t\timg,\n",
    "\t\t)\n",
    "\t\ti += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 70ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 82ms/step\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 86ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 73ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 43ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n"
     ]
    }
   ],
   "source": [
    "# GETTING CORRECT cat IMAGES\n",
    " \n",
    "# import json, requests\n",
    "i = 0\n",
    "while i<20:\n",
    "\timg_url = json.loads(requests.get(cat_api).content)[0]['url']\n",
    "\n",
    "\timg = tf.keras.preprocessing.image.load_img(\n",
    "\t\tio.BytesIO(requests.get(img_url).content),\n",
    "\t)\n",
    "\timg_resized = img.resize(model.input_shape[1:-1])\n",
    "\timg_resized = np.array(img_resized)\n",
    "\timg_resized = np.expand_dims(img_resized, axis=0)\n",
    "\timg_resized = img_resized / 255.\n",
    "\tpred = model.predict(img_resized)[0, 0]\n",
    "\tif pred < 0.5:\n",
    "\t\ttf.keras.preprocessing.image.save_img(\n",
    "\t\t\tf\"loaded_images/cat/cat_true/cat_{i}.jpg\",\n",
    "\t\t\timg,\n",
    "\t\t)\n",
    "\t\ti += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 72ms/step\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 82ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "1/1 [==============================] - 0s 81ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "1/1 [==============================] - 0s 115ms/step\n",
      "1/1 [==============================] - 0s 91ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "1/1 [==============================] - 0s 85ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 136ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "1/1 [==============================] - 0s 84ms/step\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n"
     ]
    }
   ],
   "source": [
    "# GETTING FALSE cat IMAGES\n",
    " \n",
    "i = 0\n",
    "while i<20:\n",
    "\timg_url = json.loads(requests.get(cat_api).content)[0]['url']\n",
    "\n",
    "\timg = tf.keras.preprocessing.image.load_img(\n",
    "\t\tio.BytesIO(requests.get(img_url).content),\n",
    "\t)\n",
    "\timg_resized = img.resize(model.input_shape[1:-1])\n",
    "\timg_resized = np.array(img_resized)\n",
    "\timg_resized = np.expand_dims(img_resized, axis=0)\n",
    "\timg_resized = img_resized / 255.\n",
    "\tpred = model.predict(img_resized)[0, 0]\n",
    "\tif pred < 0.5:\n",
    "\t\ttf.keras.preprocessing.image.save_img(\n",
    "\t\t\tf\"loaded_images/cat/cat_false/cat_{i}.jpg\",\n",
    "\t\t\timg,\n",
    "\t\t)\n",
    "\t\ti += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 64, 64, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 32, 32, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 32, 32, 32)        0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 32, 32, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 16, 16, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 16, 16, 64)        0         \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 8, 8, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " random_flip_2 (RandomFlip)  (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " random_flip_3 (RandomFlip)  (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " random_rotation_1 (RandomRo  (None, 8, 8, 128)        0         \n",
      " tation)                                                         \n",
      "                                                                 \n",
      " random_zoom_1 (RandomZoom)  (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 8192)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 8193      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 101,441\n",
      "Trainable params: 101,441\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_url = \"https://cdn.britannica.com/39/7139-050-A88818BB/Himalayan-chocolate-point.jpg\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.BytesIO at 0x18eac8b8630>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import tensorflow as tf\n",
    "import io\n",
    "\n",
    "io.BytesIO(requests.get(img_url).content)\n",
    "# tf.keras.preprocessing.image.load_img(img_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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