demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from functions import to_one_hot, to_string, encrypt, decrypt, enigma_machine, enigma_machine_decrypt\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test **to_one_hot** **to_string**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teste'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_string((to_one_hot(\"teste\")))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test **Encrypt** & **Decrypt**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the Permute Matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "lines = np.array([i for i in range(27)])\n",
    "np.random.shuffle(lines)\n",
    "identity_matrix = np.eye(27)\n",
    "MP = identity_matrix[lines,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'renre'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encrypt(\"teste\", MP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teste'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decrypt(encrypt(\"teste\", MP),MP)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test **Enigma** & **De Enigma**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the Auxiliary Matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = np.array([i for i in range(27)])\n",
    "identity_matrix =np.identity(27)\n",
    "np.random.shuffle(lines)\n",
    "MA = identity_matrix[lines,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teste'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enigma_machine_decrypt(enigma_machine(\"teste\", MP, MA), MP, MA)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "def enigma_encrypt_route(text):\n",
    "    payload = {\"text\": text}\n",
    "    headers = {\"Content-Type\": \"application/json\"}\n",
    "    response = requests.post(\"http://127.0.0.1:5000/enigma/encrypt\", data=json.dumps(payload), headers=headers)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        return json.loads(response.content)[\"encrypt_text\"]\n",
    "    else:\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "def enigma_decrypt_route(encrypt_text):\n",
    "    payload = {\"encrypt_text\": encrypt_text}\n",
    "    headers = {\"Content-Type\": \"application/json\"}\n",
    "    response = requests.post(\"http://127.0.0.1:5000/enigma/decrypt\", data=json.dumps(payload), headers=headers)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        return json.loads(response.content)[\"text\"]\n",
    "    else:\n",
    "        return None\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Encrypt route test<h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iblsmd bjhczgqtzpo\n"
     ]
    }
   ],
   "source": [
    "print(enigma_encrypt_route(\"encrypt route test\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Dencrypt route test<h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encrypt route test\n"
     ]
    }
   ],
   "source": [
    "print(enigma_decrypt_route(\"iblsmd bjhczgqtzpo\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Encrypt route test and Dencrypt route test<h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the great test\n"
     ]
    }
   ],
   "source": [
    "print(enigma_decrypt_route(enigma_encrypt_route(\"the great test\")))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enigma Libary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import enigmamachine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "lines = np.array([i for i in range(27)])\n",
    "np.random.shuffle(lines)\n",
    "identity_matrix = np.eye(27)\n",
    "MP = identity_matrix[lines,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enigmamachine.to_string((enigmamachine.to_one_hot(\"test\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teste'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enigmamachine.decrypt(enigmamachine.encrypt(\"teste\", MP),MP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = np.array([i for i in range(27)])\n",
    "identity_matrix =np.identity(27)\n",
    "np.random.shuffle(lines)\n",
    "MA = identity_matrix[lines,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teste'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enigmamachine.enigma_machine_decrypt(enigmamachine.enigma_machine(\"teste\", MP, MA), MP, MA)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste rápidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from enigmamachine import to_one_hot, to_string, enigma_machine_decrypt,enigma_machine_decrypt, encrypt,decrypt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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