Personality Prediction AI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing source files\n",
    "import string\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "\n",
    "Dataset = pd.read_csv('C:\\\\Users\\\\Aakash\\\\Downloads\\\\Personality Prediction AI\\\\MBTI.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>8675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>INFP</td>\n",
       "      <td>'He really was, he still is. I honestly think ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1832</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type                                              Posts\n",
       "count   8675                                               8675\n",
       "unique    16                                               8675\n",
       "top     INFP  'He really was, he still is. I honestly think ...\n",
       "freq    1832                                                  1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset before modification\n",
    "Dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding separate field for each pair\n",
    "Dataset['IvE'] = Dataset.Type\n",
    "Dataset['NvS'] = Dataset.Type\n",
    "Dataset['FvT'] = Dataset.Type\n",
    "Dataset['PvJ'] = Dataset.Type\n",
    "\n",
    "for i, t in enumerate(Dataset.Type):\n",
    "    if 'I' in t:\n",
    "        Dataset.IvE[i] = 'I'\n",
    "    elif 'E' in t:\n",
    "        Dataset.IvE[i] = 'E'\n",
    "\n",
    "    if 'N' in t:\n",
    "        Dataset.NvS[i] = 'N'\n",
    "    elif 'S' in t:\n",
    "        Dataset.NvS[i] = 'S'\n",
    "\n",
    "    if 'F' in t:\n",
    "        Dataset.FvT[i] = 'F'\n",
    "    elif 'T' in t:\n",
    "        Dataset.FvT[i] = 'T'\n",
    "\n",
    "    if 'P' in t:\n",
    "        Dataset.PvJ[i] = 'P'\n",
    "    elif 'J' in t:\n",
    "        Dataset.PvJ[i] = 'J'\n",
    "\n",
    "Posts = Dataset.Posts.values\n",
    "IE = Dataset.IvE.values\n",
    "NS = Dataset.NvS.values\n",
    "FT = Dataset.FvT.values\n",
    "PJ = Dataset.PvJ.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Posts</th>\n",
       "      <th>IvE</th>\n",
       "      <th>NvS</th>\n",
       "      <th>FvT</th>\n",
       "      <th>PvJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "      <td>8675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>8675</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>INFP</td>\n",
       "      <td>'He really was, he still is. I honestly think ...</td>\n",
       "      <td>I</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1832</td>\n",
       "      <td>1</td>\n",
       "      <td>6676</td>\n",
       "      <td>7478</td>\n",
       "      <td>4694</td>\n",
       "      <td>5241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type                                              Posts   IvE   NvS  \\\n",
       "count   8675                                               8675  8675  8675   \n",
       "unique    16                                               8675     2     2   \n",
       "top     INFP  'He really was, he still is. I honestly think ...     I     N   \n",
       "freq    1832                                                  1  6676  7478   \n",
       "\n",
       "         FvT   PvJ  \n",
       "count   8675  8675  \n",
       "unique     2     2  \n",
       "top        F     P  \n",
       "freq    4694  5241  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset after modofication\n",
    "Dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regex rules for cleaning text\n",
    "regexes = [\n",
    "    #punctuation\n",
    "    r'(?:(\\w+)\\'s)',\n",
    "    \n",
    "    r'(?:\\s(\\w+)\\.+\\s)',\n",
    "    r'(?:\\s(\\w+),+\\s)',\n",
    "    r'(?:\\s(\\w+)\\?+\\s)',\n",
    "    r'(?:\\s(\\w+)!+\\s)',\n",
    "    \n",
    "    r'(?:\\'+(\\w+)\\'+)',\n",
    "    r'(?:\"+(\\w+)\"+)',\n",
    "    r'(?:\\[+(\\w+)\\]+)',\n",
    "    r'(?:{+(\\w+)}+)',\n",
    "    r'(?:\\(+(\\w+))',\n",
    "    r'(?:(\\w+)\\)+)',\n",
    "\n",
    "    #words containing numbers & special characters & punctuation\n",
    "    r'(?:(?:(?:[a-zA-Z])*(?:[0-9!\"#$%&\\'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~])+(?:[a-zA-Z])*)+)',\n",
    "    \n",
    "    #pure words\n",
    "    r'([a-zA-Z]+)',\n",
    "]\n",
    "\n",
    "#compiling regular expression\n",
    "regex = re.compile(r'(?:'+'|'.join(regexes)+')', re.VERBOSE | re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method for cleaning and lemmatizing text\n",
    "def PreProcessor(Texts):\n",
    "    Lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "    Punctuation = list(string.punctuation)\n",
    "    StopWords = stopwords.words('english')\n",
    "    \n",
    "    Processed_Documents = []\n",
    "    \n",
    "    for i, document in enumerate(Texts):\n",
    "        print('{0}/{1}'.format(i + 1, len(Texts)))\n",
    "\n",
    "        # tokenization\n",
    "        Tokens = regex.findall(document)\n",
    "\n",
    "        # skipping useless tokens\n",
    "        t_regex = re.compile(r\"[^a-zA-Z]\")\n",
    "        document = []\n",
    "\n",
    "        for token in Tokens:\n",
    "            token = np.array(token)\n",
    "            token = np.unique(token[token != ''])\n",
    "\n",
    "            if len(token) > 0:\n",
    "                token = token[0].lower()\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "            if re.search(t_regex, token) == None and token not in StopWords:\n",
    "                token = Lemmatizer.lemmatize(token)\n",
    "                document.append(token)\n",
    "\n",
    "        document = ' '.join(document)\n",
    "\n",
    "        # skipping\n",
    "        if len(document) >= 0:\n",
    "            Processed_Documents.append(document)\n",
    "\n",
    "    print()\n",
    "    return np.array(Processed_Documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# cleaning the dataset\n",
    "Posts = PreProcessor(Posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "CountVec = CountVectorizer(max_features=20000) # 75090\n",
    "X = CountVec.fit_transform(Posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# traing the model with dataset\n",
    "xIETrain, xIETest, yIETrain, yIETest = train_test_split(X, IE)\n",
    "xNSTrain, xNSTest, yNSTrain, yNSTest = train_test_split(X, NS)\n",
    "xFTTrain, xFTTest, yFTTrain, yFTTest = train_test_split(X, FT)\n",
    "xPJTrain, xPJTest, yPJTrain, yPJTest = train_test_split(X, PJ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "IEModel = MultinomialNB().fit(xIETrain, yIETrain)\n",
    "NSModel = MultinomialNB().fit(xNSTrain, yNSTrain)\n",
    "FTModel = MultinomialNB().fit(xFTTrain, yFTTrain)\n",
    "PJModel = MultinomialNB().fit(xPJTrain, yPJTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8091286307053942\n",
      "0.855232826187183\n",
      "0.8031350852927617\n",
      "0.7344398340248963\n"
     ]
    }
   ],
   "source": [
    "# testing the accuracy of model\n",
    "print(IEModel.score(xIETest, yIETest))\n",
    "print(NSModel.score(xNSTest, yNSTest))\n",
    "print(FTModel.score(xFTTest, yFTTest))\n",
    "print(PJModel.score(xPJTest, yPJTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Personality = [[\"INFP - MEDIATOR\",\n",
    "               \"INFJ - ADVOCATE\",\n",
    "               \"INTP - LOGICIAN\",\n",
    "               \"INTJ - ARCHITECT\",\n",
    "               \"ISFP - ADVENTURER\",\n",
    "               \"ISFJ - DEFENDER\",\n",
    "               \"ISTP - VIRTUOSO\",\n",
    "               \"ISTJ - LOGISTICIAN\",\n",
    "               \"ENFP - CAMPAIGNER\",\n",
    "               \"ENFJ - PROTAGONIST\",\n",
    "               \"ENTP - DEBATOR\",\n",
    "               \"ENTJ - COMMANDER\",\n",
    "               \"ESFP - ENTERTAINER\",\n",
    "               \"ESFJ - CONSUL\",\n",
    "               \"ESTP - ENTREPRENEUR\",\n",
    "               \"ESTJ - EXECUTIVE\"],\n",
    "               [\"The Gift of Empathy, Speaking Their Truth , In Search of a Calling\",\n",
    "               \"Standing Up for What’s Right, Connecting with Others, A Personal Mission\",\n",
    "               \"The Life of the Mind, Elementary, My Dear Logician, Mysteries of the Universe\",\n",
    "               \"A Thirst for Knowledge, Finding a Better Way, The Chess Game of Life\",\n",
    "               \"A Passion for Discovery, Creating an Inspired Life\",\n",
    "               \"We Must Be Seen to Be Believed, If I Can Protect You, I Will\",\n",
    "               \"Dare to Differ, Defying the Rules\",\n",
    "               \"A Life of Integrity, Picking Up the Slack\",\n",
    "               \"The Magic of Everyday Life, Seeking Joy\",\n",
    "               \"Speaking Up for What’s Right, Getting Involved, Leading the Way\",\n",
    "               \"Breaking the Rules, Achieving Their Potential\",\n",
    "               \"Striving for Greatness, A Worthy Challenge\",\n",
    "               \"Living with Passion, A Spontaneous Spirit\",\n",
    "               \"The Wisdom of Tradition, Ready to Help\",\n",
    "               \"Diving Right In, The Path Less Traveled\",\n",
    "               \"Anyone Worth Their Salt Sticks Up for What They Believe Is Right...\",]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as TK\n",
    "\n",
    "class GUI(TK.Frame):\n",
    "    def __init__(self, parent):\n",
    "        super().__init__(parent)\n",
    "\n",
    "        self.Base = TK.Frame(self, bg='#EEE')\n",
    "        self.Base.pack(fill='both', expand=True)\n",
    "        \n",
    "        self.Type = 0\n",
    "        LabelList = [TK.Label(self.Base, bg='#DDD', text=\"Introvert VS Extrovert\"), TK.Label(self.Base, bg='#DDD', text=\"Intuition VS Sensing\"), TK.Label(self.Base, bg='#DDD', text=\"Feeling VS Thinking\"), TK.Label(self.Base, bg='#DDD', text=\"Perceiving VS Judgement\")]\n",
    "        \n",
    "        self.Label = TK.Label(self.Base, bg='#EEE', text=\"Enter Your Text:\")\n",
    "        self.Text = TK.Entry(self.Base, bg='#DDD', bd=0)\n",
    "        self.Button = TK.Button(self.Base, bg='#DDD', width=3, bd=0, text=\"Predict\", command=self.Predict)\n",
    "\n",
    "        self.Model = [[TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD')], [TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD')], [TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD'), TK.Label(self.Base, bg='#DDD')]]\n",
    "        self.Detail = TK.Label(self.Base, bg='#DDD')\n",
    "        self.Trait = TK.Label(self.Base, bg='#DDD')\n",
    "\n",
    "        for i in range(9):\n",
    "            self.Base.rowconfigure(i, weight=1)\n",
    "\n",
    "        for i in range(4):\n",
    "            self.Base.columnconfigure(i, weight=1)\n",
    "\n",
    "        self.Label.grid(row=0, column=0, columnspan=2, padx=3, pady=3, sticky='nsw')\n",
    "        self.Text.grid(row=1, rowspan=2, column=0, columnspan=4, padx=3, pady=3, sticky='nsew')\n",
    "        self.Button.grid(row=0, column=3, padx=3, pady=3, sticky='nsew')\n",
    "        \n",
    "        for i in range(4):\n",
    "            LabelList[i].grid(row=i+3, column=0, padx=3, pady=3, sticky='nsew')\n",
    "            for j in range(3):\n",
    "                self.Model[j][i].grid(row=i+3, column=j+1, padx=3, pady=3, sticky='nsew')\n",
    "        \n",
    "        self.Detail.grid(row=7, column=0, columnspan=4, padx=3, pady=3, sticky='nsew')\n",
    "        self.Trait.grid(row=8, column=0, columnspan=4, padx=3, pady=3, sticky='nsew')\n",
    "    \n",
    "    def Predict(self):\n",
    "        if(self.Text.get()!=\"\"):\n",
    "            Result = CountVec.transform([self.Text.get()])\n",
    "\n",
    "            self.Model[0][0]['text'] = str(IEModel.predict_proba(Result)[0][0])[:4]\n",
    "            self.Model[0][1]['text'] = str(NSModel.predict_proba(Result)[0][0])[:4]\n",
    "            self.Model[0][2]['text'] = str(FTModel.predict_proba(Result)[0][0])[:4]\n",
    "            self.Model[0][3]['text'] = str(PJModel.predict_proba(Result)[0][0])[:4]\n",
    "            \n",
    "            self.Model[1][0]['text'] = str(IEModel.predict_proba(Result)[0][1])[:4]\n",
    "            self.Model[1][1]['text'] = str(NSModel.predict_proba(Result)[0][1])[:4]\n",
    "            self.Model[1][2]['text'] = str(FTModel.predict_proba(Result)[0][1])[:4]\n",
    "            self.Model[1][3]['text'] = str(PJModel.predict_proba(Result)[0][1])[:4]\n",
    "            \n",
    "            if(self.Model[0][0]['text'] > self.Model[1][0]['text']):\n",
    "                self.Model[2][0]['text'] = \"I\"\n",
    "            else:\n",
    "                self.Model[2][0]['text'] = \"E\"\n",
    "                self.Type += 8\n",
    "            \n",
    "            if(self.Model[0][1]['text'] > self.Model[1][1]['text']):\n",
    "                self.Model[2][1]['text'] = \"N\"\n",
    "            else:\n",
    "                self.Model[2][1]['text'] = \"S\"\n",
    "                self.Type += 4\n",
    "            \n",
    "            if(self.Model[0][2]['text'] > self.Model[1][2]['text']):\n",
    "                self.Model[2][2]['text'] = \"F\"\n",
    "            else:\n",
    "                self.Model[2][2]['text'] = \"T\"\n",
    "                self.Type += 2\n",
    "            \n",
    "            if(self.Model[0][3]['text'] > self.Model[1][3]['text']):\n",
    "                self.Model[2][3]['text'] = \"P\"\n",
    "            else:\n",
    "                self.Model[2][3]['text'] = \"J\"\n",
    "                self.Type += 1\n",
    "            \n",
    "            self.Detail['text'] = Personality[0][self.Type]\n",
    "            self.Trait['text'] = Personality[1][self.Type]\n",
    "                \n",
    "\n",
    "class Interface(TK.Tk):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.Screen = None\n",
    "        self.Switch(GUI)\n",
    "\n",
    "    def Switch(self, FrameClass):\n",
    "        NewFrame = FrameClass(self)\n",
    "        if self.Screen is not None:\n",
    "            self.Screen.destroy()\n",
    "        self.Screen = NewFrame\n",
    "        self.Screen.pack(fill='both', expand=True)\n",
    "\n",
    "\n",
    "App = Interface()\n",
    "\n",
    "App.title(\"Personality Prediction AI\")\n",
    "App.geometry(\"600x400+600+100\")\n",
    "App.resizable(width=False, height=True)\n",
    "\n",
    "App.mainloop()"
   ]
  }
 ],
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