Job_recommender_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import docx2txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First step read the different csv files that we extract from Indeed\n",
    "def path(x):\n",
    "    all_files = glob.glob(x + \"/*.csv\")\n",
    "    return all_files\n",
    "\n",
    "## Insert the path of the folder that contains different csv files.\n",
    "all_files = path(r\"C:\\Users\\nikos\\Desktop\\dataframes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Job Type</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Job Url</th>\n",
       "      <th>Company</th>\n",
       "      <th>Location</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Posting Date</th>\n",
       "      <th>Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Admin</td>\n",
       "      <td>COVID-19 Vaccination Programme Admin Support</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=b174d863426647...</td>\n",
       "      <td>Bank Partners</td>\n",
       "      <td>London</td>\n",
       "      <td>Providing admin support on the vaccination act...</td>\n",
       "      <td>15 days ago</td>\n",
       "      <td>Location: Barts Health NHS TrustThink you coul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Admin</td>\n",
       "      <td>Vaccine Admin Support Covid Delivery Programme</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=5abe6dee9da1e1...</td>\n",
       "      <td>Epson and St Helier University Hospitals</td>\n",
       "      <td>South West London</td>\n",
       "      <td>However, non-EEA candidates may not be appoint...</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Applications from job seekers who require Tier...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Admin</td>\n",
       "      <td>Admin Personal Assistant</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=aac567d7742e37...</td>\n",
       "      <td>Furness Primary School</td>\n",
       "      <td>London</td>\n",
       "      <td>3 days per week, term time only + 10 days.\\nAb...</td>\n",
       "      <td>30+ days ago</td>\n",
       "      <td>PART TIME SCHOOL ADMINISTRATOR/PA TO HEADTEACH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Admin</td>\n",
       "      <td>Administrative Assistant</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=bc15fcd81349ac...</td>\n",
       "      <td>Hakna</td>\n",
       "      <td>London</td>\n",
       "      <td>There may also be a need for them to be an ini...</td>\n",
       "      <td>5 days ago</td>\n",
       "      <td>Job Description for Administrative Assistant:A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Admin</td>\n",
       "      <td>Administration Assistant – Facilities</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=2545de5d84e6fa...</td>\n",
       "      <td>Clarke Willmott</td>\n",
       "      <td>London</td>\n",
       "      <td>Ensuring attention to detail and accuracy when...</td>\n",
       "      <td>Just posted</td>\n",
       "      <td>You will be joining two other full-time team m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Job Type                                       Job Title  \\\n",
       "0    Admin    COVID-19 Vaccination Programme Admin Support   \n",
       "1    Admin  Vaccine Admin Support Covid Delivery Programme   \n",
       "2    Admin                        Admin Personal Assistant   \n",
       "3    Admin                        Administrative Assistant   \n",
       "4    Admin           Administration Assistant – Facilities   \n",
       "\n",
       "                                             Job Url  \\\n",
       "0  http://www.indeed.com/rc/clk?jk=b174d863426647...   \n",
       "1  http://www.indeed.com/rc/clk?jk=5abe6dee9da1e1...   \n",
       "2  http://www.indeed.com/rc/clk?jk=aac567d7742e37...   \n",
       "3  http://www.indeed.com/rc/clk?jk=bc15fcd81349ac...   \n",
       "4  http://www.indeed.com/rc/clk?jk=2545de5d84e6fa...   \n",
       "\n",
       "                                    Company           Location  \\\n",
       "0                             Bank Partners             London   \n",
       "1  Epson and St Helier University Hospitals  South West London   \n",
       "2                    Furness Primary School             London   \n",
       "3                                     Hakna             London   \n",
       "4                           Clarke Willmott             London   \n",
       "\n",
       "                                             Summary  Posting Date  \\\n",
       "0  Providing admin support on the vaccination act...   15 days ago   \n",
       "1  However, non-EEA candidates may not be appoint...     1 day ago   \n",
       "2  3 days per week, term time only + 10 days.\\nAb...  30+ days ago   \n",
       "3  There may also be a need for them to be an ini...    5 days ago   \n",
       "4  Ensuring attention to detail and accuracy when...   Just posted   \n",
       "\n",
       "                                                Desc  \n",
       "0  Location: Barts Health NHS TrustThink you coul...  \n",
       "1  Applications from job seekers who require Tier...  \n",
       "2  PART TIME SCHOOL ADMINISTRATOR/PA TO HEADTEACH...  \n",
       "3  Job Description for Administrative Assistant:A...  \n",
       "4  You will be joining two other full-time team m...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_df(files, li):\n",
    "    for filename in files:\n",
    "        df = pd.read_csv(filename, index_col=None, header=0)\n",
    "        li.append(df)\n",
    "        df = pd.concat(li, axis=0, ignore_index=True)\n",
    "    return df\n",
    "df = create_df(all_files, [])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Droped Duplicates\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(171, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Remove duplicates\n",
    "def Shape(df):\n",
    "    shape = [df.shape]\n",
    "    if ([x[0] for x in shape][0]) > len(df[\"Job Url\"].unique()):\n",
    "        print(\"Droped Duplicates\")\n",
    "        df = df.drop_duplicates(subset=['Job Url'])\n",
    "        return df\n",
    "    else:\n",
    "        print(\"We don't have Duplicates\")\n",
    "        return df\n",
    "\n",
    "new_df = Shape(df)\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## reading the canditate's cv\n",
    "def read_cv(name):\n",
    "    # read the word file\n",
    "    x =  docx2txt.process(name)\n",
    "    return x\n",
    "    \n",
    "cv = read_cv(\"monster-cv-template-admin-assistant.docx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extract candidate email, and applying some basic text preprocessing \n",
    "def text_pre(x):\n",
    "    ## Applying some text preprocessing\n",
    "    text = x.replace(\"\\n\", \"\")\n",
    "    return text\n",
    "def extract_email(x):\n",
    "    ## Extracting the email address using regex  \n",
    "    import re\n",
    "    match = re.search(r'[\\w\\.-]+@[\\w\\.-]+', x)\n",
    "    email = match.group(0)\n",
    "    return email\n",
    "    \n",
    "text = text_pre(cv)\n",
    "email = extract_email(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\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>Job Type</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Job Url</th>\n",
       "      <th>Company</th>\n",
       "      <th>Location</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Posting Date</th>\n",
       "      <th>Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Warehouse</td>\n",
       "      <td>Order Picker Warehouse Operative</td>\n",
       "      <td>http://www.indeed.com/rc/clk?jk=8b7dd86955a721...</td>\n",
       "      <td>Premier Work Support</td>\n",
       "      <td>London</td>\n",
       "      <td>To be successful in this role you need warehou...</td>\n",
       "      <td>23 days ago</td>\n",
       "      <td>We have an exciting opportunity to join a very...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Warehouse</td>\n",
       "      <td>Warehouse Operative - Immediate Start</td>\n",
       "      <td>http://www.indeed.com/pagead/clk?mo=r&amp;ad=-6NYl...</td>\n",
       "      <td>ABC Depot</td>\n",
       "      <td>Finchley Central Station</td>\n",
       "      <td>Must have 3-5 years Builders Merchant experien...</td>\n",
       "      <td>30+ days ago</td>\n",
       "      <td>DutiesMust have 3-5 years Builders Merchant ex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Warehouse</td>\n",
       "      <td>Warehouse Operative</td>\n",
       "      <td>http://www.indeed.com/company/White-Van-Gentle...</td>\n",
       "      <td>White Van Gentlemen</td>\n",
       "      <td>Earlsfield</td>\n",
       "      <td>Organizing / keeping tidy the warehouse.\\nPall...</td>\n",
       "      <td>30+ days ago</td>\n",
       "      <td>Job DescriptionWhite Van Gentlemen is a white ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Warehouse</td>\n",
       "      <td>Warehouse Operative - Picker / Packer / Replen</td>\n",
       "      <td>http://www.indeed.com/company/All-Pet-Solution...</td>\n",
       "      <td>All Pet Solutions</td>\n",
       "      <td>Uxbridge</td>\n",
       "      <td>Working within a team of warehouse operatives....</td>\n",
       "      <td>3 days ago</td>\n",
       "      <td>All Pet Solutions is an online market leader a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>name@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uschi BarkerAddress: Flat 0, Any Road, Any Tow...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Job Type                                       Job Title  \\\n",
       "167  Warehouse                Order Picker Warehouse Operative   \n",
       "168  Warehouse           Warehouse Operative - Immediate Start   \n",
       "169  Warehouse                             Warehouse Operative   \n",
       "170  Warehouse  Warehouse Operative - Picker / Packer / Replen   \n",
       "171        NaN                                             NaN   \n",
       "\n",
       "                                               Job Url               Company  \\\n",
       "167  http://www.indeed.com/rc/clk?jk=8b7dd86955a721...  Premier Work Support   \n",
       "168  http://www.indeed.com/pagead/clk?mo=r&ad=-6NYl...             ABC Depot   \n",
       "169  http://www.indeed.com/company/White-Van-Gentle...   White Van Gentlemen   \n",
       "170  http://www.indeed.com/company/All-Pet-Solution...     All Pet Solutions   \n",
       "171                                   name@hotmail.com                   NaN   \n",
       "\n",
       "                     Location  \\\n",
       "167                    London   \n",
       "168  Finchley Central Station   \n",
       "169                Earlsfield   \n",
       "170                  Uxbridge   \n",
       "171                       NaN   \n",
       "\n",
       "                                               Summary  Posting Date  \\\n",
       "167  To be successful in this role you need warehou...   23 days ago   \n",
       "168  Must have 3-5 years Builders Merchant experien...  30+ days ago   \n",
       "169  Organizing / keeping tidy the warehouse.\\nPall...  30+ days ago   \n",
       "170  Working within a team of warehouse operatives....    3 days ago   \n",
       "171                                                NaN           NaN   \n",
       "\n",
       "                                                  Desc  \n",
       "167  We have an exciting opportunity to join a very...  \n",
       "168  DutiesMust have 3-5 years Builders Merchant ex...  \n",
       "169  Job DescriptionWhite Van Gentlemen is a white ...  \n",
       "170  All Pet Solutions is an online market leader a...  \n",
       "171  Uschi BarkerAddress: Flat 0, Any Road, Any Tow...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## So, now we can add these two attributes as a row to our main dataframe\n",
    "def insert_row(email, text, df):\n",
    "    new_row = {'Job Url':email, 'Desc': text}\n",
    "    df= df.append(new_row, ignore_index=True)\n",
    "    return df\n",
    "\n",
    "new_df = insert_row(email, text, new_df)\n",
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perfect the new candidate is into our main dataframe, So now we can start developing the recommendation  model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\nikos\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\nikos\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\nikos\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\nikos\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "## Applying text preprocessing modules to Desc column\n",
    "## text preprocessing \n",
    "import nltk \n",
    "\n",
    "nltk.download('punkt') \n",
    "\n",
    "nltk.download('averaged_perceptron_tagger') \n",
    "\n",
    "nltk.download('wordnet') \n",
    "\n",
    "    \n",
    "from nltk.stem import WordNetLemmatizer \n",
    "\n",
    "lemmatizer = WordNetLemmatizer() \n",
    "\n",
    "  \n",
    "\n",
    "from nltk.corpus import stopwords \n",
    "\n",
    "nltk.download('stopwords') \n",
    "\n",
    "stop_words = set(stopwords.words('english')) \n",
    "\n",
    "  \n",
    "\n",
    "VERB_CODES = {'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(text): \n",
    "\n",
    "    text = text.lower() \n",
    "    temp_sent =[] \n",
    "    words = nltk.word_tokenize(text) \n",
    "    tags = nltk.pos_tag(words) \n",
    "    \n",
    "    for i, word in enumerate(words): \n",
    "        if tags[i][1] in VERB_CODES:   \n",
    "            lemmatized = lemmatizer.lemmatize(word, 'v') \n",
    "        else: \n",
    "            lemmatized = lemmatizer.lemmatize(word) \n",
    "        if lemmatized not in stop_words and lemmatized.isalpha(): \n",
    "            temp_sent.append(lemmatized) \n",
    "\n",
    "    finalsent = ' '.join(temp_sent) \n",
    "    finalsent = finalsent.replace(\"n't\", \" not\") \n",
    "    finalsent = finalsent.replace(\"'m\", \" am\") \n",
    "    finalsent = finalsent.replace(\"'s\", \" is\") \n",
    "    finalsent = finalsent.replace(\"'re\", \" are\") \n",
    "    finalsent = finalsent.replace(\"'ll\", \" will\") \n",
    "    finalsent = finalsent.replace(\"'ve\", \" have\") \n",
    "    finalsent = finalsent.replace(\"'d\", \" would\") \n",
    "\n",
    "    return finalsent \n",
    "\n",
    "\n",
    "## Creating a new column that we applied the text manipulation function \n",
    "new_df[\"Desc proc\"] = new_df[\"Desc\"].apply(preprocess_sentences) \n",
    "final_data = new_df[[\"Job Url\", \"Desc proc\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After data preprocessing, we are ready to develop the recommendation model. I will use the Cosine similarity metric in order to determine text similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.04126661 0.18151655 ... 0.08260162 0.07620499 0.10476454]\n",
      " [0.04126661 1.         0.10875201 ... 0.14913013 0.0880522  0.09126919]\n",
      " [0.18151655 0.10875201 1.         ... 0.18950196 0.14201333 0.15494925]\n",
      " ...\n",
      " [0.08260162 0.14913013 0.18950196 ... 1.         0.26988337 0.12259438]\n",
      " [0.07620499 0.0880522  0.14201333 ... 0.26988337 1.         0.07983581]\n",
      " [0.10476454 0.09126919 0.15494925 ... 0.12259438 0.07983581 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity \n",
    "\n",
    "count = CountVectorizer()\n",
    "count_matrix = count.fit_transform(final_data['Desc proc'])\n",
    "cosine_sim = cosine_similarity(count_matrix)\n",
    "print(cosine_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Finding the index of the candidate's cv.\n",
    "def get_index_from_url(url):\n",
    "    return final_data[final_data[\"Job Url\"] == url].index.values[0]\n",
    "    \n",
    "url = get_index_from_url(email)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       " (57, 0.033062367956624455)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Creating a list with similarily of our CV with the job description\n",
    "similar_jobs = list(enumerate(cosine_sim[url]))\n",
    "\n",
    "## Sort the list in descending order\n",
    "sorted_similar_jobs = sorted(similar_jobs, key=lambda x:x[1], reverse=True)\n",
    "sorted_similar_jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "171    name@hotmail.com\n",
      "Name: Job Url, dtype: object\n",
      "109    http://www.indeed.com/company/CentraNic-Ltd/jobs/Group-Financial-Data-Analyst-59999e06fd4b9009?fccid=004345d9813bd437&vjs=3\n",
      "Name: Job Url, dtype: object\n",
      "13    http://www.indeed.com/company/CriterionCapital/jobs/Office-Administration-Assistant-64da8cde37d422d9?fccid=1931435125b82129&vjs=3\n",
      "Name: Job Url, dtype: object\n",
      "17    http://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0BIQv-klv4x57wzcCCXZDuUs4ETBBTY7U4BZbqajjMT5rLx4iIBIgIDjvqIt6UO8LKeIOY33Wnt4_eGGFmqJeUFdqLBu7U5oyAp-J0dXDp4UiTLVL041HcriHxDT6myJ6B1t5jySkfSP0xrQ1MSGJug_oWZSIBng5uU3tgIaZmdrw1f0HFsYk5o_w5zejOWcDSmC6lgzvZJ6vOa82rSFId3FatHT_qfXMi-PufkEZX4WyY6n0oncWV21jlODJXWsKuoJYw7GGwET4yAfy66eZiJmeyDU1xf9Dt8-V27KKcbAybIWSHq7Mjgv8OjIYZvnDEPVvDV5XHAtg10Eimq-WRh4abc6WEORw6KeHRnDyo2JYUI_WLt98BE3GEtIU9J_2zOiS1fykc7VgdRV6Zb9Gbk5l3k44WDTbVg7sH7h6rRjCG67hJvDDgIMKNCQzMTdIY=&p=2&fvj=1&vjs=3\n",
      "Name: Job Url, dtype: object\n",
      "14    http://www.indeed.com/company/Agina-ltd/jobs/Office-Administrator-d3d925d98ebd3d3d?fccid=33d409a0d113e3d3&vjs=3\n",
      "Name: Job Url, dtype: object\n",
      "149    http://www.indeed.com/company/Builder-Depot/jobs/Packing-Dispatch-Warehouse-Assistant-4128a5380a31f2fe?fccid=9ee965538d152761&vjs=3\n",
      "Name: Job Url, dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Printing some jobs that are fitting better to your CV\n",
    "pd.options.display.max_colwidth = 1000\n",
    "\n",
    "def get_title_from_url(index):\n",
    "    return final_data[final_data.index == index][\"Job Url\"]\n",
    "i=0\n",
    "for job in sorted_similar_jobs:\n",
    "    print(get_title_from_url(job[0]))\n",
    "    i=i+1\n",
    "    if i>5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>index</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109</td>\n",
       "      <td>0.267647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>0.241873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0.235511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>0.228863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>0.226240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  similarity\n",
       "0    109    0.267647\n",
       "1     13    0.241873\n",
       "2     17    0.235511\n",
       "3     14    0.228863\n",
       "4    149    0.226240"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Take only the URLS and send an email to the person that is looking for a position similar to their CV \n",
    "df = pd.DataFrame(sorted_similar_jobs[1:6], columns=[\"index\", \"similarity\"])\n",
    "df    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.indeed.com/company/CentraNic-Ltd/jobs/Group-Financial-Data-Analyst-59999e06fd4b9009?fccid=004345d9813bd437&vjs=3\n",
      "\n",
      "http://www.indeed.com/company/CriterionCapital/jobs/Office-Administration-Assistant-64da8cde37d422d9?fccid=1931435125b82129&vjs=3\n",
      "\n",
      "http://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0BIQv-klv4x57wzcCCXZDuUs4ETBBTY7U4BZbqajjMT5rLx4iIBIgIDjvqIt6UO8LKeIOY33Wnt4_eGGFmqJeUFdqLBu7U5oyAp-J0dXDp4UiTLVL041HcriHxDT6myJ6B1t5jySkfSP0xrQ1MSGJug_oWZSIBng5uU3tgIaZmdrw1f0HFsYk5o_w5zejOWcDSmC6lgzvZJ6vOa82rSFId3FatHT_qfXMi-PufkEZX4WyY6n0oncWV21jlODJXWsKuoJYw7GGwET4yAfy66eZiJmeyDU1xf9Dt8-V27KKcbAybIWSHq7Mjgv8OjIYZvnDEPVvDV5XHAtg10Eimq-WRh4abc6WEORw6KeHRnDyo2JYUI_WLt98BE3GEtIU9J_2zOiS1fykc7VgdRV6Zb9Gbk5l3k44WDTbVg7sH7h6rRjCG67hJvDDgIMKNCQzMTdIY=&p=2&fvj=1&vjs=3\n",
      "\n",
      "http://www.indeed.com/company/Agina-ltd/jobs/Office-Administrator-d3d925d98ebd3d3d?fccid=33d409a0d113e3d3&vjs=3\n",
      "\n",
      "http://www.indeed.com/company/Builder-Depot/jobs/Packing-Dispatch-Warehouse-Assistant-4128a5380a31f2fe?fccid=9ee965538d152761&vjs=3\n"
     ]
    }
   ],
   "source": [
    "## Above is a dataframe with the job's index and the degree of job similarity based on the CV that I uploaded\n",
    "## I need the Links in a text shape in order to create the message, thus I found the links using the data frame indexes\n",
    "## and I saved them into a text file\n",
    "\n",
    "text = []\n",
    "for i in df[\"index\"]:\n",
    "    text.append(final_data[\"Job Url\"].iloc[i])\n",
    "    with open(\"message.txt\", 'w') as f:\n",
    "         f.write(\"\\n\\n\".join(map(str, text)))\n",
    "\n",
    "file = open('message.txt','r')\n",
    "\n",
    "#read the numbers on the file\n",
    "body = file.read()\n",
    "\n",
    "#Close the the numbers file\n",
    "file.close()\n",
    "\n",
    "#Print the Links, in other words the text that I will produce the messages\n",
    "print(body)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The final step is to send an email to the candidate using the smtplib module. I also attached an image at the bottom of the email."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First step, open my gmail account\n",
    "## Read a file with my personal info\n",
    "file = open(\"my_personal_file.txt\")\n",
    "lines = file.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully Sent email !!!\n",
      "Successfully Sent email !!!\n"
     ]
    }
   ],
   "source": [
    "import smtplib\n",
    "import imghdr\n",
    "from email.message import EmailMessage\n",
    "Sender_Email = \"nikoskalikis@gmail.com\"\n",
    "\n",
    "# You can send the emails with two methods, 1) By sending an email to every person in your list, \n",
    "## so everyone will be able to see the other person that you have in your list\n",
    "## OR 2) to every person separately which is the method that I used.\n",
    "\n",
    "Reciever_Email = [\"nikoskalikis@gmail.com\", email]\n",
    "Password = lines[1]\n",
    "for i in Reciever_Email:\n",
    "    try:\n",
    "        newMessage = EmailMessage()                         \n",
    "        newMessage['Subject'] = \"Check some new positions for you!!!\" \n",
    "        newMessage['From'] = Sender_Email                   \n",
    "        newMessage['To'] = i                   \n",
    "        newMessage.set_content(f\"Apply to the following positions\\n\\n \" + body) \n",
    "        with open('logo.png', 'rb') as f:\n",
    "            image_data = f.read()\n",
    "            image_type = imghdr.what(f.name)\n",
    "            image_name = f.name\n",
    "        newMessage.add_attachment(image_data, maintype='image', subtype=image_type, filename=image_name)\n",
    "        with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n",
    "\n",
    "            smtp.login(Sender_Email, Password)              \n",
    "            smtp.send_message(newMessage)\n",
    "            print(\"Successfully Sent email !!!\")\n",
    "    except Exception:\n",
    "           print(\"Error: unable to send email\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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