2.a-Jobs_API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a66c1692-9c84-41ec-a135-b417ef41344d",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e786144-f1e4-45ee-b287-f2f4b87300d3",
   "metadata": {},
   "source": [
    "# **JOBS API Implementation Using FLASK**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817156a7-4bab-46b8-9a67-55f3b0564c01",
   "metadata": {},
   "source": [
    "We will be executing this code  so that the notebook **2.a-Collect Data (API).ipynb**  will be accessing this code executing on the server\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ac527ce-f921-44e2-b729-28ed03634a8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: flask in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (1.1.2)\n",
      "Requirement already satisfied: click>=5.1 in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from flask) (8.0.3)\n",
      "Requirement already satisfied: Werkzeug>=0.15 in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from flask) (2.0.2)\n",
      "Requirement already satisfied: Jinja2>=2.10.1 in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from flask) (2.11.3)\n",
      "Requirement already satisfied: itsdangerous>=0.24 in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from flask) (2.0.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from click>=5.1->flask) (0.4.4)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\alvaro\\anaconda3\\lib\\site-packages (from Jinja2>=2.10.1->flask) (1.1.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install flask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5bba17-58e7-49a4-8040-0831e93466f5",
   "metadata": {},
   "source": [
    "## Dataset Used in this Assignment\n",
    "\n",
    "The dataset used in this lab comes from the following source: [https://www.kaggle.com/promptcloud/jobs-on-naukricom](https://www.kaggle.com/promptcloud/jobs-on-naukricom?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01) under the under a **Public Domain license**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2eed468-b4cd-481a-a18e-1851e2fc7a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-08-29 20:09:51--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/module%201/Accessing%20Data%20Using%20APIs/jobs.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 12878382 (12M) [application/json]\n",
      "Saving to: 'jobs.json'\n",
      "\n",
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      "  9700K .......... .......... .......... .......... .......... 77% 2.55M 1s\n",
      "  9750K .......... .......... .......... .......... .......... 77% 2.73M 1s\n",
      "  9800K .......... .......... .......... .......... .......... 78% 4.79M 1s\n",
      "  9850K .......... .......... .......... .......... .......... 78% 4.78M 1s\n",
      "  9900K .......... .......... .......... .......... .......... 79% 3.06M 1s\n",
      "  9950K .......... .......... .......... .......... .......... 79% 4.03M 1s\n",
      " 10000K .......... .......... .......... .......... .......... 79% 2.55M 1s\n",
      " 10050K .......... .......... .......... .......... .......... 80% 5.65M 1s\n",
      " 10100K .......... .......... .......... .......... .......... 80% 3.94M 1s\n",
      " 10150K .......... .......... .......... .......... .......... 81%  791K 1s\n",
      " 10200K .......... .......... .......... .......... .......... 81% 9.14M 1s\n",
      " 10250K .......... .......... .......... .......... .......... 81% 8.69M 1s\n",
      " 10300K .......... .......... .......... .......... .......... 82% 16.6M 1s\n",
      " 10350K .......... .......... .......... .......... .......... 82% 13.1M 1s\n",
      " 10400K .......... .......... .......... .......... .......... 83%  407K 1s\n",
      " 10450K .......... .......... .......... .......... .......... 83%  970K 1s\n",
      " 10500K .......... .......... .......... .......... .......... 83% 17.1M 1s\n",
      " 10550K .......... .......... .......... .......... .......... 84% 11.8M 1s\n",
      " 10600K .......... .......... .......... .......... .......... 84% 3.54M 1s\n",
      " 10650K .......... .......... .......... .......... .......... 85%  901K 1s\n",
      " 10700K .......... .......... .......... .......... .......... 85% 3.19M 1s\n",
      " 10750K .......... .......... .......... .......... .......... 85% 2.30M 1s\n",
      " 10800K .......... .......... .......... .......... .......... 86% 1.17M 1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10850K .......... .......... .......... .......... .......... 86% 1014K 1s\n",
      " 10900K .......... .......... .......... .......... .......... 87% 13.9M 1s\n",
      " 10950K .......... .......... .......... .......... .......... 87% 13.0M 1s\n",
      " 11000K .......... .......... .......... .......... .......... 87% 3.87M 1s\n",
      " 11050K .......... .......... .......... .......... .......... 88%  750K 1s\n",
      " 11100K .......... .......... .......... .......... .......... 88% 3.25M 1s\n",
      " 11150K .......... .......... .......... .......... .......... 89% 12.2M 1s\n",
      " 11200K .......... .......... .......... .......... .......... 89% 4.64M 1s\n",
      " 11250K .......... .......... .......... .......... .......... 89%  863K 1s\n",
      " 11300K .......... .......... .......... .......... .......... 90% 6.78M 1s\n",
      " 11350K .......... .......... .......... .......... .......... 90% 3.64M 1s\n",
      " 11400K .......... .......... .......... .......... .......... 91% 4.46M 0s\n",
      " 11450K .......... .......... .......... .......... .......... 91% 1.06M 0s\n",
      " 11500K .......... .......... .......... .......... .......... 91% 5.84M 0s\n",
      " 11550K .......... .......... .......... .......... .......... 92% 2.01M 0s\n",
      " 11600K .......... .......... .......... .......... .......... 92% 1.76M 0s\n",
      " 11650K .......... .......... .......... .......... .......... 93% 2.05M 0s\n",
      " 11700K .......... .......... .......... .......... .......... 93% 3.40M 0s\n",
      " 11750K .......... .......... .......... .......... .......... 93% 2.93M 0s\n",
      " 11800K .......... .......... .......... .......... .......... 94% 2.51M 0s\n",
      " 11850K .......... .......... .......... .......... .......... 94% 9.85M 0s\n",
      " 11900K .......... .......... .......... .......... .......... 95% 1.75M 0s\n",
      " 11950K .......... .......... .......... .......... .......... 95% 3.51M 0s\n",
      " 12000K .......... .......... .......... .......... .......... 95% 11.7M 0s\n",
      " 12050K .......... .......... .......... .......... .......... 96% 4.06M 0s\n",
      " 12100K .......... .......... .......... .......... .......... 96% 2.33M 0s\n",
      " 12150K .......... .......... .......... .......... .......... 97% 3.00M 0s\n",
      " 12200K .......... .......... .......... .......... .......... 97% 2.75M 0s\n",
      " 12250K .......... .......... .......... .......... .......... 97% 2.76M 0s\n",
      " 12300K .......... .......... .......... .......... .......... 98% 4.63M 0s\n",
      " 12350K .......... .......... .......... .......... .......... 98% 2.26M 0s\n",
      " 12400K .......... .......... .......... .......... .......... 98% 4.24M 0s\n",
      " 12450K .......... .......... .......... .......... .......... 99% 4.12M 0s\n",
      " 12500K .......... .......... .......... .......... .......... 99% 2.75M 0s\n",
      " 12550K .......... .......... ......                          100% 13.0M=5.4s\n",
      "\n",
      "2022-08-29 20:09:59 (2.27 MB/s) - 'jobs.json' saved [12878382/12878382]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/module%201/Accessing%20Data%20Using%20APIs/jobs.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21697c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
     ]
    }
   ],
   "source": [
    "import flask\n",
    "from flask import request, jsonify\n",
    "import requests\n",
    "import re\n",
    "\n",
    "def get_data(key,value,current):\n",
    "    results = list()\n",
    "    pattern_dict = {\n",
    "        'C'      : '(C)',\n",
    "        'C++'    : '(C\\+\\+)',\n",
    "        'Java'   :'(Java)',\n",
    "        'C#'     : '(C\\#)',\n",
    "        'Python' :'(Python)',\n",
    "        'Scala' : '(Scala)',\n",
    "        'Oracle' : '(Oracle)',\n",
    "        'SQL Server': '(SQL Server)',\n",
    "        'MySQL Server' :'(MySQL Server)',\n",
    "        'PostgreSQL':'(PostgreSQL)',\n",
    "        'MongoDB'    : '(MongoDB)',\n",
    "        'JavaScript'    : '(JavaScript)',\n",
    "        'Los Angeles' :'(Los Angeles)',\n",
    "        'New York':'(New York)',\n",
    "        'San Francisco':'(San Francisco)',\n",
    "        'Washington DC':'(Washington DC)',\n",
    "        'Seattle':'(Seattle)',\n",
    "        'Austin':'(Austin)',\n",
    "        'Detroit':'(Detroit)',\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "    }\n",
    "    for rec in current:\n",
    "        print(rec[key])\n",
    "        print(type(rec[key]))\n",
    "        print(rec[key].find(value))\n",
    "        #if rec[key].find(value) != -1:\n",
    "        import re\n",
    "        #reex_str = \"\"\"(C)|(C\\+\\+)|(JavaScript)|(Java)|(C\\#)|(Python)|(Scala)|(Oracle)|(SQL Server)|(MySQL Server)|(PostgreSQL)|(MongoDB)\"\"\"\n",
    "        if re.search(pattern_dict[value],rec[key]) != None:\n",
    "            results.append(rec)\n",
    "    return results\n",
    "\n",
    "app = flask.Flask(__name__)\n",
    "\n",
    "import json\n",
    "data = None\n",
    "with open('jobs.json',encoding='utf-8') as f:\n",
    "    # returns JSON object as\n",
    "    # a dictionary\n",
    "    data = json.load(f)\n",
    "    \n",
    "    \n",
    "\n",
    "@app.route('/', methods=['GET'])\n",
    "def home():\n",
    "    \n",
    "    return '''<h1>Welcome to flask JOB search API</p>'''\n",
    "\n",
    "\n",
    "@app.route('/data/all', methods=['GET'])\n",
    "def api_all():\n",
    "    return jsonify(data)\n",
    "\n",
    "\n",
    "@app.route('/data', methods=['GET'])\n",
    "def api_id():\n",
    "    # Check if keys such as Job Title,KeySkills, Role Category and others  are provided as part of the URL.\n",
    "    #  Assign the keys to the corresponding variables..\n",
    "    # If no key is provided, display an error in the browser.\n",
    "    res = None\n",
    "    for req in request.args:\n",
    "        \n",
    "        if req == 'Job Title':\n",
    "            key = 'Job Title'\n",
    "        elif req == 'Job Experience Required' :\n",
    "            key='Job Experience Required'\n",
    "        elif req == 'Key Skills' :\n",
    "            key='Key Skills'\n",
    "            \n",
    "        elif req == 'Role Category' :\n",
    "            key='Role Category'\n",
    "        elif req == 'Location' :\n",
    "            key='Location'\n",
    "        \n",
    "        elif req == 'Functional Area' :\n",
    "            key='Functional Area'\n",
    "        \n",
    "        elif req == 'Industry' :\n",
    "            key='Industry'\n",
    "        elif req == 'Role' :\n",
    "            key='Role'\n",
    "        elif req==\"id\":\n",
    "             key=\"id\"\n",
    "        else:\n",
    "            pass\n",
    "    \n",
    "        value = request.args[key]\n",
    "        if (res==None):\n",
    "            res = get_data(key,value,data)\n",
    "        else:\n",
    "            res = get_data(key,value,res)\n",
    "\n",
    "    # Use the jsonify function from Flask to convert our list of\n",
    "    # Python dictionaries to the JSON format.\n",
    "    return jsonify(res)\n",
    "\n",
    "app.run()"
   ]
  }
 ],
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