JabongPriceTrackerUsingRoboBrowser.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jabong Product Price Tracking Tool made using Robobrowser Library built on top of Requests and Beautifulsoup packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lxml.html as html\n",
    "import requests\n",
    "import time\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from robobrowser import RoboBrowser\n",
    "from requests import Session\n",
    "session = Session()\n",
    "session.verify = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the url : https://www.jabong.com/us-polo-assn-navy-blue-textured-slim-fit-polo-t-shirt-5768294.htm\n"
     ]
    }
   ],
   "source": [
    "url = input(\"Enter the url : \")\n",
    "br = RoboBrowser(parser = 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Brand : U.S. Polo Assn.\n",
      "Product Description : Navy Blue Textured Slim Fit Polo T-Shirt\n",
      "MRP of the product : 2399\n",
      "Current Selling Price : 839\n",
      "Additional Offer YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "br.open(url)\n",
    "\n",
    "brand = str(br.select('span.brand')[0])\n",
    "pat = r'.*>(.*)<.*'\n",
    "res = re.match(pat, brand)\n",
    "brand = res.group(1)\n",
    "\n",
    "product = str(br.select('span.product-title')[0])\n",
    "res = re.match(pat, product)\n",
    "product = res.group(1)\n",
    "\n",
    "mrp = str(br.select('span.standard-price')[1])\n",
    "res = re.match(pat, mrp)\n",
    "mrp = int(res.group(1))\n",
    "\n",
    "sp = str(br.select('span.actual-price')[0])\n",
    "res = re.match(pat, sp)\n",
    "sp = int(res.group(1))\n",
    "\n",
    "wallet = str(br.select('span.wallet-title')[0])\n",
    "res = re.match(pat, wallet)\n",
    "wallet = res.group(1)\n",
    "\n",
    "offer = str(br.select('span.wallet-desc')[0])\n",
    "res = re.match(pat, offer)\n",
    "offer = res.group(1)\n",
    "\n",
    "print('Product Brand : %s'%(brand))\n",
    "print('Product Description : %s'%(product))\n",
    "print('MRP of the product : %d'%(mrp))\n",
    "print('Current Selling Price : %d'%(sp))\n",
    "print('Additional Offer %s %s'%(wallet,offer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------Tracking Started-------------------------\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n",
      "Current Price : 839  YES Bank:  Get 15% Instant discount on Min Spend of Rs.2500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('-------------------------Tracking Started-------------------------')\n",
    "\n",
    "xaxis = []\n",
    "actual_price = []\n",
    "selling_price = []\n",
    "\n",
    "matplotlib.rc('font', size=8)\n",
    "matplotlib.rc('font', family='sans-serif')\n",
    "c=1\n",
    "while(c<10):\n",
    "    br.open(url)\n",
    "    \n",
    "    mrp = str(br.select('span.standard-price')[1])\n",
    "    res = re.match(pat, mrp)\n",
    "    mrp = int(res.group(1))\n",
    "\n",
    "    sp = str(br.select('span.actual-price')[0])\n",
    "    res = re.match(pat, sp)\n",
    "    sp = int(res.group(1))\n",
    "    \n",
    "    wallet = str(br.select('span.wallet-title')[0])\n",
    "    res = re.match(pat, wallet)\n",
    "    wallet = res.group(1)\n",
    "\n",
    "    offer = str(br.select('span.wallet-desc')[0])\n",
    "    res = re.match(pat, offer)\n",
    "    offer = res.group(1)\n",
    "    \n",
    "    actual_price.append(mrp)\n",
    "    selling_price.append(sp)\n",
    "    \n",
    "    t = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.gmtime())\n",
    "    xaxis.append(t)\n",
    "    print('Current Price : %d  %s %s'%(sp, wallet, offer))\n",
    "    time.sleep(1)\n",
    "    c+=1\n",
    "\n",
    "x_pos = np.arange(len(xaxis))\n",
    "plt.title('Price Variation')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.xticks(x_pos, xaxis, rotation=90)\n",
    "plt.plot(xaxis, actual_price , label='original')\n",
    "plt.plot(xaxis, selling_price , label='discounted', marker='o')\n",
    "plt.legend()\n",
    "plt.grid(color='lightblue', linestyle='-.')\n",
    "plt.tight_layout()\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.savefig('output.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}