__author__ = 'DarkWeb'
# Here, we are importing the auxiliary functions to clean or convert data
from MarketPlaces.Utilities.utilities import *
# Here, we are importing BeautifulSoup to search through the HTML tree
from bs4 import BeautifulSoup
# This is the method to parse the Description Pages (one page to each Product in the Listing Pages)
def wethenorth_description_parser(soup):
# Fields to be parsed
vendor = "-1" # 0 *Vendor_Name
success = "-1" # 1 Vendor_Successful_Transactions
rating_vendor = "-1" # 2 Vendor_Rating
name = "-1" # 3 *Product_Name
describe = "-1" # 4 Product_Description
CVE = "-1" # 5 Product_CVE_Classification (Common Vulnerabilities and Exposures)
MS = "-1" # 6 Product_MS_Classification (Microsoft Security)
category = "-1" # 7 Product_Category
views = "-1" # 8 Product_Number_Of_Views
reviews = "-1" # 9 Product_Number_Of_Reviews
rating_item = "-1" # 10 Product_Rating
addDate = "-1" # 11 Product_AddedDate
BTC = "-1" # 12 Product_BTC_SellingPrice
USD = "-1" # 13 Product_USD_SellingPrice
EURO = "-1" # 14 Product_EURO_SellingPrice
sold = "-1" # 15 Product_QuantitySold
left = "-1" # 16 Product_QuantityLeft
shipFrom = "-1" # 17 Product_ShippedFrom
shipTo = "-1" # 18 Product_ShippedTo
image = "-1" # 19 Product_Image
vendor_image = "-1" # 20 Vendor_Image
# Finding Product Name
listDes = soup.find('div', {'class': "listDes"})
name = listDes.find('h2').text
name = cleanString(name)
name = name.strip()
# Finding Vendor
vendor = listDes.find('b').text
vendor = cleanString(vendor)
vendor = vendor.strip()
# Finding Vendor Rating
rating = listDes.find('span', {'class': 'levelSet'})
rating = rating.text
rating = cleanNumbers(rating)
rating_vendor = rating.strip()
# Finding Prices - all prices in We The North are in CAD, I left the CAD in the resulting String so that it would show CAD for all prices
padp = listDes.find('p', {'class': 'padp'})
USD = padp.find('span').text
USD = USD.strip()
BTC = padp.find_next_sibling('p').text
BTC = cleanNumbers(BTC)
BTC = BTC.strip()
# Finding Escrow - no escrow on WTN market
shipping_info = listDes.find('tbody')
if "Digital" not in shipping_info:
shipping_info = shipping_info.find_all('tr')
row1 = shipping_info[0].find_all('td')
# Finding Shipment Information (Origin)
shipFrom = row1[-1].text
shipFrom = cleanString(shipFrom)
shipFrom = shipFrom.strip()
if shipFrom == "":
shipFrom = "-1"
row2 = shipping_info[1].find_all('td')
# Finding Shipment Information (Destination)
shipTo = row2[-1].text
shipTo = cleanString(shipTo)
shipTo = shipTo.strip()
if shipTo == "":
shipTo = "-1"
# Finding the Product description
describe = soup.find("div", {'class': 'tabcontent'})
describe = describe.find('p').text
describe = cleanString(describe)
describe = describe.strip()
# Searching for CVE and MS categories
# no CVE or MS for WTN market
# Populating the final variable (this should be a list with all fields scraped)
row = (vendor, rating_vendor, success, name, describe, CVE, MS, category, views, reviews, rating_item, addDate,
BTC, USD, EURO, sold, left, shipFrom, shipTo, image, vendor_image)
# Sending the results
return row
# This is the method to parse the Listing Pages
def wethenorth_listing_parser(soup):
# Fields to be parsed
nm = 0 # *Total_Products (Should be Integer)
mktName = "WeTheNorth" # 0 *Marketplace_Name
vendor = [] # 1 *Vendor y
rating_vendor = [] # 2 Vendor_Rating
success = [] # 3 Vendor_Successful_Transactions
name = [] # 4 *Product_Name y
CVE = [] # 5 Product_CVE_Classification (Common Vulnerabilities and Exposures)
MS = [] # 6 Product_MS_Classification (Microsoft Security)
category = [] # 7 Product_Category y
describe = [] # 8 Product_Description
views = [] # 9 Product_Number_Of_Views
reviews = [] # 10 Product_Number_Of_Reviews
rating_item = [] # 11 Product_Rating
addDate = [] # 12 Product_AddDate
BTC = [] # 13 Product_BTC_SellingPrice
USD = [] # 14 Product_USD_SellingPrice y
EURO = [] # 15 Product_EURO_SellingPrice
sold = [] # 16 Product_QuantitySold
qLeft =[] # 17 Product_QuantityLeft
shipFrom = [] # 18 Product_ShippedFrom
shipTo = [] # 19 Product_ShippedTo
image = [] # 20 Product_Image
image_vendor = [] # 21 Vendor_Image
href = [] # 22 Product_Links
right_content = soup.find('div', {"class": "right-content"})
listing = right_content.findAll('div', {"class": "col-1search"})
listing = listing[3:]
# Populating the Number of Products
nm = len(listing)
for a in listing:
bae = a.findAll('a', href=True)
# Adding the url to the list of urls
link = bae[0].get('href')
href.append(link)
# Finding the Vendor
vendor_name = a.find('p', {'class': 'padp'})
vendor_name = vendor_name.find('a').text
vendor_name = cleanString(vendor_name)
vendor_name = vendor_name.strip()
vendor.append(vendor_name)
# Finding the Product
product = a.find('div', {'class': 'col-1centre'})
product = product.find('div', {'class': 'head'}).find('a').text
product = cleanString(product)
product = product.strip()
name.append(product)
# Finding the Category
category_name = a.find('p', {'class': 'padp'}).text
first_dash = category_name.find('-')
second_dash = category_name[first_dash+1:].find('-')
category_name = category_name[first_dash+1: first_dash + second_dash]
category_name = cleanString(category_name)
category_name = category_name.strip()
category.append(category_name)
# Finding Success Transactions
vendor_success = a.find('p', {'class': 'padp'}).text
first_dash = vendor_success.find('(')
vendor_success = vendor_success[first_dash + 1:]
vendor_success = cleanNumbers(vendor_success)
vendor_success = vendor_success.strip()
success.append(vendor_success)
# Finding Views
view_count = a.text
view_count = view_count[view_count.find('Views:'): view_count.find('Sales:')]
view_count = view_count.replace('Views:', ' ')
view_count = cleanNumbers(view_count)
view_count = view_count.strip()
views.append(view_count)
# Finding Quantity Sold
sold_count = a.text
sold_count = sold_count[sold_count.find('Sales:'): sold_count.find('Short')]
sold_count = sold_count.replace('Sales:', ' ')
sold_count = cleanNumbers(sold_count)
sold_count = sold_count.strip()
sold.append(sold_count)
right = a.find('div', {'class': 'col-1right'})
# Finding USD
usd = right.find('a').text
usd = "CAD " + usd.strip()
USD.append(usd)
# Finding BTC
btc = right.text
first_dash = btc.find('(')
second_dash = btc[first_dash + 1:].find(')')
btc = btc[first_dash + 1: first_dash + second_dash]
btc = cleanNumbers(btc)
btc = btc.strip()
BTC.append(btc)
# Finding Product Image
product_image = right.find('img')
product_image = product_image.get('src')
product_image = product_image.split('base64,')[-1]
image.append(product_image)
# Searching for CVE and MS categories
# no CVE or MS in WTN market
cve = a.findAll(text=re.compile('CVE-\d{4}-\d{4}'))
if not cve:
cveValue="-1"
else:
cee = " "
for idx in cve:
cee += (idx)
cee += " "
cee = cee.replace(',', ' ')
cee = cee.replace('\n', '')
cveValue=cee
CVE.append(cveValue)
ms = a.findAll(text=re.compile('MS\d{2}-\d{3}'))
if not ms:
MSValue="-1"
else:
me = " "
for im in ms:
me += (im)
me += " "
me = me.replace(',', ' ')
me = me.replace('\n', '')
MSValue=me
MS.append(MSValue)
# Populate the final variable (this should be a list with all fields scraped)
return organizeProducts(mktName, nm, vendor, rating_vendor, success, name, CVE, MS, category, describe, views,
reviews, rating_item, addDate, BTC, USD, EURO, sold, qLeft, shipFrom, shipTo, href, image, image_vendor)
def wethenorth_links_parser(soup):
# Returning all links that should be visited by the Crawler
href = []
right_content = soup.find('div',{"class": "right-content"})
listing = right_content.findAll('div', {"class": "col-1search"})
#cut out the irrelevant products that are in blue, the first three products of each page usually unrelated
listing = listing[3:]
for a in listing:
link = a.find('a')
link = link['href']
href.append(link)
return href