ben_second_milestone

from extract_emails import ExtractEmails
import re
import cloudscraper
from bs4 import BeautifulSoup
import pandas as pd
import os
import concurrent.futures

RESULTS = []




def locate_txt_files(path=os.getcwd):
    """ A function that locates and returns a list of txt files from a given path """

    txt_array = []
    for item in os.listdir(path()):
        if item.endswith(".txt"):
            txt_array.append(item)
    return txt_array




def open_and_prepare_urls2(txt_file_array):
    final_domains = []

    for txt_file in txt_file_array:
        with open(txt_file, encoding="utf-8") as f:
            content = f.read()
        content = content.split("au")[0:]
        domain_urls = [item.replace("|", "").replace("\n", "").replace(",", "").strip()+"au" for item in content if "." in item]
        final_domains.extend(domain_urls)
        print(f"Total of {len(domain_urls)} '.au' domains found in {txt_file}")
        print("\n")

    return final_domains



###### New Additions ####
def getEmail(url):
    em = ExtractEmails(url, depth=5, ssl_verify=True)

    return em.emails





def getPhone(source):
    phones_array = []
    phones = re.findall(r'[\+\(]?[0-9][0-9 .\-\(\)]{8,}[0-9]', source)

    prefixes = ["1300", "1800",
    "02", "03", "04", "05", "07", "08",
    "612", "613", "614", "615", "617", "618"]

    for prefix in prefixes:
        for phone in phones:
            if phone.startswith(str(prefix)) and len(phone.strip().replace(" ", "").strip()) <= 12:
                phones_array.append(phone)

    try:
        phones_array = [phone.replace(" ", "").strip() for phone in phones_array]

    except:
        pass

    try:
        phones_array = sorted(set(phones_array), key=phones_array.index)

    except:
        pass

    return phones_array






scraper = cloudscraper.CloudScraper(browser="chrome")

def has_ads(text, keyword="ads"):
    return keyword in text


def getContent(url):

    is_ad = False
    r = scraper.get(url)

    soup = BeautifulSoup(r.text, "html.parser")

    Source = str(soup)

    is_ad = has_ads(text=Source)

    try:
        fones = getPhone(Source)


    except Exception as e:
        #print(e)

        pass

    if fones:
        fones = ", ".join(fones)


    else:
        fones = ""


    try:
        e_mails = getEmail(url)


    except Exception as e:
        #print(e)
        pass

    if e_mails:
        e_mails = ", ".join(e_mails)

    else:
        e_mails = ""



    RESULTS.append((url, fones, e_mails, is_ad))




def getData(url):

    fones = ""
    e_mails = ""
    contact_link = ""

    if "http" not in url:
        url  = "http://"+url


    #perform scraping
    getContent(url)






##### Apply #####
txt_files_list = locate_txt_files()

domains = open_and_prepare_urls2(txt_files_list)

total_time = int(len(domains)) * 6.7

print("Starting scraper\nPlease wait...\nEstimated time in sec(s):\n"+ str(total_time))

with concurrent.futures.ThreadPoolExecutor() as executor:
  executor.map(getData, domains)




general_result = []
result_04 = []

for item_tuple in RESULTS:
    #Only 04 numbers
    if  item_tuple[1]:
        for item in item_tuple[1].split(", "):
            if item.startswith("04") or item.startswith("4"):
                obj_04 = {}
                obj_04["Domain"] = item_tuple[0]
                obj_04["Phone Number"] = item
                obj_04["Email"] = item_tuple[2]
                obj_04["ads"] = item_tuple[3]

                result_04.append(obj_04)

    #General numbers
    obj_gen = {}
    obj_gen["Domain"] = item_tuple[0]
    obj_gen["Phone Number"] = item_tuple[1]
    obj_gen["Email"] = item_tuple[2]
    obj_gen["ads"] = item_tuple[3]

    general_result.append(obj_gen)


df = pd.DataFrame(general_result)
df_04 = pd.DataFrame(result_04)

#Filter conditions
filt_ads = (df["ads"]== True)
filt_no_ads = (df["ads"]== False)
filt_04_ads = (df_04["ads"] == True)
filt_04_no_ads = (df_04["ads"] == False)

#create ads dataframe from df and df_04
df_ads = df[filt_ads]
df_no_ads = df[filt_no_ads]
df_04_ads = df_04[filt_04_ads]
df_04_no_ads = df_04[filt_04_no_ads]


df_ads.to_excel("result_landline_ads.xlsx", index=False)
df_no_ads.to_excel("result_landline_no_ads.xlsx", index=False)
df_04_ads.to_excel("result_04_mobile_ads.xlsx", index=False)
df_04_no_ads.to_excel("result_04_mobile_no_ads.xlsx", index=False)