from selenium import webdriver
import time
from bs4 import BeautifulSoup
import re
import datetime
import os
import pandas as pd
from icecream import ic


def get_names(soup):
    return [_.text for _ in soup.find_all('h4')]


def get_addresses(soup):
    addresses = [_.text for _ in soup.find_all('li')][9:]

    addresses_v2 = [addresses[_+1:_+3] for _ in range(len(addresses)) if
                    addresses[_] == '   ' or addresses[_] == ''][:-1]

    addresses_v3 = []
    for addresse in addresses_v2:
        if addresse[0].strip()[0].isdigit() and not addresse[0].strip()[-1].isdigit():
            addresses_v3.append(addresse[0].strip())
        elif addresse[-1].strip()[0].isdigit() and not addresse[-1].strip()[-1].isdigit():
            addresses_v3.append(addresse[-1].strip())
        else:
            addresses_v3.append('NA')

    return addresses_v3


def get_urls(soup):
    urls_indexes = [_.start() for _ in re.finditer('href="https://bestrestaurantsparis.com/fr/r', str(soup))]
    return ['/'.join(str(soup)[_:_+120][6:].split('/')[:-1]) + '/' for _ in urls_indexes]


def get_prices(url):
    options = webdriver.ChromeOptions()
    options.add_argument("--headless=new")
    driver = webdriver.Chrome(options=options)

    driver.get(url)
    time.sleep(1)
    page_source = driver.page_source
    driver.quit()
    soup = BeautifulSoup(page_source, features='html.parser')

    prices_indexes = [_.start() for _ in re.finditer('€', str(soup))][:-2]

    prices = [' '.join(str(soup)[_ - 150:_ - 1].split('label')[-1][2:].split('</div><div class="menu-price">'))
              for _ in prices_indexes]
    try:
        if prices[0][:10] == 'Prix moyen':
            prices[0] = ' '.join(prices[0].split('</div><div class="value">')).split('<')[0]
    except IndexError:
        pass

    prices_v2 = {}
    for _ in prices:
        prices_v2[' '.join(_.split(' ')[:-1])] = _.split(' ')[-1] + ' €'
    return prices_v2, soup


def get_number_of_pages(soup):
    return int([_.text for _ in soup.find_all('li') if _.text.isdigit()][-1])


def scrap(soup):
    names = get_names(soup)
    addresses = get_addresses(soup)
    urls = get_urls(soup)

    df = pd.DataFrame(columns=['Name', 'Date', 'Address', 'Item', 'Price', ])
    for name, address, url in zip(names, addresses, urls):
        temporary_df = pd.DataFrame(columns=['Name', 'Date', 'Address', 'Item', 'Price', ])
        ic()
        ic(name)
        # ic(url)
        temporary_df['Item'] = get_prices(url)[0].keys()
        temporary_df['Price'] = get_prices(url)[0].values()

        newpath = '/Users/oliviermeyer/Desktop/CREST/Web Scraping/HTML/Best Restaurants/' + str(datetime.date.today())
        if not os.path.exists(newpath):
            os.makedirs(newpath)

        html_path = ('/Users/oliviermeyer/Desktop/CREST/Web Scraping/HTML/Best Restaurants/' +
                     str(datetime.date.today()) + '/' + name.replace('/', '-') + '.html')

        with open(html_path, 'wt', encoding='utf-8') as html_file:
            for line in soup.prettify():
                html_file.write(line)
        for i in range(len(temporary_df)):
            temporary_df.loc[i, 'Name'] = name
            temporary_df.loc[i, 'Address'] = address
            temporary_df.loc[i, 'Date'] = datetime.date.today()

        df = pd.concat([df, temporary_df], ignore_index=True)
    return df


def get_soup(url):
    options = webdriver.ChromeOptions()
    options.add_argument("--headless=new")
    driver = webdriver.Chrome(options=options)

    driver.get(url)
    time.sleep(1)

    page_source = driver.page_source
    driver.quit()
    soup = BeautifulSoup(page_source, features='html.parser')

    return soup


def complete_scraping():
    ic()
    url = 'https://bestrestaurantsparis.com/fr/explore/'
    soup = get_soup(url)

    number_of_pages = get_number_of_pages(soup)
    df = scrap(soup)
    for i in range(2, number_of_pages+1):
        ic(i)
        new_url = url + '?pg=' + str(i) + '&sort=latest'
        ic(new_url)
        new_soup = get_soup(new_url)
        temporary_df = scrap(new_soup)
        df = pd.concat([df, temporary_df], ignore_index=True)

    df = df.dropna(subset='Address')

    prices = df['Price']
    for i in range(len(prices)):
        if 'href' in prices[i]:
            ic()
            df = df.drop(index=[i, i + 1])

    return df