Execution d'un programme avec selenium

Bonjour,

Voici un programme que j'ai récupéré en grosse partie sur Internet. Il permet de récupérer plusieurs informations de Vinted dans le but de les envoyer par message discord grâce à un bot qu'il faut que je programme.

J'ai réussi à l'exécuter après avoir téléchargé tous les modules, mais le programme ne semble pas fonctionner correctement. Il se lance, télécharge pour une raison qui m'est inconnue des drivers chrome et s'éteint, alors qu'il est sensé poser des questions pour avoir les données pour faire sa recherche sur Vinted.

Ceci me laisse penser qu'un truc fait crasher le programme, même si je n'ai pas de message d'erreur ce que je trouve bizarre.

J'ai surement raté un truc assez simple, mais voila si qq a une idée ça me serait bien utile...

Merci d'avance

# Vinted functions

import os
import urllib
from selenium import webdriver
import time
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
import numpy as np
import pandas as pd
import re
import sys
from datetime import datetime

global dict_order, dict_catalog, dict_size, dict_color, dict_brand, dict_condition
dict_order = dict({'relevance': '&order=relevance','decreasing price' : '&order=price_high_to_low',\
'increasing price' : '&order=price_low_to_high', 'most recent first' : '&order=newest_first'})
dict_catalog = dict({'men':'&catalog[]=5', 'women':'&catalog[]=1904', 'children': '&catalog[]=1193', \
'home':'&catalog[]=1918'})
dict_size = dict({'MenXS':'&size_id[]=206', 'MenS':'&size_id[]=207', 'MenM':'&size_id[]=208', 'MenL':'&size_id[]=209',\
'MenXL':'&size_id[]=210', 'MenXXL':'&size_id[]=211', 'MenXXXL':'&size_id[]=212', 'Men4XL':'&size_id[]=308',\
'Men5XL':'&size_id[]=309', 'Men6XL':'&size_id[]=1192', 'Men7XL':'&size_id[]=1193', 'Men8XL':'&size_id[]=1194',\
'Men38':'&size_id[]=776', 'Men39':'&size_id[]=778',\
'Men40':'&size_id[]=780', 'Men41':'&size_id[]=782', 'Men42':'&size_id[]=784',\
'Men42.5':'&size_id[]=785', 'Men43':'&size_id[]=786', 'Men43.5':'&size_id[]=787', 'Men44':'&size_id[]=788',\
'Men44.5':'&size_id[]=789', 'Men45':'&size_id[]=790', 'Men45.5':'&size_id[]=791', 'Men46':'&size_id[]=792',\
'Men47':'&size_id[]=794', 'Men48':'&size_id[]=1190', 'Men49':'&size_id[]=1191',\
'WomenXXXS':'&size_id[]=1226','WomenXXS':'&size_id[]=102',\
'WomenXS':'&size_id[]=2', 'WomenS':'&size_id[]=3', 'WomenM':'&size_id[]=4', 'WomenL':'&size_id[]=5',\
'WomenXL':'&size_id[]=6', 'WomenXXL':'&size_id[]=7', 'WomenXXXL':'&size_id[]=310', 'Women4XL':'&size_id[]=311',\
'Women5XL':'&size_id[]=312', 'Women6XL':'&size_id[]=1227', 'Women7XL':'&size_id[]=1228', 'Women8XL':'&size_id[]=1229',\

'Women35':'&size_id[]=55', 'Women35.5':'&size_id[]=1195',\
'Women36':'&size_id[]=56', 'Women36.5':'&size_id[]=1196', 'Women37':'&size_id[]=57',\
'Women37.5':'&size_id[]=1197', 'Women38':'&size_id[]=58', 'Women38.5':'&size_id[]=1198',\

'Women39':'&size_id[]=59', 'Women39.5':'&size_id[]=1199',\
'Women40':'&size_id[]=60', 'Women40.5':'&size_id[]=1200', 'Women41':'&size_id[]=61',\
'Women41.5':'&size_id[]=1201', 'Women42':'&size_id[]=62', 'Women43':'&size_id[]=62'})
dict_color = dict({'black':'&color_id[]=1', 'white':'&color_id[]=12', 'grey':'&color_id[]=3', \
'cream':'&color_id[]=20', 'beige':'&color_id[]=4', 'apricot':'&color_id[]=21', 'orange':'&color_id[]=11',\
'coral':'&color_id[]=22', 'red':'&color_id[]=7','burgundy':'&color_id[]=23','pink':'&color_id[]=5',\
'rose':'&color_id[]=24', 'purple':'&color_id[]=6', 'lila':'&color_id[]=25', 'light blue':'&color_id[]=26',\
'blue':'&color_id[]=9', 'navy blue':'&color_id[]=27', 'turquoise':'&color_id[]=17', 'mint':'&color_id[]=30',\
'green':'&color_id[]=10','dark green':'&color_id[]=28','khaki':'&color_id[]=16', 'brown':'&color_id[]=2',\
'mustard':'&color_id[]=29', 'yellow':'&color_id[]=8', 'silver':'&color_id[]=13', 'golden':'&color_id[]=14',\
'multicolor':'&color_id[]=15'})
dict_brand = dict({'nike':'&brand_id[]=53', 'adidas': '&brand_id[]=14', 'zara':'&brand_id[]=12', "levi's":'&brand_id[]=10',\
'h&m':'&brand_id[]=7', 'ralph lauren':'&brand_id[]=88', 'mango':'&brand_id[]=15', 'lacoste':'&brand_id[]=304', \
'calvin klein':'&brand_id[]=255', 'tommy hilfiger': '&brand_id[]=94', 'guess':'&brand_id[]=20', 'michael kors':'&brand_id[]=6005', \
'jordan':'&brand_id[]=2703', 'puma':'&brand_id[]=535', 'balenciaga':'&brand_id[]=2369', 'vans':'&brand_id[]=139'})
dict_condition = dict({'new with label':'&status[]=6', 'new': '&status[]=1', 'very good condition': '&status[]=2', \
'good condition':'&status[]=3', 'satisfactory':'&status[]=4'})

#os.chdir("C:\\Users\\Sébastien CARARO\\Desktop\\Vinted\\Data")
global driver
options = webdriver.ChromeOptions()
options.add_experimental_option('excludeSwitches', ['enable-logging'])
driver = webdriver.Chrome(options=options)

#options.add_argument('--ignore-certificate-errors')
#options.add_argument('--ignore-ssl-errors')
#options.add_experimental_option("prefs", {"profile.default_content_setting_values.cookies": 2})

driver.get("C:\webdrivers\\chromedriver.exe")


#driver.get('https://www.vinted.fr/')
#driver.find_element_by_xpath('/html/body/div/div[2]/div[2]/button[2]').click() # accept cookies



def fi(a):
    try:
        driver.find_element_by_xpath(a).text
    except:
        return False

def ffi(a):
    if fi(a) != False :
        return driver.find_element_by_xpath(a).text

def fi2(a):
    try:
        driver.find_element_by_xpath(a).click()
    except:
        return False

def ffi2(a):
    if fi2(a) != False :
        fi2(a)
        return(True)
    else:
        return(None)

def advance(data, i):
    print(i)
    usr = driver.find_elements_by_xpath('//*[contains(@id, "Catalog-react-component")]')[0]
    usr.find_element_by_xpath('./div/div/div[{}]/div/div/div/div[2]/a'.format(i)).click() # click on result n°i
    new_data = collect_info(driver.current_url)
    print(new_data)
    data = data.append(new_data)
    return(data)

def scrape_research_page(search_url):
    # (a)Initialize variables
    data = pd.DataFrame()
    c = 0
    # (b) Collect data on first result, then on every else result # print(usr.text);print(usr.id)
    for i in range(1,1000):
        if c == 10: # if there are 10 articles in a row we cannot scrape, we consider we finished to scrape the page
            return(data)
        driver.get(search_url)
        #time.sleep(5)
        try:
            data = advance(data,i)
            c=0
        except:
            print('Unable to scrape item n°{}'.format(i))
            c+=1

    return(data)


def define_criteria_string(order = [], catalog = [], size = [], color = [], brand = [], min_price = 'Na', max_price = 'Na', condition = []):   
    # I - Create criteria string based on these dictionaries
    order_str = ''.join([dict_order[y] for y in order])
    catalog_str = ''.join([dict_catalog[y] for y in catalog])
    size_str = ''.join([dict_size[y] for y in size])
    color_str = ''.join([dict_color[y] for y in color])
    brand_str = ''.join([dict_brand[y] for y in brand])
    condition_str = ''.join([dict_condition[y] for y in condition])

    if min_price != 'Na':
        min_price_str = '&price_from={}'.format(min_price)
    else:
        min_price_str = ''

    if max_price != 'Na':
        max_price_str = '&price_to={}'.format(max_price)
    else:
        max_price_str = ''

    criteria_string = order_str + catalog_str + size_str + color_str + \
        brand_str + min_price_str + max_price_str + condition_str

    return(criteria_string)






def research_and_scrape(query, criteria_string, end_page = 2):
    start_page = 1
    # https://www.vinted.fr/vetements?search_text=nike+air+max
    DATA_ALL = pd.DataFrame()
    if start_page == 1 :
        # start first page
        page = 1
        print('We start to scrape page n° {}'.format(page))
        search_url = 'https://www.vinted.fr/vetements?search_text=' + query.replace(' ', '%20') + criteria_string
        driver.get(search_url)
        input("You might need to manually accept the cookies (only necessary at page 1).\n Press Enter to continue when it is done (or not displayed on the site)...")
        print('Thanks!')
        DATA_ALL = DATA_ALL.append(scrape_research_page(search_url))
        print('We finished to scrape page n° {}'.format(page))
    
    for page in range(2, end_page + 1):
        print('We start to scrape page n° {}'.format(page))
        search_url = 'https://www.vinted.fr/vetements?search_text=' + query.replace(' ', '%20') + criteria_string + '&page={}'.format(page) # 
        DATA_ALL = DATA_ALL.append(scrape_research_page(search_url))
        print('We finished to scrape page n° {}'.format(page))


    if not os.path.exists('./Scraped'):
        os.makedirs('./Scraped')

    DATA_ALL.to_csv("./Scraped/{}_{}_page{}_page{}.csv".format(query.replace(' ', '-'), str(datetime.now()).replace(' ','-').\
        replace(':','-').replace('.','-'), start_page, end_page), sep = ";", index = False)

  

    print('We finished scraping the query {} between page {} and page {}!'.format(query, start_page, end_page))
    return(DATA_ALL) # Everything went good

def collect_info(page_url):
    '''Date de l’annonce, Prix, Marque, Taille, ÉTAT, Couleur, Emplacement, Nombre de Vues, 
    Nombre d’intéressés, catégorie'''
    driver.get(page_url)
    #time.sleep(4)
    # (a) Collect basic info
    data = ffi('/html/body/div[4]/div/section/div/div[2]/main/aside/div[1]')

    # (b) Transform the data into a cleaner form
    clean_data = data.split('\n')

    # (c) Exctract info from the clean form
    try:
        price = re.split(' €', clean_data[0])[0]
    except:
        price = 'Na'

    try:
        brand = clean_data[np.where([(y[:6]=='MARQUE') for y in clean_data])[0][0]][6:].strip() # obtain brand without any spaces
    except:
        brand = 'Na'

    try:
        colors = clean_data[np.where([(y[:7]=='COULEUR') for y in clean_data])[0][0]][7:].strip() 
    except:
        colors = 'Na'

    try:
        size = clean_data[np.where([(y[:6]=='TAILLE') for y in clean_data])[0][0]][6:].strip() 
    except:
        size = 'Na'

    try:
        condition = clean_data[np.where([(y[:4]=='ÉTAT') for y in clean_data])[0][0]][4:].strip() 
    except:
        condition = 'Na'

    try:
        views = clean_data[np.where([(y[:14]=='NOMBRE DE VUES') for y in clean_data])[0][0]][14:].strip() 
    except:
        views = '0'

    try:
        interested = re.split(' ',clean_data[np.where([(y[:14]=='INTÉRESSÉS·ÉES') for y in clean_data])[0][0]][14:].strip())[0]
    except:
        interested = '0'

    try:
        location = clean_data[np.where([(y[:11]=='EMPLACEMENT') for y in clean_data])[0][0]][11:].strip() 
    except:
        location = 'Na'

    try:
        date = driver.find_element_by_xpath('/html/body/div[4]/div/section/div/div[2]/main/aside/div[1]/div[1]/div[2]/div[8]/div[2]/time').get_attribute('title')
    except:
        try:
            date = driver.find_element_by_xpath('/html/body/div[4]/div/section/div/div[2]/main/aside/div[1]/div[1]/div[2]/div[9]/div[2]/time').get_attribute('title')
        except:
            date = 'Na'

    try:
        description = clean_data[np.where([(y[:13]=='AJOUTÉ IL Y A') for y in clean_data])[0][0] + 1].strip() 
    except:
        description = 'Na'

    try:
        number = re.split('-',re.split('/', page_url)[-1])[0]
    except:
        number = 'Na'

    try:
        category = re.split('/', page_url)[-2]
    except:
        category = 'Na'

    return(pd.DataFrame({'Description':description,
                        'category': category,
                        'Prix': price,
                        'Marque':brand,
                        'Couleurs': colors,
                        'Taille':size,
                        'Etat':condition,
                        'Vues':views,
                        'Interesses':interested,
                        'Emplacement':location,
                        'Date':date,
   'Current_date' : datetime.now(),
                        'No':number,
                        'url':driver.current_url
                        }, index = [0]))


def interactive_version():
    # I - Ask query and criteria 
    query = input("Which item do you want to research on vinted.fr ? \n")

    print('\n Now a few criteria, you can skip any by pressing enter directly \n ')
    order_str = [input("Indicate order of search (1 choice max) among following choices : \n {} \n".format(dict_order.keys()))]
    order_str = [element for item in order_str for element in item.split(',')] 
    catalog_str = [input("Indicate catalog for search (1 choice max) among following choices : \n {} \n".format(dict_catalog.keys()))]
    catalog_str = [element for item in catalog_str for element in item.split(',')] 
    size_str = [input("Indicate size for search (no max choices) among following choices : \n {} \n".format(dict_size.keys()))]
    size_str = [element for item in size_str for element in item.split(',')] 
    color_str = [input("Indicate colors for search (2 choices max) among following choices : \n {} \n".format(dict_color.keys()))]
    color_str = [element for item in color_str for element in item.split(',')] 
    brand_str = [input("Indicate brand for search (no max choices) among following choices : \n {} \n".format(dict_brand.keys()))]
    brand_str = [element for item in brand_str for element in item.split(',')] 
    condition_str = [input("Indicate confitions for search (5 choices max) among following choices : \n {} \n".format(dict_condition.keys()))]
    condition_str = [element for item in condition_str for element in item.split(',')] 

    min_price, max_price = 'Na', 'Na'
    min_price = input("Do you want a minimal price ? ")
    max_price = input("Do you want a maximal price ? ")

    end_page = input('Until which page do you want to collect data? (max = 400)')
    if order_str == ['']:
        order_str = []
    if catalog_str == ['']:
        catalog_str = []
    if size_str == ['']:
        size_str = []
    if color_str == ['']:
        color_str = []
    if brand_str == ['']:
        brand_str = []
    if condition_str == ['']:
        condition_str = []
    
    criteria_string = define_criteria_string(order = order_str, catalog = catalog_str, size = size_str, color = color_str,\
     brand = brand_str, min_price = min_price, max_price = max_price, condition = condition_str)


    print(criteria_string)
    scraped_data = research_and_scrape(query, criteria_string,  end_page = end_page)