poshmark/posh.py

import requests
from bs4 import BeautifulSoup as b
import time
import re
import concurrent.futures
import numpy as np
# import matplotlib.pyplot as plt


def url_base_builder(search_query):
    genders = ['Men', 'Women']

    posh_colors = ['Red', 'Pink', 'Orange', 'Yellow', 'Green', 'Blue', 'Purple',
                   'Gold', 'Silver', 'Black', 'Gray', 'White', 'Cream', 'Brown', 'Tan']

    for i in range(0, len(posh_colors)):
        if posh_colors[i] in search_query:
            url_color = '&color[]=' + posh_colors[i]
            color = posh_colors[i]
            break
        else:
            color = ''
            url_color = ''
    for i in range(0, len(genders)):
        if genders[i] in search_query:
            url_gender = '&department=' + genders[i]
            gender = genders[i]
            break
        else:
            gender = ''
            url_gender = '&department=All'

    sq = search_query.replace(color, '').replace(gender, '').replace('NEW', '').replace(' ', '+')

    all_sold_url_base = 'https://poshmark.com/search?query=' + sq + \
        "&availability=sold_out" + url_color + url_gender + '&max_id='

    new_sold_url_base = 'https://poshmark.com/search?query=' + sq + '&availability=sold_out' + \
        '&condition=nwt_and_ret' + url_color + url_gender + '&max_id='

    return all_sold_url_base, new_sold_url_base


def all_sold_list_builder(i):
    bases = url_base_builder(search_query)
    all_sold_url_base = bases[0]
    all_sold_prices = []
    url = all_sold_url_base + str(i)
    html = requests.get(url).text
    soup = b(html, "lxml")
    # last_page = soup.find(string = re.compile('No Listings Found'))
    for price in soup.find_all('span', {'class': 'p--t--1 fw--bold'}):
        price = price.get_text()
        dollar_index = price.find('$')
        price = price[dollar_index+1:]
        space = price.find(' ')
        price = int(price[:space-1])
        all_sold_prices.append(price)
    return all_sold_prices


def new_sold_list_builder(i):
    bases = url_base_builder(search_query)
    new_sold_url_base = bases[1]
    new_sold_prices = []
    url = new_sold_url_base + str(i)
    html = requests.get(url).text
    soup = b(html, "lxml")
    # last_page = soup.find(string = re.compile('No Listings Found'))#this is present in all pages that don't have a full 48 listings on them. So you end up with an empty price list becuase of your conditional statement

    for price in soup.find_all('span', {'class': 'p--t--1 fw--bold'}):
        price = price.get_text()
        dollar_index = price.find('$')
        price = price[dollar_index+1:]
        space = price.find(' ')
        price = int(price[:space-1])
        new_sold_prices.append(price)
    return new_sold_prices


def main():

    search_query = str(input('Title Search: '))

    start = time.time()

    page_list = list(range(1, 2))
    all_sold_list = []
    new_sold_list = []

    with concurrent.futures.ThreadPoolExecutor() as executor:
        for future in executor.map(all_sold_list_builder, page_list):
            all_sold_list.extend(future)

    with concurrent.futures.ThreadPoolExecutor() as executor:
        for future in executor.map(new_sold_list_builder, page_list):
            new_sold_list.extend(future)# if you can pull the nwt price simultaneously with used then you won't have to use this


    for element in new_sold_list:
        all_sold_list.remove(element)
    used_sold_list = all_sold_list

    average_used_sold_price = '$' + str(round(np.mean(used_sold_list), 2))
    average_new_sold_price = '$' + str(round(np.mean(new_sold_list), 2))

    used_sold_results = str(len(used_sold_list)) + ' Used Results'
    new_sold_results = str(len(new_sold_list)) + ' NWT Results'
    total_results = str(len(used_sold_list) + len(new_sold_list)) + ' Total Results'

    end = time.time()

    print(end - start, 'seconds')

    print('Average Used Sold Price', average_used_sold_price, used_sold_results)
    print('Average New Sold Price', average_new_sold_price, new_sold_results)
    print(total_results)

main()