Datasets:
File size: 5,259 Bytes
ead407c 3ea6a16 ead407c 3ea6a16 ead407c 3ea6a16 ead407c 3ea6a16 ead407c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import json
import random
import re
import time
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
from project_settings import project_path
pattern_without_rating = r"""
name":"(.*)","image":\[(?:.*)\],"brand":"(.*)","review":"(.*)","description":"(.*)","mpn":"(.*)","color":"(.*)","size":"(.*)","sku":"(.*?)".*,"offers":{"@type":"Offer","availability":"(?:.*)","priceCurrency":"(.*)","price":"(.*)","priceValidUntil":"(.*)","url
"""
pattern_without_rating = str(pattern_without_rating).strip()
pattern_review = r"""
aggregateRating":(?:.*)ratingValue":"(.*)","reviewCount":"(.*)"},"offers
"""
pattern_review = str(pattern_review).strip()
pattern_description = r"""
<div class="des-title-div">.*<h2 class="des-title">
"""
pattern_description = str(pattern_description).strip()
def parse_text(text: str):
match = re.search(pattern_without_rating, text, flags=re.IGNORECASE)
title = match.group(1)
brand = match.group(2)
review = match.group(3)
description = match.group(4)
mpn = match.group(5)
sku = match.group(8)
price = match.group(10)
match = re.search(pattern_review, text, flags=re.IGNORECASE)
if match is not None:
rating_value = match.group(1)
review_count = match.group(2)
else:
rating_value = None
review_count = None
soup = BeautifulSoup(text, features="html.parser")
matches = soup.find_all(class_="description-item")
result = list()
for match in matches:
match_text = match.text.strip()
match_text = str(match_text).strip().split("\n")
for row in match_text:
row = str(row).strip()
if len(row) == 0:
continue
if row in ("Size Chart", "Photos"):
break
result.append(row)
overview = "\n".join(result)
matches = soup.find_all(class_="breadcrumbAB")
result = list()
for match in matches:
match_text = match.text.strip()
match_text = str(match_text).strip().split("\n")
for row in match_text:
row = str(row).strip()
if len(row) == 0:
continue
result.append(row)
category = "".join(result)
category = " > ".join(category.split(">"))
match_select_color = soup.find("select", class_="select-color-show")
matches = match_select_color.find_all("option")
color = list()
for match in matches:
match_text = match.text.strip()
if match_text == "Color":
continue
color.append(match_text)
match_selects = soup.find_all("select", class_="attr-list-select")
color_size = [[], []]
color_size_idx = None
for match_select in match_selects:
matches = match_select.find_all("option")
for idx, match in enumerate(matches):
match_text = match.text.strip()
if idx == 0:
if match_text == "Color":
color_size_idx = 0
elif match_text == "Size":
color_size_idx = 1
else:
break
continue
color_size[color_size_idx].append(match_text)
color = color_size[0]
size = color_size[1]
row = {
"title": title,
"brand": brand,
"review": str(review).strip() if len(str(review).strip()) != 0 else None,
"description": description,
"mpn": mpn,
"color": color,
"size": size,
"sku": sku,
"ratingValue": rating_value,
"reviewCount": review_count,
"overview": overview,
"category": category,
"url": "https://www.lightinthebox.com/en/p/_p{}.html".format(mpn)
}
return row
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
}
finished_mpn = set()
output_file = project_path / "data/product.jsonl"
with open(output_file, "r", encoding="utf-8") as f:
for row in f:
row = str(row).strip()
row = json.loads(row)
mpn = row["mpn"]
finished_mpn.add(mpn)
print("finished count: {}".format(len(finished_mpn)))
sleep_time = 1
for mpn in tqdm(range(9156615, 9999999)):
# mpn = random.randint(9000000, 9999999)
if mpn in finished_mpn:
continue
finished_mpn.add(mpn)
url = "https://www.lightinthebox.com/en/p/_p{}.html".format(mpn)
print("url: {}".format(url))
try:
time.sleep(2)
resp = requests.get(url, headers=headers, timeout=2)
except Exception:
print("sleep: {}".format(sleep_time))
time.sleep(sleep_time)
sleep_time += 1
continue
text = resp.text
try:
row = parse_text(text)
except Exception:
continue
sleep_time = 1
row = json.dumps(row, ensure_ascii=False)
with open(output_file, "a+", encoding="utf-8") as f:
f.write("{}\n".format(row))
f.flush()
print(row)
if __name__ == '__main__':
pass
|