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from typing import List
import csv
from pathlib import Path
import re
###################################Part for fetching testing - ignore#######################################################
# file_path = Path("data/raw/Artworks.csv")
#
# def normalize_nationality(raw: str) -> List[str]:
# # extract all text inside parentheses
# entries = re.findall(r"\((.*?)\)", raw)
# # strip and remove empty entries
# entries = [e.strip() for e in entries if e.strip()]
# return entries
#
# def extract_nationalities(csv_path: str):
# nationalities = set()
#
# with open(csv_path, mode='r', encoding='utf-8') as f:
# reader = csv.DictReader(f)
# for row in reader:
# raw_nat = row.get("Nationality", "").strip()
# if not raw_nat:
# continue
#
# extracted = normalize_nationality(raw_nat)
# nationalities.update(extracted)
#
# return sorted(nationalities)
#
# def fetch_nationalities():
# nationalities = extract_nationalities(file_path)
#
# print(f"Found {len(nationalities)} unique nationalities:\n")
# for nat in nationalities:
# print(nat)
#
# from collections import defaultdict
# def count_classifications(csv_path: str):
# classification_counts = defaultdict(int)
# total_items = 0
#
# with open(csv_path, mode='r', encoding='utf-8') as f:
# reader = csv.DictReader(f)
# for row in reader:
# total_items += 1
# classification = row.get("Classification", "").strip()
# if classification:
# classification_counts[classification] += 1
#
# return total_items, classification_counts
#
#
# def fetch_classifications():
# file_path = Path("data/raw/Artworks.csv") # ✅ adjust path if needed
#
# total_items, classification_counts = count_classifications(file_path)
#
# print(f"Total items: {total_items}")
# print(f"Found {len(classification_counts)} unique classifications:\n")
#
# # Sort by count descending
# sorted_counts = sorted(classification_counts.items(), key=lambda x: -x[1])
#
# for classification, count in sorted_counts:
# print(f"{classification}: {count} items")
#
#
# def extract_departments(csv_path: str):
# departments = defaultdict(int)
# total_items = 0
#
# with open(csv_path, mode='r', encoding='utf-8') as f:
# reader = csv.DictReader(f)
# for row in reader:
# total_items += 1
# department = row.get("Department", "").strip()
# if department:
# departments[department] += 1
#
# return total_items, departments
#
#
# def fetch_departments():
# file_path = Path("data/raw/Artworks.csv") # ✅ your real path
#
# total_items, departments = extract_departments(file_path)
#
# print(f"Total items: {total_items}")
# print(f"Found {len(departments)} unique departments:\n")
#
# # Sort by count descending
# sorted_counts = sorted(departments.items(), key=lambda x: -x[1])
#
# for department, count in sorted_counts:
# print(f"{department}: {count} items")
#
# def extract_mediums(csv_path: str):
# mediums = defaultdict(int)
# total_items = 0
#
# with open(csv_path, mode='r', encoding='utf-8') as f:
# reader = csv.DictReader(f)
# for row in reader:
# total_items += 1
# medium = row.get("Medium", "").strip()
# if medium:
# mediums[medium] += 1
#
# return total_items, mediums
#
# def fetch_mediums():
# file_path = Path("data/raw/Artworks.csv") # ✅ your actual path here
#
# total_items, mediums = extract_mediums(file_path)
#
# print(f"Total items: {total_items}")
# print(f"Found {len(mediums)} unique mediums:\n")
#
# # Sort by count descending
# sorted_counts = sorted(mediums.items(), key=lambda x: -x[1])
#
# for medium, count in sorted_counts:
# if count > 40: # ✅ Only output mediums with more than 40 items
# print(f"{medium}: {count} items")
#########################################################################################################
from curl_cffi import requests
from curl_cffi.requests import BrowserType
import json, ssl, time, math, warnings, os, re
from urllib.parse import unquote, urljoin
from pyquery import PyQuery as pq
import pandas as pd
import inspect
ssl._create_default_https_context = ssl._create_unverified_context
warnings.filterwarnings("ignore")
class DzSpider(object):
def __init__(self):
self.all_objects = []
self.data = {}
self.folder = fr'{os.getcwd()}'
self.start_page = 1
self.end_page = 50
self.spider_num = 1
self.page_size = 40
self.has_finish = False
self.reset_end_page = True
self.session = None
self.headers = {
'accept': '*/*',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
'cache-control': 'no-cache',
'content-type': 'text/plain',
'pragma': 'no-cache',
'referer': 'https://www.moma.org/collection/',
'sec-ch-ua': '"Google Chrome";v="137", "Chromium";v="137", "Not/A)Brand";v="24"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/137.0.0.0 Safari/537.36',
'x-requested-with': 'XMLHttpRequest'
}
def init_session(self):
"""Initialize session with proper configuration"""
# Try different browser types in order of preference
browser_types = [
'chrome120',
'chrome110',
'chrome101',
'chrome99',
'safari15_5',
'edge99'
]
for browser_type in browser_types:
try:
if hasattr(BrowserType, browser_type):
self.session = requests.Session(
impersonate=getattr(BrowserType, browser_type),
timeout=30
)
print(f"Session initialized successfully with {browser_type}")
return True
except Exception as e:
print(f"Failed to initialize with {browser_type}: {e}")
continue
# Fallback: try without impersonate
try:
self.session = requests.Session(timeout=30)
print("Session initialized successfully (fallback mode)")
return True
except Exception as e:
print(f"Failed to initialize session completely: {e}")
return False
def run_task(self):
if not self.init_session():
print("Failed to initialize session, exiting...")
return
page_index = self.start_page
self.reset_end_page = True
self.has_finish = False
print(f"Starting scraper from page {self.start_page} to {self.end_page}")
while page_index <= self.end_page:
try:
print(f"\n{'=' * 50}")
print(f"Processing page {page_index}")
print(f"{'=' * 50}")
objects = self.get_one_page(page_index)
if not objects:
print("No more objects found. Stopping.")
break
self.all_objects.extend(objects)
print(f"✅ Scraped {len(objects)} items on page {page_index}")
page_index += 1
time.sleep(1)
except KeyboardInterrupt:
print("\nScraping interrupted by user")
break
except Exception as e:
print(f"Error processing page {page_index}: {e}")
break
print(f"\nScraping completed! Total items processed: {self.spider_num - 1}")
def get_one_page(self, page_index: int) -> List[DesignObject]:
req_url = "https://www.moma.org/collection/"
params = {
"classifications": "37",
"date_begin": "1960",
"date_end": "2010",
"include_uncataloged_works": "false",
"on_view": "false",
"recent_acquisitions": "false",
"with_images": "true",
"page": page_index,
}
print(f"Fetching page {page_index} …")
html = self.session.get(req_url, headers=self.headers, params=params).text
doc = pq(html)
items = doc("li a")
if items.length == 0:
print(f"⚠️ Page {page_index} returned no items.")
return []
objects: list[DesignObject] = []
for i, a in enumerate(items.items(), 1):
href = urljoin(req_url, a.attr("href") or "")
makers = a("div>div:eq(1) p:eq(0)").text()
title = a("div>div:eq(1) p:eq(1)").text()
year = a("div>div:eq(1) p:eq(2)").text()
detail = self.get_detail(href)
obj = DesignObject(
name=title,
year=year, # raw string
classification=detail["classification"],
dimension=detail["dimension"],
makers=[makers], # raw, unsplit
image_urls=detail["image_urls"],
country="USA",
source="https://www.moma.org/",
)
objects.append(obj)
# print(f"✔ {i}/{len(items)} {obj}")
return objects
def get_detail(self, href: str) -> dict:
detail = {
"image_urls": [],
"dimension": "N/A",
"classification": "N/A", # department → classification
}
try:
resp = self.session.get(href, headers=self.headers, timeout=30)
resp.raise_for_status()
doc = pq(resp.text)
# -------- images before <h1> -----------
main = doc("#main") or doc
title = main.find("h1").eq(0)
seen = set()
for node in main.find("*"):
if node is title[0]:
break
for img in pq(node).find("img").items():
src = img.attr("src")
if src and src not in seen:
seen.add(src)
detail["image_urls"].append(urljoin(href, src))
# -------- dimensions --------
dim_dd = (
doc('#caption dt:contains("Dimensions")').next("dd")
or doc('dt:contains("Dimensions")').next("dd")
)
if dim_dd:
detail["dimension"] = dim_dd.text().strip()
# -------- department classification -------------
dept_dd = (
doc('#caption dt:contains("Department")').next("dd")
or doc('dt:contains("Department")').next("dd")
)
if dept_dd:
detail["classification"] = dept_dd.text().strip()
except Exception as exc:
print(f"Error scraping {href}: {exc}")
return detail
def save_design_objects_to_xlsx(objects: List[DesignObject], delimiter: str = "|||"):
# Resolve ../../data relative to current file
data_dir = Path(__file__).resolve().parent.parent.parent / "data" / "metadata"
data_dir.mkdir(parents=True, exist_ok=True)
# Get current Python filename without extension
script_name = Path(inspect.stack()[-1].filename).stem
filename = f"{script_name}.xlsx"
output_path = data_dir / filename
# Flatten DesignObjects
rows = []
for obj in objects:
rows.append({
"name": obj.name,
"year": obj.year,
"classification": obj.classification,
"dimension": obj.dimension,
"makers": delimiter.join(obj.makers),
"image_urls": delimiter.join(obj.image_urls),
"country": obj.country,
"price": obj.price or "",
"popularity": obj.popularity or "",
"source": obj.source or "",
})
pd.DataFrame(rows).to_excel(output_path, index=False)
print(f"✅ Saved {len(rows)} records to: {output_path}")
if __name__ == '__main__':
print("Starting MoMA Collection Scraper")
spider = DzSpider()
spider.run_task()
save_design_objects_to_xlsx(spider.all_objects)
print("\nScraper finished!") |