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import scrapy
from scrapy.crawler import CrawlerProcess
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor
from scrapy.exceptions import CloseSpider
import os
# Maximum file size in GB before scraper is closed
MAX_FILE_SIZE_GB = float(os.environ.get("SCRAPY_MAX_FILE_SIZE_GB", 0.5))
# Maximum number of concurrent requests
MAX_CONCURRENT_REQUESTS = int(os.environ.get("SCRAPY_MAX_CONCURRENT_REQUESTS", 8))
# Maximum concurrent requests per domain
MAX_REQUESTS_PER_DOMAIN = int(os.environ.get("SCRAPY_MAX_REQUESTS_PER_DOMAIN", 4))
# Output CSV File
OUTPUT_FILE = os.environ.get("SCRAPY_OUTPUT_FILE", "computerscience_data.csv")
class ComputerScienceSpyder(CrawlSpider):
"""
Main Scrapy Spider to extract information about computer science pages.
"""
name = 'computerscience_data'
allowed_domains = ['c-sharpcorner.com', 'wikipedia.org', 'javatpoint.com']
custom_settings = {
'CONCURRENT_REQUESTS': MAX_CONCURRENT_REQUESTS,
'CONCURRENT_REQUESTS_PER_DOMAIN': MAX_REQUESTS_PER_DOMAIN,
'DOWNLOAD_DELAY': 1.0,
'AUTOTHROTTLE_ENABLED': True,
'AUTOTHROTTLE_START_DELAY': 2,
'AUTOTHROTTLE_TARGET_CONCURRENCY': MAX_CONCURRENT_REQUESTS,
'HTTPCACHE_ENABLED': True,
'HTTPCACHE_EXPIRATION_SECS': 1800,
}
def __init__(self, *args, **kwargs):
super(ComputerScienceSpyder, self).__init__(*args, **kwargs)
# Set to prevent duplicate pages
self.scraped_urls = set()
def parsing_data_func(self, result):
# Check if we've scraped this URL already
if result.url in self.scraped_urls:
return
self.scraped_urls.add(result.url)
# Extract page text
content = ' '.join(result.xpath('//p/text()').getall()).strip()
# Extract image source URLs
src_list = result.css('img::attr(src)').extract()
image_urls = []
for url in src_list:
full_url = result.urljoin(url)
image_urls.append(full_url)
yield {
'Domain': result.url.split('/')[2],
'Title': result.css('title::text').get(),
'ParsedContent': content,
'ImageResourceLocator': '|'.join(image_urls),
'ResourceLocator': result.url,
}
file_size_bytes = os.path.getsize(OUTPUT_FILE)
bytes_per_gigabyte = 1024 * 1024 * 1024
file_size_gigabytes = file_size_bytes / bytes_per_gigabyte
print(f"The file size is {file_size_gigabytes} GB")
# Close if enough data is scraped
if file_size_gigabytes >= MAX_FILE_SIZE_GB:
raise CloseSpider("Done with Crawling")
rules = (
# Follow all links we encounter
Rule(LinkExtractor(allow=()), callback='parsing_data_func', follow=True),
)
process = CrawlerProcess(settings={
'USER_AGENT': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.99 Safari/537.36',
'ROBOTSTXT_OBEY': True,
'FEEDS': {
OUTPUT_FILE: {
'format': 'csv',
'fields': ['Domain', 'Title', 'ParsedContent', 'ImageResourceLocator', 'ResourceLocator'],
},
},
})
# Start the crawling process for each domain
process.crawl(ComputerScienceSpyder, start_urls=[
'https://www.javatpoint.com/javascript-tutorial'
'https://www.javatpoint.com/c-programming-language-tutorial'
'https://www.javatpoint.com/cloud-computing'
'https://www.javatpoint.com/ajax-tutorial'
'https://www.javatpoint.com/json-tutorial'
'https://en.wikipedia.org/wiki/BERT_(language_model)'
'https://en.wikipedia.org/wiki/Computer_vision'
'https://www.c-sharpcorner.com/interview-questions-by-technology/android-programming'
'https://www.c-sharpcorner.com/interview-questions-by-technology/dot_net_2015',
'https://www.c-sharpcorner.com/interview-questions-by-technology/android-programming',
'https://www.c-sharpcorner.com/interview-questions-by-technology/databases-and-dba',
'https://www.c-sharpcorner.com/interview-questions-by-technology/ios',
'https://en.wikipedia.org/wiki/C_Sharp_(programming_language)',
'https://en.wikipedia.org/wiki/C%2B%2B',
'https://en.wikipedia.org/wiki/U-Net',
])
process.start()
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