Proper right here is an occasion of implementing web crawling using Python, specializing in scraping Google search outcomes with Scrapy:
pip arrange beautifulsoup4
pip arrange scrapy
import scrapy
from scrapy.exporters import CsvItemExporterclass GoogleSpider(scrapy.Spider):
establish = "google"
allowed_domains = ["google.com"]
start_urls = [
"https://www.google.com/search?q=python",
]
def parse(self, response):
exporter = CsvItemExporter(open('outcomes.csv', 'wb'))
for finish in response.css('div.g'):
data = {
'title': consequence.css('h3::textual content material').extract_first(),
'url': consequence.css('h3 a::attr(href)').extract_first(),
'description': consequence.css('span.st::textual content material').extract_first(),
}
exporter.export_item(data)
yield data
To run the crawler:
scrapy crawl google
Since Clojure runs on the Java Digital Machine, Java libraries could be utilized for web crawling. Below is an occasion using clj-http for HTTP requests and jsoup for HTML parsing:
(ns myapp.crawler
(:require [clj-http.client :as http]
[org.jsoup.Jsoup :as jsoup]))(defn get-page [url]
(let [response (http/get url)]
(if (= (:standing response) 200)
(:physique response)
(throw (ex-info "Didn't retrieve net web page" {:url url})))))
(defn extract-data [html]
(let [doc (jsoup/parse html)]
(map #(str (.textual content material %) ", " (.attr % "href")) (.select doc "a"))))
(let [url "https://www.example.com"
html (get-page url)
data (extract-data html)]
(println data))
Web crawling is a versatile method essential for quite a few generative AI capabilities. By leveraging static and dynamic web crawling, along with API-based methods, big portions of data could be gathered for AI teaching and analysis. Integrating AI utilized sciences enhances the effectivity and accuracy of web crawling, making it a helpful instrument for creating refined AI fashions.
