Tired Of Web Scraping? Make The AI Do It

April 9, 2023 by Donald Papp 19 Comments

[James Turk] has a novel approach to the problem of scraping web content in a structured way without needing to write the kind of page-specific code web scrapers usually have to deal with. How? Just enlist the help of a natural language AI. Scrapeghost relies on OpenAI’s GPT API to parse a web page’s content, pull out and classify any salient bits, and format it in a useful way.

What makes Scrapeghost different is how data gets organized. For example, when instantiating scrapeghost one defines the data one wishes to extract. For example:

from scrapeghost import SchemaScraper
scrape_legislators = SchemaScraper(
schema={
"name": "string",
"url": "url",
"district": "string",
"party": "string",
"photo_url": "url",
"offices": [{"name": "string", "address": "string", "phone": "string"}],
}
)

The kicker is that this format is entirely up to you! The GPT models are very, very good at processing natural language, and scrapeghost uses GPT to process the scraped data and find (using the example above) whatever looks like a name, district, party, photo, and office address and format it exactly as requested.

It’s an experimental tool and you’ll need an API key from OpenAI to use it, but it has useful features and is certainly a novel approach. There’s a tutorial and even a command-line interface, so check it out.

“Lazier” Web Scraping Is Better Web Scraping

February 7, 2022 by Donald Papp 13 Comments

Ever needed to get data from a web page? Parsing the content for data is called web scraping, and [Doug Guthrie] has a few tips for making the process of digging data out of a web page simpler and more efficient, complete with code examples in Python. He uses getting data from Yahoo Finance as an example, because it’s apparently a pretty common use case judging by how often questions about it pop up on Stack Overflow. The general concepts are pretty widely applicable, however.

[Doug] shows that while parsing a web page for a specific piece of data (for example, a stock price) is not difficult, there are sometimes easier and faster ways to go about it. In the case of Yahoo Finance, the web page most of us look at isn’t really the actual source of the data being displayed, it’s just a front end.

One way to more efficiently scrape data is to get to the data’s source. In the case of Yahoo Finance, the data displayed on a web page comes from a JavaScript variable that is perfectly accessible to the end user, and much easier to parse and work with. Another way is to go one level lower, and retrieve JSON-formatted data from the same place that the front-end web page does; ignoring the front end altogether and essentially treating it as an unofficial API. Either way is not only easier than parsing the end result, but faster and more reliable, to boot.

How does one find these resources? [Doug] gives some great tips on how exactly to do so, including how to use a web browser’s developer tools to ferret out XHR requests. These methods won’t work for everything, but they are definitely worth looking into to see if they are an option. Another resource to keep in mind is woob (web outside of browsers), which has an impressive list of back ends available for reading and interacting with web content. So if you need data for your program, but it’s on a web page? Don’t let that stop you!

Hack The Web Without A Browser

January 17, 2022 by Al Williams 20 Comments

It is a classic problem. You want data for use in your program but it is on a webpage. Some websites have an API, of course, but usually, you are on your own. You can load the whole page via HTTP and parse it. Or you can use some tools to “scrape” the site. One interesting way to do this is woob — web outside of browsers.

The system uses a series of backends tailored at particular sites. There’s a collection of official backends, and you can also create your own. Once you have a backend, you can configure it and use it from Python. Here’s an example of finding a bank account balance:

>>> from woob.core import Woob
>>> from woob.capabilities.bank import CapBank
>>> w = Woob()
>>> w.load_backends(CapBank)
{'societegenerale': <Backend 'societegenerale'>, 'creditmutuel': <Backend 'creditmutuel'>}
>>> pprint(list(w.iter_accounts()))
[<Account id='7418529638527412' label=u'Compte de ch\xe8ques'>,
<Account id='9876543216549871' label=u'Livret A'>,
<Account id='123456789123456789123EUR' label=u'C/C Eurocompte Confort M Roger Philibert'>]
>>> acc = next(iter(w.iter_accounts()))
>>> acc.balance
Decimal('87.32')

The list of available backends is impressive, but eventually, you’ll want to create your own modules. Thankfully, there’s plenty of documentation about how to do that. The framework allows you to post data to the website and easily read the results. Each backend also has a test which can detect if a change in the website breaks the code, which is a common problem with such schemes.

We didn’t see a Hackaday backend. Too bad. There are, however, many application examples, both console-based and using QT. For example, you can search for movies, manage recipes, or dating sites.

Of course, there are many approaches possible to this problem. Maybe you need to find out when the next train is leaving.

Job Application Script Automates The Boring Stuff With Python

May 30, 2020 by Sven Gregori 15 Comments

Job hunting can certainly require a good amount of hoop-jumping in today’s age. Even if you’re lucky enough to have your application read by an actual human, there’s no guarantee the person on the other end has much of an understanding about your skill set. Oftentimes, the entire procedure is futile from the start, and as a recent graduate, [harshibar] is well aware of the soul-crushing experience investing a lot of time in it can be. Well, as the saying goes: if you can’t beat them, join them — and if you can’t join them, automate the hell out of the application process.

As the final piece of a “5 Python Projects in 5 Days” challenge [harshibar] set for herself — which also spawned a “Tinder for Netflix” for the web development section of it — she essentially created a web-scraper that gathers job openings for a specific search term, and automatically sends an application to each and every one of them. Using Beautiful Soup to parse the scraped pages of a certain job portal, Selenium’s browser automation functionality to fill out the online application forms, she can get all her information into the form saving countless hours in comparison to the manual alternative. The program even hits the apply button.

While the quantity-over-quality approach may not be for everyone, there’s of course room for more filtering and being more selective about the job openings beforehand, which [harshibar] also addresses in her video about the project (embedded below). And while this won’t fix the application process itself, we can definitely see the satisfaction a beating-them-at-their-own-game might provide — plus, it can’t have a worse miss rate than your typical LinkedIn “recruiter”. Still, if you’re looking for a more systematic approach, have a look at [Lewin Day]’s view on the subject, he even has advice job hunting is still further down the road for you.

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Web Scraping Amazon And Rotten Tomatoes

January 23, 2013 by Mike Szczys 29 Comments

web-scraping-amazon-and-rotten-tomatos

[Rajesh] put web scraping to good use in order to gather the information important to him. He’s published two posts about it. One scrapes Amazon daily to see if the books he wants to read have reached a certain price threshold. The other scrapes Rotten Tomatoes in order to display the audience score next to the critics score for the top renting movies.

Web scraping uses scripts to gather information programmatically from HTML rather than using an API to access data. We recently featured a conceptual tutorial on the topic, and even came across a hack that scraped all of our own posts. [Rajesh’s] technique is pretty much the same.

He’s using Python scripts with the Beautiful Soup module to parse the DOM tree for the information he’s after. In the case of the Amazon script he sets a target price for a specific book he’s after and will get an email automatically when it gets there. With Rotten Tomatoes he sometimes likes to see the audience score when considering a movie, but you can’t get it on the list at the website; you have to click through to each movie. His script keeps a database so that it doesn’t continually scrape the same information. The collected numbers are displayed alongside the critics scores as seen above.

Picture Frame That Scrapes Train Times From The Web

January 8, 2013 by Mike Szczys 22 Comments

rpi-train-times-fixture

Whenever [Gareth James] needs to catch a train he has only to push a button on this frame and the next three departure times will be displayed. As you can see from the post-processing in the photo, this is accomplished by a Raspberry Pi board using a few familiar tools.

Let’s take a look at the hardware first. He acquired a 7″ LCD display which he removed from its plastic case. The bare screen will easily fit inside of the rather deep wood frame and its composite video input makes it quite simple to interface with the RPi board. There was a little work to be done for power. The LCD needs 12V so he’s using a 12V wall wart to feed the frame, and including a USB car charger to power the RPi. The last thing he added is a button connected to the GPIO header to tell the system to fetch a new set of times.

A Python script monitors the button and uses Beautiful Soup to scrape the train info off of a website. To get the look he wanted [Gareth] wrote a GUI using tkinter. Don’t miss the demo after the jump.

If you need a bit of a primer on scraping web data take a look at this guide.

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Web Scraping Tutorial

December 10, 2012 by Mike Szczys 36 Comments

Web scraping is the act of programmatically harvesting data from a webpage. It consists of finding a way to format the URLs to pages containing useful information, and then parsing the DOM tree to get at the data. It’s a bit finicky, but our experience is that this is easier than it sounds. That’s especially true if you take some of the tips from this web scraping tutorial.

It is more of an intermediate tutorial as it doesn’t feature any code. But if you can bring yourself up to speed on using BeautifulSoup and Python the rest is not hard to implement by trial and error. [Hartley Brody] discusses investigating how the GET requests are formed on your webpage of choice. Once that URL syntax has been figured out just look through the source code for tags (css or otherwise) that can be used as hooks to get at your target data.

So what can this be used for? A lot of things. We’d suggest reading the Reddit comments as there are several real world uses discussed there. But one that immediately pops to mind is the picture harvesting [Mark Zuckerburg] used when he created Facemash.