Web Scraping with GPT-4 Vision AI + Puppeteer is Mind-Blowingly EASY!

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⏱️ Timestamps:
00:00 Intro
00:48 ChatGPT for HTML
02:01 OpenAI API
04:08 Puppeteer
04:28 Avoid restrictions (Bright Data)
05:11 Get HTML with Puppeteer + Proxy
08:35 Processing HTML
10:00 Give HTML to OpenAI
12:19 ChatGPT for scraper code
14:41 Vision API
19:40 Vision API pricing
21:12 Vision vs Text pricing
22:52 Future of web scraping (AI + Bright Data)
#webdevelopment #programming #coding #reactjs #nextjs

[@hxxzxtf]

🎯 Key Takeaways for quick navigation: 00:00 *🌐 Web scraping has been revolutionized by AI, particularly with the latest Vision AI model, making data extraction more efficient.* 01:07 *💻 Manually copying HTML and using Chat GPT for extraction is one method, but OpenAI's API offers programmable solutions for scalability.* 02:16 *🔄 Using Puppeteer with Bright Data's scraping browser helps circumvent website restrictions and rate limiting during scraping.* 05:33 *🖥️ Puppeteer allows for easy scraping of HTML content, but there's a need to manage and clean up the extracted data before analysis.* 08:35 *💡 Extracting only necessary data from HTML can optimize costs when using OpenAI's models for analysis.* 12:17 *💰 Text-based scraping methods can be cost-effective, but they require ongoing maintenance due to HTML structure changes.* 14:49 *📸 Utilizing OpenAI's GPT-4 Vision API enables data extraction from screenshots, potentially offering a more robust solution for complex web scraping tasks.* 17:52 *🖼️ Using base64 encoding allows passing images to models, enhancing data processing capabilities.* 18:49 *💸 Consider cost-effectiveness when choosing between complex HTML-based or text-based approaches for web scraping.* 19:58 *🎚️ Adjusting image resolution can significantly decrease token usage in web scraping, but it may increase the likelihood of errors.* 20:53 *🖼️🔄 Balance image resolution and price when utilizing Vision API for web scraping, as higher resolution images incur higher costs.* 21:19 *🧹 Clean up HTML before web scraping to reduce token usage and ensure accuracy in results.* 22:57 *🤖 Explore advanced features of AI tools, such as identifying clickable elements, to enhance web scraping automation.* Made with HARPA AI

[@sidouglas]

This is such a timely video - i'm doing something similar to resurrect a website from the wayback machine.

[@nomicgaming5730]

thank you a lot ♥

[@reidevanson181]

what an amazing video - like its so niche but so useful

[@ByteGrad]

Glad you liked it

[@imranhrafi]

It's interesting, but what if I want pagination? I will still need to select next button in old way. Is there any other way of doing the pagination?

[@hellokevin_133]

Hey man, mind if I ask what programming languages you know other than Javascript/TS ?

[@hishamazmy8189]

amazing

[@juliushernandez9855]

Can you create a video how to deploy puppeteer and next js to vercel?

[@Garejoor]

can crewAI do this as well?

[@user-vo8kk8dv8l]

elegant

[@RobShocks]

Have you thought about or tried using a local model to scrape, it would save all the costs

[@Lars16]

This is a great video. But the problem with scraping has hardly ever been parsing the HTML or maintaining the parsers. The biggest problem is efficiently accessing websites that actively try to block you by gating their content being a login or captchas. Then comes IP blocking (or worse data obfuscation) if you Scrape their website in a large volume.

[@binhtruongdac2861]

That’s why you need smth like Bright Data, yes, it’s not free unfortunately

[@amitjangra6454]

I am scrapping (dropping html) with python code with selenium (aprrox 60,000 articles) and later creating vector embeddings for Llama 3 and asking it to write article for me.

[@subhranshudas8862]

how do you handle paginated data?

[@binhtruongdac2861]

You just need to use the URL with page number in query params then run for loop to request multiple html page

[@dmytroocheretianyi7577]

Perhaps it will be cheaper on Claude.

[@LifeTrekchannel]

How to do this using Braina AI? Braina can run GPT-4 Vision.

[@amadeuszg1491]

I am interested in creating a price comparison website featuring approximately 10-20 shops, each offering around 10,000 similar products. Unfortunately, these shops do not provide APIs for direct access to their data. What would be the most efficient approach to setting up such a website while keeping maintenance costs reasonable?

[@Braincompiler]

Make it like the other comparison sites and provide an upload for CSV, XML and so on or YOU provide the API for them so their shop systems can push the data ;) Crawling by yourself is the last option and could be made with XPath and stuff.

[@amadeuszg1491]

@@Braincompiler Yes, but in this case store needs to send me the csv, xml file with their products. What if they dont?

[@Braincompiler]

@@amadeuszg1491 Yes of course. If your comparison site has a benefit for them be sure they will.

[@abhisycvirat]

I did this 6 years ago, scraped each website and compared the price using SKU

[@UserAliyev]

First

[@semyaza555]

2nd