What programming languages can I use to execute web scraping tasks with GPT prompts?

You can use a variety of programming languages to execute web scraping tasks with GPT (Generative Pre-trained Transformer) prompts. The choice of language often depends on the specific requirements of the project, such as the complexity of the task, performance needs, and the experience of the developer. Below are several popular programming languages that are commonly used for web scraping along with GPT prompts:

Python

Python is the most popular language for web scraping due to its simplicity and the powerful libraries available for both web scraping (e.g., requests, BeautifulSoup, lxml, Scrapy) and interacting with AI models like GPT (e.g., openai or transformers by Hugging Face). Here's a simple example using Python with requests and BeautifulSoup for scraping, and openai for GPT prompts:

import requests
from bs4 import BeautifulSoup
import openai

# Set up GPT prompt
gpt_prompt = "Summarize the following text:"

# Web scraping
url = 'https://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
text_to_summarize = soup.get_text()

# Generate summary with GPT
openai.api_key = 'your-api-key'
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=gpt_prompt + text_to_summarize,
  max_tokens=50
)

print(response.choices[0].text.strip())

JavaScript (Node.js)

JavaScript, with Node.js, is another excellent choice for web scraping, especially for web applications that require real-time data extraction. Libraries like axios for HTTP requests, cheerio for parsing HTML, and puppeteer for controlling headless browsers are widely used. For GPT, you can use the openai npm package.

const axios = require('axios');
const cheerio = require('cheerio');
const { Configuration, OpenAIApi } = require('openai');

const url = 'https://example.com';

// Web scraping
axios.get(url).then(response => {
  const $ = cheerio.load(response.data);
  const textToSummarize = $('body').text();

  // GPT prompt
  const gptPrompt = 'Summarize the following text:';

  // Configure OpenAI
  const configuration = new Configuration({
    apiKey: 'your-api-key',
  });
  const openai = new OpenAIApi(configuration);

  // Generate summary with GPT
  openai.createCompletion({
    model: "text-davinci-003",
    prompt: gptPrompt + textToSummarize,
    max_tokens: 50,
  }).then(response => {
    console.log(response.data.choices[0].text.trim());
  });
});

Ruby

Ruby, with its elegant syntax, is also used for web scraping tasks. Gems like nokogiri for HTML parsing and httparty for making HTTP requests are popular. For GPT, you can use the openai gem.

require 'nokogiri'
require 'httparty'
require 'openai'

# Web scraping
url = 'https://example.com'
response = HTTParty.get(url)
document = Nokogiri::HTML(response.body)
text_to_summarize = document.text

# GPT prompt
gpt_prompt = "Summarize the following text:"

# Configure OpenAI
OpenAI.api_key = 'your-api-key'

# Generate summary with GPT
response = OpenAI::Completion.create(
  engine: "text-davinci-003",
  prompt: gpt_prompt + text_to_summarize,
  max_tokens: 50
)

puts response['choices'][0]['text'].strip

PHP

PHP is not as common for web scraping as the other languages mentioned, but it is still a viable option. Libraries such as Guzzle for HTTP requests and Symfony DomCrawler for HTML parsing are useful. For GPT, you would typically interact with the OpenAI API using HTTP requests since there might not be a dedicated PHP library.

<?php
// Assuming you have composer installed and have required guzzle and symfony/dom-crawler
use GuzzleHttp\Client;
use Symfony\Component\DomCrawler\Crawler;

$client = new Client();

// Web scraping
$url = 'https://example.com';
$response = $client->request('GET', $url);
$htmlContent = (string) $response->getBody();
$crawler = new Crawler($htmlContent);
$textToSummarize = $crawler->filter('body')->text();

// GPT prompt
$gptPrompt = "Summarize the following text:";

// OpenAI API request
$apiKey = 'your-api-key';
$openaiClient = new Client([
    'base_uri' => 'https://api.openai.com/v1/engines/text-davinci-003/completions',
    'headers' => ['Authorization' => "Bearer $apiKey"],
]);
$gptResponse = $openaiClient->request('POST', '', [
    'json' => [
        'prompt' => $gptPrompt . $textToSummarize,
        'max_tokens' => 50,
    ],
]);

$summary = json_decode($gptResponse->getBody(), true)['choices'][0]['text'];
echo trim($summary);
?>

In each case, you need to ensure that you're following ethical guidelines and obeying the terms of service of the websites you're scraping, as well as being mindful of any legal implications. Use these languages and tools responsibly, and always respect the privacy and copyright of the content owners.