چگونه هوش مصنوعی در ایجاد نقص وب: تکنیک ها و نمونه های کد متحول می شود

# Install dependencies: pip install playwright torch ultralytics numpy  
from playwright.sync_api import sync_playwright
from PIL import Image
import numpy as np
from ultralytics import YOLO

def detect_elements():
    model = YOLO("yolov8n.pt")  # Pre-trained model
    with sync_playwright() as p:
        browser = p.chromium.launch()
        page = browser.new_page()
        page.goto("https://example.com")

        # Capture screenshot
        page.screenshot(path="screenshot.png")
        img = Image.open("screenshot.png")

        # Detect elements (e.g., buttons, text)
        results = model(img)
        boxes = results[0].boxes.xyxy  # Bounding boxes

        # Extract coordinates of detected elements
        for box in boxes:
            x1, y1, x2, y2 = box.numpy()
            print(f"Detected element at position: ({x1}, {y1}) to ({x2}, {y2})")

        browser.close()

detect_elements()

# Install: pip install transformers requests beautifulsoup4  
from transformers import pipeline
import requests
from bs4 import BeautifulSoup

# Scrape raw text
url = "https://example-news.com/article"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
article_text = soup.find("div", class_="article-body").text

# Use NLP to extract entities (people, organizations)
ner_pipeline = pipeline("ner", model="dslim/bert-base-NER")
entities = ner_pipeline(article_text)

# Filter and print entities
for entity in entities:
    if entity["score"] > 0.9:
        print(f"{entity['word']} ({entity['entity']})")

Apple (ORG)  
Elon Musk (PER)  
New York (LOC)  

const { chromium } = require('playwright');
const axios = require('axios');

(async () => {
  const browser = await chromium.launch({ headless: false });
  const page = await browser.newPage();
  await page.goto('https://website-with-captcha.com');

  // Solve CAPTCHA using 2Captcha API
  const captchaImage = await page.$eval('#captcha-image', el => el.src);
  const apiKey = 'YOUR_2CAPTCHA_KEY';

  // Submit CAPTCHA to 2Captcha for solving
  const { data: { requestId } } = await axios.post(
    `https://2captcha.com/in.php?key=${apiKey}&method=base64&body=${captchaImage}`
  );

  // Poll for solution
  let solution;
  while (!solution) {
    const { data } = await axios.get(
      `https://2captcha.com/res.php?key=${apiKey}&action=get&id=${requestId}`
    );
    if (data.includes('OK')) {
      solution = data.split('|')[1];
      break;
    }
    await new Promise(resolve => setTimeout(resolve, 5000));
  }

  // Submit solved CAPTCHA
  await page.fill('#captcha-input', solution);
  await page.click('#submit');
  await browser.close();
})();

# Simplified example: Train a model to predict CSS selectors
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# Sample training data (features: HTML attributes, label: selector)
training_data = pd.DataFrame([
    {"tag": "div", "class": "price", "id": "price", "selector": ".price"},
    {"tag": "h1", "class": "title", "id": None, "selector": "h1.title"},
])

X = training_data[["tag", "class", "id"]]
y = training_data["selector"]

model = RandomForestClassifier()
model.fit(pd.get_dummies(X), y)

# Predict selector for new element
new_element = {"tag": "span", "class": "price-new", "id": None}
predicted_selector = model.predict(pd.get_dummies(pd.DataFrame([new_element])))[0]
print(f"Predicted selector: {predicted_selector}")