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ModelMaker
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ModelMaker, an advanced AI tool, transforms brief problem descriptions into comprehensive guides on how to develop AI models. It provides detailed instructions, code, a custom synthetic dataset, and necessary training scripts, delivering technically accurate information in a simple, user-friendly format.
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AI-powered wizard for crafting Chrome extensions effortlessly
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Thank you for providing the information about the Facebook group member extraction extension. I'll guide you through creating this Chrome extension, explaining each component and the rationale behind the code. Please note that scraping user data from Facebook may violate their terms of service, so proceed with caution and ensure you have proper authorization.
Here's a step-by-step guide to create the extension:
1. Create the manifest file (manifest.json):
```json
{
"manifest_version": 2,
"name": "Facebook Group Member Extractor",
"version": "1.0",
"description": "Extract member information from a Facebook group and save as CSV",
"permissions": [
"activeTab",
"storage",
"*://*.facebook.com/*"
],
"browser_action": {
"default_popup": "popup.html",
"default_icon": {
"16": "icon16.png",
"48": "icon48.png",
"128": "icon128.png"
}
},
"content_scripts": [
{
"matches": ["*://*.facebook.com/groups/*"],
"js": ["content.js"]
}
],
"background": {
"scripts": ["background.js"],
"persistent": false
}
}
```
This manifest file defines the extension's properties, permissions, and components. We're using manifest version 2 for broader compatibility. The permissions allow the extension to access the active tab, use storage, and interact with Facebook.com.
2. Create the popup interface (popup.html):
```html
<!DOCTYPE html>
<html>
<head>
<title>Facebook Group Member Extractor</title>
<style>
body { width: 300px; padding: 10px; }
button { margin-top: 10px; }
</style>
</head>
<body>
<h2>Facebook Group Member Extractor</h2>
<button id="extract">Extract Members</button>
<div id="status"></div>
<script src="popup.js"></script>
</body>
</html>
```
This creates a simple popup with a button to trigger the extraction process.
3. Create the popup logic (popup.js):
```javascript
document.getElementById('extract').addEventListener('click', function() {
chrome.tabs.query({active: true, currentWindow: true}, function(tabs) {
chrome.tabs.sendMessage(tabs[0].id, {action: "extract"}, function(response) {
document.getElementById('status').textContent = "Extraction in progress...";
});
});
});
chrome.runtime.onMessage.addListener(function(request, sender, sendResponse) {
if (request.action === "extractionComplete") {
document.getElementById('status').textContent = "Extraction complete! CSV file downloaded.";
}
});
```
This script adds a click listener to the extract button, which sends a message to the content script to start the extraction process.
4. Create the content script (content.js):
```javascript
chrome.runtime.onMessage.addListener(function(request, sender, sendResponse) {
if (request.action === "extract") {
extractMembers();
}
});
function extractMembers() {
let members = [];
// This selector may need to be updated based on Facebook's current DOM structure
const memberElements = document.querySelectorAll('div[data-visualcompletion="ignore-dynamic"][role="gridcell"]');
memberElements.forEach(element => {
const name = element.querySelector('a[role="link"]').textContent;
const profileUrl = element.querySelector('a[role="link"]').href;
members.push({name, profileUrl});
});
if (members.length > 0) {
const csv = convertToCSV(members);
downloadCSV(csv);
chrome.runtime.sendMessage({action: "extractionComplete"});
}
}
function convertToCSV(objArray) {
const array = typeof objArray !== 'object' ? JSON.parse(objArray) : objArray;
let str = 'Name,Profile URL\r\n';
for (let i = 0; i < array.length; i++) {
let line = '';
line += array[i].name + ',' + array[i].profileUrl;
str += line + '\r\n';
}
return str;
}
function downloadCSV(csv) {
const blob = new Blob([csv], { type: 'text/csv;charset=utf-8;' });
const link = document.createElement("a");
if (link.download !== undefined) {
const url = URL.createObjectURL(blob);
link.setAttribute("href", url);
link.setAttribute("download", "facebook_group_members.csv");
link.style.visibility = 'hidden';
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
}
```
This content script listens for the "extract" message, then scrapes the member information from the Facebook group page, converts it to CSV format, and triggers a download.
5. Create a background script (background.js):
```javascript
chrome.runtime.onInstalled.addListener(function() {
console.log("Facebook Group Member Extractor installed.");
});
```
This simple background script logs a message when the extension is installed. You can expand this to handle more complex background tasks if needed.
6. Create icon files (icon16.png, icon48.png, icon128.png) for your extension.
To use this extension:
1. Load it as an unpacked extension in Chrome's developer mode.
2. Navigate to a Facebook group page.
3. Click the extension icon and then the "Extract Members" button.
4. The extension will scrape member information and download a CSV file.
Remember to regularly update the content script's selectors, as Facebook's DOM structure may change over time. Also, be mindful of rate limiting and respect Facebook's robots.txt file to avoid potential issues.
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**Web Scraper Script**
=======================
### Overview
This Python script is designed to scrape data from a public website and output it in a CSV format. The script handles potential connection errors, respects the site's robots.txt, and allows customization of the user-agent.
### Dependencies
* `beautifulsoup4` for parsing HTML content
* `requests` for making HTTP requests
* `csv` for writing CSV output
* `robotparser` for parsing robots.txt
* `argparse` for command-line argument parsing
### Installation
To install the dependencies, run:
```bash
pip install beautifulsoup4 requests
```
### Code
```python
import argparse
import csv
import logging
import os
import re
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
from urllib.robotparser import RobotFileParser
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def scrape_website(url, user_agent, output_file):
"""
Scrapes data from a website and writes it to a CSV file.
Args:
url (str): URL of the website to scrape
user_agent (str): User-agent to use for HTTP requests
output_file (str): Path to the output CSV file
"""
# Parse robots.txt
rp = RobotFileParser()
rp.set_url(urljoin(url, "/robots.txt"))
rp.read()
# Check if scraping is allowed
if not rp.can_fetch(user_agent, url):
logger.error(f"Scraping is not allowed by robots.txt for {url}")
return
try:
# Make HTTP request
headers = {"User-Agent": user_agent}
response = requests.get(url, headers=headers)
response.raise_for_status() # Raise an exception for HTTP errors
except requests.RequestException as e:
logger.error(f"Connection error: {e}")
return
# Parse HTML content
soup = BeautifulSoup(response.text, "html.parser")
# Find all relevant data (e.g., links, images, text)
data = []
for link in soup.find_all("a"):
href = link.get("href")
if href:
data.append({"URL": href, "Text": link.text.strip()})
# Write data to CSV file
with open(output_file, "w", newline="") as csvfile:
fieldnames = ["URL", "Text"]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for row in data:
writer.writerow(row)
logger.info(f"Data scraped and written to {output_file}")
def main():
parser = argparse.ArgumentParser(description="Web Scraper Script")
parser.add_argument("-u", "--url", required=True, help="URL of the website to scrape")
parser.add_argument("-o", "--output", required=True, help="Path to the output CSV file")
parser.add_argument("-a", "--user-agent", default="Web Scraper Bot/1.0", help="User-agent to use")
args = parser.parse_args()
scrape_website(args.url, args.user_agent, args.output)
if __name__ == "__main__":
main()
```
### Documentation
#### Functions
* `scrape_website(url, user_agent, output_file)`: Scrapes data from a website and writes it to a CSV file.
* `main()`: Parses command-line arguments and calls `scrape_website()`.
#### Command-Line Arguments
* `-u`, `--url`: URL of the website to scrape (required)
* `-o`, `--output`: Path to the output CSV file (required)
* `-a`, `--user-agent`: User-agent to use (default: "Web Scraper Bot/1.0")
#### Usage Examples
* Scrape a website with default user-agent:
```bash
python web_scraper.py -u https://example.com -o output.csv
```
* Scrape a website with custom user-agent:
```bash
python web_scraper.py -u https://example.com -o output.csv -a "Custom User Agent/1.0"
```
### Best Practices
* Always respect the site's robots.txt and do not scrape data that is not allowed.
* Use a reasonable user-agent to identify your scraper.
* Handle potential connection errors and exceptions.
* Test your scraper thoroughly to ensure it works as expected.
### Edge Cases
* Handling non-HTML content: The script assumes that the website returns HTML content. If the website returns a different type of content (e.g., JSON, XML), you may need to modify the script accordingly.
* Handling JavaScript-heavy websites: The script uses BeautifulSoup to parse HTML content. If the website uses a lot of JavaScript to load its content, you may need to use a more advanced tool like Selenium to render the JavaScript.
### Commit Message Guidelines
* Follow standard professional guidelines for commit messages.
Example commit message:
```
Add web scraper script
* Implemented web scraper script to scrape data from a public website
* Added support for custom user-agent and handling of connection errors
* Included detailed documentation and usage examples
```
-
834,344635v1.6 released 14d agoFree + from $12/mo
Reducing manual efforts in first-pass during code-review process helps speed up the "final check" before merging PRs -
28,780394v1.0 released 11mo agoFree + from $35/mo
This team took the time to understand the industry, problem and its users and designed a perfectly engineered solution. Kudos.
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