AI Digital Assistant Hub

AI Assistant Capabilities

Harness the full potential of AI with our comprehensive set of tools for data collection, analysis, and interaction.

Web Scraping
API Integration
Natural Language Processing (NLP)
Real-time Data Processing

Web Scraping Example

Using Python and BeautifulSoup to scrape data from websites:

import requests
from bs4 import BeautifulSoup

def scrape_website(url):
    response = requests.get(url)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        data = [item.text.strip() for item in soup.find_all('div', {'class': 'data'})]
        return data
    return []

url = "https://example.com"
scraped_data = scrape_website(url)
print(scraped_data)

API Integration Example

Performing a GET request to an API using requests library in Python:

import requests

def fetch_data(api_url):
    response = requests.get(api_url)
    if response.status_code == 200:
        return response.json()
    return {}

api_url = "https://jsonplaceholder.typicode.com/todos/1"
data = fetch_data(api_url)
print(data)

Natural Language Processing Example

Using NLTK to tokenize and process text data:

import nltk
from nltk.tokenize import word_tokenize

nltk.download('punkt')

def process_text(text):
    tokens = word_tokenize(text)
    return tokens

sample_text = "Hello, how are you today?"
tokens = process_text(sample_text)
print(tokens)

WebSim AI API

Interact with simulation using WebSim AI API:

import requests

API_KEY = 'YOUR_API_KEY'
HEADERS = {'Authorization': f'Bearer {API_KEY}'}

def create_simulation(url, depth=2, max_pages=10):
    api_url = "https://api.websim.ai/v1/simulations"
    payload = {
        'url': url,
        'depth': depth,
        'maxPages': max_pages
    }
    response = requests.post(api_url, headers=HEADERS, json=payload)
    return response.json()

simulation = create_simulation("https://example.com")
print(simulation)