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import asyncio
import aiohttp
import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt

# Function for synchronous web scraping
def scrape_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    numbers = [int(s.text) for s in soup.find_all('span', class_='number')]
    return numbers

# Function for asynchronous web scraping
async def async_scrape_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            text = await response.text()
            soup = BeautifulSoup(text, 'html.parser')
            numbers = [int(s.text) for s in soup.find_all('span', class_='number')]
            return numbers

# Function for data processing
def process_data(data):
    df = pd.DataFrame(data, columns=['Value'])
    df['RollingAverage'] = df['Value'].rolling(window=5).mean()
    return df

# Function for data visualization
def plot_data(df):
    plt.plot(df.index, df['Value'], label='Value')
    plt.plot(df.index, df['RollingAverage'], label='Rolling Average', linestyle='--')
    plt.xlabel('Index')
    plt.ylabel('Values')
    plt.title('Data Visualization')
    plt.legend()
    plt.show()

# Async main function
async def main(urls):
    tasks = [asyncio.create_task(async_scrape_data(url)) for url in urls]
    results = await asyncio.gather(*tasks)
    return results

# URLs for demonstration (Replace with actual URLs)
urls = ["http://example.com/data1", "http://example.com/data2"]

# Run the async main function and process/visualize data
scraped_data = asyncio.run(main(urls))
for data in scraped_data:
    df = process_data(data)
    plot_data(df)