File size: 1,936 Bytes
925ac7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import asyncio
from pyppeteer import launch
from bs4 import BeautifulSoup
import pandas as pd # Add this import for DataFrame functionality
async def main():
# Launch the browser in headless mode
browser = await launch(headless=True)
page = await browser.newPage()
# Find the table in the HTML
await page.goto('https://llm-price.com/')
await page.waitForSelector('#tableBody')
content = await page.content()
soup = BeautifulSoup(content, 'html.parser')
table = soup.find('table')
# Check if the table is found
if table:
model_names = []
# providers = [] # Skip providers, only uses .svg logos on the website, use sources (col4) instead
input_tokens = []
output_tokens = []
sources = []
updated_times = []
# Iterate through the rows of the table
for row in table.find_all('tr')[1:]: # Skip the header row
cols = row.find_all('td')
if len(cols) > 0:
model_names.append(cols[0].text.strip())
# providers.append(cols[1].text.strip())
input_tokens.append(cols[2].text.strip())
output_tokens.append(cols[3].text.strip())
sources.append(cols[4].text.strip())
updated_times.append(cols[5].text.strip())
# Create a DataFrame from the collected data
data = {
'Model Name': model_names,
# 'Providers': providers,
'1 M Input Tokens': input_tokens,
'1 M Output Tokens': output_tokens,
'Source': sources,
'Updated Time': updated_times
}
df = pd.DataFrame(data)
return df
else:
print("Table not found.")
# Close the browser
await browser.close()
# Run the main function
def get_pricing_df():
price_df = asyncio.get_event_loop().run_until_complete(main())
return price_df |