fetch prices

app.py

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+import pandas as pd
+import gradio as gr
+import asyncio  # Ensure asyncio is imported
+
+from src.pricing import get_pricing_df
+
+
+text_leaderboard = get_pricing_df()
+llm_calc_app = gr.Blocks()
+with llm_calc_app:
+
+    leaderboard_table = gr.Dataframe(
+                            value=text_leaderboard,
+                            elem_id="text-leaderboard-table",
+                            interactive=False,
+                            visible=True,
+                            height=800
+                        )
+    llm_calc_app.load()
+
+# Ensure the app runs in an asyncio event loop
+async def main():
+    llm_calc_app.queue()
+    await llm_calc_app.launch()
+
+# Run the async main function
+asyncio.run(main())

requirements.txt

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+beautifulsoup4==4.12.3
+pandas==2.2.3
+selenium==4.25.0

src/pricing.py

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+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