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import requests |
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from bs4 import BeautifulSoup |
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import pandas as pd |
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import gradio as gr |
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import plotly.express as px |
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BASE_URL = "https://scale.com/leaderboard" |
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LEADERBOARDS = { |
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"Coding": "/coding", |
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"Adversarial Robustness": "/adversarial_robustness", |
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"Instruction Following": "/instruction_following", |
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"Math": "/math" |
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} |
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def scrape_leaderboard(leaderboard): |
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url = BASE_URL + LEADERBOARDS[leaderboard] |
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response = requests.get(url) |
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soup = BeautifulSoup(response.content, 'html.parser') |
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leaderboard_div = soup.find('div', class_='flex flex-col gap-4 sticky top-20') |
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if not leaderboard_div: |
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raise ValueError("Leaderboard div not found. The page structure might have changed.") |
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table = leaderboard_div.find('table', class_='w-full caption-bottom text-sm') |
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if not table: |
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raise ValueError("Leaderboard table not found within the div.") |
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data = [] |
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for row in table.find('tbody').find_all('tr'): |
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cols = row.find_all('td') |
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rank = cols[0].find('div', class_='flex').text.strip().split()[0] |
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model = cols[0].find('a').text.strip() |
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score = cols[1].text.strip() |
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confidence = cols[2].text.strip() |
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data.append([rank, model, score, confidence]) |
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df = pd.DataFrame(data, columns=['Rank', 'Model', 'Score', '95% Confidence']) |
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df['Score'] = pd.to_numeric(df['Score']) |
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return df |
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def create_chart(df): |
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fig = px.bar(df, x='Model', y='Score', title='Model Scores Comparison', |
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labels={'Score': 'Overall Score', 'Model': 'Model Name'}, |
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color='Score', color_continuous_scale='viridis') |
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fig.update_layout(xaxis_tickangle=-45, xaxis_title=None) |
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return fig |
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def update_leaderboard(leaderboard): |
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try: |
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df = scrape_leaderboard(leaderboard) |
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chart = create_chart(df) |
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return chart, df.to_html(index=False) |
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except Exception as e: |
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return None, f"An error occurred: {str(e)}" |
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with gr.Blocks() as iface: |
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gr.Markdown("# Scale AI Leaderboard Viewer") |
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dropdown = gr.Dropdown(choices=list(LEADERBOARDS.keys()), label="Select Leaderboard", value="Coding") |
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chart_output = gr.Plot() |
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table_output = gr.HTML() |
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def on_load(): |
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chart, html = update_leaderboard("Coding") |
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return chart, html |
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dropdown.change(update_leaderboard, inputs=[dropdown], outputs=[chart_output, table_output]) |
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iface.load(on_load, outputs=[chart_output, table_output]) |
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iface.launch() |
