Create app.py
Browse files
app.py
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import re
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import streamlit as st
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import requests
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import pandas as pd
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from io import StringIO
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import plotly.graph_objs as go
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from yall import create_yall
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def convert_markdown_table_to_dataframe(md_content):
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"""
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Converts markdown table to Pandas DataFrame, handling special characters and links,
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extracts Hugging Face URLs, and adds them to a new column.
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"""
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# Remove leading and trailing | characters
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cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE)
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# Create DataFrame from cleaned content
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df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python')
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# Remove the first row after the header
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df = df.drop(0, axis=0)
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# Strip whitespace from column names
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df.columns = df.columns.str.strip()
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# Extract Hugging Face URLs and add them to a new column
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model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)'
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df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None)
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# Clean Model column to have only the model link text
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df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x))
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return df
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def create_bar_chart(df, category):
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"""Create and display a bar chart for a given category."""
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st.write(f"### {category} Scores")
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# Sort the DataFrame based on the category score
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sorted_df = df[['Model', category]].sort_values(by=category, ascending=True)
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# Create the bar chart with color gradient
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fig = go.Figure(go.Bar(
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x=sorted_df[category],
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y=sorted_df['Model'],
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orientation='h',
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marker=dict(color=sorted_df[category], colorscale='Magma')
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))
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# Update layout for better readability
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fig.update_layout(
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xaxis_title=category,
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yaxis_title="Model",
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margin=dict(l=20, r=20, t=20, b=20)
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)
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st.plotly_chart(fig, use_container_width=True)
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def main():
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st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
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st.title("🏆 YALL - Yet Another LLM Leaderboard")
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st.markdown("Leaderboard made with [🧐 LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite. It's a collection of my own evaluations.")
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content = create_yall()
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if content:
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try:
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score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
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# Display dataframe
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df = convert_markdown_table_to_dataframe(content)
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for col in score_columns:
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df[col] = pd.to_numeric(df[col].str.strip(), errors='coerce')
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st.dataframe(df, use_container_width=True)
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# Full-width plot for the first category
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create_bar_chart(df, score_columns[0])
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# Next two plots in two columns
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col1, col2 = st.columns(2)
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with col1:
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create_bar_chart(df, score_columns[1])
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with col2:
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create_bar_chart(df, score_columns[2])
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# Last two plots in two columns
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col3, col4 = st.columns(2)
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with col3:
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create_bar_chart(df, score_columns[3])
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with col4:
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create_bar_chart(df, score_columns[4])
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except Exception as e:
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st.error("An error occurred while processing the markdown table.")
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st.error(str(e))
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else:
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st.error("Failed to download the content from the URL provided.")
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if __name__ == "__main__":
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main()
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