adding app

app.py

@@ -0,0 +1,88 @@
+import re
+import streamlit as st
+import requests
+import pandas as pd
+from io import StringIO
+import plotly.graph_objs as go
+
+
+def convert_markdown_table_to_dataframe(md_content):
+    """
+    Converts a markdown table to a Pandas DataFrame, handling special characters, links, 
+    and extracting Hugging Face URLs.
+    """
+    cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE)
+    df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python')
+    df = df.drop(0, axis=0)  # Remove first row if it's not the header
+    df.columns = df.columns.str.strip()  # Clean column names
+
+    # Extract Model names and URLs
+    model_link_pattern = r'\[(.*?)\]\((.*?)\)'
+    df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None)
+    df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x))
+    return df
+
+
+def create_bar_chart(df, metric):
+    """
+    Creates and displays a bar chart for a given metric.
+    """
+    st.write(f"### {metric} Scores")
+    if metric not in df.columns:
+        st.write(f"No data available for {metric}.")
+        return
+
+    sorted_df = df[['Model', metric]].dropna().sort_values(by=metric, ascending=True)
+    fig = go.Figure(go.Bar(
+        x=sorted_df[metric],
+        y=sorted_df['Model'],
+        orientation='h',
+        marker=dict(color=sorted_df[metric], colorscale='Inferno')
+    ))
+    fig.update_layout(margin=dict(l=20, r=20, t=20, b=20))
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def main():
+    st.set_page_config(page_title="LLM Leaderboard", layout="wide")
+    st.title("🏆 LLM Leaderboard")
+
+    # URL to your markdown file
+    md_url = st.text_input("Enter the URL to the markdown file", "https://raw.githubusercontent.com/yourrepo/README.md")
+    
+    if not md_url:
+        st.error("Please provide a valid URL to a markdown file containing the leaderboard table.")
+        return
+
+    try:
+        response = requests.get(md_url)
+        response.raise_for_status()
+        md_content = response.text
+
+        df = convert_markdown_table_to_dataframe(md_content)
+        
+        # Automatically detect metrics (all columns except 'Model' and 'URL')
+        metric_columns = [col for col in df.columns if col not in ['Model', 'URL']]
+        
+        # Convert metric columns to numeric, handling errors gracefully
+        for col in metric_columns:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # Sortable leaderboard table
+        st.dataframe(
+            df[['Model'] + metric_columns + ['URL']],
+            use_container_width=True,
+            hide_index=True,
+        )
+
+        # Bar charts for each metric
+        for metric in metric_columns:
+            create_bar_chart(df, metric)
+
+    except Exception as e:
+        st.error(f"An error occurred while processing the markdown table: {e}")
+
+
+if __name__ == "__main__":
+    main()
+