Update app.py

app.py

@@ -21,6 +21,13 @@ def extract_table_from_markdown(markdown_text, table_start):
             table_content.append(line)
     return '\n'.join(table_content)
 
+def parse_markdown_link(text):
+    """Parse a Markdown link and return the display text and URL."""
+    match = re.match(r'\[(.*?)\]\((.*?)\)', text)
+    if match:
+        return match.group(1), match.group(2)
+    return text, None
+
 def markdown_table_to_df(table_content):
     """Convert markdown table to pandas DataFrame."""
     # Split the table content into lines
@@ -34,14 +41,17 @@ def markdown_table_to_df(table_content):
     for line in lines[2:]:  # Skip the header separator line
         row = [cell.strip() for cell in line.split('|') if cell.strip()]
         if row and len(row) == len(headers):  # Ensure row has the correct number of columns
-            data.append(row)
+            # Parse the Model Name column for Markdown links
+            model_name, url = parse_markdown_link(row[0])
+            row[0] = model_name
+            data.append(row + [url])  # Add URL as a new column
     
     # Create DataFrame
-    df = pd.DataFrame(data, columns=headers)
+    df = pd.DataFrame(data, columns=headers + ['URL'])
     
     # Convert numeric columns to float
     for col in df.columns:
-        if col not in ["Model Name", "Publisher", "Open?", "Basemodel", "Matryoshka"]:
+        if col not in ["Model Name", "Publisher", "Open?", "Basemodel", "Matryoshka", "URL"]:
             df[col] = pd.to_numeric(df[col], errors='coerce')
     
     return df
@@ -60,19 +70,29 @@ def display_leaderboard(df):
     present_non_benchmark_cols = [col for col in POSSIBLE_NON_BENCHMARK_COLS if col in df.columns]
     
     # Add filters
-    columns_to_filter = [col for col in df.columns if col not in present_non_benchmark_cols]
+    columns_to_filter = [col for col in df.columns if col not in present_non_benchmark_cols and col != 'URL']
     selected_columns = st.multiselect("Select benchmarks to display:", columns_to_filter, default=columns_to_filter)
     
     # Filter dataframe
     df_display = df[present_non_benchmark_cols + selected_columns]
     
-    # Display dataframe
-    st.dataframe(df_display.style.format("{:.4f}", subset=[col for col in df_display.columns if df_display[col].dtype == 'float64']))
+    # Create a copy of the dataframe for display
+    df_display_with_links = df_display.copy()
+    
+    # Create clickable links in the Model Name column
+    df_display_with_links['Model Name'] = df_display_with_links.apply(
+        lambda row: f'<a href="{row["URL"]}" target="_blank">{row["Model Name"]}</a>' if pd.notnull(row["URL"]) else row["Model Name"],
+        axis=1
+    )
+    
+    # Display dataframe with clickable links
+    st.write(df_display_with_links.to_html(escape=False, index=False), unsafe_allow_html=True)
     
     # Download buttons
     csv = df_display.to_csv(index=False)
     st.download_button(label="Download as CSV", data=csv, file_name="leaderboard.csv", mime="text/csv")
 
+
 def display_evaluation():
     """Display the evaluation section."""
     st.header("🧪 Evaluation")