Update app.py

app.py

@@ -39,7 +39,7 @@ def make_link(mname):
     display_name = parts[1] if len(parts) > 1 else mname
     return f'[{display_name}](https://huggingface.co/{mname})'
 
-# --- Leaderboard Table Functions (Modified to dynamically calculate max energy) ---
+# --- Leaderboard Table Functions (Modified to sort BEFORE creating HTML bar) ---
 
 def create_minimal_bar_html(energy_value_wh, energy_score, max_energy_value):
     """Generates HTML for the minimal bar chart with dynamic max energy."""
@@ -64,6 +64,8 @@ def get_model_names(task):
         df = df.iloc[:, 1:]
     df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion
     df['energy_score'] = df['energy_score'].astype(int)
+
+    df = df.sort_values(by='total_gpu_energy') # Sort BEFORE creating HTML column
     max_energy_for_task = df['total_gpu_energy'].max() # Calculate max energy for this task
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_task
@@ -72,7 +74,6 @@ def get_model_names(task):
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns
-    df = df.sort_values(by='total_gpu_energy') # Sort by underlying energy value for table order
     df = df.drop('total_gpu_energy', axis=1) # remove the original energy column that was used for sorting
     return df
 
@@ -83,16 +84,17 @@ def get_all_model_names():
         df = pd.read_csv('data/energy/' + task)
         df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion
         df['energy_score'] = df['energy_score'].astype(int)
-        max_energy_overall = max(max_energy_overall, df['total_gpu_energy'].max()) # Update overall max
-
         all_df = pd.concat([all_df, df], ignore_index=True)
+
     all_df = all_df.drop_duplicates(subset=['model'])
+    all_df = all_df.sort_values(by='total_gpu_energy') # Sort ALL DATA before calculating max and creating HTML
+
+    max_energy_overall = all_df['total_gpu_energy'].max() # Calculate overall max AFTER sorting
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_overall
     all_df['GPU Energy (Wh)'] = all_df.apply(lambda row: create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_overall), axis=1)
     all_df['Model'] = all_df['model'].apply(make_link)
     all_df['Score'] = all_df['energy_score'].apply(format_stars)
-    all_df = all_df.sort_values(by='total_gpu_energy') # Sort by underlying energy value for table order
     all_df = all_df.drop('total_gpu_energy', axis=1) # remove the original energy column that was used for sorting
     return all_df[['Model', 'GPU Energy (Wh)', 'Score']]
 
@@ -105,6 +107,8 @@ def get_text_generation_model_names(model_class):
         df = df[df['class'] == model_class]
     df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion
     df['energy_score'] = df['energy_score'].astype(int)
+
+    df = df.sort_values(by='total_gpu_energy') # Sort BEFORE creating HTML column
     max_energy_for_class = df['total_gpu_energy'].max() # Calculate max energy for this class
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_class
@@ -113,7 +117,6 @@ def get_text_generation_model_names(model_class):
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns
-    df = df.sort_values(by='total_gpu_energy') # Sort by underlying energy value for table order
     df = df.drop('total_gpu_energy', axis=1) # remove the original energy column that was used for sorting
     return df