oops

app.py

@@ -61,7 +61,7 @@ def get_model_names(task):
     all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
     all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2)
     all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
-    model_names= model_names.sort_values('Total GPU Energy (Wh)')
+    all_df= all_df.sort_values('Total GPU Energy (Wh)')
     model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']]
     return model_names
 
@@ -80,7 +80,7 @@ def get_all_model_names():
     all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
     all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2)
     all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
-    model_names= model_names.sort_values('Total GPU Energy (Wh)')
+    all_df= all_df.sort_values('Total GPU Energy (Wh)')
     model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']]
     return model_names
 
@@ -181,7 +181,7 @@ with demo:
                     plot = gr.Plot(get_all_plots)
                 with gr.Column():
                     table = gr.Dataframe(get_all_model_names)
-                    
+
     with gr.Accordion("Methodology", open = False):
         gr.Markdown(
         """For each of the ten tasks above, we created a custom dataset with 1,000 entries (see all of the datasets on our [org Hub page](https://huggingface.co/EnergyStarAI)).