ok how about this

app.py

@@ -27,12 +27,13 @@ def get_plots(task):
     return fig
 
 def get_all_plots():
+    all_df = pd.DataFrame(columns = ['model', 'parameters', 'total_gpu_energy']
     for task in tasks:
         task_df= pd.read_csv('data/energy/'+task)
         params_df = pd.read_csv('data/params/'+task)
         params_df= params_df.rename(columns={"Link": "model"})
         tasks_df = pd.merge(task_df, params_df, on='model')
-        all_df = pd.DataFrame(columns = tasks_df.columns)
+        tasks_df= tasks_df[['model', 'parameters', 'total_gpu_energy']]
         all_df = pd.concat([all_df, tasks_df])
     all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
     all_df = all_df.sort_values(by=['Total GPU Energy (Wh)'])
@@ -67,13 +68,13 @@ def get_model_names(task):
 
 def get_all_model_names():
     #TODO: add link to results in model card of each model
+    all_df = pd.DataFrame(columns = ['model', 'parameters', 'total_gpu_energy']
     for task in tasks:
         task_df= pd.read_csv('data/params/'+task)
         energy_df= pd.read_csv('data/energy/'+task)
         task_df= task_df.rename(columns={"Link": "model"})
         tasks_df = pd.merge(task_df, energy_df, on='model')
-        print(tasks_df.columns)
-        all_df = pd.DataFrame(columns = tasks_df.columns)
+        tasks_df= tasks_df[['model', 'parameters', 'total_gpu_energy']]
         all_df = pd.concat([all_df, tasks_df])
     all_df=all_df.drop_duplicates(subset=['model'])
     all_df['Parameters'] = all_df['parameters'].apply(format_params)