removing grouping

app.py

@@ -4,14 +4,10 @@ from huggingface_hub import list_models
 import plotly.express as px
 
 def get_plots(task_df):
-    grouped_df = task_df[['total_gpu_energy', 'model']].groupby('model').mean().sort_values('total_gpu_energy',ascending = False)
-    grouped_df = grouped_df.reset_index()
-    grouped_df['model'] = grouped_df['model'].str.split('/').str[-1]
-    grouped_df['task'] = 'text_classification'
-    grouped_df['total_gpu_energy (Wh)'] = grouped_df['total_gpu_energy']*1000
-    grouped_df['energy_star'] = pd.cut(grouped_df['total_gpu_energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
-    grouped_df = px.scatter(grouped_df, x="model", y="total_gpu_energy (Wh)", height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
-    return grouped_df
+    task_df['total_gpu_energy (Wh)'] = task_df['total_gpu_energy']*1000
+    task_df['energy_star'] = pd.cut(task_df['total_gpu_energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
+    task_df = px.scatter(task_df, x="model", y="total_gpu_energy (Wh)", height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
+    return task_df
 
 # %% app.ipynb 3
 demo = gr.Blocks()