Update app.py

app.py

@@ -41,9 +41,9 @@ def get_plots(task):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
-    # Ensure total_gpu_energy is a float so that values are not misinterpreted
+    # Convert GPU energy to float so that very small numbers are preserved
     df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
-    # Convert energy_score to a categorical string for discrete coloring
+    # Convert energy_score to categorical string for proper discrete coloring
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
     
@@ -59,10 +59,11 @@ def get_plots(task):
         width=800,
         color_discrete_map=color_map
     )
+    # GPU Energy now shows 4 decimals in the hover text
     fig.update_traces(
         hovertemplate="<br>".join([
             "Model: %{y}",
-            "GPU Energy (Wh): %{x}",
+            "GPU Energy (Wh): %{x:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
@@ -96,7 +97,7 @@ def get_all_plots():
     fig.update_traces(
         hovertemplate="<br>".join([
             "Model: %{y}",
-            "GPU Energy (Wh): %{x}",
+            "GPU Energy (Wh): %{x:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
@@ -108,16 +109,12 @@ def get_model_names(task):
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
     df['energy_score'] = df['energy_score'].astype(int)
+    # Ensure GPU Energy is a float and format it to 4 decimals
     df['GPU Energy (Wh)'] = df['total_gpu_energy'].astype(float).apply(lambda x: f"{x:.4f}")
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
-    
-    if 'class' in df.columns:
-        df['Class'] = df['class']
-        df = df[['Model', 'GPU Energy (Wh)', 'Score', 'Class']]
-    else:
-        df = df[['Model', 'GPU Energy (Wh)', 'Score']]
-        
+    # Remove any Class column
+    df = df[['Model', 'GPU Energy (Wh)', 'Score']]
     df = df.sort_values(by='GPU Energy (Wh)')
     return df
 
@@ -139,7 +136,7 @@ def get_text_generation_plots(model_class):
     df = pd.read_csv('data/energy/text_generation.csv')
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
-    # Filter to the selected model class (if a "class" column exists)
+    # Filter by the selected model class if the "class" column exists
     if 'class' in df.columns:
         df = df[df['class'] == model_class]
     df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
@@ -161,7 +158,7 @@ def get_text_generation_plots(model_class):
     fig.update_traces(
         hovertemplate="<br>".join([
             "Model: %{y}",
-            "GPU Energy (Wh): %{x}",
+            "GPU Energy (Wh): %{x:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
@@ -178,11 +175,8 @@ def get_text_generation_model_names(model_class):
     df['GPU Energy (Wh)'] = df['total_gpu_energy'].astype(float).apply(lambda x: f"{x:.4f}")
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
-    if 'class' in df.columns:
-        df['Class'] = df['class']
-        df = df[['Model', 'GPU Energy (Wh)', 'Score', 'Class']]
-    else:
-        df = df[['Model', 'GPU Energy (Wh)', 'Score']]
+    # Remove the Class column if it exists
+    df = df[['Model', 'GPU Energy (Wh)', 'Score']]
     df = df.sort_values(by='GPU Energy (Wh)')
     return df
 
@@ -218,14 +212,16 @@ Click through the tasks below to see how different models measure up in terms of
     with gr.Tabs():
         # --- Text Generation Tab with Dropdown for Model Class ---
         with gr.TabItem("Text Generation 💬"):
+            # Dropdown moved above the plot and leaderboard
+            model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
+                                                 label="Select Model Class",
+                                                 value="A")
             with gr.Row():
                 with gr.Column(scale=1.3):
                     tg_plot = gr.Plot(get_text_generation_plots("A"))
                 with gr.Column(scale=1):
                     tg_table = gr.Dataframe(get_text_generation_model_names("A"), datatype="markdown")
-            model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
-                                                 label="Select Model Class",
-                                                 value="A")
+            # Update plot and table when the dropdown value changes
             model_class_dropdown.change(fn=update_text_generation,
                                         inputs=model_class_dropdown,
                                         outputs=[tg_plot, tg_table])