Update app.py

app.py

@@ -41,9 +41,8 @@ def get_plots(task):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
-    # Convert GPU energy to float (use the raw values from the CSV)
-    df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
-    # Convert energy_score to a categorical string for proper discrete coloring
+    # Use the raw numeric value from the CSV
+    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
     
@@ -59,7 +58,8 @@ def get_plots(task):
         width=800,
         color_discrete_map=color_map
     )
-    # Hover text shows GPU Energy rounded to 4 decimals
+    # Hover text shows GPU Energy rounded to 4 decimals,
+    # but note that no formatting is applied to the x-axis ticks.
     fig.update_traces(
         hovertemplate="<br>".join([
             "Model: %{y}",
@@ -67,7 +67,6 @@ def get_plots(task):
             "Energy Score: %{customdata[0]}"
         ])
     )
-    # Remove forced tickformat so the x-axis uses the raw numbers
     fig.update_layout(
         xaxis_title="GPU Energy (Wh)",
         yaxis_title="Model"
@@ -80,7 +79,7 @@ def get_all_plots():
         df = pd.read_csv('data/energy/' + task)
         if df.columns[0].startswith("Unnamed:"):
             df = df.iloc[:, 1:]
-        df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
+        df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
         df['energy_score'] = df['energy_score'].astype(int).astype(str)
         df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
         all_df = pd.concat([all_df, df], ignore_index=True)
@@ -117,7 +116,7 @@ def get_model_names(task):
         df = df.iloc[:, 1:]
     df['energy_score'] = df['energy_score'].astype(int)
     # For leaderboard display, format GPU Energy to 4 decimals
-    df['GPU Energy (Wh)'] = df['total_gpu_energy'].astype(float).apply(lambda x: f"{x:.4f}")
+    df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     # Remove any Class column if it exists
@@ -130,7 +129,7 @@ def get_all_model_names():
     for task in tasks:
         df = pd.read_csv('data/energy/' + task)
         df['energy_score'] = df['energy_score'].astype(int)
-        df['GPU Energy (Wh)'] = df['total_gpu_energy'].astype(float).apply(lambda x: f"{x:.4f}")
+        df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
         df['Model'] = df['model'].apply(make_link)
         df['Score'] = df['energy_score'].apply(format_stars)
         all_df = pd.concat([all_df, df], ignore_index=True)
@@ -146,8 +145,8 @@ def get_text_generation_plots(model_class):
     # Filter by the selected model class if the "class" column exists
     if 'class' in df.columns:
         df = df[df['class'] == model_class]
-    # Use the raw GPU energy values from the CSV
-    df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
+    # Use the raw numeric value from the CSV
+    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
     
@@ -183,7 +182,7 @@ def get_text_generation_model_names(model_class):
     if 'class' in df.columns:
         df = df[df['class'] == model_class]
     df['energy_score'] = df['energy_score'].astype(int)
-    df['GPU Energy (Wh)'] = df['total_gpu_energy'].astype(float).apply(lambda x: f"{x:.4f}")
+    df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     # Remove the Class column if it exists