Update app.py

app.py

@@ -39,14 +39,14 @@ def make_link(mname):
     display_name = parts[1] if len(parts) > 1 else mname
     return f'[{display_name}](https://huggingface.co/{mname})'
 
-# --- Plot Functions (Bar Chart - Modified with explicit tickvals) ---
+# --- Plot Functions (Bar Chart - Modified with kWh to Wh conversion and explicit tickvals) ---
 
 def get_plots(task):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
-    # Use the raw numeric value from the CSV for GPU Energy
-    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
+    # Use the raw numeric value from the CSV for GPU Energy and convert kWh to Wh
+    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     # Create a display model column for labeling
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
@@ -78,7 +78,7 @@ def get_plots(task):
         yaxis_title="GPU Energy (Wh)",
         yaxis = dict(
             tickformat=".4f",
-            tickvals = list(np.arange(0, df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
+            tickvals = list(np.arange(0, df['total_gpu_energy'].max() * 1.1, 100)) # Ticks every 100 Wh, adjust as needed
         )
     )
     return fig
@@ -89,7 +89,7 @@ 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'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
+        df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
         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)
@@ -119,12 +119,12 @@ def get_all_plots():
         yaxis_title="GPU Energy (Wh)",
         yaxis = dict(
             tickformat=".4f",
-            tickvals = list(np.arange(0, all_df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
+            tickvals = list(np.arange(0, all_df['total_gpu_energy'].max() * 1.1, 100)) # Ticks every 100 Wh, adjust as needed
         )
     )
     return fig
 
-# --- New functions for Text Generation filtering by model class (with Bar Chart - Modified explicit tickvals) ---
+# --- New functions for Text Generation filtering by model class (with Bar Chart - Modified kWh to Wh and explicit tickvals) ---
 
 def get_text_generation_plots(model_class):
     df = pd.read_csv('data/energy/text_generation.csv')
@@ -133,7 +133,7 @@ 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]
-    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
+    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
 
@@ -162,22 +162,21 @@ def get_text_generation_plots(model_class):
         yaxis_title="GPU Energy (Wh)",
         yaxis = dict(
             tickformat=".4f",
-            tickvals = list(np.arange(0, df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
+            tickvals = list(np.arange(0, df['total_gpu_energy'].max() * 1.1, 100)) # Ticks every 100 Wh, adjust as needed
         )
     )
     return fig
 
 
-# --- Leaderboard Table Functions and Gradio Interface are unchanged ---
-# (Keep the rest of the code same as previous response)
+# --- Leaderboard Table Functions (Modified kWh to Wh conversion) ---
 
 def get_model_names(task):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
         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)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
+    # For leaderboard display, format GPU Energy to 4 decimals and convert kWh to Wh
+    df['GPU Energy (Wh)'] = (pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000).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
@@ -190,7 +189,7 @@ 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)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
+        df['GPU Energy (Wh)'] = (pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000).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)
@@ -206,7 +205,7 @@ 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)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
+    df['GPU Energy (Wh)'] = (pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000).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