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:]
-    # Convert GPU energy to float so that very small numbers are preserved
+    # 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 categorical string for proper discrete coloring
+    # Convert energy_score to a 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])
     
@@ -59,7 +59,7 @@ def get_plots(task):
         width=800,
         color_discrete_map=color_map
     )
-    # Hover text now shows GPU Energy (Wh) with 4 decimals
+    # Hover text shows GPU Energy rounded to 4 decimals
     fig.update_traces(
         hovertemplate="<br>".join([
             "Model: %{y}",
@@ -67,11 +67,10 @@ def get_plots(task):
             "Energy Score: %{customdata[0]}"
         ])
     )
-    # Set the x-axis tickformat to 4 decimals
+    # Remove forced tickformat so the x-axis uses the raw numbers
     fig.update_layout(
         xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model",
-        xaxis=dict(tickformat=".4f")
+        yaxis_title="Model"
     )
     return fig
 
@@ -108,8 +107,7 @@ def get_all_plots():
     )
     fig.update_layout(
         xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model",
-        xaxis=dict(tickformat=".4f")
+        yaxis_title="Model"
     )
     return fig
 
@@ -118,7 +116,7 @@ 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 for display
+    # 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['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
@@ -148,6 +146,7 @@ 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]
+    # Use the raw GPU energy values from the CSV
     df['total_gpu_energy'] = df['total_gpu_energy'].astype(float)
     df['energy_score'] = df['energy_score'].astype(int).astype(str)
     df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
@@ -173,8 +172,7 @@ def get_text_generation_plots(model_class):
     )
     fig.update_layout(
         xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model",
-        xaxis=dict(tickformat=".4f")
+        yaxis_title="Model"
     )
     return fig
 
@@ -216,10 +214,7 @@ with demo:
     gr.Markdown(
         """# AI Energy Score Leaderboard
 ### Welcome to the leaderboard for the [AI Energy Score Project!](https://huggingface.co/AIEnergyScore)
-Click through the tasks below to see how different models measure up in terms of energy efficiency."""
-    )
-    gr.Markdown(
-        """Test your own models via the [submission portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)"""
+Select different tasks to see scored models. Submit open models for testing and learn about testing proprietary models via the [submission portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)"""
     )
     
     with gr.Tabs():