Update app.py

app.py

@@ -30,46 +30,51 @@ def format_stars(score):
         score_int = int(score)
     except Exception:
         score_int = 0
-    return f'<span style="color: #3fa45bff !important; font-size:2em !important;">{"★" * score_int}</span>'
+    # Render stars in black with a slightly larger font
+    return f'<span style="color: black !important; font-size:1.5em !important;">{"★" * score_int}</span>'
 
 def make_link(mname):
     parts = str(mname).split('/')
     display_name = parts[1] if len(parts) > 1 else mname
     return f'[{display_name}](https://huggingface.co/{mname})'
 
+# --- Plot Functions (Axes swapped) ---
+
 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
+    # Use the raw numeric value from the CSV for GPU Energy
     df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
     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])
     
+    # Use the energy score to control color
     color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}
     
+    # Now plot with the model name on the X axis and GPU Energy on the Y axis.
     fig = px.scatter(
         df,
-        x="total_gpu_energy",
-        y="Display Model",
+        x="Display Model",
+        y="total_gpu_energy",
         color="energy_score",
         custom_data=['energy_score'],
         height=500,
         width=800,
         color_discrete_map=color_map
     )
-    # Hover text shows GPU Energy rounded to 4 decimals,
-    # but note that no formatting is applied to the x-axis ticks.
+    # Update hover text to show the model and GPU Energy (with 4 decimals)
     fig.update_traces(
         hovertemplate="<br>".join([
-            "Model: %{y}",
-            "GPU Energy (Wh): %{x:.4f}",
+            "Model: %{x}",
+            "GPU Energy (Wh): %{y:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
     fig.update_layout(
-        xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model"
+        xaxis_title="Model",
+        yaxis_title="GPU Energy (Wh)"
     )
     return fig
 
@@ -89,8 +94,8 @@ def get_all_plots():
     
     fig = px.scatter(
         all_df,
-        x="total_gpu_energy",
-        y="Display Model",
+        x="Display Model",
+        y="total_gpu_energy",
         color="energy_score",
         custom_data=['energy_score'],
         height=500,
@@ -99,17 +104,19 @@ def get_all_plots():
     )
     fig.update_traces(
         hovertemplate="<br>".join([
-            "Model: %{y}",
-            "GPU Energy (Wh): %{x:.4f}",
+            "Model: %{x}",
+            "GPU Energy (Wh): %{y:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
     fig.update_layout(
-        xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model"
+        xaxis_title="Model",
+        yaxis_title="GPU Energy (Wh)"
     )
     return fig
 
+# --- Leaderboard Table Functions (unchanged except stars) ---
+
 def get_model_names(task):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
@@ -137,7 +144,8 @@ def get_all_model_names():
     all_df = all_df.sort_values(by='GPU Energy (Wh)')
     return all_df[['Model', 'GPU Energy (Wh)', 'Score']]
 
-# === New functions for Text Generation filtering by model class ===
+# --- New functions for Text Generation filtering by model class (with swapped axes) ---
+
 def get_text_generation_plots(model_class):
     df = pd.read_csv('data/energy/text_generation.csv')
     if df.columns[0].startswith("Unnamed:"):
@@ -145,7 +153,6 @@ 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 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])
@@ -154,8 +161,8 @@ def get_text_generation_plots(model_class):
     
     fig = px.scatter(
         df,
-        x="total_gpu_energy",
-        y="Display Model",
+        x="Display Model",
+        y="total_gpu_energy",
         color="energy_score",
         custom_data=['energy_score'],
         height=500,
@@ -164,14 +171,14 @@ def get_text_generation_plots(model_class):
     )
     fig.update_traces(
         hovertemplate="<br>".join([
-            "Model: %{y}",
-            "GPU Energy (Wh): %{x:.4f}",
+            "Model: %{x}",
+            "GPU Energy (Wh): %{y:.4f}",
             "Energy Score: %{customdata[0]}"
         ])
     )
     fig.update_layout(
-        xaxis_title="GPU Energy (Wh)",
-        yaxis_title="Model"
+        xaxis_title="Model",
+        yaxis_title="GPU Energy (Wh)"
     )
     return fig
 
@@ -195,7 +202,8 @@ def update_text_generation(model_class):
     table = get_text_generation_model_names(model_class)
     return plot, table
 
-# === Build the Gradio Interface ===
+# --- Build the Gradio Interface ---
+
 demo = gr.Blocks(css="""
 .gr-dataframe table {
     table-layout: fixed;