Update app.py

app.py

@@ -39,13 +39,9 @@ def make_link(mname):
 
 def generate_html_table_from_df(df):
     """
-    Given a dataframe that already includes:
-      - 'gpu_energy_numeric': numeric energy (in Wh)
-      - 'Model': the model link HTML,
-      - 'Score': the HTML stars,
-      - and 'energy_score' as an integer,
-    generate an HTML table that shows the energy value plus a horizontal bar whose width is computed
-    relative to the maximum energy.
+    Generate an HTML table from the given DataFrame.
+    Each GPU Energy cell contains both the numeric energy (Wh) and a horizontal bar
+    whose width is computed relative to the maximum energy in the table.
     """
     max_energy = df['gpu_energy_numeric'].max() if not df.empty else 1
     color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}
@@ -59,7 +55,7 @@ def generate_html_table_from_df(df):
     for _, row in df.iterrows():
         energy_numeric = row['gpu_energy_numeric']
         energy_str = f"{energy_numeric:.4f}"
-        # Compute the relative width (as a percentage) for the horizontal bar
+        # Calculate the relative width as a percentage
         bar_width = (energy_numeric / max_energy) * 100
         score_val = row['energy_score']
         bar_color = color_map.get(str(score_val), "gray")
@@ -83,7 +79,8 @@ def get_model_names_html(task):
     df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
-    df = df.sort_values(by='gpu_energy_numeric')
+    # Sort descending (high to low)
+    df = df.sort_values(by='gpu_energy_numeric', ascending=False)
     return generate_html_table_from_df(df)
 
 def get_all_model_names_html():
@@ -98,23 +95,33 @@ def get_all_model_names_html():
         df['Score'] = df['energy_score'].apply(format_stars)
         all_df = pd.concat([all_df, df], ignore_index=True)
     all_df = all_df.drop_duplicates(subset=['model'])
-    all_df = all_df.sort_values(by='gpu_energy_numeric')
+    # Sort descending
+    all_df = all_df.sort_values(by='gpu_energy_numeric', ascending=False)
     return generate_html_table_from_df(all_df)
 
 def get_text_generation_model_names_html(model_class):
     df = pd.read_csv('data/energy/text_generation.csv')
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
+    # Filter by model class if the "class" column exists
     if 'class' in df.columns:
         df = df[df['class'] == model_class]
     df['energy_score'] = df['energy_score'].astype(int)
     df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
-    df = df.sort_values(by='gpu_energy_numeric')
+    # Sort descending
+    df = df.sort_values(by='gpu_energy_numeric', ascending=False)
     return generate_html_table_from_df(df)
 
-def update_text_generation(model_class):
+def update_text_generation(selected_display):
+    # Mapping from display text to the internal value
+    mapping = {
+        "A (Single Consumer GPU) <20B parameters": "A",
+        "B (Single Cloud GPU) 20-66B parameters": "B",
+        "C (Multiple Cloud GPUs) >66B parameters": "C"
+    }
+    model_class = mapping.get(selected_display, "A")
     table_html = get_text_generation_model_names_html(model_class)
     return table_html
 
@@ -139,17 +146,37 @@ with demo:
 ### Welcome to the leaderboard for the [AI Energy Score Project!](https://huggingface.co/AIEnergyScore)
 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)"""
     )
-
+    
+    # Visually appealing header links
+    gr.HTML('''
+    <div style="text-align: center; margin-bottom: 20px;">
+        <a href="https://huggingface.co/spaces/AIEnergyScore/submission_portal" style="margin: 0 15px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Submission Portal</a>
+        <a href="https://huggingface.co/spaces/AIEnergyScore/README/discussions" style="margin: 0 15px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Community</a>
+        <a href="https://huggingface.github.io/AIEnergyScore/#faq" style="margin: 0 15px; text-decoration: none; font-weight: bold; font-size: 1.1em;">FAQ</a>
+        <a href="https://huggingface.github.io/AIEnergyScore/#documentation" style="margin: 0 15px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Documentation</a>
+    </div>
+    ''')
+    
     with gr.Tabs():
         # --- Text Generation Tab with Dropdown for Model Class ---
         with gr.TabItem("Text Generation 💬"):
-            model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
-                                                 label="Select Model Class",
-                                                 value="A")
+            # Define the dropdown with descriptive text options.
+            model_class_options = [
+                "A (Single Consumer GPU) <20B parameters",
+                "B (Single Cloud GPU) 20-66B parameters",
+                "C (Multiple Cloud GPUs) >66B parameters"
+            ]
+            model_class_dropdown = gr.Dropdown(
+                choices=model_class_options,
+                label="Select Model Class",
+                value=model_class_options[0]
+            )
             tg_table = gr.HTML(get_text_generation_model_names_html("A"))
-            model_class_dropdown.change(fn=update_text_generation,
-                                        inputs=model_class_dropdown,
-                                        outputs=tg_table)
+            model_class_dropdown.change(
+                fn=update_text_generation,
+                inputs=model_class_dropdown,
+                outputs=tg_table
+            )
 
         with gr.TabItem("Image Generation 📷"):
             gr.HTML(get_model_names_html('image_generation.csv'))
@@ -189,8 +216,6 @@ Select different tasks to see scored models. Submit open models for testing and
             lines=10,
             show_copy_button=True,
         )
-    gr.Markdown(
-        """Last updated: February 2025"""
-    )
+    gr.Markdown("""Last updated: February 2025""")
 
 demo.launch()