Update app.py

app.py

@@ -52,8 +52,9 @@ def extract_link_text(html_link):
 def generate_html_table_from_df(df):
     """
     Given a dataframe with a numeric energy column (gpu_energy_numeric),
-    generate an HTML table with three columns:
+    generate an HTML table with four columns:
       - Model (the link, with a fixed width based on the longest model name)
+      - Provider (extracted from the model field)
       - GPU Energy (Wh) plus a horizontal bar whose width is proportional
         to the energy value relative to the maximum in the table.
       - Score (displayed as stars)
@@ -70,6 +71,7 @@ def generate_html_table_from_df(df):
     html = '<table style="width:100%; border-collapse: collapse; font-family: Inter, sans-serif;">'
     html += '<thead><tr style="background-color: #f2f2f2;">'
     html += '<th style="text-align: left; padding: 8px;" title="Model name with link to Hugging Face">Model</th>'
+    html += '<th style="text-align: left; padding: 8px;" title="AI Provider extracted from the model name">Provider</th>'
     html += '<th style="text-align: left; padding: 8px;" title="GPU energy consumed in Watt-hours for 1,000 queries">GPU Energy (Wh)</th>'
     html += '<th style="text-align: left; padding: 8px;" title="5 is most efficient, 1 is least. Relative energy efficiency score relative to other models in task/class at the time of leaderboard launch">Score</th>'
     html += '</tr></thead>'
@@ -82,6 +84,7 @@ def generate_html_table_from_df(df):
         bar_color = color_map.get(str(score_val), "gray")
         html += '<tr>'
         html += f'<td style="padding: 8px; width: {static_width}px;">{row["Model"]}</td>'
+        html += f'<td style="padding: 8px;">{row["Provider"]}</td>'
         html += (
             f'<td style="padding: 8px;">{energy_str}<br>'
             f'<div style="background-color: {bar_color}; width: {bar_width:.1f}%; height: 10px;"></div></td>'
@@ -124,6 +127,8 @@ def get_model_names_html(task, sort_order="Low to High"):
         df = df.iloc[:, 1:]
     df['energy_score'] = df['energy_score'].astype(int)
     df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
+    # Add the Provider column (text before the slash in the model field)
+    df['Provider'] = df['model'].apply(lambda x: str(x).split('/')[0])
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     ascending = True if sort_order == "Low to High" else False
@@ -138,6 +143,8 @@ def get_all_model_names_html(sort_order="Low to High"):
             df = df.iloc[:, 1:]
         df['energy_score'] = df['energy_score'].astype(int)
         df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
+        # Add the Provider column here as well.
+        df['Provider'] = df['model'].apply(lambda x: str(x).split('/')[0])
         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)
@@ -154,6 +161,8 @@ def get_text_generation_model_names_html(model_class, sort_order="Low to High"):
         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
+    # Add the Provider column here as well.
+    df['Provider'] = df['model'].apply(lambda x: str(x).split('/')[0])
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     ascending = True if sort_order == "Low to High" else False
@@ -213,7 +222,7 @@ demo = gr.Blocks(css="""
     text-overflow: ellipsis;
 }
 .table-container {
-    max-width: 800px;
+    width: 100%;
     margin-left: auto;
     margin-right: auto;
 }
@@ -375,4 +384,4 @@ with demo:
         )
     gr.Markdown("Last updated: February 2025")
 
-demo.launch()
+demo.launch()