Remove open/closed distinction from graph, use company logos as data points

- Replaced colored markers (pink/yellow) with company logo images on scatter plots
- Downloaded official logos from SimpleIcons CDN (Anthropic, Google, Meta, HuggingFace)
- Created recognizable logos for OpenAI, Mistral, DeepSeek, Cohere, Moonshot
- Updated graph legend to show 'Company Logos' instead of 'Model Openness'
- Maintained invisible hover markers for tooltip functionality
- Model names still labeled on Pareto frontier points
- Tables still show company logos in first column

Co-authored-by: openhands <openhands@all-hands.dev>

leaderboard_transformer.py

@@ -548,60 +548,58 @@ def _plot_scatter_plotly(
     # Use consistent shape for all points (no tooling distinction)
     data_plot['shape_symbol'] = default_shape
 
-    # --- Section 6: Plot Markers by "Openness" Category with Company Logos ---
+    # --- Section 6: Plot Company Logo Images as Markers (replacing open/closed distinction) ---
     # Collect layout images for company logos
     layout_images = []
     
-    for category in category_order:
-        group = data_plot[data_plot['Openness'] == category]
-        if group.empty:
-            continue
-
-        fig.add_trace(go.Scatter(
-            x=group[x_col_to_use],
-            y=group[y_col_to_use],
-            mode='markers',
-            name=category,
-            showlegend=False,
-            text=group['hover_text'],
-            hoverinfo='text',
-            marker=dict(
-                color=color_map.get(category, 'black'),
-                symbol=default_shape,
-                size=15,
-                opacity=0.8,
-                line=dict(width=1, color='deeppink')
-            )
-        ))
+    # Add invisible markers for hover functionality (all points together, no color distinction)
+    fig.add_trace(go.Scatter(
+        x=data_plot[x_col_to_use],
+        y=data_plot[y_col_to_use],
+        mode='markers',
+        name='Models',
+        showlegend=False,
+        text=data_plot['hover_text'],
+        hoverinfo='text',
+        marker=dict(
+            color='rgba(0,0,0,0)',  # Invisible markers
+            size=25,  # Large enough for hover detection
+            opacity=0
+        )
+    ))
+    
+    # Add company logo images for each data point
+    for _, row in data_plot.iterrows():
+        model_name = row.get('Language Model', '')
+        company_info = get_company_from_model_name(model_name)
+        logo_path = company_info['path']
         
-        # Add company logo images for each point in this group
-        for _, row in group.iterrows():
-            model_name = row.get('Language Model', '')
-            company_info = get_company_from_model_name(model_name)
-            logo_path = company_info['path']
-            
-            # Read the SVG file and encode as base64 data URI
-            if os.path.exists(logo_path):
-                try:
-                    with open(logo_path, 'rb') as f:
-                        encoded_logo = base64.b64encode(f.read()).decode('utf-8')
-                        logo_uri = f"data:image/svg+xml;base64,{encoded_logo}"
-                        
-                        # Add image at data point position with small offset
-                        layout_images.append(dict(
-                            source=logo_uri,
-                            xref="x",
-                            yref="y",
-                            x=row[x_col_to_use],
-                            y=row[y_col_to_use],
-                            sizex=max_reported_cost * 0.06 if max_reported_cost > 0 else 0.5,  # Scale relative to data
-                            sizey=3,  # Fixed y size in data units
-                            xanchor="center",
-                            yanchor="bottom",
-                            layer="above"
-                        ))
-                except Exception as e:
-                    logger.warning(f"Could not load logo {logo_path}: {e}")
+        # Read the SVG file and encode as base64 data URI
+        if os.path.exists(logo_path):
+            try:
+                with open(logo_path, 'rb') as f:
+                    encoded_logo = base64.b64encode(f.read()).decode('utf-8')
+                    logo_uri = f"data:image/svg+xml;base64,{encoded_logo}"
+                    
+                    # Calculate logo size based on data range
+                    logo_size_x = max_reported_cost * 0.08 if max_reported_cost > 0 else 0.5
+                    logo_size_y = 4  # Fixed y size in data units
+                    
+                    # Add image at data point position, centered on the point
+                    layout_images.append(dict(
+                        source=logo_uri,
+                        xref="x",
+                        yref="y",
+                        x=row[x_col_to_use],
+                        y=row[y_col_to_use],
+                        sizex=logo_size_x,
+                        sizey=logo_size_y,
+                        xanchor="center",
+                        yanchor="middle",
+                        layer="above"
+                    ))
+            except Exception as e:
+                logger.warning(f"Could not load logo {logo_path}: {e}")
 
     # --- Section 7: Add Model Name Labels to Frontier Points ---
     if frontier_rows:

ui_components.py

@@ -307,20 +307,23 @@ def create_legend_markdown(which_table: str) -> str:
     """
     return legend_markdown
 
-# Create HTML for plot legend with SVG icons and keys
-openness_legend_items = []
-for name, info in OPENNESS_SVG_MAP.items():
-    uri = get_svg_as_data_uri(info["path"])
+# Create HTML for plot legend with company logos
+company_legend_items = []
+# Show a sample of company logos in the legend
+sample_companies = [
+    ("Anthropic", "assets/logo-anthropic.svg"),
+    ("OpenAI", "assets/logo-openai.svg"),
+    ("Google", "assets/logo-google.svg"),
+    ("Meta", "assets/logo-meta.svg"),
+    ("Mistral", "assets/logo-mistral.svg"),
+]
+for name, path in sample_companies:
+    uri = get_svg_as_data_uri(path)
     if uri:
-        openness_legend_items.append(
+        company_legend_items.append(
             f'<div class="plot-legend-item">'
-                f'<img class="plot-legend-item-svg" src="{uri}" alt="{name}" title="{name}">'
-                f'<div class="plot-legend-item-text">'
-                    f'<div>'
-                        f'<span>{name}</span>'
-                    f'</div>'
-                    f'<span class="description">{info["description"]}</span>'
-                f'</div>'
+                f'<img class="plot-legend-item-svg" src="{uri}" alt="{name}" title="{name}" style="width: 20px; height: 20px;">'
+                f'<span>{name}</span>'
             f'</div>'
         )
 
@@ -339,9 +342,9 @@ plot_legend_html = f"""
             </div>
         </div>
         <div>
-            <span class="plot-legend-category-heading">Model Openness</span>
+            <span class="plot-legend-category-heading">Company Logos</span>
             <div style="margin-top: 8px;">
-                {''.join(openness_legend_items)}
+                {''.join(company_legend_items)}
             </div>
         </div>
     </div>