Empty PR for testing

app.py

@@ -379,37 +379,14 @@ with demo.route("About", "/about"):
 logger.info("All routes configured")
 
 # Mount the REST API on /api
-from fastapi import FastAPI, Request
-from fastapi.responses import RedirectResponse
-from starlette.middleware.base import BaseHTTPMiddleware
+from fastapi import FastAPI
 from api import api_app
 
-
-class RootRedirectMiddleware(BaseHTTPMiddleware):
-    """Middleware to redirect root path "/" to "/home".
-    
-    This fixes the 307 trailing slash redirect issue (Gradio bug #11071) that 
-    occurs when Gradio is mounted at "/" - FastAPI's default behavior redirects 
-    "/" to "//", which breaks routing on HuggingFace Spaces.
-    
-    See: https://github.com/gradio-app/gradio/issues/11071
-    """
-    async def dispatch(self, request: Request, call_next):
-        if request.url.path == "/":
-            return RedirectResponse(url="/home", status_code=302)
-        return await call_next(request)
-
-
-# Create a parent FastAPI app with redirect_slashes=False to prevent
-# automatic trailing slash redirects that cause issues with Gradio
-root_app = FastAPI(redirect_slashes=False)
-
-# Add middleware to handle root path redirect to /home
-root_app.add_middleware(RootRedirectMiddleware)
-
+# Create a parent FastAPI app that will host both the API and Gradio
+root_app = FastAPI()
 root_app.mount("/api", api_app)
 
-# Mount Gradio app at root path
+# Mount Gradio app - root redirect is handled by the proxy
 app = gr.mount_gradio_app(root_app, demo, path="/")
 logger.info("REST API mounted at /api, Gradio app mounted at /")
 

main_page.py

@@ -1,7 +1,6 @@
 import matplotlib
 matplotlib.use('Agg')
 import gradio as gr
-import pandas as pd
 
 
 from ui_components import (
@@ -27,32 +26,6 @@ from constants import MARK_BY_DEFAULT
 CACHED_VIEWERS = {}
 CACHED_TAG_MAPS = {}
 
-
-def filter_complete_entries(df: pd.DataFrame) -> pd.DataFrame:
-    if df.empty:
-        return df.copy()
-
-    category_score_columns = [
-        'Issue Resolution Score',
-        'Frontend Score',
-        'Greenfield Score',
-        'Testing Score',
-        'Information Gathering Score',
-    ]
-
-    if all(column in df.columns for column in category_score_columns):
-        return df[df[category_score_columns].notna().all(axis=1)].copy()
-
-    if 'Categories Completed' in df.columns:
-        categories_completed = pd.to_numeric(df['Categories Completed'], errors='coerce')
-        return df[categories_completed >= 5].copy()
-
-    if 'Categories Attempted' in df.columns:
-        return df[df['Categories Attempted'] == '5/5'].copy()
-
-    return df.copy()
-
-
 def build_page():
     with gr.Row(elem_id="intro-row"):
         with gr.Column(scale=1):
@@ -65,91 +38,78 @@ def build_page():
 
     test_df, test_tag_map = get_full_leaderboard_data("test")
     if not test_df.empty:
-        show_incomplete_checkbox, show_open_only_checkbox, mark_by_dropdown = create_leaderboard_display(
+        # Get the checkbox and dropdown returned from create_leaderboard_display
+        show_open_only_checkbox, mark_by_dropdown = create_leaderboard_display(
             full_df=test_df,
             tag_map=test_tag_map,
             category_name=CATEGORY_NAME,
             split_name="test"
         )
-
-        test_df_complete = filter_complete_entries(test_df)
-        has_complete_entries = len(test_df_complete) > 0
-
+        
+        # Prepare open-only filtered dataframe for Winners and Evolution
         if 'Openness' in test_df.columns:
             test_df_open = test_df[test_df['Openness'].str.lower() == 'open'].copy()
         else:
             test_df_open = test_df.copy()
-        test_df_complete_open = filter_complete_entries(test_df_open)
-
-        initial_df = test_df_complete if has_complete_entries else test_df
-
+        
         # --- Winners by Category Section ---
         gr.Markdown("---")
         gr.HTML('<h2>Winners by Category</h2>', elem_id="winners-header")
         gr.Markdown("Top 5 performing systems in each benchmark category.")
-
-        winners_component = gr.HTML(
-            create_winners_by_category_html(initial_df, top_n=5),
-            elem_id="winners-by-category",
-        )
-
+        
+        # Create both all and open-only versions of winners HTML
+        winners_html_all = create_winners_by_category_html(test_df, top_n=5)
+        winners_html_open = create_winners_by_category_html(test_df_open, top_n=5)
+        
+        winners_component = gr.HTML(winners_html_all, elem_id="winners-by-category")
+        
         # --- New Visualization Sections ---
         gr.Markdown("---")
-
+        
         # Evolution Over Time Section
         gr.HTML('<h2>Evolution Over Time</h2>', elem_id="evolution-header")
         gr.Markdown("Track how model performance has improved over time based on release dates.")
-
-        evolution_component = gr.Plot(
-            value=create_evolution_over_time_chart(initial_df, MARK_BY_DEFAULT),
-            elem_id="evolution-chart",
-        )
-
+        
+        # Create initial evolution chart with default mark_by
+        evolution_fig_all = create_evolution_over_time_chart(test_df, MARK_BY_DEFAULT)
+        
+        evolution_component = gr.Plot(value=evolution_fig_all, elem_id="evolution-chart")
+        
         gr.Markdown("---")
-
+        
         # Open Model Accuracy by Size Section (always shows open models only by design)
         gr.HTML('<h2>Open Model Accuracy by Size</h2>', elem_id="size-accuracy-header")
         gr.Markdown("Compare open-weights model performance against their parameter count.")
-
-        size_component = gr.Plot(
-            value=create_accuracy_by_size_chart(initial_df, MARK_BY_DEFAULT),
-            elem_id="size-accuracy-chart",
-        )
-
-        def update_extra_sections(show_incomplete, show_open_only, mark_by):
-            include_incomplete = show_incomplete or not has_complete_entries
-            base_df = test_df if include_incomplete else test_df_complete
-            base_df_open = test_df_open if include_incomplete else test_df_complete_open
-            winners_df = base_df_open if show_open_only else base_df
-
-            winners_html = create_winners_by_category_html(winners_df, top_n=5)
-            evolution_fig = create_evolution_over_time_chart(winners_df, mark_by)
-            size_fig = create_accuracy_by_size_chart(base_df, mark_by)
-
+        
+        size_fig = create_accuracy_by_size_chart(test_df, MARK_BY_DEFAULT)
+        size_component = gr.Plot(value=size_fig, elem_id="size-accuracy-chart")
+        
+        # Update function for Winners, Evolution, and Size charts based on filters
+        def update_extra_sections(show_open_only, mark_by):
+            # Select the appropriate dataframe based on open_only filter
+            df_to_use = test_df_open if show_open_only else test_df
+            
+            # Winners HTML (not affected by mark_by, only open_only)
+            winners_html = winners_html_open if show_open_only else winners_html_all
+            
+            # Regenerate charts with current mark_by setting
+            evolution_fig = create_evolution_over_time_chart(df_to_use, mark_by)
+            size_fig = create_accuracy_by_size_chart(test_df, mark_by)  # Size chart always uses full df (filters internally)
+            
             return winners_html, evolution_fig, size_fig
-
-        show_incomplete_input = show_incomplete_checkbox if show_incomplete_checkbox is not None else gr.State(value=True)
-        show_open_only_input = show_open_only_checkbox if show_open_only_checkbox is not None else gr.State(value=False)
-        extra_section_inputs = [show_incomplete_input, show_open_only_input, mark_by_dropdown]
-
-        if show_incomplete_checkbox is not None:
-            show_incomplete_checkbox.change(
-                fn=update_extra_sections,
-                inputs=extra_section_inputs,
-                outputs=[winners_component, evolution_component, size_component]
-            )
-
+        
+        # Connect both checkbox and dropdown to update all extra sections
         if show_open_only_checkbox is not None:
             show_open_only_checkbox.change(
                 fn=update_extra_sections,
-                inputs=extra_section_inputs,
+                inputs=[show_open_only_checkbox, mark_by_dropdown],
                 outputs=[winners_component, evolution_component, size_component]
             )
-
+        
         if mark_by_dropdown is not None:
             mark_by_dropdown.change(
                 fn=update_extra_sections,
-                inputs=extra_section_inputs,
+                inputs=[show_open_only_checkbox if show_open_only_checkbox else gr.State(value=False), mark_by_dropdown],
                 outputs=[winners_component, evolution_component, size_component]
             )
         

ui_components.py

@@ -1032,8 +1032,8 @@ def create_leaderboard_display(
                 outputs=[dataframe_component, cost_plot_component, runtime_plot_component]
             )
 
-    # Return the filter controls so they can be used to update other sections
-    return show_incomplete_checkbox, show_open_only_checkbox, mark_by_dropdown
+    # Return the show_open_only_checkbox and mark_by_dropdown so they can be used to update other sections
+    return show_open_only_checkbox, mark_by_dropdown
 
 # # --- Detailed Benchmark Display ---
 def create_benchmark_details_display(