Try to make bokeh plots low-weight with bokeh2html()

analyze_winscore.py

@@ -12,6 +12,15 @@ from bokeh.models import LinearColorMapper, HoverTool
 from bokeh.models import CustomJS
 from bokeh.palettes import Turbo256  # A color palette with enough colors
 
+def bokeh2html(obj):
+    from bokeh.embed import components
+    from bokeh.resources import CDN
+    
+    script, div = components(obj, CDN)
+    bokeh_html = f"{CDN.render()}\n{div}\n{script}"
+    
+    return bokeh_html
+
 def bokeh2json(obj):
     from bokeh.document import Document
     

app.py

@@ -740,7 +740,7 @@ def gradio_app():
                         )
                     
                     with gr.Row():
-                        leaderboard_scatter_plot = gr.Plot(
+                        leaderboard_scatter_plot = gr.HTML(
                             value=leaderboard_server.get_leaderboard_scatter_plot(category=leaderboard_server.TASKS_CATEGORY_OVERALL),
                             label='Scatter plot',
                         )
@@ -748,7 +748,7 @@ def gradio_app():
                     gr.Markdown(LEADERBOARD_TAB_BELLOW_SCATTER_PLOT_MARKDOWN)
                     
                     with gr.Row():
-                        leaderboard_heatmap = gr.Plot(
+                        leaderboard_heatmap = gr.HTML(
                             value=leaderboard_server.get_leaderboard_heatmap(category=leaderboard_server.TASKS_CATEGORY_OVERALL),
                             label='Heatmap',
                             elem_classes="leaderboard-heatmap",

server.py

@@ -28,6 +28,7 @@ import pandas as pd
 from huggingface_hub import HfApi, snapshot_download
 
 from compare_significance import SUPPORTED_METRICS
+from analyze_winscore import bokeh2html
 
 VISIBLE_METRICS = SUPPORTED_METRICS + ["macro_f1"]
 
@@ -376,12 +377,12 @@ class LeaderboardServer:
             self.tournament_dataframes_csv = tournament_dataframes_csv
         
         leaderboard_scatter_plots = {kind_of_p_value: {
-            category: self._get_leaderboard_scatter_plot(category=category, kind_of_p_value=kind_of_p_value)
+            category: bokeh2html(self._get_leaderboard_scatter_plot(category=category, kind_of_p_value=kind_of_p_value))
             for category in categories
         } for kind_of_p_value in self.KINDS_OF_P_VALUE}
         
         leaderboard_heatmaps = {kind_of_p_value: {
-            category: self._get_leaderboard_heatmap(category=category, kind_of_p_value=kind_of_p_value)
+            category: bokeh2html(self._get_leaderboard_heatmap(category=category, kind_of_p_value=kind_of_p_value))
             for category in categories
         } for kind_of_p_value in self.KINDS_OF_P_VALUE}
         
@@ -738,7 +739,7 @@ class LeaderboardServer:
             with self.var_lock.ro:
                 return self.leaderboard_scatter_plots[kind_of_p_value][category]
         else:
-            return self._get_leaderboard_scatter_plot(pre_submit=pre_submit, category=category, kind_of_p_value=kind_of_p_value)
+            return bokeh2html(self._get_leaderboard_scatter_plot(pre_submit=pre_submit, category=category, kind_of_p_value=kind_of_p_value))
 
     def _get_leaderboard_scatter_plot(self, pre_submit=None, category=None, kind_of_p_value=None):
         import numpy as np
@@ -794,7 +795,7 @@ class LeaderboardServer:
             with self.var_lock.ro:
                 return self.leaderboard_heatmaps[kind_of_p_value][category]
         else:
-            return self._get_leaderboard_heatmap(pre_submit=pre_submit, category=category, kind_of_p_value=kind_of_p_value)
+            return bokeh2html(self._get_leaderboard_heatmap(pre_submit=pre_submit, category=category, kind_of_p_value=kind_of_p_value))
 
     def _get_leaderboard_heatmap(self, pre_submit=None, category=None, kind_of_p_value=None):
         from analyze_winscore import get_ldb_records, create_heatmap