Merge branch 'per_task_results' into pr/26

app.py

@@ -26,23 +26,28 @@ def make_default_md(arena_df, elo_results):
 | [Vote](https://chat.lmsys.org) | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
 
 LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals.
-We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system.
+We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system. Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)!
 """
     return leaderboard_md
 
 
-def make_arena_leaderboard_md(arena_df):
+def make_arena_leaderboard_md(arena_df, arena_subset_df=None, name="Overall"):
     total_votes = sum(arena_df["num_battles"]) // 2
     total_models = len(arena_df)
-
+    space = "   "
+    if arena_subset_df is not None:
+        total_subset_votes = sum(arena_subset_df["num_battles"]) // 2
+        total_subset_models = len(arena_subset_df)
+        vote_str = f"{space} {name} #models: **{total_subset_models}**.{space} {name} #votes: **{'{:,}'.format(total_subset_votes)}**."
+    else:
+        vote_str = ""
     leaderboard_md = f"""
-Total #models: **{total_models}**. Total #votes: **{total_votes}**. Last updated: March 29, 2024.
+Total #models: **{total_models}**.{space} Total #votes: **{"{:,}".format(total_votes)}**.{vote_str}{space} Last updated: March 29, 2024.
 
-Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}).
+**NEW!** Click the buttons below to view the ELO leaderboard and stats for different input categories. You are currently viewing **{name}** inputs.
 """
     return leaderboard_md
 
-
 def make_full_leaderboard_md(elo_results):
     leaderboard_md = f"""
 Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**.
@@ -202,51 +207,82 @@ def get_full_table(arena_df, model_table_df):
     values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
     return values
 
-
-def get_arena_table(arena_df, model_table_df):
-    # sort by rating
+def create_ranking_str(ranking, ranking_difference):
+    if ranking_difference > 0:
+        return f"{int(ranking)} (\u2191{int(ranking_difference)})"
+    elif ranking_difference < 0:
+        return f"{int(ranking)} (\u2193{int(-ranking_difference)})"
+    else:
+        return f"{int(ranking)}"
+    
+def get_arena_table(arena_df, model_table_df, arena_subset_df=None):
     arena_df = arena_df.sort_values(by=["rating"], ascending=False)
+    arena_df = arena_df.sort_values(by=["final_ranking"], ascending=True)
+    # sort by rating
+    if arena_subset_df is not None: 
+        arena_subset_df = arena_subset_df.sort_values(by=["rating"], ascending=False)
+        arena_subset_df = arena_subset_df.sort_values(by=["final_ranking"], ascending=True)
+        # join arena_df and arena_subset_df on index
+        arena_df = arena_subset_df.join(arena_df["final_ranking"], rsuffix="_global", how="inner")
+        arena_df['ranking_difference'] =  arena_df['final_ranking_global'] - arena_df['final_ranking']
+        arena_df["final_ranking"] = arena_df.apply(lambda x: create_ranking_str(x["final_ranking"], x["ranking_difference"]), axis=1)
+    
     values = []
     for i in range(len(arena_df)):
         row = []
         model_key = arena_df.index[i]
-        model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
-            0
-        ]
-
-        # rank
-        ranking = arena_df.iloc[i].get("final_ranking") or i+1
-        row.append(ranking)
-        # model display name
-        row.append(model_name)
-        # elo rating
-        row.append(round(arena_df.iloc[i]["rating"]))
-        upper_diff = round(
-            arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
-        )
-        lower_diff = round(
-            arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
-        )
-        row.append(f"+{upper_diff}/-{lower_diff}")
-        # num battles
-        row.append(round(arena_df.iloc[i]["num_battles"]))
-        # Organization
-        row.append(
-            model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
-        )
-        # license
-        row.append(
-            model_table_df[model_table_df["key"] == model_key]["License"].values[0]
-        )
+        try:
+            model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
+                0
+            ]
+
+            # rank
+            ranking = arena_df.iloc[i].get("final_ranking") or i+1
+            row.append(ranking)
+            # model display name
+            row.append(model_name)
+            # elo rating
+            row.append(round(arena_df.iloc[i]["rating"]))
+            upper_diff = round(
+                arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
+            )
+            lower_diff = round(
+                arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
+            )
+            row.append(f"+{upper_diff}/-{lower_diff}")
+            # num battles
+            row.append(round(arena_df.iloc[i]["num_battles"]))
+            # Organization
+            row.append(
+                model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
+            )
+            # license
+            row.append(
+                model_table_df[model_table_df["key"] == model_key]["License"].values[0]
+            )
 
-        cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0]
-        if cutoff_date == "-":
-            row.append("Unknown")
-        else:
-            row.append(cutoff_date)
-        values.append(row)
+            cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0]
+            if cutoff_date == "-":
+                row.append("Unknown")
+            else:
+                row.append(cutoff_date)
+            values.append(row)
+        except Exception as e:
+            print(f"{model_key} - {e}")
     return values
 
+def update_leaderboard_and_plots(button, arena_df, model_table_df, arena_subset_df, elo_subset_results):
+    arena_values = get_arena_table(arena_df, model_table_df, arena_subset_df)
+    p1 = elo_subset_results["win_fraction_heatmap"]
+    p2 = elo_subset_results["battle_count_heatmap"]
+    p3 = elo_subset_results["bootstrap_elo_rating"]
+    p4 = elo_subset_results["average_win_rate_bar"]
+    more_stats_md = f"""## More Statistics for Chatbot Arena ({button})
+    """
+    leaderboard_md = make_arena_leaderboard_md(arena_df, arena_subset_df, name=button)
+    return arena_values, p1, p2, p3, p4, more_stats_md, leaderboard_md
+
+
 def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False):
     if elo_results_file is None:  # Do live update
         default_md = "Loading ..."
@@ -255,6 +291,9 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
         with open(elo_results_file, "rb") as fin:
             elo_results = pickle.load(fin)
             if "full" in elo_results:
+                elo_chinese_results = elo_results["chinese"]
+                elo_long_results = elo_results["long"]
+                elo_english_results = elo_results["english"]
                 elo_results = elo_results["full"]
 
         p1 = elo_results["win_fraction_heatmap"]
@@ -262,9 +301,13 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
         p3 = elo_results["bootstrap_elo_rating"]
         p4 = elo_results["average_win_rate_bar"]
         arena_df = elo_results["leaderboard_table_df"]
+        arena_chinese_df = elo_chinese_results["leaderboard_table_df"]
+        arena_long_df = elo_long_results["leaderboard_table_df"]
+        arena_english_df = elo_english_results["leaderboard_table_df"]
         default_md = make_default_md(arena_df, elo_results)
 
     md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
+    # md = make_arena_leaderboard_md(arena_df, arena_chinese_df, arena_long_df, arena_english_df)
     if leaderboard_table_file:
         data = load_leaderboard_table_csv(leaderboard_table_file)
         model_table_df = pd.DataFrame(data)
@@ -274,8 +317,21 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
             arena_table_vals = get_arena_table(arena_df, model_table_df)
             with gr.Tab("Arena Elo", id=0):
                 md = make_arena_leaderboard_md(arena_df)
-                gr.Markdown(md, elem_id="leaderboard_markdown")
-                gr.Dataframe(
+                leaderboard_markdown = gr.Markdown(md, elem_id="leaderboard_markdown")
+                with gr.Row():
+                    overall_rating = gr.Button("Overall")
+                    # update_overall_rating_df = lambda _: get_arena_table(arena_df, model_table_df)
+                    update_overall_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, None, elo_results)
+                    english_rating = gr.Button("English")
+                    update_english_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_english_df, elo_english_results)
+                    # update_english_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_english_df)
+                    chinese_rating = gr.Button("Chinese")
+                    update_chinese_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_chinese_df, elo_chinese_results)
+                    # update_chinese_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_chinese_df)
+                    long_context_rating = gr.Button("Long Context")
+                    update_long_context_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_long_df, elo_long_results)
+                    # update_long_context_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_long_df)
+                elo_display_df = gr.Dataframe(
                     headers=[
                         "Rank",
                         "🤖 Model",
@@ -299,9 +355,10 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
                     value=arena_table_vals,
                     elem_id="arena_leaderboard_dataframe",
                     height=700,
-                    column_widths=[50, 200, 120, 100, 100, 150, 150, 100],
+                    column_widths=[70, 190, 110, 100, 90, 160, 150, 140],
                     wrap=True,
                 )
+
             with gr.Tab("Full Leaderboard", id=1):
                 md = make_full_leaderboard_md(elo_results)
                 gr.Markdown(md, elem_id="leaderboard_markdown")
@@ -335,7 +392,7 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Fa
     gr.Markdown(
         f"""Note: we take the 95% confidence interval into account when determining a model's ranking.
 A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score.
-See Figure 3 below for visualization of the confidence intervals.
+See Figure 3 below for visualization of the confidence intervals. Code to recreate these tables and plots in this [notebook]({notebook_url}) and more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
 """,
         elem_id="leaderboard_markdown"
     )
@@ -343,35 +400,38 @@ See Figure 3 below for visualization of the confidence intervals.
     leader_component_values[:] = [default_md, p1, p2, p3, p4]
 
     if show_plot:
-        gr.Markdown(
-            f"""## More Statistics for Chatbot Arena\n
-Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}).
-You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
-    """,
-            elem_id="leaderboard_markdown"
-        )
+        # more_stats_md = gr.Markdown(
+        #     f"""## More Statistics for Chatbot Arena (Overall)""",
+        #     elem_id="leaderboard_markdown"
+        # )
+        more_stats_md = gr.Button("More Statistics for Chatbot Arena (Overall)", elem_id="non-interactive-button")
         with gr.Row():
             with gr.Column():
                 gr.Markdown(
-                    "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
+                    "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles", elem_id="plot-title", variant="panel"
                 )
-                plot_1 = gr.Plot(p1, show_label=False)
+                plot_1 = gr.Plot(p1, show_label=False, elem_id="plot-container")
             with gr.Column():
                 gr.Markdown(
-                    "#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
+                    "#### Figure 2: Battle Count for Each Combination of Models (without Ties)", elem_id="plot-title"
                 )
                 plot_2 = gr.Plot(p2, show_label=False)
         with gr.Row():
             with gr.Column():
                 gr.Markdown(
-                    "#### Figure 3: Confidence Intervals on Model Strength (via Bootstrapping)"
+                    "#### Figure 3: Confidence Intervals on Model Strength (via Bootstrapping)", elem_id="plot-title"
                 )
                 plot_3 = gr.Plot(p3, show_label=False)
             with gr.Column():
                 gr.Markdown(
-                    "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
+                    "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)", elem_id="plot-title"
                 )
                 plot_4 = gr.Plot(p4, show_label=False)
+                
+    overall_rating.click(fn=update_overall_rating_df, inputs=overall_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
+    english_rating.click(fn=update_english_rating_df, inputs=english_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
+    chinese_rating.click(fn=update_chinese_rating_df, inputs=chinese_rating ,outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
+    long_context_rating.click(fn=update_long_context_rating_df, inputs=long_context_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown])
 
     gr.Markdown(acknowledgment_md)
 
@@ -379,6 +439,7 @@ You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12
         return [md_1, plot_1, plot_2, plot_3, plot_4]
     return [md_1]
 
+
 block_css = """
 #notice_markdown {
     font-size: 104%
@@ -397,9 +458,30 @@ block_css = """
     padding-top: 6px;
     padding-bottom: 6px;
 }
+
 #leaderboard_dataframe td {
     line-height: 0.1em;
 }
+
+#plot-title {
+    text-align: center;
+    display:block;
+}
+
+#non-interactive-button {
+  display: inline-block;
+  padding: 10px 10px;
+  background-color: #f7f7f7; /* Super light grey background */
+  color: #000000; /* Black text */
+  text-align: center;
+  font-size: 26px; /* Larger text */
+  border-radius: 0; /* Straight edges, no border radius */
+  border: 0px solid #dcdcdc; /* A light grey border to match the background */
+  font-weight: bold;
+  user-select: none; /* The text inside the button is not selectable */
+  pointer-events: none; /* The button is non-interactive */
+}
+
 footer {
     display:none !important
 }
@@ -429,10 +511,12 @@ We thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a1
 
 def build_demo(elo_results_file, leaderboard_table_file):
     text_size = gr.themes.sizes.text_lg
-
+    theme = gr.themes.Base(text_size=text_size)
+    theme.set(button_secondary_background_fill_hover="*primary_300", button_secondary_background_fill_hover_dark="*primary_700")
     with gr.Blocks(
         title="Chatbot Arena Leaderboard",
-        theme=gr.themes.Base(text_size=text_size),
+        theme=theme,
+        # theme = gr.themes.Base.load("theme.json"),
         css=block_css,
     ) as demo:
         leader_components = build_leaderboard_tab(

theme.json

@@ -0,0 +1 @@
+{"theme": {"_font": [{"__gradio_font__": true, "name": "Rubik", "class": "google"}, {"__gradio_font__": true, "name": "ui-sans-serif", "class": "font"}, {"__gradio_font__": true, "name": "system-ui", "class": "font"}, {"__gradio_font__": true, "name": "sans-serif", "class": "font"}], "_font_mono": [{"__gradio_font__": true, "name": "Inconsolata", "class": "google"}, {"__gradio_font__": true, "name": "ui-monospace", "class": "font"}, {"__gradio_font__": true, "name": "Consolas", "class": "font"}, {"__gradio_font__": true, "name": "monospace", "class": "font"}], "_stylesheets": ["https://fonts.googleapis.com/css2?family=Rubik:wght@400;500&display=swap", "https://fonts.googleapis.com/css2?family=Inconsolata:wght@400;500&display=swap"], "text_size": "20px", "background_fill_primary": "white", "background_fill_primary_dark": "*neutral_950", "background_fill_secondary": "*neutral_50", "background_fill_secondary_dark": "*neutral_900", "block_background_fill": "*background_fill_primary", "block_background_fill_dark": "*neutral_800", "block_border_color": "*border_color_primary", "block_border_color_dark": "*border_color_primary", "block_border_width": "1px", "block_border_width_dark": "1px", "block_info_text_color": "*body_text_color_subdued", "block_info_text_color_dark": "*body_text_color_subdued", "block_info_text_size": "*text_sm", "block_info_text_weight": "400", "block_label_background_fill": "*background_fill_primary", "block_label_background_fill_dark": "*background_fill_secondary", "block_label_border_color": "*border_color_primary", "block_label_border_color_dark": "*border_color_primary", "block_label_border_width": "1px", "block_label_border_width_dark": "1px", "block_label_margin": "0", "block_label_padding": "*spacing_sm *spacing_lg", "block_label_radius": "calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0", "block_label_right_radius": "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)", "block_label_shadow": "*block_shadow", "block_label_text_color": "*neutral_500", "block_label_text_color_dark": "*neutral_200", "block_label_text_size": "*text_sm", "block_label_text_weight": "400", "block_padding": "*spacing_xl calc(*spacing_xl + 2px)", "block_radius": "*radius_lg", "block_shadow": "none", "block_shadow_dark": "none", "block_title_background_fill": "none", "block_title_background_fill_dark": "none", "block_title_border_color": "none", "block_title_border_color_dark": "none", "block_title_border_width": "0px", "block_title_border_width_dark": "0px", "block_title_padding": "0", "block_title_radius": "none", "block_title_text_color": "*neutral_500", "block_title_text_color_dark": "*neutral_200", "block_title_text_size": "*text_md", "block_title_text_weight": "400", "body_background_fill": "*background_fill_primary", "body_background_fill_dark": "*background_fill_primary", "body_text_color": "*neutral_700", "body_text_color_dark": "*neutral_200", "body_text_color_subdued": "*neutral_400", "body_text_color_subdued_dark": "*neutral_500", "body_text_size": "*text_md", "body_text_weight": "400", "border_color_accent": "*primary_300", "border_color_accent_dark": "*neutral_600", "border_color_primary": "*neutral_200", "border_color_primary_dark": "*neutral_700", "button_border_width": "*input_border_width", "button_border_width_dark": "*input_border_width", "button_cancel_background_fill": "*button_secondary_background_fill", "button_cancel_background_fill_dark": "*button_secondary_background_fill", "button_cancel_background_fill_hover": "*button_cancel_background_fill", "button_cancel_background_fill_hover_dark": "*button_cancel_background_fill", "button_cancel_border_color": "*button_secondary_border_color", "button_cancel_border_color_dark": "*button_secondary_border_color", "button_cancel_border_color_hover": "*button_cancel_border_color", "button_cancel_border_color_hover_dark": "*button_cancel_border_color", "button_cancel_text_color": "*button_secondary_text_color", "button_cancel_text_color_dark": "*button_secondary_text_color", "button_cancel_text_color_hover": "*button_cancel_text_color", "button_cancel_text_color_hover_dark": "*button_cancel_text_color", "button_large_padding": "*spacing_lg calc(2 * *spacing_lg)", "button_large_radius": "*radius_lg", "button_large_text_size": "*text_lg", "button_large_text_weight": "500", "button_primary_background_fill": "*primary_200", "button_primary_background_fill_dark": "*primary_700", "button_primary_background_fill_hover": "*button_primary_background_fill", "button_primary_background_fill_hover_dark": "*button_primary_background_fill", "button_primary_border_color": "*primary_200", "button_primary_border_color_dark": "*primary_600", "button_primary_border_color_hover": "*button_primary_border_color", "button_primary_border_color_hover_dark": "*button_primary_border_color", "button_primary_text_color": "*primary_600", "button_primary_text_color_dark": "white", "button_primary_text_color_hover": "*button_primary_text_color", "button_primary_text_color_hover_dark": "*button_primary_text_color", "button_secondary_background_fill": "*neutral_200", "button_secondary_background_fill_dark": "*neutral_600", "button_secondary_background_fill_hover": "*neutral_300", "button_secondary_background_fill_hover_dark": "*neutral_500", "button_secondary_border_color": "*neutral_200", "button_secondary_border_color_dark": "*neutral_600", "button_secondary_border_color_hover": "*button_secondary_border_color", "button_secondary_border_color_hover_dark": "*button_secondary_border_color", "button_secondary_text_color": "*neutral_700", "button_secondary_text_color_dark": "white", "button_secondary_text_color_hover": "*button_secondary_text_color", "button_secondary_text_color_hover_dark": "*button_secondary_text_color", "button_shadow": "none", "button_shadow_active": "none", "button_shadow_hover": "none", "button_small_padding": "*spacing_sm calc(2 * *spacing_sm)", "button_small_radius": "*radius_lg", "button_small_text_size": "*text_md", "button_small_text_weight": "400", "button_transition": "background-color 0.2s ease", "checkbox_background_color": "*background_fill_primary", "checkbox_background_color_dark": "*neutral_800", "checkbox_background_color_focus": "*checkbox_background_color", "checkbox_background_color_focus_dark": "*checkbox_background_color", "checkbox_background_color_hover": "*checkbox_background_color", "checkbox_background_color_hover_dark": "*checkbox_background_color", "checkbox_background_color_selected": "*secondary_600", "checkbox_background_color_selected_dark": "*secondary_600", "checkbox_border_color": "*neutral_300", "checkbox_border_color_dark": "*neutral_700", "checkbox_border_color_focus": "*secondary_500", "checkbox_border_color_focus_dark": "*secondary_500", "checkbox_border_color_hover": "*neutral_300", "checkbox_border_color_hover_dark": "*neutral_600", "checkbox_border_color_selected": "*secondary_600", "checkbox_border_color_selected_dark": "*secondary_600", "checkbox_border_radius": "*radius_sm", "checkbox_border_width": "*input_border_width", "checkbox_border_width_dark": "*input_border_width", "checkbox_check": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")", "checkbox_label_background_fill": "*button_secondary_background_fill", "checkbox_label_background_fill_dark": "*button_secondary_background_fill", "checkbox_label_background_fill_hover": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_hover_dark": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_selected": "*checkbox_label_background_fill", "checkbox_label_background_fill_selected_dark": "*checkbox_label_background_fill", "checkbox_label_border_color": "*border_color_primary", "checkbox_label_border_color_dark": "*border_color_primary", "checkbox_label_border_color_hover": "*checkbox_label_border_color", "checkbox_label_border_color_hover_dark": "*checkbox_label_border_color", "checkbox_label_border_width": "*input_border_width", "checkbox_label_border_width_dark": "*input_border_width", "checkbox_label_gap": "*spacing_lg", "checkbox_label_padding": "*spacing_md calc(2 * *spacing_md)", "checkbox_label_shadow": "none", "checkbox_label_text_color": "*body_text_color", "checkbox_label_text_color_dark": "*body_text_color", "checkbox_label_text_color_selected": "*checkbox_label_text_color", "checkbox_label_text_color_selected_dark": "*checkbox_label_text_color", "checkbox_label_text_size": "*text_md", "checkbox_label_text_weight": "400", "checkbox_shadow": "*input_shadow", "color_accent": "*primary_500", "color_accent_soft": "*primary_50", "color_accent_soft_dark": "*neutral_700", "container_radius": "*radius_lg", "embed_radius": "*radius_md", "error_background_fill": "#fee2e2", "error_background_fill_dark": "*background_fill_primary", "error_border_color": "#fecaca", "error_border_color_dark": "*border_color_primary", "error_border_width": "1px", "error_border_width_dark": "1px", "error_text_color": "#ef4444", "error_text_color_dark": "#ef4444", "font": "'Rubik', 'ui-sans-serif', 'system-ui', sans-serif", "font_mono": "'Inconsolata', 'ui-monospace', 'Consolas', monospace", "form_gap_width": "0px", "input_background_fill": "*neutral_100", "input_background_fill_dark": "*neutral_700", "input_background_fill_focus": "*secondary_500", "input_background_fill_focus_dark": "*secondary_600", "input_background_fill_hover": "*input_background_fill", "input_background_fill_hover_dark": "*input_background_fill", "input_border_color": "*border_color_primary", "input_border_color_dark": "*border_color_primary", "input_border_color_focus": "*secondary_300", "input_border_color_focus_dark": "*neutral_700", "input_border_color_hover": "*input_border_color", "input_border_color_hover_dark": "*input_border_color", "input_border_width": "0px", "input_border_width_dark": "0px", "input_padding": "*spacing_xl", "input_placeholder_color": "*neutral_400", "input_placeholder_color_dark": "*neutral_500", "input_radius": "*radius_lg", "input_shadow": "none", "input_shadow_dark": "none", "input_shadow_focus": "*input_shadow", "input_shadow_focus_dark": "*input_shadow", "input_text_size": "*text_md", "input_text_weight": "400", "layout_gap": "*spacing_xxl", "link_text_color": "*secondary_600", "link_text_color_active": "*secondary_600", "link_text_color_active_dark": "*secondary_500", "link_text_color_dark": "*secondary_500", "link_text_color_hover": "*secondary_700", "link_text_color_hover_dark": "*secondary_400", "link_text_color_visited": "*secondary_500", "link_text_color_visited_dark": "*secondary_600", "loader_color": "*color_accent", "loader_color_dark": "*color_accent", "name": "base", "neutral_100": "#f5f5f4", "neutral_200": "#e7e5e4", "neutral_300": "#d6d3d1", "neutral_400": "#a8a29e", "neutral_50": "#fafaf9", "neutral_500": "#78716c", "neutral_600": "#57534e", "neutral_700": "#44403c", "neutral_800": "#292524", "neutral_900": "#1c1917", "neutral_950": "#0f0e0d", "panel_background_fill": "*background_fill_secondary", "panel_background_fill_dark": "*background_fill_secondary", "panel_border_color": "*border_color_primary", "panel_border_color_dark": "*border_color_primary", "panel_border_width": "0", "panel_border_width_dark": "0", "primary_100": "#e0f2fe", "primary_200": "#bae6fd", "primary_300": "#7dd3fc", "primary_400": "#38bdf8", "primary_50": "#f0f9ff", "primary_500": "#0ea5e9", "primary_600": "#0284c7", "primary_700": "#0369a1", "primary_800": "#075985", "primary_900": "#0c4a6e", "primary_950": "#0b4165", "prose_header_text_weight": "500", "prose_text_size": "*text_md", "prose_text_weight": "400", "radio_circle": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")", "radius_lg": "3px", "radius_md": "3px", "radius_sm": "3px", "radius_xl": "3px", "radius_xs": "3px", "radius_xxl": "3px", "radius_xxs": "3px", "secondary_100": "#e0f2fe", "secondary_200": "#bae6fd", "secondary_300": "#7dd3fc", "secondary_400": "#38bdf8", "secondary_50": "#f0f9ff", "secondary_500": "#0ea5e9", "secondary_600": "#0284c7", "secondary_700": "#0369a1", "secondary_800": "#075985", "secondary_900": "#0c4a6e", "secondary_950": "#0b4165", "section_header_text_size": "*text_md", "section_header_text_weight": "400", "shadow_drop": "rgba(0,0,0,0.05) 0px 1px 2px 0px", "shadow_drop_lg": "0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)", "shadow_inset": "rgba(0,0,0,0.05) 0px 2px 4px 0px inset", "shadow_spread": "3px", "shadow_spread_dark": "1px", "slider_color": "*primary_600", "slider_color_dark": "*primary_600", "spacing_lg": "8px", "spacing_md": "6px", "spacing_sm": "4px", "spacing_xl": "10px", "spacing_xs": "2px", "spacing_xxl": "16px", "spacing_xxs": "1px", "stat_background_fill": "*primary_300", "stat_background_fill_dark": "*primary_500", "table_border_color": "*neutral_300", "table_border_color_dark": "*neutral_700", "table_even_background_fill": "white", "table_even_background_fill_dark": "*neutral_950", "table_odd_background_fill": "*neutral_50", "table_odd_background_fill_dark": "*neutral_900", "table_radius": "*radius_lg", "table_row_focus": "*color_accent_soft", "table_row_focus_dark": "*color_accent_soft", "text_lg": "20px", "text_md": "16px", "text_sm": "14px", "text_xl": "24px", "text_xs": "12px", "text_xxl": "28px", "text_xxs": "10px"}, "version": "0.0.1"}