Add average of averages

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 from css_html_js import custom_css, trigger_plot
 from parse import read_json, read_data
-from utils import model_hyperlink, filter_RTLRepo, filter_bench, handle_special_cases
+from utils import model_hyperlink, filter_RTLRepo, filter_bench, filter_bench_all, handle_special_cases
 from typing import Union
 from about import CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT
 import numpy as np
@@ -13,15 +13,19 @@ import plotly.express as px
 from gradio.themes.utils import colors
 
 def filter_leaderboard(benchmark, model_type, search_query, max_params):
-    subset = df[df['Benchmark'] == benchmark]
+    subset = df.copy()
+    if benchmark != 'All':
+        subset = df[df['Benchmark'] == benchmark]
     if model_type != 'All':
         subset = subset[subset['Model Type'] == model_type]
     if search_query:
         subset = subset[subset['Model'].str.contains(search_query, case=False, na=False)]
     max_params = float(max_params)
     subset = subset[subset['Params'] <= max_params]
-    
-    if benchmark == 'RTL-Repo':
+
+    if benchmark == 'All':
+        return filter_bench_all(subset)
+    elif benchmark == 'RTL-Repo':
         return filter_RTLRepo(subset)
     else:
         return filter_bench(subset)
@@ -93,6 +97,7 @@ function refresh() {
         
 with gr.Blocks(css=custom_css, js=js_func, theme=gr.themes.Default(primary_hue=colors.emerald)) as app:
     df, benchmarks, metrics, default_metric = read_data()
+    benchmarks = ["All"] + benchmarks
     # gr.Markdown("""# TuRTLe 🐢 Model Leaderboard""")
     gr.HTML("""
     <p align="center" style="margin-bottom: -10px;">

utils.py

@@ -45,3 +45,23 @@ def filter_bench(subset: pd.DataFrame) -> pd.DataFrame:
     pivot_df.insert(0, '', range(1, len(pivot_df) + 1))
     return pivot_df
 
+def filter_bench_all(subset: pd.DataFrame) -> pd.DataFrame:
+    details = subset[['Model', 'Model URL', 'Model Type', 'Params']].drop_duplicates('Model')
+    pivot_df = subset.pivot_table(index='Model', columns='Metric', values='Score', aggfunc='mean').reset_index().round(2)
+    pivot_df['🐢 Score (Avg of all) ⬆️'] = pivot_df.mean(axis=1, numeric_only=True).round(2)
+    pivot_df = pd.merge(pivot_df, details, on='Model', how='left')
+    pivot_df['Model'] = pivot_df.apply(lambda row: model_hyperlink(row["Model URL"], row["Model"]), axis=1)
+    pivot_df['Type'] = pivot_df['Model Type'].map(lambda x: type_emoji.get(x, ""))
+    pivot_df.rename(columns={
+        'Syntax (STX)': 'Avg STX', 
+        'Functionality (FNC)': 'Avg FNC', 
+        'Synthesis (SYN)': 'Avg SYN', 
+        'Power': 'Avg Power',
+        'Performance': 'Avg Perf',
+        'Area': 'Avg Area',
+    }, inplace=True)
+    columns_order = ['Type', 'Model', 'Params', '🐢 Score (Avg of all) ⬆️', 'Avg STX', 'Avg FNC', 'Avg SYN', 'Avg Power', 'Avg Perf', 'Avg Area']
+    pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
+    pivot_df = pivot_df.sort_values(by='🐢 Score (Avg of all) ⬆️', ascending=False).reset_index(drop=True)
+    pivot_df.insert(0, '', range(1, len(pivot_df) + 1))
+    return pivot_df