Add different column names for NST

app.py

@@ -80,7 +80,6 @@ def make_leaderboard_tab(state: Simulator, name: str, html: str=None):
             wrap=True,
             datatype=["html", "html"],
             interactive=False,
-            column_widths=["7%", "28%", "13%", "10%", "13%", "10%", "14%"],
             elem_classes=elem_classes,
         )
 

config/constants.py

@@ -48,6 +48,20 @@ COLUMN_ORDER = [
     "Post-Synthesis",
 ]
 
+NOTSOTINY_COLUMN_ORDER = [
+    "Type",
+    "Model",
+    "Parameters (B)",
+    "TT06",
+    "TT07",
+    "TT08",
+    "TT09",
+    "TT10 IHP 02",
+    "TT10 IHP 25a",
+    "TTSky25a",
+    "AVG",
+]
+
 TYPE_COLORS = {"General": "green", "Coding": "yellow", "RTL-Specific": "blue"}
 
 Y_AXIS_LIMITS = {

data_processing.py

@@ -13,7 +13,7 @@ from config.constants import (
     Y_AXIS_LIMITS,
     DISCARDED_MODELS,
 )
-from utils import filter_bench, filter_bench_all, filter_RTLRepo, handle_special_cases
+from utils import filter_bench, filter_bench_all, filter_NotSoTiny, filter_RTLRepo, handle_special_cases
 
 
 # this is just a simple class to load the correct data depending on which sim we are at
@@ -93,6 +93,8 @@ def filter_leaderboard(task, benchmark, model_type, search_query, max_params, st
             return filter_RTLRepo(subset, name=name)
     elif benchmark == "RTL-Repo":
         return filter_RTLRepo(subset, name=name)
+    elif benchmark == "NotSoTiny":
+        return filter_NotSoTiny(subset, name=name)
     else:
         agg_column = None
         if benchmark == "VerilogEval S2R":
@@ -103,8 +105,6 @@ def filter_leaderboard(task, benchmark, model_type, search_query, max_params, st
             agg_column = "Agg RTLLM"
         elif benchmark == "VeriGen":
             agg_column = "Agg VeriGen"
-        elif benchmark == "NotSoTiny":
-            agg_column = "Agg NotSoTiny"
 
         return filter_bench(subset, state.get_current_agg(), agg_column, name=name)
 

style/css_html_js.py

@@ -123,12 +123,37 @@ input[role="listbox"] {
 .metrics-page p {
     text-align: left !important;
 }
+/* Enable horizontal scrolling for tables with many columns (e.g., NotSoTiny) */
+.dataframe-leaderboard,
+.dataframe-other-models {
+  overflow-x: auto !important;
+  display: block !important;
+  max-width: 100% !important;
+}
+
+.dataframe-leaderboard .table-wrap,
+.dataframe-leaderboard .wrap,
+.dataframe-leaderboard > div,
+.dataframe-leaderboard table,
+.dataframe-other-models .table-wrap,
+.dataframe-other-models .wrap,
+.dataframe-other-models > div,
+.dataframe-other-models table {
+  overflow-x: auto !important;
+  max-width: 100% !important;
+  width: max-content !important;
+}
+
+/* Default cell styling - centered with no wrapping */
 .dataframe-leaderboard td,
 .dataframe-leaderboard th {
   text-align: center !important;
   vertical-align: middle !important;
+  white-space: nowrap !important;
+  min-width: 80px !important;
 }
 
+/* Center-align all table headers by default */
 .dataframe-leaderboard th .cell-wrap,
 .dataframe-leaderboard th .header-content,
 .dataframe-leaderboard th .header-button,
@@ -140,6 +165,7 @@ input[role="listbox"] {
   text-align: center !important;
 }
 
+/* Left-align Type and Model columns in main leaderboard */
 .dataframe-leaderboard tbody td:nth-child(3),
 .dataframe-leaderboard tbody td:nth-child(4) {
   text-align: left !important;

utils.py

@@ -7,7 +7,7 @@ import plotly.express as px
 import plotly.graph_objects as go
 import re
 
-from config.constants import COLUMN_MAPPINGS, COLUMN_ORDER, TYPE_EMOJI, DISCARDED_MODELS
+from config.constants import COLUMN_MAPPINGS, COLUMN_ORDER, NOTSOTINY_COLUMN_ORDER, TYPE_EMOJI, DISCARDED_MODELS
 
 
 def model_hyperlink(link, model_name, release, thinking=False):
@@ -81,6 +81,53 @@ def filter_RTLRepo(subset: pd.DataFrame, name=str) -> pd.DataFrame:
     return filtered_df
 
 
+def filter_NotSoTiny(subset: pd.DataFrame, name=str) -> pd.DataFrame:
+    if subset.empty:
+        return pd.DataFrame(columns=NOTSOTINY_COLUMN_ORDER)
+
+    subset = subset.drop(subset[subset["Score"] < 0.0].index)
+
+    if subset.empty:
+        return pd.DataFrame(columns=NOTSOTINY_COLUMN_ORDER)
+
+    details = subset[["Model", "Model URL", "Model Type", "Params", "Release", "Thinking"]].drop_duplicates(
+        "Model"
+    )
+
+    pivot_df = subset.pivot_table(index="Model", columns="Metric", values="Score").reset_index().round(2)
+
+    # AVG of TT cols...
+    tt_columns = ["TT06", "TT07", "TT08", "TT09", "TT10 IHP 02", "TT10 IHP 25a", "TTSky25a"]
+    available_tt_columns = [col for col in tt_columns if col in pivot_df.columns]
+    if available_tt_columns:
+        pivot_df["AVG"] = pivot_df[available_tt_columns].mean(axis=1).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"],
+            row["Release"],
+            row["Thinking"],
+        ),
+        axis=1,
+    )
+    pivot_df["Type"] = pivot_df["Model Type"].map(lambda x: TYPE_EMOJI.get(x, ""))
+    pivot_df.rename(columns={"Params": "Parameters (B)"}, inplace=True)
+
+    pivot_df = pivot_df[[col for col in NOTSOTINY_COLUMN_ORDER if col in pivot_df.columns]]
+
+    if "AVG" in pivot_df.columns:
+        pivot_df = pivot_df.sort_values(by="AVG", ascending=False).reset_index(drop=True)
+
+    if name == "Other Models":
+        pivot_df["Date Discarded"] = pivot_df["Model"].apply(lambda x: DISCARDED_MODELS.get(extract_name_from_link(x), "N/A"))
+        cols = ["Type", "Model", "Parameters (B)", "Date Discarded"] + tt_columns + ["AVG"]
+        pivot_df = pivot_df[[c for c in cols if c in pivot_df.columns]]
+
+    return pivot_df
+
+
 def filter_bench(subset: pd.DataFrame, df_agg=None, agg_column=None, name=str) -> pd.DataFrame:
     if subset.empty:
         return pd.DataFrame(columns=COLUMN_ORDER)