Spacing between cols

app.py

@@ -280,15 +280,13 @@ with gr.Blocks(
                 ],
                 interactive=False,
                 column_widths=[
-                    "7%",
-                    "25%",
-                    "10%",
-                    "8%",
-                    "8%",
-                    "8%",
-                    "8%",
-                    "8%",
-                    "8%",
+                    "5%",
+                    "27%",
+                    "15%",
+                    "9%",
+                    "12%",
+                    "9%",
+                    "14%",
                 ],
             )
 

utils.py

@@ -86,29 +86,32 @@ def filter_bench(subset: pd.DataFrame, df_agg=None, agg_column=None) -> pd.DataF
         axis=1,
     )
     pivot_df["Type"] = pivot_df["Model Type"].map(lambda x: type_emoji.get(x, ""))
+    pivot_df["Post-Synthesis (PSQ)"] = (
+        pivot_df[["Power", "Performance", "Area"]].mean(axis=1).round(2)
+    )
+
     pivot_df.rename(
         columns={
-            "Syntax (STX)": "STX",
-            "Functionality (FNC)": "FNC",
-            "Synthesis (SYN)": "SYN",
-            "Performance": "Perf",
+            "Syntax (STX)": "Syntax",
+            "Functionality (FNC)": "Functionality",
+            "Synthesis (SYN)": "Synthesis",
+            "Post-Synthesis (PSQ)": "Post-Synthesis",
         },
         inplace=True,
     )
-
     columns_order = [
         "Type",
         "Model",
         "Params",
-        "STX",
-        "FNC",
-        "SYN",
-        "Power",
-        "Perf",
-        "Area",
+        "Syntax",
+        "Functionality",
+        "Synthesis",
+        "Post-Synthesis",
     ]
     pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
-    pivot_df = pivot_df.sort_values(by="FNC", ascending=False).reset_index(drop=True)
+    pivot_df = pivot_df.sort_values(by="Functionality", ascending=False).reset_index(
+        drop=True
+    )
     return pivot_df
 
 
@@ -165,16 +168,17 @@ def filter_bench_all(
         axis=1,
     )
     pivot_df["Type"] = pivot_df["Model Type"].map(lambda x: type_emoji.get(x, ""))
+    pivot_df["Post-Synthesis Quality"] = (
+        pivot_df[["Power", "Performance", "Area"]].mean(axis=1).round(2)
+    )
 
     pivot_df.rename(
         columns={
             "Exact Matching (EM)": "EM",
-            "Syntax (STX)": "Agg STX",
-            "Functionality (FNC)": "Agg FNC",
-            "Synthesis (SYN)": "Agg SYN",
-            "Power": "Agg Power",
-            "Performance": "Agg Perf",
-            "Area": "Agg Area",
+            "Syntax (STX)": "Syntax",
+            "Functionality (FNC)": "Functionality",
+            "Synthesis (SYN)": "Synthesis",
+            "Post-Synthesis Quality": "Post-Synthesis",
         },
         inplace=True,
     )
@@ -183,15 +187,13 @@ def filter_bench_all(
         "Type",
         "Model",
         "Params",
-        "Agg STX",
-        "Agg FNC",
-        "Agg SYN",
-        "Agg Power",
-        "Agg Perf",
-        "Agg Area",
+        "Syntax",
+        "Functionality",
+        "Synthesis",
+        "Post-Synthesis",
     ]
     pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
-    pivot_df = pivot_df.sort_values(by="Agg FNC", ascending=False).reset_index(
+    pivot_df = pivot_df.sort_values(by="Functionality", ascending=False).reset_index(
         drop=True
     )
     return pivot_df