Update dashboard/app.py

dashboard/app.py

@@ -84,6 +84,23 @@ def run(from_results_dir, datasource, port):
         # This is the raw data with correct dtypes for sorting
         raw_data_for_sorting = data.copy()
 
+        # Define columns that should be numeric for sorting and have specific formatting
+        # These names must match the column names *after* renaming by column_mappings
+        numeric_cols_to_ensure = [
+            'ITL P90 (ms)', 'TTFT P90 (ms)', 'E2E P90 (ms)', 
+            'Throughput (tokens/s)', 'QPS'
+        ]
+
+        for col in numeric_cols_to_ensure:
+            if col in raw_data_for_sorting.columns:
+                # Convert column to numeric, coercing errors to NaN
+                # NaN values will be handled by the formatter's try-except or display as "nan"
+                raw_data_for_sorting[col] = pd.to_numeric(raw_data_for_sorting[col], errors='coerce')
+            # else:
+                # Optionally, log a warning if a column expected to be numeric is missing
+                # print(f"Warning: Column '{col}' not found for numeric conversion in summary_table.")
+
+
         headers = raw_data_for_sorting.columns.tolist()
 
         # Formatter for display purposes
@@ -111,11 +128,12 @@ def run(from_results_dir, datasource, port):
             display_values_list.append(display_row)
 
         # Create a styling array (list of lists for CSS). Empty strings if no specific CSS.
+        # Corrected the extra parenthesis at the end of this line
         styling_array = [["" for _ in headers] for _ in range(len(raw_data_for_sorting))]
 
         return {
-            # Convert the DataFrame to a list of lists for the "data" field
-            "data": raw_data_for_sorting.values.tolist(), 
+            # Convert the DataFrame (with corrected numeric dtypes) to a list of lists for the "data" field
+            "data": raw_data_for_sorting.values.tolist(),
             "headers": headers,
             "metadata": {
                 "display_value": display_values_list,