Add metric range filters for each metric type

app.py

@@ -139,7 +139,30 @@ def build_tab(df, name):
                     elem_classes="filter-group"
                 )
 
-        def update(search, md, org, cols):
+                # Add metric filters
+                gr.Markdown("**Filter by Metrics**")
+                metric_filters = {}
+                for metric in metric_cols:
+                    # Get all numeric values for this metric across all dataset columns
+                    metric_values = []
+                    for col in metric_combo_cols:
+                        if f"({metric})" in col:
+                            values = pd.to_numeric(pivoted_df[col], errors='coerce').dropna()
+                            metric_values.extend(values.tolist())
+
+                    if metric_values:
+                        min_val = min(metric_values)
+                        max_val = max(metric_values)
+                        metric_filters[metric] = gr.Slider(
+                            minimum=min_val,
+                            maximum=max_val,
+                            value=min_val,
+                            step=(max_val - min_val) / 100,
+                            label=f"Min {metric}",
+                            elem_classes="metric-filter"
+                        )
+
+        def update(search, md, org, cols, *metric_values):
             filtered = pivoted_df.copy()
 
             if search:
@@ -151,16 +174,33 @@ def build_tab(df, name):
             if org:
                 filtered = filtered[filtered["Organization"].isin(org)]
 
+            # Apply metric filters
+            metric_names = list(metric_filters.keys())
+            for i, metric_name in enumerate(metric_names):
+                min_threshold = metric_values[i]
+                # Filter rows where at least one column with this metric meets the threshold
+                metric_columns = [col for col in metric_combo_cols if f"({metric_name})" in col and col in filtered.columns]
+                if metric_columns:
+                    mask = filtered[metric_columns].apply(
+                        lambda row: any(pd.to_numeric(val, errors='coerce') >= min_threshold if pd.notna(val) and val != "-" else False for val in row),
+                        axis=1
+                    )
+                    filtered = filtered[mask]
+
             if cols:
                 display_cols = [col for col in cols if col in filtered.columns]
                 filtered = filtered[display_cols]
 
             return filtered
 
-        search_bar.change(update, [search_bar, model_filter, org_filter, col_selector], table)
-        model_filter.change(update, [search_bar, model_filter, org_filter, col_selector], table)
-        org_filter.change(update, [search_bar, model_filter, org_filter, col_selector], table)
-        col_selector.change(update, [search_bar, model_filter, org_filter, col_selector], table)
+        inputs = [search_bar, model_filter, org_filter, col_selector] + list(metric_filters.values())
+
+        search_bar.change(update, inputs, table)
+        model_filter.change(update, inputs, table)
+        org_filter.change(update, inputs, table)
+        col_selector.change(update, inputs, table)
+        for metric_filter in metric_filters.values():
+            metric_filter.change(update, inputs, table)
 
 
 custom_css = """
@@ -243,6 +283,14 @@ custom_css = """
     font-size: 14px;
 }
 
+.metric-filter {
+    margin-bottom: 1em;
+}
+
+.metric-filter label {
+    font-size: 14px;
+}
+
 .column-select {
     margin-bottom: 1.5em;
 }