Make KPI card in Inventory tab filter by the item selected in the table

streamlit_app.py

@@ -2386,13 +2386,51 @@ with tab_inv:
             & (inv_filt["Date"].dt.month == _m_sel)
         ]
 
+        # ── Read prior table selection (if any) BEFORE computing KPIs ────
+        # `st.dataframe(on_select="rerun", key="inv_table")` (rendered
+        # further down) stores its selection in st.session_state under
+        # that key. By reading it here at the top of the rerun we can
+        # use the picked item to filter both the KPI tiles AND the
+        # downstream charts — keeping all three in sync.
+        _ranked_preview = (
+            latest_rows[latest_rows[sort_by] > 0]
+            .sort_values(sort_by, ascending=False)
+            .head(100)
+        )
+        _selected_item: "str | None" = None
+        _prior_table_state = st.session_state.get("inv_table")
+        if _prior_table_state is not None and "Item" in _ranked_preview.columns:
+            try:
+                # The state object exposes `.selection.rows` in recent
+                # Streamlit; fall back to dict access for older builds.
+                if hasattr(_prior_table_state, "selection"):
+                    _sel_rows = list(_prior_table_state.selection.rows)
+                elif isinstance(_prior_table_state, dict):
+                    _sel_rows = list(_prior_table_state.get("selection", {}).get("rows", []))
+                else:
+                    _sel_rows = []
+                if _sel_rows:
+                    _i = _sel_rows[0]
+                    if 0 <= _i < len(_ranked_preview):
+                        _selected_item = str(_ranked_preview.iloc[_i]["Item"])
+            except Exception:
+                _selected_item = None
+
+        # KPI source rows: when a row is selected, narrow to just that
+        # item; otherwise use the full month-filtered set.
+        _kpi_rows = (
+            latest_rows[latest_rows["Item"] == _selected_item]
+            if (_selected_item is not None and "Item" in latest_rows.columns)
+            else latest_rows
+        )
+
         # ── KPI tiles — total Value_Used in the month + per-customer rate
         # Customers for the same month come from kpi_daily, filtered by
         # the same sidebar Restaurant / Branch filters so the ratio is
         # consistent with whichever scope the user is viewing.
         _value_used_total = (
-            float(latest_rows["Value_Used"].sum())
-            if "Value_Used" in latest_rows.columns else 0.0
+            float(_kpi_rows["Value_Used"].sum())
+            if "Value_Used" in _kpi_rows.columns else 0.0
         )
         _cust_in_month = kpi_daily.copy() if not kpi_daily.empty else pd.DataFrame()
         if not _cust_in_month.empty and "Date" in _cust_in_month.columns:
@@ -2412,8 +2450,8 @@ with tab_inv:
         _value_per_cust = (_value_used_total / _cust_total) if _cust_total > 0 else None
 
         _qty_used_total = (
-            float(latest_rows["Qty_Used"].sum())
-            if "Qty_Used" in latest_rows.columns else 0.0
+            float(_kpi_rows["Qty_Used"].sum())
+            if "Qty_Used" in _kpi_rows.columns else 0.0
         )
         _qty_per_cust = (_qty_used_total / _cust_total) if _cust_total > 0 else None