Add filter mechanic to Inventory tab

streamlit_app.py

@@ -127,6 +127,8 @@ LANG = {
         "inv_kpi_value_used":      "Total Value Used",
         "inv_kpi_value_per_cust":  "Value Used / Customer",
         "inv_chart_vpc_trend":     "Value Used / Customer — monthly trend",
+        "inv_chart_vpc_item":      "Value Used / Customer — {item} (monthly)",
+        "inv_table_hint":          "Click any row to filter the chart below to that item. Click the same row again to clear.",
         "inv_store_filter":        "Store",
         # Sign-in screen
         "auth_title":   "Copper Group Dashboard",
@@ -242,6 +244,8 @@ LANG = {
         "inv_kpi_value_used":      "มูลค่าที่ใช้ทั้งหมด",
         "inv_kpi_value_per_cust":  "มูลค่าที่ใช้ต่อลูกค้า",
         "inv_chart_vpc_trend":     "มูลค่าที่ใช้ต่อลูกค้า — แนวโน้มรายเดือน",
+        "inv_chart_vpc_item":      "มูลค่าที่ใช้ต่อลูกค้า — {item} (รายเดือน)",
+        "inv_table_hint":          "คลิกแถวใดก็ได้เพื่อกรองกราฟด้านล่างเฉพาะรายการนั้น คลิกแถวเดิมอีกครั้งเพื่อล้าง",
         "inv_store_filter":        "สโตร์",
         # Sign-in screen
         "auth_title":   "แดชบอร์ดคอปเปอร์กรุ๊ป",
@@ -2405,14 +2409,62 @@ with tab_inv:
             fmt_money(_value_per_cust) if _value_per_cust is not None else "—",
         )
 
+        # ── Snapshot table (with row selection) ──────────────────────
+        # Render the table FIRST. A single-row selection here drives the
+        # chart below — clicking an item filters its monthly Value Used
+        # / Customer trend; clicking again clears.
+        ranked = latest_rows[latest_rows[sort_by] > 0].sort_values(sort_by, ascending=False).head(100)
+
+        cols_to_show = [c for c in
+                        ["Item", "Restaurant", "Branch", "Store Name",
+                         "Unit", "Qty_Closing", "Value_Closing",
+                         "Qty_Used", "Value_Used"]
+                        if c in ranked.columns]
+        disp = ranked[cols_to_show].copy()
+        for c in ("Qty_Closing", "Qty_Used"):
+            if c in disp.columns:
+                disp[c] = disp[c].map(fmt_qty)
+        for c in ("Value_Closing", "Value_Used"):
+            if c in disp.columns:
+                disp[c] = disp[c].map(fmt_money)
+        disp = disp.rename(columns={
+            "Value_Closing": "Value Closing (THB)",
+            "Value_Used":    "Value Used (THB)",
+        })
+        st.caption(t("inv_table_hint"))
+        _table_event = st.dataframe(
+            disp,
+            use_container_width=True, hide_index=True,
+            on_select="rerun",
+            selection_mode="single-row",
+            key="inv_table",
+        )
+
+        # Translate the picked row index back to the underlying Item name.
+        # The picker indexes into the displayed (formatted) DataFrame,
+        # which has the same row order as `ranked`, so we can look it up
+        # there to recover the original (unformatted) Item value.
+        _selected_item: "str | None" = None
+        try:
+            _sel_rows = _table_event.selection.rows  # list[int]
+            if _sel_rows and "Item" in ranked.columns:
+                _idx = _sel_rows[0]
+                if 0 <= _idx < len(ranked):
+                    _selected_item = str(ranked.iloc[_idx]["Item"])
+        except Exception:
+            _selected_item = None
+
         # ── Monthly trend: Value Used / Customer ─────────────────────
-        # Aggregates over ALL months in the sidebar date range (not just
-        # the picked month). Numerator = sum(Value_Used) per month from
-        # fact_inventory; denominator = sum(Customers) per month from
-        # kpi_daily under the same Restaurant/Branch filter.
+        # If a row was selected above, filter the numerator (Value_Used)
+        # to that item only — the denominator (Customers) stays as the
+        # period total because we're asking "for this item, how much
+        # value per customer did we burn each month?".
         _inv_for_trend = inv_filt.copy()
         _inv_for_trend["Date"] = pd.to_datetime(_inv_for_trend["Date"], errors="coerce")
         _inv_for_trend = _inv_for_trend.dropna(subset=["Date"])
+        if _selected_item is not None and "Item" in _inv_for_trend.columns:
+            _inv_for_trend = _inv_for_trend[_inv_for_trend["Item"] == _selected_item]
+
         if not _inv_for_trend.empty and "Value_Used" in _inv_for_trend.columns:
             _inv_for_trend["YearMonth"] = _inv_for_trend["Date"].dt.to_period("M").astype(str)
             _inv_for_trend["Year"]      = _inv_for_trend["Date"].dt.year
@@ -2471,34 +2523,16 @@ with tab_inv:
                     textfont=dict(size=9),
                 )
                 fig.update_yaxes(tickformat=",.0f")
+                _chart_title = (
+                    t("inv_chart_vpc_item", item=_selected_item)
+                    if _selected_item is not None
+                    else t("inv_chart_vpc_trend")
+                )
                 fig.update_layout(
-                    title=t("inv_chart_vpc_trend"),
+                    title=_chart_title,
                     xaxis_title=None, yaxis_title=None,
                     showlegend=False,
                 )
                 st.plotly_chart(style_plotly(fig, height=320), use_container_width=True)
 
-        ranked = latest_rows[latest_rows[sort_by] > 0].sort_values(sort_by, ascending=False).head(100)
-
-        cols_to_show = [c for c in
-                        ["Item", "Restaurant", "Branch", "Store Name",
-                         "Unit", "Qty_Closing", "Value_Closing",
-                         "Qty_Used", "Value_Used"]
-                        if c in ranked.columns]
-        disp = ranked[cols_to_show].copy()
-        for c in ("Qty_Closing", "Qty_Used"):
-            if c in disp.columns:
-                disp[c] = disp[c].map(fmt_qty)
-        for c in ("Value_Closing", "Value_Used"):
-            if c in disp.columns:
-                disp[c] = disp[c].map(fmt_money)
-        disp = disp.rename(columns={
-            "Value_Closing": "Value Closing (THB)",
-            "Value_Used":    "Value Used (THB)",
-        })
-        st.dataframe(
-            disp,
-            use_container_width=True, hide_index=True,
-        )
-