Add kpi to Inventory tab and the card at the top of the dashboard

streamlit_app.py

@@ -63,6 +63,10 @@ LANG = {
         "all_dates":        "all dates",
         "n_restaurants":    "{n} restaurant(s)",
         "n_branches":       "{n} branch(es)",
+        "kpi_total_revenue":   "Total Revenue",
+        "kpi_total_customers": "Total Customers",
+        "kpi_rev_per_head":    "Revenue / Head",
+        "kpi_yoy_suffix":      "YoY",
         # Overview tab
         "ov_monthly_revenue_trend": "Monthly Revenue Trend",
         "ov_channel_mix":           "Channel Mix",
@@ -120,7 +124,8 @@ LANG = {
         "inv_month_picker": "Month",
         "inv_sort_by":      "Sort by",
         "inv_snapshot":     "Inventory snapshot ({ym})",
-        "inv_closing_by_branch": "Closing inventory value by branch",
+        "inv_kpi_value_used":      "Total Value Used",
+        "inv_kpi_value_per_cust":  "Value Used / Customer",
         # Sign-in screen
         "auth_title":   "Copper Group Dashboard",
         "auth_intro":   "Restricted to members of the <code>CB-Group</code> organization on Hugging Face. "
@@ -171,6 +176,10 @@ LANG = {
         "all_dates":        "ทุกวันที่",
         "n_restaurants":    "{n} ร้าน",
         "n_branches":       "{n} สาขา",
+        "kpi_total_revenue":   "รายได้รวม",
+        "kpi_total_customers": "ลูกค้ารวม",
+        "kpi_rev_per_head":    "รายได้ต่อหัว",
+        "kpi_yoy_suffix":      "YoY",
         # Overview tab
         "ov_monthly_revenue_trend": "แนวโน้มรายได้รายเดือน",
         "ov_channel_mix":           "สัดส่วนช่องทาง",
@@ -228,7 +237,8 @@ LANG = {
         "inv_month_picker": "เดือน",
         "inv_sort_by":      "เรียงตาม",
         "inv_snapshot":     "ภาพรวมสินค้าคงคลัง ({ym})",
-        "inv_closing_by_branch": "มูลค่าคงเหลือตามสาขา",
+        "inv_kpi_value_used":      "มูลค่าที่ใช้ทั้งหมด",
+        "inv_kpi_value_per_cust":  "มูลค่าที่ใช้ต่อลูกค้า",
         # Sign-in screen
         "auth_title":   "แดชบอร์ดคอปเปอร์กรุ๊ป",
         "auth_intro":   "เฉพาะสมาชิกขององค์กร <code>CB-Group</code> บน Hugging Face เท่านั้น "
@@ -1200,10 +1210,46 @@ total_iqty  = filtered_daily["ItemQty"].sum()   if "ItemQty"   in filtered_daily
 total_irev  = filtered_daily["ItemRevenue"].sum() if "ItemRevenue" in filtered_daily.columns else 0
 rev_phead   = (total_rev / total_cust) if total_cust else 0
 
+# ── Year-on-year comparison ────────────────────────────────────────────
+# Pull the same date window one calendar year earlier from kpi_daily,
+# apply the same Restaurant / Branch sidebar filters, and compute the
+# same three totals. The percent delta is what we show under each tile.
+# DateOffset(years=1) handles the Feb-29 → Feb-28 edge case for us.
+yoy_rev = yoy_cust = yoy_rph = None
+if date_from is not None and date_to is not None and not kpi_daily.empty:
+    try:
+        _yoy_from = (pd.Timestamp(date_from) - pd.DateOffset(years=1))
+        _yoy_to   = (pd.Timestamp(date_to)   - pd.DateOffset(years=1))
+        _y = kpi_daily.copy()
+        _y["Date"] = pd.to_datetime(_y["Date"], errors="coerce")
+        _y = _y[(_y["Date"] >= _yoy_from) & (_y["Date"] <= _yoy_to)]
+        if sel_restaurants and "Restaurant" in _y.columns:
+            _y = _y[_y["Restaurant"].isin(sel_restaurants)]
+        if sel_branches and "Branch" in _y.columns:
+            _y = _y[_y["Branch"].isin(sel_branches)]
+        if not _y.empty:
+            yoy_rev  = float(_y["Revenue"].sum())   if "Revenue"   in _y.columns else None
+            yoy_cust = float(_y["Customers"].sum()) if "Customers" in _y.columns else None
+            yoy_rph  = (yoy_rev / yoy_cust) if (yoy_rev is not None and yoy_cust) else None
+    except Exception:
+        pass
+
+def _yoy_delta(current, previous) -> "str | None":
+    """% change vs prior year, formatted with a sign + 'YoY' suffix.
+    Returns None when there's no comparable prior-year value so the
+    delta indicator is hidden instead of misleading."""
+    if previous is None or previous == 0 or pd.isna(previous):
+        return None
+    pct = (current - previous) / previous * 100
+    return f"{pct:+.1f}% {t('kpi_yoy_suffix')}"
+
 c1, c2, c3 = st.columns(3)
-c1.metric("Total Revenue",   fmt_money(total_rev))
-c2.metric("Total Customers", fmt_num(total_cust))
-c3.metric("Revenue / Head",  fmt_money(rev_phead))
+c1.metric(t("kpi_total_revenue"),   fmt_money(total_rev),
+          delta=_yoy_delta(total_rev,  yoy_rev))
+c2.metric(t("kpi_total_customers"), fmt_num(total_cust),
+          delta=_yoy_delta(total_cust, yoy_cust))
+c3.metric(t("kpi_rev_per_head"),    fmt_money(rev_phead),
+          delta=_yoy_delta(rev_phead,  yoy_rph))
 
 st.divider()
 
@@ -2305,6 +2351,39 @@ with tab_inv:
             (inv_filt["Date"].dt.year == _y_sel)
             & (inv_filt["Date"].dt.month == _m_sel)
         ]
+
+        # ── 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
+        )
+        _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:
+            _cust_in_month["Date"] = pd.to_datetime(_cust_in_month["Date"], errors="coerce")
+            _cust_in_month = _cust_in_month[
+                (_cust_in_month["Date"].dt.year == _y_sel)
+                & (_cust_in_month["Date"].dt.month == _m_sel)
+            ]
+            if sel_restaurants and "Restaurant" in _cust_in_month.columns:
+                _cust_in_month = _cust_in_month[_cust_in_month["Restaurant"].isin(sel_restaurants)]
+            if sel_branches and "Branch" in _cust_in_month.columns:
+                _cust_in_month = _cust_in_month[_cust_in_month["Branch"].isin(sel_branches)]
+        _cust_total = (
+            float(_cust_in_month["Customers"].sum())
+            if "Customers" in _cust_in_month.columns and not _cust_in_month.empty else 0.0
+        )
+        _value_per_cust = (_value_used_total / _cust_total) if _cust_total > 0 else None
+
+        ik1, ik2 = st.columns(2)
+        ik1.metric(t("inv_kpi_value_used"), fmt_money(_value_used_total))
+        ik2.metric(
+            t("inv_kpi_value_per_cust"),
+            fmt_money(_value_per_cust) if _value_per_cust is not None else "—",
+        )
+
         ranked = latest_rows[latest_rows[sort_by] > 0].sort_values(sort_by, ascending=False).head(100)
 
         cols_to_show = [c for c in
@@ -2328,21 +2407,4 @@ with tab_inv:
             use_container_width=True, hide_index=True,
         )
 
-        # Per-branch inventory totals
-        st.subheader(t("inv_closing_by_branch"))
-        totals = (
-            latest_rows.groupby(["Restaurant", "Branch"], as_index=False)["Value_Closing"]
-            .sum()
-            .sort_values("Value_Closing", ascending=True)
-        )
-        if not totals.empty:
-            totals["Label"] = totals["Restaurant"] + " / " + totals["Branch"]
-            fig = px.bar(
-                totals, x="Value_Closing", y="Label", orientation="h",
-                color="Restaurant", color_discrete_map=RESTAURANT_COLOR,
-                text=totals["Value_Closing"].apply(fmt_money),
-            )
-            fig.update_layout(xaxis_title="Closing Value (THB)", yaxis_title=None, showlegend=False)
-            fig.update_xaxes(tickformat=",.0f")
-            st.plotly_chart(style_plotly(fig, height=380), use_container_width=True)