Update the Sales tab to adjust the data show in the tables

streamlit_app.py

@@ -1559,7 +1559,10 @@ with tab_summary:
                 # rest of the revenue is "Normal" (à la carte food +
                 # beverage). Derive Normal as Revenue − Delivery so the
                 # column lines up with the kpi_monthly Revenue total
-                # that drives the other tiles.
+                # that drives the other tiles. Premium / Party Pack
+                # don't apply here — the columns are intentionally NOT
+                # added so they're omitted from both the table and the
+                # stacked-bar chart legend.
                 delivery_rows = (fi[fi["Type"] == "Delivery"]
                                  if "Type" in fi.columns else fi.iloc[0:0])
                 _bucket_into(delivery_rows, "Delivery")
@@ -1567,9 +1570,6 @@ with tab_summary:
                     m["Normal"] = (m["Revenue"] - m.get("Delivery", 0.0)).clip(lower=0)
                 else:
                     m["Normal"] = 0.0
-                # Tiew Copper has no Premium / Party Pack channels.
-                m["Premium"]   = 0.0
-                m["PartyPack"] = 0.0
 
             else:
                 # Group-level rows (Holding / CK / Conso) — no channel
@@ -1894,6 +1894,74 @@ with tab_summary:
                 d_tail = [c for c in ["Rev_Per_Head"] if c in d.columns]
                 d_round_cols: list[str] = []
                 d_metric_cols: list[str] = []
+                d_channel_cols: list[str] = []
+
+                # ── Daily channel split — Normal / Premium / Delivery /
+                # Party Pack per (Date, Branch). Same source rules as the
+                # Monthly summary version (GrossRev + SVC; Copper Buffet
+                # uses Type=Package + SubType; Tiew Copper uses
+                # Type='Delivery' with Normal derived as Revenue −
+                # Delivery). The result merges onto `d` so the daily
+                # table renders the same column set as the monthly one.
+                if not fact_items.empty:
+                    fi_d = fact_items.copy()
+                    if "Date" in fi_d.columns:
+                        fi_d["Date"] = pd.to_datetime(fi_d["Date"], errors="coerce")
+                        fi_d = fi_d.dropna(subset=["Date"])
+                    if "Restaurant" in fi_d.columns:
+                        fi_d = fi_d[fi_d["Restaurant"] == restaurant_name]
+                    if sel_branches and "Branch" in fi_d.columns:
+                        fi_d = fi_d[fi_d["Branch"].isin(sel_branches)]
+                    if date_from is not None and "Date" in fi_d.columns:
+                        fi_d = fi_d[fi_d["Date"] >= pd.to_datetime(date_from)]
+                    if date_to is not None and "Date" in fi_d.columns:
+                        fi_d = fi_d[fi_d["Date"] <= pd.to_datetime(date_to)]
+                    if not fi_d.empty:
+                        _g = pd.to_numeric(fi_d.get("GrossRev", 0), errors="coerce").fillna(0)
+                        _s = pd.to_numeric(fi_d.get("SVC",      0), errors="coerce").fillna(0)
+                        fi_d["_rev"] = _g + _s
+
+                    def _bucket_into_daily(source: pd.DataFrame, dest_col: str) -> None:
+                        nonlocal d
+                        if source.empty:
+                            d[dest_col] = 0.0
+                            return
+                        agg = (
+                            source.groupby(["Date", "Branch"], as_index=False)["_rev"]
+                                  .sum().rename(columns={"_rev": dest_col})
+                        )
+                        d = d.merge(agg, on=["Date", "Branch"], how="left")
+                        d[dest_col] = d[dest_col].fillna(0.0)
+
+                    if restaurant_name == "Copper Buffet":
+                        fi_pkg_d = (fi_d[fi_d["Type"] == "Package"]
+                                    if "Type" in fi_d.columns else fi_d.iloc[0:0])
+                        def _by_subtype_d(sub_value: str) -> pd.DataFrame:
+                            if "SubType" not in fi_pkg_d.columns:
+                                return fi_pkg_d.iloc[0:0]
+                            return fi_pkg_d[fi_pkg_d["SubType"] == sub_value]
+                        _bucket_into_daily(_by_subtype_d("Normal"),     "Normal")
+                        _bucket_into_daily(_by_subtype_d("Premium"),    "Premium")
+                        _bucket_into_daily(_by_subtype_d("Delivery"),   "Delivery")
+                        _bucket_into_daily(_by_subtype_d("Party Pack"), "PartyPack")
+                    elif restaurant_name == "Tiew Copper":
+                        # Same restaurant-specific rules as the monthly
+                        # version above: only Normal + Delivery; Premium
+                        # and Party Pack columns are not added so they're
+                        # absent from the table and the chart legend.
+                        delivery_rows_d = (fi_d[fi_d["Type"] == "Delivery"]
+                                           if "Type" in fi_d.columns else fi_d.iloc[0:0])
+                        _bucket_into_daily(delivery_rows_d, "Delivery")
+                        if "Revenue" in d.columns:
+                            d["Normal"] = (d["Revenue"] - d.get("Delivery", 0.0)).clip(lower=0)
+                        else:
+                            d["Normal"] = 0.0
+                    else:
+                        for c in ("Normal", "Premium", "Delivery", "PartyPack"):
+                            d[c] = 0.0
+                    d_channel_cols = [c for c in
+                                      ("Normal", "Premium", "Delivery", "PartyPack")
+                                      if c in d.columns]
 
                 if restaurant_name == "Copper Buffet" and not fact_shift_items.empty:
                     si_all = _summary_filter(fact_shift_items)
@@ -1981,10 +2049,11 @@ with tab_summary:
                             d = d.drop(columns=["_Cap", "_TotCust2"])
                             d_metric_cols.append("%Cap")
 
-                # Column order: Date · Branch · Revenue · Customers · %Cap ·
-                # %Premium · Rev/Head · rounds — same as the Monthly table.
+                # Column order matches the Monthly summary table:
+                #   Date · Branch · Revenue · Customers · channels ·
+                #   %Cap · %Premium · Rev/Head · rounds.
                 metric_cols_d = [c for c in ["%Cap", "%Premium"] if c in d.columns]
-                cols = d_base + metric_cols_d + d_tail + d_round_cols
+                cols = d_base + d_channel_cols + metric_cols_d + d_tail + d_round_cols
 
                 disp = d[cols].copy()
                 for c in ("Revenue", "Rev_Per_Head"):
@@ -1992,11 +2061,19 @@ with tab_summary:
                         disp[c] = disp[c].map(fmt_money)
                 if "Customers" in disp.columns:
                     disp["Customers"] = disp["Customers"].map(fmt_num)
+                for c in d_channel_cols:
+                    disp[c] = disp[c].map(fmt_money)
                 for c in d_round_cols:
                     disp[c] = disp[c].map(fmt_num)
                 for c in metric_cols_d:
                     disp[c] = disp[c].map(fmt_pct)
-                disp = disp.rename(columns={"Rev_Per_Head": "Rev / Head"})
+                disp = disp.rename(columns={
+                    "Rev_Per_Head": "Rev / Head",
+                    "Normal":       t("sm_col_normal"),
+                    "Premium":      t("sm_col_premium"),
+                    "Delivery":     t("sm_col_delivery"),
+                    "PartyPack":    t("sm_col_partypack"),
+                })
                 st.dataframe(
                     disp,
                     use_container_width=True, hide_index=True,