Updated Summary Tab

streamlit_app.py

@@ -846,20 +846,18 @@ 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
 
-c1, c2, c3, c4, c5 = st.columns(5)
-c1.metric("Total Revenue",     fmt_money(total_rev))
-c2.metric("Item Revenue (Grand Total)", fmt_money(total_irev))
-c3.metric("Total Customers",   fmt_num(total_cust))
-c4.metric("Revenue / Head",    fmt_money(rev_phead))
-c5.metric("Items Sold (Food)", fmt_num(total_iqty))
+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))
 
 st.divider()
 
 # ─────────────────────────────────────────────────────────────────────────────
 # Tabs
 # ─────────────────────────────────────────────────────────────────────────────
-tab_overview, tab_branch, tab_pl, tab_inv, tab_data = st.tabs(
-    ["Overview", "Branches", "P&L", "Inventory", "Raw tables"]
+tab_overview, tab_summary, tab_pl, tab_inv = st.tabs(
+    ["Overview", "Summary", "P&L", "Inventory"]
 )
 
 # ── Overview ───────────────────────────────────────────────────────────────
@@ -944,63 +942,112 @@ with tab_overview:
             fig.update_yaxes(tickformat=",.0f")
             st.plotly_chart(style_plotly(fig, height=320), use_container_width=True)
 
-# ── Branches ────────────────────────────────────────────────────────────────
-with tab_branch:
-    if filtered_daily.empty:
-        st.info("No data for the current filters.")
-    else:
-        st.subheader("Revenue and Customers by Branch")
-        br = (
-            filtered_daily.groupby(["Restaurant", "Branch"], as_index=False)
-            .agg(Revenue=("Revenue", "sum"),
-                 Customers=("Customers", "sum"),
-                 ItemRevenue=("ItemRevenue", "sum"))
-        )
-        br["Label"] = br["Restaurant"] + " / " + br["Branch"]
-
-        col1, col2 = st.columns(2)
-        with col1:
-            ds = br.sort_values("Revenue")
-            fig = px.bar(
-                ds, x="Revenue", y="Label", orientation="h", color="Restaurant",
-                color_discrete_map=RESTAURANT_COLOR,
-                text=ds["Revenue"].apply(fmt_money),
-            )
-            fig.update_layout(xaxis_title="Revenue", yaxis_title=None, showlegend=False)
-            fig.update_xaxes(tickformat=",.0f")
-            st.plotly_chart(style_plotly(fig, height=420), use_container_width=True)
-        with col2:
-            ds = br.sort_values("Customers")
-            fig = px.bar(
-                ds, x="Customers", y="Label", orientation="h", color="Restaurant",
-                color_discrete_map=RESTAURANT_COLOR,
-                text=ds["Customers"].apply(fmt_num),
+# ── Summary ─────────────────────────────────────────────────────────────────
+# Restaurant-by-restaurant summary tables, mirroring the layout of the
+# original Summary.xlsx (Copper Buffet) and Summary_Tiew.xlsx (Tiew Copper).
+# Restaurant filter is intentionally ignored here so both restaurants are
+# always shown; Branch / date / year filters still apply.
+with tab_summary:
+    def _summary_filter(df: pd.DataFrame, *, use_date: bool = True) -> pd.DataFrame:
+        """Like apply_filters() but skips the Restaurant filter."""
+        if df.empty:
+            return df
+        out = df
+        if "Branch" in out.columns and sel_branches:
+            out = out[out["Branch"].isin(sel_branches)]
+        if use_date and "Date" in out.columns:
+            if date_from is not None:
+                out = out[out["Date"] >= pd.to_datetime(date_from)]
+            if date_to is not None:
+                out = out[out["Date"] <= pd.to_datetime(date_to)]
+        if sel_year != "All years":
+            if "Year" in out.columns:
+                out = out[out["Year"] == sel_year]
+            elif "Date" in out.columns:
+                out = out[out["Date"].dt.year == sel_year]
+        return out
+
+    def _render_restaurant_summary(restaurant_name: str) -> None:
+        st.subheader(restaurant_name)
+
+        monthly = _summary_filter(kpi_monthly, use_date=False)
+        monthly = monthly[monthly.get("Restaurant", "") == restaurant_name] \
+                  if "Restaurant" in monthly.columns else monthly.iloc[0:0]
+        daily   = _summary_filter(kpi_daily)
+        daily   = daily[daily.get("Restaurant", "") == restaurant_name] \
+                  if "Restaurant" in daily.columns else daily.iloc[0:0]
+
+        if monthly.empty and daily.empty:
+            st.info(f"No data for {restaurant_name} in the current filters.")
+            return
+
+        # ── Top-line totals for this restaurant ──────────────────────────
+        tot_rev  = daily["Revenue"].sum()      if "Revenue"     in daily.columns else 0
+        tot_cust = daily["Customers"].sum()    if "Customers"   in daily.columns else 0
+        tot_iqty = daily["ItemQty"].sum()      if "ItemQty"     in daily.columns else 0
+        tot_irev = daily["ItemRevenue"].sum()  if "ItemRevenue" in daily.columns else 0
+        rev_ph   = (tot_rev / tot_cust) if tot_cust else 0
+
+        kc1, kc2, kc3, kc4 = st.columns(4)
+        kc1.metric("Revenue",     fmt_money(tot_rev))
+        kc2.metric("Customers",   fmt_num(tot_cust))
+        kc3.metric("Rev / Head",  fmt_money(rev_ph))
+        kc4.metric("Item Revenue (Grand Total)", fmt_money(tot_irev))
+
+        # ── Monthly summary table ────────────────────────────────────────
+        st.markdown("**Monthly summary**")
+        if not monthly.empty:
+            m = monthly.copy().sort_values(
+                ["Year", "Month", "Branch"], ascending=[False, False, True]
             )
-            fig.update_layout(xaxis_title="Customers", yaxis_title=None, showlegend=False)
-            fig.update_xaxes(tickformat=",.0f")
-            st.plotly_chart(style_plotly(fig, height=420), use_container_width=True)
-
-        st.subheader("Monthly Summary")
-        mf = apply_filters(kpi_monthly, use_date=False)
-        if not mf.empty:
-            mf = mf.sort_values(
-                ["Year", "Month", "Restaurant", "Branch"],
-                ascending=[False, False, True, True],
-            )
-            display_cols = [c for c in
-                            ["Year", "Month", "Restaurant", "Branch",
-                             "Revenue", "ItemRevenue", "Customers", "Rev_Per_Head"]
-                            if c in mf.columns]
+            cols = [c for c in
+                    ["Year", "Month", "Branch", "Revenue", "Customers",
+                     "Rev_Per_Head", "ItemRevenue"]
+                    if c in m.columns]
             st.dataframe(
-                mf[display_cols],
+                m[cols],
                 use_container_width=True, hide_index=True,
                 column_config={
+                    "Year":         st.column_config.NumberColumn(format="%d"),
+                    "Month":        st.column_config.NumberColumn(format="%d"),
                     "Revenue":      st.column_config.NumberColumn(format="฿%.0f"),
-                    "ItemRevenue":  st.column_config.NumberColumn("Item Revenue", format="฿%.0f"),
                     "Customers":    st.column_config.NumberColumn(format="%.0f"),
                     "Rev_Per_Head": st.column_config.NumberColumn("Rev / Head", format="฿%.0f"),
+                    "ItemRevenue":  st.column_config.NumberColumn("Item Revenue", format="฿%.0f"),
                 },
             )
+        else:
+            st.caption("No monthly rows in this filter window.")
+
+        # ── Daily detail (collapsed by default — can be long) ────────────
+        with st.expander("Daily detail", expanded=False):
+            if not daily.empty:
+                d = daily.copy().sort_values(["Date", "Branch"], ascending=[False, True])
+                # Compute Rev/Head on the fly so daily rows have the same column.
+                if "Revenue" in d.columns and "Customers" in d.columns:
+                    d["Rev_Per_Head"] = d["Revenue"] / d["Customers"].replace(0, np.nan)
+                cols = [c for c in
+                        ["Date", "Branch", "DayType", "Revenue", "Customers",
+                         "Rev_Per_Head", "ItemRevenue", "ItemQty"]
+                        if c in d.columns]
+                st.dataframe(
+                    d[cols],
+                    use_container_width=True, hide_index=True,
+                    column_config={
+                        "Date":         st.column_config.DateColumn(format="YYYY-MM-DD"),
+                        "Revenue":      st.column_config.NumberColumn(format="฿%.0f"),
+                        "Customers":    st.column_config.NumberColumn(format="%.0f"),
+                        "Rev_Per_Head": st.column_config.NumberColumn("Rev / Head", format="฿%.0f"),
+                        "ItemRevenue":  st.column_config.NumberColumn("Item Revenue", format="฿%.0f"),
+                        "ItemQty":      st.column_config.NumberColumn("Items Sold", format="%.0f"),
+                    },
+                )
+            else:
+                st.caption("No daily rows in this filter window.")
+
+    _render_restaurant_summary("Copper Buffet")
+    st.divider()
+    _render_restaurant_summary("Tiew Copper")
 
 # ── P&L ─────────────────────────────────────────────────────────────────────
 with tab_pl:
@@ -1108,21 +1155,3 @@ with tab_inv:
             fig.update_xaxes(tickformat=",.0f")
             st.plotly_chart(style_plotly(fig, height=380), use_container_width=True)
 
-# ── Raw tables (browser) ────────────────────────────────────────────────────
-with tab_data:
-    st.markdown("Quick browser for every sheet in the workbook.")
-    sheet_choice = st.selectbox("Sheet", list(sheets.keys()))
-    df = sheets[sheet_choice]
-    apply_to_table = st.checkbox(
-        "Apply current sidebar filters", value=True,
-        help="When checked, the table respects the Restaurant / Branch / date filters above.",
-    )
-    if apply_to_table:
-        df = apply_filters(df)
-    st.caption(f"{len(df):,} rows × {len(df.columns)} columns")
-    # Keep the table snappy by capping the rendered length
-    cap = 5000
-    if len(df) > cap:
-        st.caption(f"Showing first {cap:,} rows (use filters to narrow the view).")
-        df = df.head(cap)
-    st.dataframe(df, use_container_width=True, hide_index=True)