Forecast Tiew Copper revenue and customer

streamlit_app.py

@@ -86,6 +86,9 @@ LANG = {
         "fc_month_customers": "Forecast Customers (this month)",
         "fc_month_revenue":   "Forecast Revenue (this month, est.)",
         "fc_month_basis":     "Revenue is estimated as forecast customers × trailing 3-month Rev/Head per branch.",
+        "fc_month_basis_full":"Copper Buffet uses the model-based daily forecast (Prediction × Rev/Head per branch). "
+                              "Tiew Copper has no per-day forecast, so its current-month tiles are estimated as the "
+                              "trailing 3-month average of monthly Customers and Revenue per branch.",
         "fc_header":          "Copper Buffet — Forecast & Bookings",
         "fc_caption":         "Forecasted customer counts and confirmed bookings for upcoming "
                               "service dates. Data is captured only for Copper Buffet.",
@@ -173,6 +176,9 @@ LANG = {
         "fc_month_customers": "พยากรณ์จำนวนลูกค้า (เดือนนี้)",
         "fc_month_revenue":   "พยากรณ์รายได้ (เดือนนี้, ประมาณการ)",
         "fc_month_basis":     "ประมาณการรายได้จาก: พยากรณ์จำนวนลูกค้า × รายได้ต่อหัวเฉลี่ย 3 เดือนล่าสุดของแต่ละสาขา",
+        "fc_month_basis_full":"คอปเปอร์บุฟเฟ่ต์ใช้พยากรณ์รายวันจากโมเดล (พยากรณ์ลูกค้า × รายได้ต่อหัวต่อสาขา) "
+                              "เตี่ยวคอปเปอร์ไม่มีพยากรณ์รายวัน จึงประมาณค่าของเดือนนี้จาก "
+                              "ค่าเฉลี่ยลูกค้าและรายได้ต่อเดือน 3 เดือนล่าสุดของแต่ละสาขา",
         "fc_header":          "คอปเปอร์บุฟเฟ่ต์ — พยากรณ์และการจอง",
         "fc_caption":         "พยากรณ์จำนวนลูกค้าและการจองที่ยืนยันแล้วสำหรับวันที่บริการในอนาคต "
                               "ข้อมูลมีเฉพาะของคอปเปอร์บุฟเฟ่ต์เท่านั้น",
@@ -1690,59 +1696,91 @@ with tab_forecast:
         st.subheader(t("fc_header"))
         st.caption(t("fc_caption"))
 
-        # ── This Month forecast — total customers + estimated revenue ────
-        # Sum the latest Prediction snapshot for every day in the current
-        # calendar month, across whichever branches the sidebar is
-        # filtered to. Revenue is derived per-branch by multiplying that
-        # branch's forecast customers by its trailing 3-month average
-        # Rev/Head (from kpi_monthly).
+        # ── This Month forecast — per-restaurant customer + revenue ─────
+        # Copper Buffet uses fact_predictions (model-based daily prediction
+        # × trailing 3-month Rev/Head per branch). Tiew Copper has no
+        # per-day forecast in the dataset, so it falls back to the
+        # trailing 3-month average of monthly Customers / Revenue per
+        # branch — a reasonable "what we usually do" baseline.
         st.markdown(f"**{t('fc_month_title')}**")
         _now = pd.Timestamp(_dt.now().date())
         _month_start = _now.replace(day=1)
         _month_end   = (_month_start + pd.offsets.MonthEnd(0)).normalize()
 
-        _mp = fact_predictions.copy() if not fact_predictions.empty else pd.DataFrame()
-        if not _mp.empty:
+        def _cb_month_forecast() -> tuple[int, float]:
+            """Copper Buffet — predict from fact_predictions × Rev/Head."""
+            _mp = fact_predictions.copy() if not fact_predictions.empty else pd.DataFrame()
+            if _mp.empty:
+                return 0, 0.0
             _mp["Date"] = pd.to_datetime(_mp["Date"], errors="coerce")
             _mp = _mp[(_mp["Date"] >= _month_start) & (_mp["Date"] <= _month_end)]
-            if sel_branches and "Branch" in _mp.columns:
-                _mp = _mp[_mp["Branch"].isin(sel_branches)]
             if "Restaurant" in _mp.columns:
                 _mp = _mp[_mp["Restaurant"] == "Copper Buffet"]
+            if sel_branches and "Branch" in _mp.columns:
+                _mp = _mp[_mp["Branch"].isin(sel_branches)]
+            if _mp.empty:
+                return 0, 0.0
             # Latest snapshot per (Date, Branch).
-            if "Date_Diff" in _mp.columns and not _mp.empty:
+            if "Date_Diff" in _mp.columns:
                 _mp = _mp.assign(_a=_mp["Date_Diff"].abs()) \
                          .sort_values("_a") \
                          .drop_duplicates(["Date", "Branch"], keep="first") \
                          .drop(columns="_a")
-
-        _month_cust = int(_mp["Prediction"].sum()) if "Prediction" in _mp.columns and not _mp.empty else 0
-
-        # Trailing 3-month Rev/Head per branch from kpi_monthly.
-        _rph_map: dict[str, float] = {}
-        if not kpi_monthly.empty and {"Restaurant", "Branch", "Rev_Per_Head", "Year", "Month"}.issubset(kpi_monthly.columns):
-            _hist = kpi_monthly[kpi_monthly["Restaurant"] == "Copper Buffet"].copy()
-            _hist = _hist.sort_values(["Year", "Month"])
-            _rph_map = (
-                _hist.groupby("Branch")
-                     .tail(3)
-                     .groupby("Branch")["Rev_Per_Head"]
-                     .mean()
-                     .to_dict()
+            cust = int(_mp["Prediction"].sum())
+
+            # Per-branch trailing 3-month Rev/Head from kpi_monthly.
+            rev = 0.0
+            if not kpi_monthly.empty and {"Restaurant", "Branch", "Rev_Per_Head", "Year", "Month"}.issubset(kpi_monthly.columns):
+                hist = kpi_monthly[kpi_monthly["Restaurant"] == "Copper Buffet"].sort_values(["Year", "Month"])
+                rph_map = (
+                    hist.groupby("Branch").tail(3)
+                        .groupby("Branch")["Rev_Per_Head"].mean().to_dict()
+                )
+                fallback = sum(rph_map.values()) / len(rph_map) if rph_map else 0.0
+                for _br, _cust in _mp.groupby("Branch")["Prediction"].sum().items():
+                    rev += float(_cust) * rph_map.get(_br, fallback)
+            return cust, rev
+
+        def _avg_month_forecast(restaurant: str) -> tuple[float, float]:
+            """Trailing 3-month avg of monthly Customers and Revenue,
+            summed across the branches the sidebar is filtered to."""
+            if kpi_monthly.empty:
+                return 0.0, 0.0
+            df = kpi_monthly[kpi_monthly.get("Restaurant", "") == restaurant].copy()
+            if sel_branches and "Branch" in df.columns:
+                df = df[df["Branch"].isin(sel_branches)]
+            if df.empty or not {"Year", "Month"}.issubset(df.columns):
+                return 0.0, 0.0
+            # One row per (Year, Month) — sum across whichever branches survived.
+            monthly_totals = (
+                df.groupby(["Year", "Month"], as_index=False)
+                  .agg(Customers=("Customers", "sum"),
+                       Revenue=("Revenue", "sum"))
+                  .sort_values(["Year", "Month"])
+                  .tail(3)
             )
-
-        _month_rev = 0.0
-        if not _mp.empty and _rph_map:
-            _fallback = sum(_rph_map.values()) / len(_rph_map) if _rph_map else 0.0
-            _branch_cust = _mp.groupby("Branch")["Prediction"].sum()
-            for _br, _cust in _branch_cust.items():
-                _month_rev += float(_cust) * _rph_map.get(_br, _fallback)
-
-        mc1, mc2 = st.columns(2)
-        mc1.metric(t("fc_month_customers"), fmt_num(_month_cust))
-        mc2.metric(t("fc_month_revenue"),
-                   fmt_money(_month_rev) if _month_rev > 0 else "—")
-        st.caption(t("fc_month_basis"))
+            if monthly_totals.empty:
+                return 0.0, 0.0
+            return monthly_totals["Customers"].mean(), monthly_totals["Revenue"].mean()
+
+        cb_cust, cb_rev = _cb_month_forecast()
+        tc_cust, tc_rev = _avg_month_forecast("Tiew Copper")
+
+        # Copper Buffet block
+        st.markdown("**Copper Buffet**")
+        cb1, cb2 = st.columns(2)
+        cb1.metric(t("fc_month_customers"), fmt_num(cb_cust))
+        cb2.metric(t("fc_month_revenue"),
+                   fmt_money(cb_rev) if cb_rev > 0 else "—")
+
+        # Tiew Copper block
+        st.markdown("**Tiew Copper**")
+        tc1, tc2 = st.columns(2)
+        tc1.metric(t("fc_month_customers"), fmt_num(tc_cust))
+        tc2.metric(t("fc_month_revenue"),
+                   fmt_money(tc_rev) if tc_rev > 0 else "—")
+
+        st.caption(t("fc_month_basis_full"))
         st.divider()
 
         # Local horizon control — the sidebar date range is historical-focused