Add Monthly forecast and sub-cat in P&L

streamlit_app.py

@@ -42,7 +42,7 @@ LANG = {
     "en": {
         # Tab labels
         "tab_overview":  "Overview",
-        "tab_summary":   "Summary",
+        "tab_summary":   "Sales",
         "tab_forecast":  "Forecast",
         "tab_pl":        "P&L",
         "tab_inventory": "Inventory",
@@ -82,6 +82,10 @@ LANG = {
         "sm_chart_premium":    "%Premium — premium share of customers",
         "sm_chart_rounds":     "Customers by Round (monthly)",
         # Forecast tab
+        "fc_month_title":     "This Month Forecast",
+        "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_header":          "Copper Buffet — Forecast & Bookings",
         "fc_caption":         "Forecasted customer counts and confirmed bookings for upcoming "
                               "service dates. Data is captured only for Copper Buffet.",
@@ -104,6 +108,8 @@ LANG = {
         "pl_monthly_ts":        "Monthly P&L Time Series",
         "pl_cat_picker":        "Filter to one category",
         "pl_all_categories":    "All categories",
+        "pl_subcat_picker":     "Filter to one sub-category",
+        "pl_all_subcats":       "All sub-categories",
         "pl_amount_axis":       "Amount (THB)",
         # Inventory tab
         "inv_no_data":      "No inventory rows for the current filters.",
@@ -123,7 +129,7 @@ LANG = {
     "th": {
         # Tab labels
         "tab_overview":  "ภาพรวม",
-        "tab_summary":   "สรุป",
+        "tab_summary":   "ยอดขาย",
         "tab_forecast":  "พยากรณ์",
         "tab_pl":        "งบกำไรขาดทุน",
         "tab_inventory": "สินค้าคงคลัง",
@@ -163,6 +169,10 @@ LANG = {
         "sm_chart_premium":    "%ลูกค้าพรีเมียม",
         "sm_chart_rounds":     "ลูกค้าตามรอบ (รายเดือน)",
         # Forecast tab
+        "fc_month_title":     "พยากรณ์ของเดือนนี้",
+        "fc_month_customers": "พยากรณ์จำนวนลูกค้า (เดือนนี้)",
+        "fc_month_revenue":   "พยากรณ์รายได้ (เดือนนี้, ประมาณการ)",
+        "fc_month_basis":     "ประมาณการรายได้จาก: พยากรณ์จำนวนลูกค้า × รายได้ต่อหัวเฉลี่ย 3 เดือนล่าสุดของแต่ละสาขา",
         "fc_header":          "คอปเปอร์บุฟเฟ่ต์ — พยากรณ์และการจอง",
         "fc_caption":         "พยากรณ์จำนวนลูกค้าและการจองที่ยืนยันแล้วสำหรับวันที่บริการในอนาคต "
                               "ข้อมูลมีเฉพาะของคอปเปอร์บุฟเฟ่ต์เท่านั้น",
@@ -185,6 +195,8 @@ LANG = {
         "pl_monthly_ts":        "งบกำไรขาดทุนรายเดือน",
         "pl_cat_picker":        "กรองเฉพาะหมวด",
         "pl_all_categories":    "ทุกหมวด",
+        "pl_subcat_picker":     "กรองเฉพาะหมวดย่อย",
+        "pl_all_subcats":       "ทุกหมวดย่อย",
         "pl_amount_axis":       "จำนวน (บาท)",
         # Inventory tab
         "inv_no_data":      "ไม่มีข้อมูลสินค้าคงคลังสำหรับตัวกรองปัจจุบัน",
@@ -1678,6 +1690,61 @@ 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).
+        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:
+            _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"]
+            # Latest snapshot per (Date, Branch).
+            if "Date_Diff" in _mp.columns and not _mp.empty:
+                _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()
+            )
+
+        _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"))
+        st.divider()
+
         # Local horizon control — the sidebar date range is historical-focused
         # by default (Jan 1 → yesterday), so the forecast tab keeps its own.
         horizon_days = st.slider(
@@ -1876,10 +1943,26 @@ with tab_pl:
         st.subheader(t("pl_monthly_ts"))
         pl_filt = pl_filt.copy()
         pl_filt["YearMonth"] = pl_filt["Date"].dt.to_period("M").astype(str)
-        _all_cats_label = t("pl_all_categories")
+
+        # Two cascading filters: Category, then Sub-Category (only the
+        # sub-cats that exist inside the chosen Cat are offered).
+        _all_cats_label    = t("pl_all_categories")
+        _all_subcats_label = t("pl_all_subcats")
         cats = [_all_cats_label] + sorted(pl_filt["Cat"].dropna().unique().tolist())
-        cat_pick = st.selectbox(t("pl_cat_picker"), cats)
-        ts_src = pl_filt if cat_pick == _all_cats_label else pl_filt[pl_filt["Cat"] == cat_pick]
+        cat_col, subcat_col = st.columns(2)
+        with cat_col:
+            cat_pick = st.selectbox(t("pl_cat_picker"), cats, key="pl_ts_cat")
+        # Narrow pool first by category, then offer the sub-cats that exist
+        # in that pool. If "All categories" is selected we draw sub-cats
+        # from the whole filtered set.
+        ts_pool = pl_filt if cat_pick == _all_cats_label else pl_filt[pl_filt["Cat"] == cat_pick]
+        if "SubCat" in ts_pool.columns:
+            subcats = [_all_subcats_label] + sorted(ts_pool["SubCat"].dropna().unique().tolist())
+            with subcat_col:
+                subcat_pick = st.selectbox(t("pl_subcat_picker"), subcats, key="pl_ts_subcat")
+            ts_src = ts_pool if subcat_pick == _all_subcats_label else ts_pool[ts_pool["SubCat"] == subcat_pick]
+        else:
+            ts_src = ts_pool
         ts = (
             ts_src.groupby(["YearMonth", "Restaurant"], as_index=False)["Amount"].sum()
             .sort_values("YearMonth")