Support Thai language

streamlit_app.py

@@ -31,6 +31,190 @@ import pandas as pd
 import plotly.express as px
 import streamlit as st
 
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Bilingual support (English / Thai)
+# ─────────────────────────────────────────────────────────────────────────────
+# Restaurant names ("Copper Buffet" / "Tiew Copper") and data cell values are
+# intentionally NOT translated — they're brand names / source-system labels.
+# Only UI chrome (tabs, widget labels, headings, chart titles) flips.
+LANG = {
+    "en": {
+        # Tab labels
+        "tab_overview":  "Overview",
+        "tab_summary":   "Summary",
+        "tab_forecast":  "Forecast",
+        "tab_pl":        "P&L",
+        "tab_inventory": "Inventory",
+        # Sidebar
+        "sb_language":      "Language",
+        "sb_filters":       "Filters",
+        "sb_restaurant":    "Restaurant",
+        "sb_branch":        "Branch",
+        "sb_date_range":    "Date range",
+        "sb_source":        "Source",
+        "sb_sheets_loaded": "Sheets loaded",
+        "sb_signed_in_as":  "Signed in as",
+        "sb_sign_out":      "Sign out",
+        # Top header / period caption
+        "title":            "Copper Group Dashboard",
+        "filtered_period":  "Filtered period",
+        "all_dates":        "all dates",
+        "n_restaurants":    "{n} restaurant(s)",
+        "n_branches":       "{n} branch(es)",
+        # Overview tab
+        "ov_monthly_revenue_trend": "Monthly Revenue Trend",
+        "ov_channel_mix":           "Channel Mix",
+        "ov_daytype_perf":          "Day Type Performance",
+        "ov_no_monthly":            "No monthly rows match the current filters.",
+        "ov_no_channel":            "No channel data for the current filters.",
+        "ov_rev_per_head":          "Revenue per Head (THB)",
+        # Summary tab
+        "sm_trends":           "Trends",
+        "sm_monthly_summary":  "Monthly summary",
+        "sm_daily_detail":     "Daily detail",
+        "sm_no_data":          "No data for {name} in the current filters.",
+        "sm_no_monthly_rows":  "No monthly rows in this filter window.",
+        "sm_no_daily_rows":    "No daily rows in this filter window.",
+        "sm_chart_revenue":    "Monthly Revenue (THB)",
+        "sm_chart_customers":  "Monthly Customers",
+        "sm_chart_cap":        "%Cap — Capacity utilised",
+        "sm_chart_premium":    "%Premium — premium share of customers",
+        "sm_chart_rounds":     "Customers by Round (monthly)",
+        # Forecast tab
+        "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.",
+        "fc_no_data":         "No booking or forecast data is loaded.",
+        "fc_horizon":         "Forecast horizon (days from today)",
+        "fc_kpi_forecast":    "Forecast (next {n}d)",
+        "fc_kpi_booked":      "Booked so far (next {n}d)",
+        "fc_kpi_pct_booked":  "% Booked vs Forecast",
+        "fc_outlook":         "Daily outlook",
+        "fc_no_horizon":      "No forecast rows in the selected horizon.",
+        "fc_no_bookings":     "No booking rows in the selected horizon.",
+        "fc_chart_trend":     "Predicted Customers — next {n} days",
+        "fc_section_bookings":"Booked seats by round",
+        "fc_chart_booked":    "Booked Seats by Round — next {n} days",
+        "fc_section_trend":   "Forecast trend",
+        # P&L tab
+        "pl_no_data":           "No P&L rows for the current filters.",
+        "pl_month_picker":      "Month",
+        "pl_top_subcat_title":  "P&L — Top Sub-Categories ({ym})",
+        "pl_monthly_ts":        "Monthly P&L Time Series",
+        "pl_cat_picker":        "Filter to one category",
+        "pl_all_categories":    "All categories",
+        "pl_amount_axis":       "Amount (THB)",
+        # Inventory tab
+        "inv_no_data":      "No inventory rows for the current filters.",
+        "inv_month_picker": "Month",
+        "inv_sort_by":      "Sort by",
+        "inv_snapshot":     "Inventory snapshot ({ym})",
+        "inv_closing_by_branch": "Closing inventory value by branch",
+        # Sign-in screen
+        "auth_title":   "Copper Group Dashboard",
+        "auth_intro":   "Restricted to members of the <code>CB-Group</code> organization on Hugging Face. "
+                        "Sign in with your HF account to continue.",
+        "auth_button":  "Sign in with Hugging Face",
+        "auth_no_acct": "Don't have an HF account? Ask the dashboard owner to invite you to the "
+                        "<code>CB-Group</code> org, then",
+        "auth_signup":  "sign up here",
+    },
+    "th": {
+        # Tab labels
+        "tab_overview":  "ภาพรวม",
+        "tab_summary":   "สรุป",
+        "tab_forecast":  "พยากรณ์",
+        "tab_pl":        "งบกำไรขาดทุน",
+        "tab_inventory": "สินค้าคงคลัง",
+        # Sidebar
+        "sb_language":      "ภาษา",
+        "sb_filters":       "ตัวกรอง",
+        "sb_restaurant":    "ร้านอาหาร",
+        "sb_branch":        "สาขา",
+        "sb_date_range":    "ช่วงวันที่",
+        "sb_source":        "แหล่งข้อมูล",
+        "sb_sheets_loaded": "จำนวนชีท",
+        "sb_signed_in_as":  "เข้าสู่ระบบในนาม",
+        "sb_sign_out":      "ออกจากระบบ",
+        # Top header / period caption
+        "title":            "แดชบอร์ดคอปเปอร์กรุ๊ป",
+        "filtered_period":  "ช่วงที่กรอง",
+        "all_dates":        "ทุกวันที่",
+        "n_restaurants":    "{n} ร้าน",
+        "n_branches":       "{n} สาขา",
+        # Overview tab
+        "ov_monthly_revenue_trend": "แนวโน้มรายได้รายเดือน",
+        "ov_channel_mix":           "สัดส่วนช่องทาง",
+        "ov_daytype_perf":          "ผลการดำเนินงานตามประเภทวัน",
+        "ov_no_monthly":            "ไม่มีข้อมูลรายเดือนสำหรับตัวกรองปัจจุบัน",
+        "ov_no_channel":            "ไม่มีข้อมูลช่องทางสำหรับตัวกรองปัจจุบัน",
+        "ov_rev_per_head":          "รายได้ต่อหัว (บาท)",
+        # Summary tab
+        "sm_trends":           "แนวโน้ม",
+        "sm_monthly_summary":  "สรุปรายเดือน",
+        "sm_daily_detail":     "รายละเอียดรายวัน",
+        "sm_no_data":          "ไม่มีข้อมูล {name} สำหรับตัวกรองปัจจุบัน",
+        "sm_no_monthly_rows":  "ไม่มีข้อมูลรายเดือนในช่วงที่เลือก",
+        "sm_no_daily_rows":    "ไม่มีข้อมูลรายวันในช่วงที่เลือก",
+        "sm_chart_revenue":    "รายได้รายเดือน (บาท)",
+        "sm_chart_customers":  "ลูกค้ารายเดือน",
+        "sm_chart_cap":        "%ใช้พื้นที่",
+        "sm_chart_premium":    "%ลูกค้าพรีเมียม",
+        "sm_chart_rounds":     "ลูกค้าตามรอบ (รายเดือน)",
+        # Forecast tab
+        "fc_header":          "คอปเปอร์บุฟเฟ่ต์ — พยากรณ์และการจอง",
+        "fc_caption":         "พยากรณ์จำนวนลูกค้าและการจองที่ยืนยันแล้วสำหรับวันที่บริการในอนาคต "
+                              "ข้อมูลมีเฉพาะของคอปเปอร์บุฟเฟ่ต์เท่านั้น",
+        "fc_no_data":         "ไม่มีข้อมูลการจองหรือพยากรณ์ที่โหลดอยู่",
+        "fc_horizon":         "ระยะเวลาพยากรณ์ (วันจากวันนี้)",
+        "fc_kpi_forecast":    "พยากรณ์ ({n} วันข้างหน้า)",
+        "fc_kpi_booked":      "จองแล้ว ({n} วันข้างหน้า)",
+        "fc_kpi_pct_booked":  "% จองเทียบกับพยากรณ์",
+        "fc_outlook":         "ภาพรวมรายวัน",
+        "fc_no_horizon":      "ไม่มีข้อมูลพยากรณ์ในช่วงที่เลือก",
+        "fc_no_bookings":     "ไม่มีข้อมูลการจองในช่วงที่เลือก",
+        "fc_chart_trend":     "พยากรณ์จำนวนลูกค้า — {n} วันข้างหน้า",
+        "fc_section_bookings":"ที่นั่งที่จองตามรอบ",
+        "fc_chart_booked":    "ที่นั่งที่จองตามรอบ — {n} วันข้างหน้า",
+        "fc_section_trend":   "แนวโน้มพยากรณ์",
+        # P&L tab
+        "pl_no_data":           "ไม่มีข้อมูลงบกำไรขาดทุนสำหรับตัวกรองปัจจุบัน",
+        "pl_month_picker":      "เดือน",
+        "pl_top_subcat_title":  "งบกำไรขาดทุน — หมวดย่อยอันดับต้น ({ym})",
+        "pl_monthly_ts":        "งบกำไรขาดทุนรายเดือน",
+        "pl_cat_picker":        "กรองเฉพาะหมวด",
+        "pl_all_categories":    "ทุกหมวด",
+        "pl_amount_axis":       "จำนวน (บาท)",
+        # Inventory tab
+        "inv_no_data":      "ไม่มีข้อมูลสินค้าคงคลังสำหรับตัวกรองปัจจุบัน",
+        "inv_month_picker": "เดือน",
+        "inv_sort_by":      "เรียงตาม",
+        "inv_snapshot":     "ภาพรวมสินค้าคงคลัง ({ym})",
+        "inv_closing_by_branch": "มูลค่าคงเหลือตามสาขา",
+        # Sign-in screen
+        "auth_title":   "แดชบอร์ดคอปเปอร์กรุ๊ป",
+        "auth_intro":   "เฉพาะสมาชิกขององค์กร <code>CB-Group</code> บน Hugging Face เท่านั้น "
+                        "เข้าสู่ระบบด้วยบัญชี HF ของคุณเพื่อดำเนินการต่อ",
+        "auth_button":  "เข้าสู่ระบบด้วย Hugging Face",
+        "auth_no_acct": "ยังไม่มีบัญชี HF? ขอให้เจ้าของแดชบอร์ดเชิญคุณเข้า "
+                        "องค์กร <code>CB-Group</code> จากนั้น",
+        "auth_signup":  "สมัครได้ที่นี่",
+    },
+}
+
+
+def t(key: str, **kwargs) -> str:
+    """Return the user-facing string for ``key`` in the current language.
+
+    Falls back to English if a key is missing in Thai, and falls back to
+    the raw key if it's missing in both — so a missed translation shows up
+    as e.g. ``sm_chart_revenue`` instead of crashing.
+    """
+    lang = st.session_state.get("_lang", "en")
+    s = LANG.get(lang, {}).get(key) or LANG["en"].get(key, key)
+    return s.format(**kwargs) if kwargs else s
+
 # ─────────────────────────────────────────────────────────────────────────────
 # Page setup
 # ─────────────────────────────────────────────────────────────────────────────
@@ -282,11 +466,9 @@ def _oauth_gate() -> None:
         f"""
         <div style="max-width: 420px; margin: 80px auto 0 auto; text-align: center;">
             <div style="font-size: 32px; margin-bottom: 8px;">🔒</div>
-            <h2 style="margin: 0 0 8px 0; color: #1E2B3A;">Copper Group Dashboard</h2>
+            <h2 style="margin: 0 0 8px 0; color: #1E2B3A;">{t("auth_title")}</h2>
             <p style="color: #6B7280; margin: 0 0 28px 0; line-height: 1.5;">
-                Restricted to members of the
-                <code>CB-Group</code> organization on Hugging Face.
-                Sign in with your HF account to continue.
+                {t("auth_intro")}
             </p>
             <a href="{auth_url}" target="_self" style="
                 display: inline-flex;
@@ -300,12 +482,11 @@ def _oauth_gate() -> None:
                 font-size: 15px;
                 text-decoration: none;
                 box-shadow: 0 1px 3px rgba(30, 43, 58, 0.15);
-            ">🤗 Sign in with Hugging Face</a>
+            ">🤗 {t("auth_button")}</a>
             <p style="color: #6B7280; font-size: 12px; margin-top: 32px;">
-                Don't have an HF account? Ask the dashboard owner to invite
-                you to the <code>CB-Group</code> org, then
+                {t("auth_no_acct")}
                 <a href="https://huggingface.co/join" target="_blank"
-                   style="color: #976A4D;">sign up here</a>.
+                   style="color: #976A4D;">{t("auth_signup")}</a>.
             </p>
         </div>
         """,
@@ -569,7 +750,7 @@ if secrets_token is None:
     secrets_token = os.environ.get("HF_TOKEN") or None
 
 # Header (we show it early so the loading status is visible even on slow links)
-st.title("Copper Group Dashboard")
+st.title(t("title"))
 
 
 def _show_diagnostics(exc: "FetchError", source_hint: str) -> None:
@@ -723,12 +904,29 @@ _restaurant_defaults = [r for r in _DEFAULT_RESTAURANTS if r in restaurants_all]
 _branch_defaults     = [b for b in _DEFAULT_BRANCHES if b in branches_all] \
                        or branches_all
 
-st.sidebar.title("Filters")
+# Language selector — placed BEFORE other widgets so labels switch
+# immediately on the same rerun.
+_lang_label = {"en": "English", "th": "ภาษาไทย"}
+_lang_default = st.session_state.get("_lang", "en")
+_lang_choice = st.sidebar.radio(
+    t("sb_language"),
+    options=["en", "th"],
+    format_func=lambda code: _lang_label[code],
+    horizontal=True,
+    index=0 if _lang_default == "en" else 1,
+    key="_lang_radio",
+)
+if _lang_choice != st.session_state.get("_lang"):
+    st.session_state["_lang"] = _lang_choice
+    st.rerun()
+st.sidebar.divider()
+
+st.sidebar.title(t("sb_filters"))
 sel_restaurants = st.sidebar.multiselect(
-    "Restaurant", restaurants_all, default=_restaurant_defaults
+    t("sb_restaurant"), restaurants_all, default=_restaurant_defaults
 )
 sel_branches = st.sidebar.multiselect(
-    "Branch", branches_all, default=_branch_defaults
+    t("sb_branch"), branches_all, default=_branch_defaults
 )
 
 # Year + date range
@@ -756,7 +954,7 @@ if min_date and max_date:
         _default_start, _default_end = min_date, max_date
 
     sel_dates = st.sidebar.date_input(
-        "Date range",
+        t("sb_date_range"),
         value=(_default_start, _default_end),
         min_value=min_date,
         max_value=max_date,
@@ -769,16 +967,16 @@ else:
     date_from = date_to = None
 
 st.sidebar.divider()
-st.sidebar.caption(f"Source: {source_label}")
-st.sidebar.caption(f"Sheets loaded: {len(sheets)}")
+st.sidebar.caption(f"{t('sb_source')}: {source_label}")
+st.sidebar.caption(f"{t('sb_sheets_loaded')}: {len(sheets)}")
 
 # ── Signed-in user badge + sign-out (only shown when OAuth is active) ────────
 _oauth_user = st.session_state.get("_oauth_user")
 if _oauth_user:
     _name = _oauth_user.get("preferred_username") or _oauth_user.get("name") or "user"
     st.sidebar.divider()
-    st.sidebar.markdown(f"Signed in as **{_name}**")
-    if st.sidebar.button("Sign out", use_container_width=True):
+    st.sidebar.markdown(f"{t('sb_signed_in_as')} **{_name}**")
+    if st.sidebar.button(t("sb_sign_out"), use_container_width=True):
         # Drop everything OAuth-related and force the sign-in screen on rerun.
         for _k in ("_oauth_user", "_oauth_state"):
             st.session_state.pop(_k, None)
@@ -914,10 +1112,10 @@ BRAND_SEQUENCE = [
 # Header + KPI tiles
 # ─────────────────────────────────────────────────────────────────────────────
 # (Title already rendered up-top; just print the filtered-period caption.)
-period_str = f"{date_from} → {date_to}" if date_from else "all dates"
+period_str = f"{date_from} → {date_to}" if date_from else t("all_dates")
 st.markdown(
-    f"<span class='small-caption'>Filtered period: <b>{period_str}</b> · "
-    f"{len(sel_restaurants)} restaurant(s), {len(sel_branches)} branch(es)</span>",
+    f"<span class='small-caption'>{t('filtered_period')}: <b>{period_str}</b> · "
+    f"{t('n_restaurants', n=len(sel_restaurants))}, {t('n_branches', n=len(sel_branches))}</span>",
     unsafe_allow_html=True,
 )
 
@@ -940,7 +1138,8 @@ st.divider()
 # Tabs
 # ─────────────────────────────────────────────────────────────────────────────
 tab_overview, tab_summary, tab_forecast, tab_pl, tab_inv = st.tabs(
-    ["Overview", "Summary", "Forecast", "P&L", "Inventory"]
+    [t("tab_overview"), t("tab_summary"), t("tab_forecast"),
+     t("tab_pl"), t("tab_inventory")]
 )
 
 # ── Overview ───────────────────────────────────────────────────────────────
@@ -948,7 +1147,7 @@ with tab_overview:
     left, right = st.columns([2, 1])
 
     with left:
-        st.subheader("Monthly Revenue Trend")
+        st.subheader(t("ov_monthly_revenue_trend"))
         if not kpi_monthly.empty:
             mf = apply_filters(kpi_monthly)
             if not mf.empty:
@@ -975,10 +1174,10 @@ with tab_overview:
                 fig.update_layout(xaxis_title=None, yaxis_title="Revenue (THB)")
                 st.plotly_chart(style_plotly(fig, height=420), use_container_width=True)
             else:
-                st.info("No monthly rows match the current filters.")
+                st.info(t("ov_no_monthly"))
 
     with right:
-        st.subheader("Channel Mix")
+        st.subheader(t("ov_channel_mix"))
         if not fact_sales.empty:
             sf = apply_filters(fact_sales)
             # Exclude roll-up rows from the source data — "Grand Total" /
@@ -1008,9 +1207,9 @@ with tab_overview:
                 )
                 st.plotly_chart(style_plotly(fig, height=420), use_container_width=True)
             else:
-                st.info("No channel data for the current filters.")
+                st.info(t("ov_no_channel"))
 
-    st.subheader("Day Type Performance")
+    st.subheader(t("ov_daytype_perf"))
     if not filtered_daily.empty and "DayType" in filtered_daily.columns:
         order = ["Weekday", "Weekend", "Holiday"]
         daytype = (
@@ -1035,7 +1234,7 @@ with tab_overview:
                          color_discrete_map=_DAYTYPE_COLOR,
                          text=daytype["Rev_Per_Head"].apply(lambda v: f"฿{v:,.0f}" if pd.notna(v) else ""))
             fig.update_layout(showlegend=False, xaxis_title=None,
-                              yaxis_title="Revenue per Head (THB)")
+                              yaxis_title=t("ov_rev_per_head"))
             fig.update_yaxes(tickformat=",.0f")
             st.plotly_chart(style_plotly(fig, height=320), use_container_width=True)
         with col2:
@@ -1087,7 +1286,7 @@ with tab_summary:
                   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.")
+            st.info(t("sm_no_data", name=restaurant_name))
             return
 
         # Pre-compute extended monthly DataFrame *once*, reused for both the
@@ -1192,7 +1391,7 @@ with tab_summary:
 
         # ── Trends — small charts above the tables ───────────────────────
         if not m.empty:
-            st.markdown("**Trends**")
+            st.markdown(f"**{t('sm_trends')}**")
             mf = m.copy()
             mf["YearMonth"] = (
                 mf["Year"].astype(int).astype(str)
@@ -1214,7 +1413,7 @@ with tab_summary:
                     textfont=dict(size=9),
                 )
                 fig.update_yaxes(tickformat=",.0f")
-                fig.update_layout(title="Monthly Revenue (THB)",
+                fig.update_layout(title=t("sm_chart_revenue"),
                                   xaxis_title=None, yaxis_title=None)
                 st.plotly_chart(style_plotly(fig, height=280), use_container_width=True)
             with tc2:
@@ -1229,7 +1428,7 @@ with tab_summary:
                     textfont=dict(size=9),
                 )
                 fig.update_yaxes(tickformat=",.0f")
-                fig.update_layout(title="Monthly Customers",
+                fig.update_layout(title=t("sm_chart_customers"),
                                   xaxis_title=None, yaxis_title=None)
                 st.plotly_chart(style_plotly(fig, height=280), use_container_width=True)
 
@@ -1249,7 +1448,7 @@ with tab_summary:
                             textfont=dict(size=9),
                         )
                         fig.update_yaxes(ticksuffix="%")
-                        fig.update_layout(title="%Cap — Capacity utilised",
+                        fig.update_layout(title=t("sm_chart_cap"),
                                           xaxis_title=None, yaxis_title=None)
                         st.plotly_chart(style_plotly(fig, height=280), use_container_width=True)
                 if "%Premium" in mf.columns:
@@ -1265,7 +1464,7 @@ with tab_summary:
                             textfont=dict(size=9),
                         )
                         fig.update_yaxes(ticksuffix="%")
-                        fig.update_layout(title="%Premium — premium share of customers",
+                        fig.update_layout(title=t("sm_chart_premium"),
                                           xaxis_title=None, yaxis_title=None)
                         st.plotly_chart(style_plotly(fig, height=280), use_container_width=True)
 
@@ -1294,12 +1493,12 @@ with tab_summary:
                     insidetextanchor="middle",
                 )
                 fig.update_yaxes(tickformat=",.0f")
-                fig.update_layout(title="Customers by Round (monthly)",
-                                  xaxis_title=None, yaxis_title="Customers")
+                fig.update_layout(title=t("sm_chart_rounds"),
+                                  xaxis_title=None, yaxis_title=None)
                 st.plotly_chart(style_plotly(fig, height=340), use_container_width=True)
 
         # ── Monthly summary table ────────────────────────────────────────
-        st.markdown("**Monthly summary**")
+        st.markdown(f"**{t('sm_monthly_summary')}**")
         if not m.empty:
             base_cols = [c for c in
                          ["Year", "Month", "Branch", "Revenue", "Customers"]
@@ -1331,13 +1530,13 @@ with tab_summary:
                 },
             )
         else:
-            st.caption("No monthly rows in this filter window.")
+            st.caption(t("sm_no_monthly_rows"))
 
         # ── Daily detail (collapsed by default — can be long) ────────────
         # Mirror the Monthly summary column layout, just with Date instead
         # of Year / Month. Adds %Cap, %Premium and per-round customer
         # columns for Copper Buffet.
-        with st.expander("Daily detail", expanded=False):
+        with st.expander(t("sm_daily_detail"), expanded=False):
             if not daily.empty:
                 d = daily.copy().sort_values(["Date", "Branch"], ascending=[True, True])
                 if "Revenue" in d.columns and "Customers" in d.columns:
@@ -1460,7 +1659,7 @@ with tab_summary:
                     },
                 )
             else:
-                st.caption("No daily rows in this filter window.")
+                st.caption(t("sm_no_daily_rows"))
 
     _render_restaurant_summary("Copper Buffet")
     st.divider()
@@ -1474,21 +1673,15 @@ with tab_summary:
 # service date.
 with tab_forecast:
     if fact_predictions.empty and fact_bookings.empty:
-        st.info(
-            "No booking or forecast data is loaded. The collection script "
-            "writes `fact_bookings` and `fact_predictions` for Copper Buffet."
-        )
+        st.info(t("fc_no_data"))
     else:
-        st.subheader("Copper Buffet — Forecast & Bookings")
-        st.caption(
-            "Forecasted customer counts and confirmed bookings for upcoming "
-            "service dates. Data is captured only for Copper Buffet."
-        )
+        st.subheader(t("fc_header"))
+        st.caption(t("fc_caption"))
 
         # 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(
-            "Forecast horizon (days from today)",
+            t("fc_horizon"),
             min_value=7, max_value=60, value=14, step=1,
         )
         today    = pd.Timestamp(_dt.now().date())
@@ -1528,15 +1721,15 @@ with tab_forecast:
         avg_pct_booked = (total_booked / total_forecast * 100) if total_forecast > 0 else None
 
         fk1, fk2, fk3 = st.columns(3)
-        fk1.metric(f"Forecast (next {horizon_days}d)", fmt_num(total_forecast))
-        fk2.metric(f"Booked so far (next {horizon_days}d)", fmt_num(total_booked))
-        fk3.metric("% Booked vs Forecast",
+        fk1.metric(t("fc_kpi_forecast", n=horizon_days), fmt_num(total_forecast))
+        fk2.metric(t("fc_kpi_booked",   n=horizon_days), fmt_num(total_booked))
+        fk3.metric(t("fc_kpi_pct_booked"),
                    fmt_pct(avg_pct_booked) if avg_pct_booked is not None else "—")
 
         # ── Daily outlook table ──────────────────────────────────────────
-        st.markdown("**Daily outlook**")
+        st.markdown(f"**{t('fc_outlook')}**")
         if preds.empty:
-            st.info("No forecast rows in the selected horizon.")
+            st.info(t("fc_no_horizon"))
         else:
             out = preds[["Date", "Branch", "DayType", "Prediction",
                          "Round_1", "Round_2", "Round_3", "Round_4", "Round_5"]].copy()
@@ -1573,9 +1766,9 @@ with tab_forecast:
             )
 
         # ── Forecast trend line — predicted customers per day ────────────
-        st.markdown("**Forecast trend**")
+        st.markdown(f"**{t('fc_section_trend')}**")
         if preds.empty:
-            st.caption("No forecast rows in the selected horizon.")
+            st.caption(t("fc_no_horizon"))
         else:
             fig = px.line(
                 preds, x="Date", y="Prediction", color="Branch",
@@ -1587,14 +1780,14 @@ with tab_forecast:
                 textfont=dict(size=9),
             )
             fig.update_yaxes(tickformat=",.0f")
-            fig.update_layout(title=f"Predicted Customers — next {horizon_days} days",
-                              xaxis_title=None, yaxis_title="Customers")
+            fig.update_layout(title=t("fc_chart_trend", n=horizon_days),
+                              xaxis_title=None, yaxis_title=None)
             st.plotly_chart(style_plotly(fig, height=320), use_container_width=True)
 
         # ── Booked seats stacked by round ────────────────────────────────
-        st.markdown("**Booked seats by round**")
+        st.markdown(f"**{t('fc_section_bookings')}**")
         if books.empty:
-            st.caption("No booking rows in the selected horizon.")
+            st.caption(t("fc_no_bookings"))
         else:
             ROUND_NUM_LABEL = {1: "Breakfast", 2: "Lunch", 3: "Dinner",
                                4: "Late Dinner", 5: "Special"}
@@ -1625,9 +1818,8 @@ with tab_forecast:
             )
             fig.update_yaxes(tickformat=",.0f")
             fig.update_layout(
-                title=f"Booked Seats by Round — next {horizon_days} days",
-                xaxis_title=None, yaxis_title="Booked Seats",
-                legend_title="Round",
+                title=t("fc_chart_booked", n=horizon_days),
+                xaxis_title=None, yaxis_title=None,
             )
             st.plotly_chart(style_plotly(fig, height=320), use_container_width=True)
 
@@ -1635,15 +1827,28 @@ with tab_forecast:
 with tab_pl:
     pl_filt = apply_filters(fact_pl)
     if pl_filt.empty:
-        st.info("No P&L rows for the current filters. (Try selecting the Group restaurant to see Conso/CK/CH.)")
+        st.info(t("pl_no_data"))
     else:
-        latest = pl_filt["Date"].max()
-        ym = f"{latest.year}-{latest.month:02d}"
+        # Month picker — list every (Year, Month) present in the filtered
+        # data, newest first; default to the most recent one.
+        _pl_ym = (
+            pl_filt[["Date"]].dropna()
+            .assign(_y=lambda d: d["Date"].dt.year,
+                    _m=lambda d: d["Date"].dt.month)
+            [["_y", "_m"]].drop_duplicates()
+            .sort_values(["_y", "_m"], ascending=[False, False])
+        )
+        _pl_options = [f"{int(y)}-{int(m):02d}" for y, m in _pl_ym.itertuples(index=False)]
+        if not _pl_options:
+            st.info("No P&L rows for the current filters.")
+            st.stop()
+        ym = st.selectbox(t("pl_month_picker"), _pl_options, index=0, key="pl_month")
+        _y_sel, _m_sel = (int(p) for p in ym.split("-"))
 
-        st.subheader(f"P&L — Top Sub-Categories ({ym})")
+        st.subheader(t("pl_top_subcat_title", ym=ym))
         latest_rows = pl_filt[
-            (pl_filt["Date"].dt.year == latest.year)
-            & (pl_filt["Date"].dt.month == latest.month)
+            (pl_filt["Date"].dt.year == _y_sel)
+            & (pl_filt["Date"].dt.month == _m_sel)
         ]
         group_col = "SubCat" if "SubCat" in latest_rows.columns else "Cat"
         agg = (
@@ -1664,16 +1869,17 @@ with tab_pl:
             text="AmountLabel",
         )
         fig.update_traces(textposition="outside", cliponaxis=False)
-        fig.update_layout(yaxis_title=None, xaxis_title="Amount (THB)", showlegend=True)
+        fig.update_layout(yaxis_title=None, xaxis_title=t("pl_amount_axis"), showlegend=True)
         fig.update_xaxes(tickformat=",.0f")
         st.plotly_chart(style_plotly(fig, height=520), use_container_width=True)
 
-        st.subheader("Monthly P&L Time Series")
+        st.subheader(t("pl_monthly_ts"))
         pl_filt = pl_filt.copy()
         pl_filt["YearMonth"] = pl_filt["Date"].dt.to_period("M").astype(str)
-        cats = ["All categories"] + sorted(pl_filt["Cat"].dropna().unique().tolist())
-        cat_pick = st.selectbox("Filter to one category", cats)
-        ts_src = pl_filt if cat_pick == "All categories" else pl_filt[pl_filt["Cat"] == cat_pick]
+        _all_cats_label = t("pl_all_categories")
+        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]
         ts = (
             ts_src.groupby(["YearMonth", "Restaurant"], as_index=False)["Amount"].sum()
             .sort_values("YearMonth")
@@ -1694,23 +1900,39 @@ with tab_pl:
 with tab_inv:
     inv_filt = apply_filters(fact_inventory)
     if inv_filt.empty:
-        st.info("No inventory rows for the current filters.")
+        st.info(t("inv_no_data"))
     else:
-        latest = inv_filt["Date"].max()
-        ym = f"{latest.year}-{latest.month:02d}"
+        # Month picker — list every (Year, Month) present in the filtered
+        # inventory data, newest first; default to the most recent.
+        _inv_ym = (
+            inv_filt[["Date"]].dropna()
+            .assign(_y=lambda d: d["Date"].dt.year,
+                    _m=lambda d: d["Date"].dt.month)
+            [["_y", "_m"]].drop_duplicates()
+            .sort_values(["_y", "_m"], ascending=[False, False])
+        )
+        _inv_options = [f"{int(y)}-{int(m):02d}" for y, m in _inv_ym.itertuples(index=False)]
+        if not _inv_options:
+            st.info("No inventory rows for the current filters.")
+            st.stop()
 
-        col1, col2 = st.columns(2)
+        col1, col2, col3 = st.columns([2, 1, 1])
         with col1:
-            st.subheader(f"Inventory snapshot ({ym})")
+            ym = st.selectbox(t("inv_month_picker"), _inv_options, index=0, key="inv_month")
         with col2:
             sort_by = st.selectbox(
-                "Sort by",
+                t("inv_sort_by"),
                 ["Value_Closing", "Qty_Closing", "Value_Used", "Qty_Used"],
                 index=0,
+                key="inv_sort",
             )
+        with col3:
+            st.markdown(" ")  # spacer
+        _y_sel, _m_sel = (int(p) for p in ym.split("-"))
+        st.subheader(t("inv_snapshot", ym=ym))
         latest_rows = inv_filt[
-            (inv_filt["Date"].dt.year == latest.year)
-            & (inv_filt["Date"].dt.month == latest.month)
+            (inv_filt["Date"].dt.year == _y_sel)
+            & (inv_filt["Date"].dt.month == _m_sel)
         ]
         ranked = latest_rows[latest_rows[sort_by] > 0].sort_values(sort_by, ascending=False).head(100)
 
@@ -1736,7 +1958,7 @@ with tab_inv:
         )
 
         # Per-branch inventory totals
-        st.subheader("Closing inventory value by branch")
+        st.subheader(t("inv_closing_by_branch"))
         totals = (
             latest_rows.groupby(["Restaurant", "Branch"], as_index=False)["Value_Closing"]
             .sum()