Change theme to match the brand

.streamlit/config.toml

@@ -1,9 +1,14 @@
 [theme]
-base = "dark"
-primaryColor = "#4dabf7"
-backgroundColor = "#0f1419"
-secondaryBackgroundColor = "#1a2028"
-textColor = "#e8eaed"
+# Light theme tuned to the Copper Group brand palette.
+#   primaryColor             = Copper Buffet wordmark color (#906848)
+#   backgroundColor          = warm off-white / cream
+#   secondaryBackgroundColor = slightly darker cream for sidebar + cards
+#   textColor                = dark espresso-brown for body text
+base = "light"
+primaryColor = "#906848"
+backgroundColor = "#FAF7F2"
+secondaryBackgroundColor = "#F2EBE0"
+textColor = "#2A1F15"
 font = "sans serif"
 
 [server]

streamlit_app.py

@@ -44,29 +44,54 @@ st.set_page_config(
 st.markdown(
     """
 <style>
+    /* Light-theme Copper Group palette.
+       --copper        = Copper Buffet wordmark (primary brand)
+       --copper-soft   = lighter wash for borders / dividers
+       --cream         = page surface
+       --cream-2       = card / sidebar surface
+       --ink           = body text
+       --muted         = secondary text */
+    :root {
+        --copper:      #906848;
+        --copper-soft: #D4C4B0;
+        --cream:       #FAF7F2;
+        --cream-2:     #F2EBE0;
+        --ink:         #2A1F15;
+        --muted:       #7A6248;
+    }
     div[data-testid="stMetric"] {
-        background: #1a2028;
-        border: 1px solid #2d3744;
+        background: var(--cream-2);
+        border: 1px solid var(--copper-soft);
         border-radius: 10px;
         padding: 16px 20px;
     }
     div[data-testid="stMetric"] [data-testid="stMetricValue"] {
         font-size: 28px;
         font-weight: 600;
+        color: var(--ink);
     }
     div[data-testid="stMetric"] [data-testid="stMetricLabel"] {
-        color: #9aa4b2;
+        color: var(--muted);
         font-size: 12px;
         letter-spacing: 0.5px;
         text-transform: uppercase;
     }
     section[data-testid="stSidebar"] {
-        background: #1a2028;
+        background: var(--cream-2);
     }
     .small-caption {
-        color: #9aa4b2;
+        color: var(--muted);
         font-size: 12px;
     }
+    /* Make the page background pick up the cream tone consistently. */
+    .stApp { background: var(--cream); }
+    /* Tab labels lean copper to reinforce the brand on the main nav. */
+    button[data-baseweb="tab"] {
+        color: var(--muted) !important;
+    }
+    button[data-baseweb="tab"][aria-selected="true"] {
+        color: var(--copper) !important;
+    }
 
     /* ── Mobile-responsive overrides (phones / narrow tablets) ───────────
        Streamlit's default st.columns() lays out equal-width children in a
@@ -257,8 +282,8 @@ 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;">Copper Group Dashboard</h2>
-            <p style="color: #9aa4b2; margin: 0 0 28px 0; line-height: 1.5;">
+            <h2 style="margin: 0 0 8px 0; color: #2A1F15;">Copper Group Dashboard</h2>
+            <p style="color: #7A6248; 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.
@@ -267,19 +292,20 @@ def _oauth_gate() -> None:
                 display: inline-flex;
                 align-items: center;
                 gap: 10px;
-                background: #ffd21e;
-                color: #000;
+                background: #906848;
+                color: #FAF7F2;
                 padding: 12px 28px;
                 border-radius: 10px;
                 font-weight: 600;
                 font-size: 15px;
                 text-decoration: none;
+                box-shadow: 0 1px 3px rgba(42, 31, 21, 0.15);
             ">🤗 Sign in with Hugging Face</a>
-            <p style="color: #6b7280; font-size: 12px; margin-top: 32px;">
+            <p style="color: #7A6248; 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
                 <a href="https://huggingface.co/join" target="_blank"
-                   style="color: #6b7280;">sign up here</a>.
+                   style="color: #906848;">sign up here</a>.
             </p>
         </div>
         """,
@@ -719,15 +745,15 @@ if not kpi_daily.empty and "Year" in kpi_daily.columns:
     years_avail = sorted(years_avail, reverse=True)
 
 # Default the year filter to the current calendar year when present in the
-# data; otherwise fall back to "All years" so the dashboard still loads
-# something on empty / future-dated runs.
+# data; otherwise leave it empty (= "all years") so the dashboard still
+# loads something on empty / future-dated runs.
 from datetime import datetime as _dt
 _current_year = _dt.now().year
-_year_options = ["All years"] + years_avail
-_year_default_index = (
-    _year_options.index(_current_year) if _current_year in years_avail else 0
+_year_default = [_current_year] if _current_year in years_avail else []
+sel_years = st.sidebar.multiselect(
+    "Year", years_avail, default=_year_default,
+    help="Leave empty to include every year in the dataset.",
 )
-sel_year = st.sidebar.selectbox("Year", _year_options, index=_year_default_index)
 
 if min_date and max_date:
     # Default range: Jan 1 of the current year → yesterday. Clamp both ends
@@ -788,11 +814,11 @@ def apply_filters(df: pd.DataFrame, *, use_date: bool = True) -> pd.DataFrame:
             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 sel_years:
         if "Year" in out.columns:
-            out = out[out["Year"] == sel_year]
+            out = out[out["Year"].isin(sel_years)]
         elif "Date" in out.columns:
-            out = out[out["Date"].dt.year == sel_year]
+            out = out[out["Date"].dt.year.isin(sel_years)]
     return out
 
 
@@ -827,24 +853,29 @@ def fmt_qty(n) -> str:
 
 
 def style_plotly(fig, *, height: int | None = None):
-    """Common Plotly cosmetics — transparent background + theme colours."""
+    """Common Plotly cosmetics — transparent background + light-theme gridlines."""
     fig.update_layout(
         paper_bgcolor="rgba(0,0,0,0)",
         plot_bgcolor="rgba(0,0,0,0)",
         margin=dict(l=10, r=10, t=30, b=10),
         legend=dict(orientation="h", yanchor="bottom", y=-0.25, x=0),
+        font=dict(color="#2A1F15"),
     )
     if height is not None:
         fig.update_layout(height=height)
-    fig.update_xaxes(gridcolor="#2d3744", zerolinecolor="#2d3744")
-    fig.update_yaxes(gridcolor="#2d3744", zerolinecolor="#2d3744")
+    fig.update_xaxes(gridcolor="#E5DDD0", zerolinecolor="#D4C4B0", linecolor="#D4C4B0")
+    fig.update_yaxes(gridcolor="#E5DDD0", zerolinecolor="#D4C4B0", linecolor="#D4C4B0")
     return fig
 
 
+# Restaurant chart colors taken directly from the brand assets:
+#   Copper Buffet  = #906848 (wordmark color in CopperBuffet_Logo.jpg)
+#   Tiew Copper    = #D87838 (background color in TiewCopper_Logo.png)
+#   Group          = neutral copper-grey for Holding / CK / Conso rows.
 RESTAURANT_COLOR = {
-    "Copper Buffet": "#d97706",
-    "Tiew Copper":   "#3b82f6",
-    "Group":         "#6b7280",
+    "Copper Buffet": "#906848",
+    "Tiew Copper":   "#D87838",
+    "Group":         "#8B7355",
 }
 
 # ─────────────────────────────────────────────────────────────────────────────
@@ -980,11 +1011,11 @@ with tab_summary:
                 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 sel_years:
             if "Year" in out.columns:
-                out = out[out["Year"] == sel_year]
+                out = out[out["Year"].isin(sel_years)]
             elif "Date" in out.columns:
-                out = out[out["Date"].dt.year == sel_year]
+                out = out[out["Date"].dt.year.isin(sel_years)]
         return out
 
     def _render_restaurant_summary(restaurant_name: str) -> None:
@@ -1001,11 +1032,6 @@ with tab_summary:
             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
-        rev_ph   = (tot_rev / tot_cust) if tot_cust else 0
-
         # Pre-compute extended monthly DataFrame *once*, reused for both the
         # trend charts (below) and the Monthly summary table further down.
         # For Copper Buffet, append per-round customer columns + %Cap +
@@ -1106,61 +1132,6 @@ with tab_summary:
                     )
                     m = m.drop(columns=["_Cap", "_TotCust2"])
 
-        # ── KPI strip — current totals + most-recent-month MoM delta ─────
-        # MoM helper: pick the latest complete (Year, Month) pair in the
-        # monthly DataFrame and the one before it. Aggregate across the
-        # branches in the current filter so each metric is single-valued.
-        def _mom_delta(df: pd.DataFrame, value_col: str, agg: str = "sum"):
-            if df.empty or value_col not in df.columns:
-                return None, None, None
-            keys = df[["Year", "Month"]].dropna().drop_duplicates()
-            keys = keys.sort_values(["Year", "Month"])
-            if len(keys) < 1:
-                return None, None, None
-            latest = keys.iloc[-1]
-            prev   = keys.iloc[-2] if len(keys) >= 2 else None
-            def _val(row):
-                mask = (df["Year"] == row["Year"]) & (df["Month"] == row["Month"])
-                if agg == "sum":
-                    return df.loc[mask, value_col].sum()
-                return df.loc[mask, value_col].mean()
-            cur_v  = _val(latest)
-            prev_v = _val(prev) if prev is not None else None
-            delta = (cur_v - prev_v) if (prev_v is not None and pd.notna(prev_v)) else None
-            return cur_v, prev_v, delta
-
-        def _money_delta(d):
-            return None if d is None or pd.isna(d) else fmt_money(d)
-        def _num_delta(d):
-            return None if d is None or pd.isna(d) else f"{d:+,.0f}"
-        def _pct_delta(d):
-            return None if d is None or pd.isna(d) else f"{d:+.1f} pp"
-
-        rev_cur,  _, rev_delta  = _mom_delta(m, "Revenue",      "sum")
-        cust_cur, _, cust_delta = _mom_delta(m, "Customers",    "sum")
-        rph_cur,  _, rph_delta  = _mom_delta(m, "Rev_Per_Head", "mean")
-
-        # Always show the period totals as the headline numbers, but layer
-        # the most-recent-month MoM delta underneath using st.metric's
-        # delta param — keeps "all of the above" use cases in one glance.
-        has_pct = ("%Cap" in m.columns) or ("%Premium" in m.columns)
-        if has_pct:
-            kc1, kc2, kc3, kc4, kc5 = st.columns(5)
-        else:
-            kc1, kc2, kc3 = st.columns(3)
-        kc1.metric("Revenue",    fmt_money(tot_rev),  delta=_money_delta(rev_delta))
-        kc2.metric("Customers",  fmt_num(tot_cust),   delta=_num_delta(cust_delta))
-        kc3.metric("Rev / Head", fmt_money(rev_ph),   delta=_money_delta(rph_delta))
-        if has_pct:
-            cap_cur,  _, cap_delta  = _mom_delta(m, "%Cap",     "mean")
-            prem_cur, _, prem_delta = _mom_delta(m, "%Premium", "mean")
-            kc4.metric("%Cap (latest mo.)",
-                       fmt_pct(cap_cur)  if cap_cur  is not None else "—",
-                       delta=_pct_delta(cap_delta))
-            kc5.metric("%Premium (latest mo.)",
-                       fmt_pct(prem_cur) if prem_cur is not None else "—",
-                       delta=_pct_delta(prem_delta))
-
         # ── Trends — small charts above the tables ───────────────────────
         if not m.empty:
             st.markdown("**Trends**")
@@ -1430,7 +1401,7 @@ with tab_pl:
         fig = px.bar(
             agg.sort_values("Amount"),
             x="Amount", y=group_col, orientation="h", color="Flow",
-            color_discrete_map={"Income": "#51cf66", "Expense": "#ff6b6b"},
+            color_discrete_map={"Income": "#5C8A52", "Expense": "#C44A3B"},
             text=agg["Amount"].apply(fmt_money),
         )
         fig.update_layout(yaxis_title=None, xaxis_title="Amount (THB)", showlegend=True)