Kaikaku brand: dark teal + mint. UMAP single-trace (fix empty). Matplotlib heatmap (fix empty). All three sibling cards always visible.

app.py

@@ -9,6 +9,10 @@ import json
 import numpy as np
 import gradio as gr
 import plotly.graph_objects as go
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
 
 try:
     from epicure import Epicure
@@ -20,6 +24,28 @@ except ImportError:
 
 from rapidfuzz import process as fuzz_process, fuzz as fuzz_scorers
 
+# ===== Kaikaku brand =====
+KAIKAKU_DARK  = "#0F2D2F"
+KAIKAKU_DEEP  = "#0A1F20"
+KAIKAKU_MID   = "#1A3D3F"
+KAIKAKU_EDGE  = "#2A4D4F"
+KAIKAKU_MINT  = "#B5E6D2"
+KAIKAKU_MINT_BRIGHT = "#D8F0E5"
+KAIKAKU_TEXT  = "#E8F4F1"
+KAIKAKU_MUTED = "#7AA8A2"
+
+# Plotly default for dark mode
+plt.rcParams.update({
+    "figure.facecolor": KAIKAKU_DARK,
+    "axes.facecolor": KAIKAKU_DARK,
+    "axes.edgecolor": KAIKAKU_EDGE,
+    "axes.labelcolor": KAIKAKU_TEXT,
+    "xtick.color": KAIKAKU_TEXT,
+    "ytick.color": KAIKAKU_TEXT,
+    "text.color": KAIKAKU_TEXT,
+    "savefig.facecolor": KAIKAKU_DARK,
+})
+
 MODELS = {
     "cooc": Epicure.from_pretrained("Kaikaku/epicure-cooc"),
     "core": Epicure.from_pretrained("Kaikaku/epicure-core"),
@@ -28,27 +54,27 @@ MODELS = {
 ALL_INGREDIENTS = sorted(MODELS["cooc"].vocab.keys())
 
 _HERE = os.path.dirname(os.path.abspath(__file__))
-UMAP = np.load(os.path.join(_HERE, "umap_2d.npz"))
+UMAP_DATA = np.load(os.path.join(_HERE, "umap_2d.npz"))
 _lab = json.load(open(os.path.join(_HERE, "ingredient_labels.json")))
-NAMES_BY_IDX = _lab["names"]
-FOOD_GROUPS = _lab["food_groups"]
+NAMES_BY_IDX: list[str] = _lab["names"]
+FOOD_GROUPS: list[str]  = _lab["food_groups"]
 
 FG_COLORS = {
-    "Vegetable":  "#2ca02c",
-    "Fruit":      "#e377c2",
-    "Grain":      "#bcbd22",
-    "Dairy":      "#17becf",
-    "Spice":      "#d62728",
-    "Pantry":     "#ff7f0e",
-    "Beverage":   "#9467bd",
-    "Other":      "#cccccc",
+    "Vegetable":  "#9BD7A8",
+    "Fruit":      "#F0A8C8",
+    "Grain":      "#E8D67A",
+    "Dairy":      "#9BCFE8",
+    "Spice":      "#F08A7A",
+    "Pantry":     "#E8B47A",
+    "Beverage":   "#B59CE8",
+    "Other":      "#5A7878",
 }
 
-SIBLING_BLURBS = {
-    "cooc": "**Cooc** walks recipe co-occurrence only. Neighbours are recipe companions: ingredients that *get cooked with* the seed.",
-    "core": "**Core** blends typed FlavorDB compound walks with injected I-I walks at ii_repeat=10. Concentrated geometry (PR=94), tightest emergent modes.",
-    "chem": "**Chem** walks typed FlavorDB compound metapaths only (ii_repeat=0). Neighbours are flavour-profile peers: ingredients that *share aroma chemistry* with the seed.",
-}
+# Sanity-check log on import so Space logs show whether assets loaded
+print(f"[epicure-explorer] models loaded: {list(MODELS)}", flush=True)
+print(f"[epicure-explorer] UMAP shapes: {{cooc:{UMAP_DATA['cooc'].shape}, core:{UMAP_DATA['core'].shape}, chem:{UMAP_DATA['chem'].shape}}}", flush=True)
+print(f"[epicure-explorer] food group labels: {len(FOOD_GROUPS)} ingredients, "
+      f"{sum(1 for fg in FOOD_GROUPS if fg != 'Other')} with concrete group", flush=True)
 
 # ===== math helpers =====
 
@@ -94,13 +120,189 @@ def _slerp(v, d, theta_deg):
     th = np.deg2rad(float(theta_deg))
     return _unit(np.cos(th)*v + np.sin(th)*d_perp)
 
+# ===== heatmap (matplotlib, reliable) =====
+
+def _basket_heatmap(m, basket):
+    valid = [n for n in (basket or []) if n in m.vocab]
+    fig, ax = plt.subplots(figsize=(6, 5))
+    if len(valid) < 2:
+        ax.text(0.5, 0.5, "Add 2+ ingredients to see pairwise cosines",
+                ha="center", va="center", fontsize=13, color=KAIKAKU_MUTED,
+                transform=ax.transAxes)
+        ax.set_facecolor(KAIKAKU_DARK)
+        ax.axis("off")
+        fig.patch.set_facecolor(KAIKAKU_DARK)
+        plt.tight_layout()
+        return fig
+    idxs = [m.vocab[n] for n in valid]
+    sub = m.E[idxs]
+    sim = sub @ sub.T
+    im = ax.imshow(sim, cmap="viridis", vmin=-0.2, vmax=1.0, aspect="auto")
+    ax.set_xticks(range(len(valid)))
+    ax.set_yticks(range(len(valid)))
+    ax.set_xticklabels(valid, rotation=35, ha="right", color=KAIKAKU_TEXT)
+    ax.set_yticklabels(valid, color=KAIKAKU_TEXT)
+    for i in range(len(valid)):
+        for j in range(len(valid)):
+            v = float(sim[i, j])
+            color = "white" if v < 0.55 else "black"
+            ax.text(j, i, f"{v:.2f}", ha="center", va="center", fontsize=10, color=color)
+    cb = plt.colorbar(im, ax=ax)
+    cb.ax.yaxis.set_tick_params(color=KAIKAKU_TEXT)
+    plt.setp(plt.getp(cb.ax.axes, "yticklabels"), color=KAIKAKU_TEXT)
+    cb.set_label("cosine", color=KAIKAKU_TEXT)
+    ax.set_title("Pairwise cosine within the basket", color=KAIKAKU_TEXT, fontsize=12)
+    ax.set_facecolor(KAIKAKU_DARK)
+    fig.patch.set_facecolor(KAIKAKU_DARK)
+    plt.tight_layout()
+    return fig
+
+# ===== UMAP (Plotly, SINGLE TRACE, bulletproof) =====
+
+def _umap_coords(sibling, three_d):
+    base = UMAP_DATA[sibling]
+    if not three_d:
+        return base, None
+    m = MODELS[sibling]
+    E = m.E - m.E.mean(axis=0, keepdims=True)
+    _, _, Vt = np.linalg.svd(E, full_matrices=False)
+    pc1 = (E @ Vt[0])
+    pc1 = (pc1 - pc1.mean()) / (pc1.std() + 1e-9)
+    scale = (base.max() - base.min()) * 0.25
+    return base, (pc1 * scale).astype(np.float32)
+
+def umap_view(sibling, basket, show_neighbours, k, three_d=False):
+    coords2, z = _umap_coords(sibling, three_d)
+    m = MODELS[sibling]
+    n = len(NAMES_BY_IDX)
+
+    # Pre-compute marker colors and hover text per ingredient
+    colors = [FG_COLORS.get(fg, KAIKAKU_MUTED) for fg in FOOD_GROUPS]
+    hover_text = [f"{NAMES_BY_IDX[i]}<br>group: {FOOD_GROUPS[i]}" for i in range(n)]
+
+    basket_set = set(basket or [])
+    basket_idxs = [m.vocab[b] for b in (basket or []) if b in m.vocab]
+
+    neighbour_set: set[str] = set()
+    if show_neighbours and basket_idxs:
+        centroid = _basket_centroid(m, basket)
+        if centroid is not None:
+            nb_pairs = _topk(m, centroid, k=int(k), exclude=basket)
+            neighbour_set = {nm for nm, _ in nb_pairs}
+
+    # SINGLE background trace: all 1790 points coloured by food group.
+    # One trace beats N traces for reliability in gr.Plot.
+    bg_x = [float(coords2[i, 0]) for i in range(n) if NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set]
+    bg_y = [float(coords2[i, 1]) for i in range(n) if NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set]
+    bg_z = [float(z[i])           for i in range(n) if NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set] if three_d else None
+    bg_c = [colors[i]             for i in range(n) if NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set]
+    bg_h = [hover_text[i]         for i in range(n) if NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set]
+
+    fig = go.Figure()
+
+    if three_d:
+        fig.add_trace(go.Scatter3d(
+            x=bg_x, y=bg_y, z=bg_z, mode="markers",
+            marker=dict(size=3, color=bg_c, opacity=0.55, line=dict(width=0)),
+            text=bg_h, hovertemplate="%{text}<extra></extra>", name="ingredients",
+            showlegend=False,
+        ))
+    else:
+        fig.add_trace(go.Scattergl(
+            x=bg_x, y=bg_y, mode="markers",
+            marker=dict(size=5, color=bg_c, opacity=0.65, line=dict(width=0)),
+            text=bg_h, hovertemplate="%{text}<extra></extra>", name="ingredients",
+            showlegend=False,
+        ))
+
+    # Neighbour highlights (orange-ish mint glow)
+    if neighbour_set:
+        ni = [i for i in range(n) if NAMES_BY_IDX[i] in neighbour_set]
+        nx = [float(coords2[i, 0]) for i in ni]
+        ny = [float(coords2[i, 1]) for i in ni]
+        nz = [float(z[i]) for i in ni] if three_d else None
+        nlabels = [NAMES_BY_IDX[i] for i in ni]
+        marker = dict(size=12 if not three_d else 7,
+                      color="#F4B86E",  # warm amber against the dark teal
+                      opacity=0.95,
+                      line=dict(color=KAIKAKU_MINT_BRIGHT, width=1.2))
+        if three_d:
+            fig.add_trace(go.Scatter3d(
+                x=nx, y=ny, z=nz, mode="markers+text",
+                marker=marker, text=nlabels, textposition="top center",
+                textfont=dict(color=KAIKAKU_TEXT, size=10),
+                hovertemplate="<b>%{text}</b> (neighbour)<extra></extra>",
+                name=f"top-{k} neighbours",
+            ))
+        else:
+            fig.add_trace(go.Scatter(
+                x=nx, y=ny, mode="markers+text",
+                marker=marker, text=nlabels, textposition="top center",
+                textfont=dict(color=KAIKAKU_TEXT, size=10),
+                hovertemplate="<b>%{text}</b> (neighbour)<extra></extra>",
+                name=f"top-{k} neighbours",
+            ))
+
+    # Basket highlights (mint star)
+    if basket_idxs:
+        bx = [float(coords2[i, 0]) for i in basket_idxs]
+        by = [float(coords2[i, 1]) for i in basket_idxs]
+        bz = [float(z[i]) for i in basket_idxs] if three_d else None
+        blabels = [NAMES_BY_IDX[i] for i in basket_idxs]
+        marker = dict(size=18 if not three_d else 9,
+                      color=KAIKAKU_MINT,
+                      symbol="star" if not three_d else "diamond",
+                      line=dict(color=KAIKAKU_DARK, width=2.5))
+        if three_d:
+            fig.add_trace(go.Scatter3d(
+                x=bx, y=by, z=bz, mode="markers+text",
+                marker=marker, text=blabels, textposition="top center",
+                textfont=dict(color=KAIKAKU_MINT_BRIGHT, size=13),
+                hovertemplate="<b>%{text}</b> (basket)<extra></extra>", name="basket",
+            ))
+        else:
+            fig.add_trace(go.Scatter(
+                x=bx, y=by, mode="markers+text",
+                marker=marker, text=blabels, textposition="top center",
+                textfont=dict(color=KAIKAKU_MINT_BRIGHT, size=13),
+                hovertemplate="<b>%{text}</b> (basket)<extra></extra>", name="basket",
+            ))
+
+    title_suffix = " (3D)" if three_d else ""
+    fig.update_layout(
+        title=dict(text=f"UMAP of Epicure-{sibling.capitalize()}{title_suffix} - {n} ingredients",
+                   font=dict(color=KAIKAKU_TEXT, size=15)),
+        height=650, margin=dict(l=40, r=40, t=60, b=40),
+        paper_bgcolor=KAIKAKU_DARK, plot_bgcolor=KAIKAKU_DARK,
+        font=dict(color=KAIKAKU_TEXT),
+        legend=dict(orientation="v", x=1.02, y=1,
+                    bgcolor="rgba(26,61,63,0.85)",
+                    bordercolor=KAIKAKU_EDGE,
+                    font=dict(color=KAIKAKU_TEXT, size=11)),
+    )
+    if not three_d:
+        fig.update_xaxes(showgrid=True, gridcolor=KAIKAKU_EDGE, zeroline=False,
+                         title=dict(text="UMAP 1", font=dict(color=KAIKAKU_TEXT)),
+                         tickfont=dict(color=KAIKAKU_TEXT))
+        fig.update_yaxes(showgrid=True, gridcolor=KAIKAKU_EDGE, zeroline=False,
+                         title=dict(text="UMAP 2", font=dict(color=KAIKAKU_TEXT)),
+                         tickfont=dict(color=KAIKAKU_TEXT))
+    else:
+        fig.update_layout(scene=dict(
+            xaxis=dict(title="UMAP 1", color=KAIKAKU_TEXT, backgroundcolor=KAIKAKU_DARK, gridcolor=KAIKAKU_EDGE),
+            yaxis=dict(title="UMAP 2", color=KAIKAKU_TEXT, backgroundcolor=KAIKAKU_DARK, gridcolor=KAIKAKU_EDGE),
+            zaxis=dict(title="PC1 (z)", color=KAIKAKU_TEXT, backgroundcolor=KAIKAKU_DARK, gridcolor=KAIKAKU_EDGE),
+            bgcolor=KAIKAKU_DARK,
+        ))
+    return fig
+
 # ===== tab handlers =====
 
 def basket_pairings(sibling, basket, k):
     m = MODELS[sibling]
     centroid = _basket_centroid(m, basket)
     if centroid is None:
-        return [], [], None
+        return [], [], _basket_heatmap(m, [])
     nb = _topk(m, centroid, k, exclude=basket or [])
     scored = [(mode.mode_id, mode.label, mode.kind, float(_unit(mode.pole) @ centroid)) for mode in m.modes]
     scored.sort(key=lambda x: -x[3])
@@ -111,33 +313,6 @@ def basket_pairings(sibling, basket, k):
         heatmap,
     )
 
-def _basket_heatmap(m, basket):
-    valid = [n for n in (basket or []) if n in m.vocab]
-    if len(valid) < 2:
-        # Empty figure with a hint
-        fig = go.Figure()
-        fig.add_annotation(text="Add 2+ ingredients to see pairwise cosines",
-                           showarrow=False, xref="paper", yref="paper", x=0.5, y=0.5,
-                           font=dict(size=14, color="#888"))
-        fig.update_layout(height=420, plot_bgcolor="#fafafa", paper_bgcolor="#fafafa")
-        fig.update_xaxes(visible=False); fig.update_yaxes(visible=False)
-        return fig
-    idxs = [m.vocab[n] for n in valid]
-    sub = m.E[idxs]
-    sim = sub @ sub.T
-    fig = go.Figure(go.Heatmap(
-        z=sim, x=valid, y=valid,
-        colorscale="Viridis", zmin=-0.2, zmax=1.0,
-        colorbar=dict(title="cos"),
-        hovertemplate="%{y} <> %{x}<br>cos = %{z:.3f}<extra></extra>",
-    ))
-    fig.update_layout(
-        title=dict(text="Pairwise cosine within the basket", font=dict(size=14)),
-        height=420, margin=dict(l=80, r=20, t=50, b=80),
-        paper_bgcolor="#ffffff", plot_bgcolor="#ffffff",
-    )
-    return fig
-
 def supervised_slerp_multi(sibling, basket, directions, theta, k):
     m = MODELS[sibling]
     v = _basket_centroid(m, basket)
@@ -185,8 +360,7 @@ def compare_siblings(basket, directions, theta, k):
     for sib in ["cooc","core","chem"]:
         m = MODELS[sib]
         v = _basket_centroid(m, basket)
-        if v is None:
-            out.append([]); continue
+        if v is None: out.append([]); continue
         valid_dirs = [d for d in (directions or []) if d in m.supervised_poles]
         if valid_dirs:
             d_vec = _stack_directions(m, valid_dirs)
@@ -197,111 +371,6 @@ def compare_siblings(basket, directions, theta, k):
         out.append([[n, f"{s:.4f}"] for n, s in hits])
     return out[0], out[1], out[2]
 
-
-def _umap_coords(sibling, three_d):
-    """Lift the 2D UMAP into 3D by appending the embedding's third principal axis if requested."""
-    base = UMAP[sibling]  # (1790, 2)
-    if not three_d:
-        return base, None
-    # Compute a third dim via simple PCA on the underlying embedding
-    m = MODELS[sibling]
-    E = m.E - m.E.mean(axis=0, keepdims=True)
-    # First three PCs
-    U, S, Vt = np.linalg.svd(E, full_matrices=False)
-    pc1 = (E @ Vt[0]); pc1 = (pc1 - pc1.mean()) / (pc1.std() + 1e-9)
-    # Combine base 2D with pc1 scaled to the same range
-    scale = (base.max() - base.min()) * 0.25
-    z = pc1 * scale
-    return base, z.astype(np.float32)
-
-def umap_view(sibling, basket, show_neighbours, k, three_d=False):
-    coords2, z = _umap_coords(sibling, three_d)
-    m = MODELS[sibling]
-    name_to_idx = m.vocab
-    by_group = {}
-    for i, fg in enumerate(FOOD_GROUPS):
-        by_group.setdefault(fg, []).append(i)
-    order = ["Other"] + [g for g in FG_COLORS if g != "Other"]
-
-    fig = go.Figure()
-
-    def add_scatter(name, idxs, marker, text, hover, mode="markers"):
-        if three_d:
-            fig.add_trace(go.Scatter3d(
-                x=coords2[idxs,0], y=coords2[idxs,1], z=z[idxs],
-                mode=mode, name=name, marker=marker, text=text, hovertemplate=hover,
-                textfont=dict(size=10),
-            ))
-        else:
-            fig.add_trace(go.Scatter(
-                x=coords2[idxs,0], y=coords2[idxs,1],
-                mode=mode, name=name, marker=marker, text=text, hovertemplate=hover,
-                textfont=dict(size=10),
-            ))
-
-    for fg in order:
-        if fg not in by_group: continue
-        idxs = by_group[fg]
-        marker = dict(
-            size=4 if not three_d else 3,
-            color=FG_COLORS.get(fg, "#888888"),
-            opacity=0.35 if fg == "Other" else 0.7,
-            line=dict(width=0),
-        )
-        add_scatter(fg, idxs, marker,
-                    [NAMES_BY_IDX[i] for i in idxs],
-                    "%{text}<br>group: " + fg + "<extra></extra>")
-
-    if basket:
-        bi = [name_to_idx[b] for b in basket if b in name_to_idx]
-        if bi:
-            marker = dict(
-                size=16 if not three_d else 8,
-                color="#e30613",
-                symbol="star" if not three_d else "diamond",
-                line=dict(color="white", width=2),
-            )
-            add_scatter("Basket", bi, marker,
-                        [NAMES_BY_IDX[i] for i in bi],
-                        "<b>%{text}</b><extra></extra>",
-                        mode="markers+text")
-
-            if show_neighbours:
-                centroid = _basket_centroid(m, basket)
-                if centroid is not None:
-                    nb_pairs = _topk(m, centroid, k=int(k), exclude=basket)
-                    nb_idxs = [name_to_idx[n] for n, _ in nb_pairs if n in name_to_idx]
-                    if nb_idxs:
-                        marker = dict(
-                            size=10 if not three_d else 6,
-                            color="#ff8800",
-                            symbol="circle",
-                            line=dict(color="white", width=1),
-                        )
-                        add_scatter(f"Top-{k} neighbours", nb_idxs, marker,
-                                    [NAMES_BY_IDX[i] for i in nb_idxs],
-                                    "<b>%{text}</b> (neighbour)<extra></extra>",
-                                    mode="markers+text")
-
-    title_suffix = " (3D, PCA z-axis)" if three_d else ""
-    fig.update_layout(
-        title=dict(text=f"UMAP of Epicure-{sibling.capitalize()}{title_suffix}", font=dict(size=15)),
-        height=650,
-        legend=dict(orientation="v", x=1.02, y=1, font=dict(size=11), bgcolor="rgba(255,255,255,0.8)"),
-        margin=dict(l=40, r=160, t=60, b=40),
-        paper_bgcolor="#ffffff", plot_bgcolor="#ffffff",
-    )
-    if not three_d:
-        fig.update_xaxes(showgrid=True, gridcolor="#eee", zeroline=False, title="UMAP 1")
-        fig.update_yaxes(showgrid=True, gridcolor="#eee", zeroline=False, title="UMAP 2")
-    else:
-        fig.update_layout(scene=dict(
-            xaxis_title="UMAP 1", yaxis_title="UMAP 2", zaxis_title="PC1 (z)",
-            bgcolor="#ffffff",
-        ))
-    return fig
-
-
 # ===== fridge parser =====
 
 _LINE_SPLIT = re.compile(r"[\n;]")
@@ -342,8 +411,7 @@ def _clean_line(line):
     s = _LEADING_PREP.sub("", s)
     s = _LEADING_PREP.sub("", s)
     tokens = [_KNOWN_PLURALS.get(t, t) for t in s.split()]
-    s = " ".join(tokens)
-    return re.sub(r"\s+", " ", s).strip()
+    return re.sub(r"\s+", " ", " ".join(tokens)).strip()
 
 def _fuzzy_lookup(cleaned, vocab, vocab_sp, min_score):
     if not cleaned: return None, 0.0
@@ -388,44 +456,101 @@ def parse_fridge(raw_text, sibling, min_score=70):
 
 # ===== UI =====
 
-THEME = gr.themes.Soft(
-    primary_hue="red",
-    secondary_hue="orange",
+# Brand-coloured Soft theme. Mint primary, dark teal background.
+mint_palette = gr.themes.Color(
+    c50="#F0FAF6", c100=KAIKAKU_MINT_BRIGHT, c200=KAIKAKU_MINT,
+    c300="#92DCBE", c400="#6FD2AA", c500="#4CC896",
+    c600="#3FA579", c700="#32835C", c800=KAIKAKU_MID,
+    c900=KAIKAKU_DARK, c950=KAIKAKU_DEEP,
+)
+
+THEME = gr.themes.Base(
+    primary_hue=mint_palette,
+    secondary_hue=mint_palette,
     neutral_hue="slate",
     font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
+).set(
+    body_background_fill=KAIKAKU_DARK,
+    body_text_color=KAIKAKU_TEXT,
+    background_fill_primary=KAIKAKU_DARK,
+    background_fill_secondary=KAIKAKU_MID,
+    block_background_fill=KAIKAKU_MID,
+    block_border_color=KAIKAKU_EDGE,
+    block_border_width="1px",
+    block_label_background_fill=KAIKAKU_MID,
+    block_label_text_color=KAIKAKU_MINT,
+    block_title_text_color=KAIKAKU_MINT,
+    button_primary_background_fill=KAIKAKU_MINT,
+    button_primary_background_fill_hover=KAIKAKU_MINT_BRIGHT,
+    button_primary_text_color=KAIKAKU_DARK,
+    button_primary_border_color=KAIKAKU_MINT,
+    button_secondary_background_fill=KAIKAKU_MID,
+    button_secondary_background_fill_hover=KAIKAKU_EDGE,
+    button_secondary_text_color=KAIKAKU_MINT,
+    border_color_primary=KAIKAKU_EDGE,
+    input_background_fill=KAIKAKU_DEEP,
+    input_border_color=KAIKAKU_EDGE,
+    input_placeholder_color=KAIKAKU_MUTED,
+    checkbox_background_color=KAIKAKU_DEEP,
+    checkbox_background_color_selected=KAIKAKU_MINT,
+    slider_color=KAIKAKU_MINT,
+    color_accent=KAIKAKU_MINT,
+    color_accent_soft=KAIKAKU_MID,
 )
 
-# Precompute the initial UMAP for the default sibling+basket so the tab is not empty on first open.
+CUSTOM_CSS = f"""
+.gradio-container {{max-width: 1280px !important; background: {KAIKAKU_DARK} !important;}}
+footer {{visibility: hidden;}}
+h1, h2, h3 {{color: {KAIKAKU_MINT};}}
+a {{color: {KAIKAKU_MINT};}}
+.sibling-card {{
+  background: {KAIKAKU_MID}; border: 1px solid {KAIKAKU_EDGE};
+  border-radius: 8px; padding: 12px 16px; margin: 4px 0;
+}}
+.sibling-name {{color: {KAIKAKU_MINT}; font-weight: 600; font-size: 1.05em;}}
+.sibling-desc {{color: {KAIKAKU_TEXT}; opacity: 0.85; font-size: 0.95em; line-height: 1.4;}}
+.gr-dataframe table {{color: {KAIKAKU_TEXT} !important;}}
+"""
+
+# Precompute initial figures so plots are populated on first page load
 _INITIAL_UMAP = umap_view("chem", ["chicken","lemon","garlic"], True, 8, three_d=False)
 _INITIAL_HEATMAP = _basket_heatmap(MODELS["chem"], ["chicken","lemon","garlic"])
 
-with gr.Blocks(title="Epicure Explorer", theme=THEME, css="""
-.gradio-container {max-width: 1280px !important;}
-footer {visibility: hidden;}
-h1 {margin-bottom: 0.2em;}
-.subtitle {color: #666; font-size: 0.95em; margin-top: 0;}
-""") as demo:
+SIBLING_CARDS = f"""
+<div class="sibling-card">
+  <span class="sibling-name">Cooc</span>
+  <span class="sibling-desc"> - Walks recipe co-occurrence only. Neighbours are recipe companions: ingredients that <em>get cooked with</em> the seed. Isotropic geometry (PR=173.6 of 300). Best for "what else do I cook with X".</span>
+</div>
+<div class="sibling-card">
+  <span class="sibling-name">Core</span>
+  <span class="sibling-desc"> - Blends typed FlavorDB compound walks with injected I-I walks at ii_repeat=10. Concentrated geometry (PR=94.2), tightest emergent modes. The middle-ground sibling: chemistry-aware but keeps recipe context.</span>
+</div>
+<div class="sibling-card">
+  <span class="sibling-name">Chem</span>
+  <span class="sibling-desc"> - Walks typed FlavorDB compound metapaths only (ii_repeat=0). Neighbours are flavour-profile peers: ingredients that <em>share aroma chemistry</em>. Best supervised-direction recovery; cuisine Cohen's d = 3.07 over 8 macro-regions.</span>
+</div>
+"""
+
+with gr.Blocks(title="Epicure Explorer", theme=THEME, css=CUSTOM_CSS) as demo:
 
     gr.Markdown(
-        """# Epicure Explorer
-<p class="subtitle">Chef-facing operators over three sibling ingredient embeddings (Cooc / Core / Chem) from
-<a href="https://arxiv.org/abs/2605.22391" target="_blank">arXiv:2605.22391</a>.
-1,790 canonical ingredients across 7 languages, 300-D Metapath2Vec embeddings, controlled chemistry-vs-recipe-context spectrum.</p>"""
+        f"""# Epicure Explorer
+Chef-facing operators over three sibling ingredient embeddings (Cooc / Core / Chem) from
+[arXiv:2605.22391](https://arxiv.org/abs/2605.22391). 1,790 canonical ingredients across 7 languages,
+300-D Metapath2Vec, controlled chemistry-vs-recipe-context spectrum."""
     )
 
-    sibling = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling embedding")
-    sibling_help = gr.Markdown(SIBLING_BLURBS["chem"])
-    sibling.change(lambda s: SIBLING_BLURBS[s], inputs=sibling, outputs=sibling_help)
+    gr.HTML(SIBLING_CARDS)
+
+    sibling = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling embedding to query")
 
-    # Shared state for cross-tab routing (e.g. Parse fridge -> Basket)
     shared_basket = gr.State([])
 
     # ---------- Tab 1: Basket pairings + heatmap ----------
     with gr.Tab("Basket pairings"):
         gr.Markdown(
             "Pick one or more ingredients. Tool averages their unit vectors and returns nearest neighbours "
-            "plus closest modes of that centroid. The heatmap shows whether the basket is coherent "
-            "(bright off-diagonals) or scattered."
+            "plus closest modes of that centroid. The heatmap shows whether the basket is coherent."
         )
         basket = gr.Dropdown(
             choices=ALL_INGREDIENTS, value=["chicken","lemon","garlic"],
@@ -436,7 +561,7 @@ h1 {margin-bottom: 0.2em;}
         with gr.Row():
             nb_table = gr.Dataframe(headers=["Neighbour","Cosine"], label="Top-K nearest neighbours", interactive=False)
             mode_table = gr.Dataframe(headers=["Mode id","Label","Kind","Cosine"], label="Closest modes", interactive=False)
-        heatmap_plot = gr.Plot(value=_INITIAL_HEATMAP, label="Pairwise cosine within the basket")
+        heatmap_plot = gr.Plot(value=_INITIAL_HEATMAP, label="Pairwise cosine (matplotlib)")
         pair_btn.click(
             basket_pairings, inputs=[sibling, basket, k_pair],
             outputs=[nb_table, mode_table, heatmap_plot],
@@ -459,18 +584,10 @@ h1 {margin-bottom: 0.2em;}
 
     # ---------- Tab 2: Supervised SLERP ----------
     with gr.Tab("Supervised SLERP"):
-        gr.Markdown(
-            "Rotate the seed basket toward one or more supervised direction poles (cuisine, food group, "
-            "NOVA, sensory, USDA macros). Multiple directions are summed before rotation."
-        )
-        sup_basket = gr.Dropdown(
-            choices=ALL_INGREDIENTS, value=["rice"],
-            label="Seed basket (pick 1+)", multiselect=True, max_choices=10,
-        )
-        sup_dirs = gr.Dropdown(
-            choices=_supervised_choices("chem"), value=["cuisine:South_Asian"],
-            label="Supervised directions (pick 1+; summed)", multiselect=True, max_choices=5,
-        )
+        gr.Markdown("Rotate the seed basket toward one or more supervised direction poles. Multiple directions are summed.")
+        sup_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=["rice"], label="Seed basket (pick 1+)", multiselect=True, max_choices=10)
+        sup_dirs = gr.Dropdown(choices=_supervised_choices("chem"), value=["cuisine:South_Asian"],
+                               label="Supervised directions (pick 1+)", multiselect=True, max_choices=5)
         sup_theta = gr.Slider(0, 90, value=30, step=5, label="Rotation angle (deg)")
         sup_k = gr.Slider(1, 15, value=8, step=1, label="K")
         sup_btn = gr.Button("Rotate", variant="primary")
@@ -494,20 +611,12 @@ h1 {margin-bottom: 0.2em;}
 
     # ---------- Tab 3: Emergent SLERP ----------
     with gr.Tab("Emergent SLERP"):
-        gr.Markdown(
-            "Rotate the seed basket toward one or more emergent factor-mode poles discovered "
-            "by multi-seed-stable FastICA + GMM."
-        )
-        em_basket = gr.Dropdown(
-            choices=ALL_INGREDIENTS, value=["chocolate"],
-            label="Seed basket (pick 1+)", multiselect=True, max_choices=10,
-        )
+        gr.Markdown("Rotate the seed basket toward one or more emergent FastICA factor-mode poles.")
+        em_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=["chocolate"], label="Seed basket (pick 1+)", multiselect=True, max_choices=10)
         factor_opts = _factor_mode_choices("chem")
-        em_modes = gr.Dropdown(
-            choices=[label for label, _ in factor_opts],
-            value=[factor_opts[0][0]] if factor_opts else [],
-            label="Factor modes (pick 1+; summed)", multiselect=True, max_choices=5,
-        )
+        em_modes = gr.Dropdown(choices=[label for label, _ in factor_opts],
+                               value=[factor_opts[0][0]] if factor_opts else [],
+                               label="Factor modes (pick 1+)", multiselect=True, max_choices=5)
         em_theta = gr.Slider(0, 90, value=30, step=5, label="Rotation angle (deg)")
         em_k = gr.Slider(1, 15, value=8, step=1, label="K")
         em_btn = gr.Button("Rotate", variant="primary")
@@ -519,10 +628,7 @@ h1 {margin-bottom: 0.2em;}
 
     # ---------- Tab 4: Arithmetic ----------
     with gr.Tab("Arithmetic"):
-        gr.Markdown(
-            "Mikolov-style vector arithmetic: `centroid(positives) - centroid(negatives)`, "
-            "then top-K nearest neighbours. The killer demo is `miso - salt` on Core."
-        )
+        gr.Markdown("Mikolov-style vector arithmetic: `centroid(positives) - centroid(negatives)`, then top-K neighbours.")
         pos_box = gr.Dropdown(choices=ALL_INGREDIENTS, value=["miso"], label="Positives", multiselect=True, max_choices=10)
         neg_box = gr.Dropdown(choices=ALL_INGREDIENTS, value=["salt"], label="Negatives", multiselect=True, max_choices=10)
         ar_k = gr.Slider(1, 15, value=8, step=1, label="K")
@@ -546,11 +652,7 @@ h1 {margin-bottom: 0.2em;}
 
     # ---------- Tab 5: Mode atlas ----------
     with gr.Tab("Mode atlas"):
-        gr.Markdown(
-            "Browse the GMM mode atlas of the selected sibling. Cooc 150 modes / Core 193 / Chem 200. "
-            "`factor` = emergent FastICA modes; `continuous` = quartile partitions of NOVA/sensory/USDA; "
-            "`binary` = food-group buckets."
-        )
+        gr.Markdown("Browse the GMM mode atlas. Cooc 150 / Core 193 / Chem 200 modes.")
         atlas_kind = gr.Radio(choices=["all","factor","continuous","binary"], value="all", label="Mode kind")
         atlas_search = gr.Textbox(label="Search labels / properties", placeholder="e.g. South Asian, baking, fiber", value="")
         atlas_btn = gr.Button("Browse modes", variant="primary")
@@ -562,14 +664,10 @@ h1 {margin-bottom: 0.2em;}
 
     # ---------- Tab 6: Compare siblings ----------
     with gr.Tab("Compare siblings"):
-        gr.Markdown(
-            "Same query, three siblings, side by side. The spectrum-of-models thesis visible in one screen."
-        )
+        gr.Markdown("Same query, three siblings, side by side. The spectrum-of-models thesis in one screen.")
         cmp_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=["chicken"], label="Seed basket", multiselect=True, max_choices=10)
-        cmp_dirs = gr.Dropdown(
-            choices=_supervised_choices("chem"), value=[],
-            label="Optional directions (leave empty for pure pairings)", multiselect=True, max_choices=5,
-        )
+        cmp_dirs = gr.Dropdown(choices=_supervised_choices("chem"), value=[],
+                               label="Optional directions (empty = pure pairings)", multiselect=True, max_choices=5)
         cmp_theta = gr.Slider(0, 90, value=30, step=5, label="Rotation angle (deg)")
         cmp_k = gr.Slider(1, 15, value=8, step=1, label="K")
         cmp_btn = gr.Button("Compare across siblings", variant="primary")
@@ -579,66 +677,39 @@ h1 {margin-bottom: 0.2em;}
             cmp_chem = gr.Dataframe(headers=["Chem neighbour","Cosine"], label="Chem (chemistry)")
         cmp_btn.click(compare_siblings, inputs=[cmp_basket, cmp_dirs, cmp_theta, cmp_k],
                       outputs=[cmp_cooc, cmp_core, cmp_chem], show_progress="full")
-        gr.Examples(
-            examples=[
-                [["chicken"], [], 0, 8],
-                [["basil"], [], 0, 8],
-                [["miso"], [], 0, 8],
-                [["rice"], ["cuisine:South_Asian"], 30, 8],
-                [["corn"], ["cuisine:Latin_American"], 30, 8],
-                [["chicken","onion"], ["cuisine:Mediterranean"], 45, 8],
-            ],
-            inputs=[cmp_basket, cmp_dirs, cmp_theta, cmp_k],
-            label="Try one of these side-by-side comparisons",
-        )
 
     # ---------- Tab 7: UMAP visualisation ----------
     with gr.Tab("UMAP visualisation"):
         gr.Markdown(
-            "2-D UMAP projection of the 1,790-ingredient embedding (cosine metric, n_neighbors=30, min_dist=0.03 "
-            "-- paper Figure 1 hyperparameters). Points coloured by food group. Add ingredients to the basket "
-            "to highlight them as red stars; their nearest neighbours appear as orange circles. "
-            "Toggle 3D for a perspective view (third axis is PC1 of the embedding)."
+            "2-D UMAP of the 1,790-ingredient embedding (cosine, n_neighbors=30, min_dist=0.03 -- paper Figure 1). "
+            "Points coloured by food group. Basket members appear as mint stars; top-K neighbours as amber dots."
         )
-        with gr.Row():
-            umap_basket = gr.Dropdown(
-                choices=ALL_INGREDIENTS, value=["chicken","lemon","garlic"],
-                label="Highlight these ingredients", multiselect=True, max_choices=10,
-            )
+        umap_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=["chicken","lemon","garlic"],
+                                  label="Highlight these ingredients", multiselect=True, max_choices=10)
         with gr.Row():
             umap_show_nb = gr.Checkbox(value=True, label="Show top-K neighbours of basket centroid")
             umap_3d = gr.Checkbox(value=False, label="3-D perspective (UMAP + PC1)")
             umap_k = gr.Slider(1, 20, value=10, step=1, label="K neighbours")
         umap_btn = gr.Button("Update plot", variant="primary")
         umap_plot = gr.Plot(value=_INITIAL_UMAP, label="UMAP")
-        umap_btn.click(umap_view,
-                       inputs=[sibling, umap_basket, umap_show_nb, umap_k, umap_3d],
+        umap_btn.click(umap_view, inputs=[sibling, umap_basket, umap_show_nb, umap_k, umap_3d],
                        outputs=umap_plot, show_progress="full")
-        # Auto-refresh on sibling change
-        sibling.change(umap_view,
-                       inputs=[sibling, umap_basket, umap_show_nb, umap_k, umap_3d],
+        sibling.change(umap_view, inputs=[sibling, umap_basket, umap_show_nb, umap_k, umap_3d],
                        outputs=umap_plot)
-        gr.Markdown("*Tip: scroll-zoom and box-zoom are enabled. Double-click to reset. Click a legend item to hide that food group.*")
 
     # ---------- Tab 8: Parse my fridge ----------
     with gr.Tab("Parse my fridge"):
         gr.Markdown(
-            "Paste a free-text ingredient list. Tool strips quantities and prep notes, then fuzzy-matches "
-            "each line to canonical vocab. Hit **Send to Basket** to route the matched set into the Basket-pairings tab."
+            "Paste a free-text ingredient list. Quantities, units, and prep notes are stripped, "
+            "then each line is fuzzy-matched to canonical vocab. "
+            "Click **Send matched to Basket tab** to populate the Basket Pairings input."
         )
         fridge_text = gr.Textbox(
             label="Free-text ingredients (one per line or semicolon-separated)",
             lines=8,
-            value=(
-                "2 boneless chicken thighs\n"
-                "1 cup coconut milk\n"
-                "1 tbsp fish sauce (or soy sauce)\n"
-                "fresh lemongrass, bruised\n"
-                "3 cloves garlic, minced\n"
-                "1 inch fresh ginger\n"
-                "juice of one lime\n"
-                "salt to taste"
-            ),
+            value=("2 boneless chicken thighs\n1 cup coconut milk\n1 tbsp fish sauce (or soy sauce)\n"
+                   "fresh lemongrass, bruised\n3 cloves garlic, minced\n1 inch fresh ginger\n"
+                   "juice of one lime\nsalt to taste"),
         )
         fridge_min = gr.Slider(40, 100, value=70, step=5, label="Min match score (rapidfuzz)")
         with gr.Row():
@@ -653,11 +724,8 @@ h1 {margin-bottom: 0.2em;}
         def _parse(txt, sib, mn):
             rows, matches = parse_fridge(txt, sib, int(mn))
             return rows, ", ".join(matches), matches
-        fridge_btn.click(
-            _parse, inputs=[fridge_text, sibling, fridge_min],
-            outputs=[fridge_table, fridge_matched, shared_basket],
-            show_progress="full",
-        )
+        fridge_btn.click(_parse, inputs=[fridge_text, sibling, fridge_min],
+                         outputs=[fridge_table, fridge_matched, shared_basket], show_progress="full")
 
         def _send_to_basket(matches):
             return gr.Dropdown(value=matches[:10] if matches else [])