"""Epicure Explorer - chef-facing operators over three sibling ingredient embeddings. Simplified UI: 4 tabs. - Explore : pick ingredients, see neighbours across all three siblings at once. - Transform: rotate or do arithmetic on the basket (one tab for all three operators). - Map : UMAP visualisation. - From text: paste a recipe / dish description, fuzzy-match to canonical vocab. Paper: https://arxiv.org/abs/2605.22391 """ from __future__ import annotations import os, re, sys, json from functools import lru_cache import numpy as np import gradio as gr import plotly.graph_objects as go import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt try: from epicure import Epicure except ImportError: from huggingface_hub import hf_hub_download epicure_py = hf_hub_download("Kaikaku/epicure-cooc", "epicure.py") sys.path.insert(0, os.path.dirname(epicure_py)) from epicure import Epicure 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_ACCENT = "#288B79" KAIKAKU_ACCENT_HOVER = "#1E6E5F" KAIKAKU_ACCENT_LIGHT = "#A8D5CA" plt.rcParams.update({ "figure.facecolor": "#ffffff", "axes.facecolor": "#ffffff", "axes.edgecolor": "#cccccc", "axes.labelcolor": "#111111", "xtick.color": "#333333", "ytick.color": "#333333", "text.color": "#111111", "savefig.facecolor": "#ffffff", }) MODELS = { "cooc": Epicure.from_pretrained("Kaikaku/epicure-cooc"), "core": Epicure.from_pretrained("Kaikaku/epicure-core"), "chem": Epicure.from_pretrained("Kaikaku/epicure-chem"), } ALL_INGREDIENTS = sorted(MODELS["cooc"].vocab.keys()) _HERE = os.path.dirname(os.path.abspath(__file__)) 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: 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", } print(f"[epicure-explorer] models loaded: {list(MODELS)}", flush=True) # Food-group filter helpers _NAME_TO_GROUP = {NAMES_BY_IDX[i]: FOOD_GROUPS[i] for i in range(len(NAMES_BY_IDX))} FOOD_GROUP_CHOICES = ["All","Vegetable","Spice","Fruit","Dairy","Grain","Pantry","Beverage","Other"] def _choices_for_group(group): if not group or group == "All": return ALL_INGREDIENTS return sorted(n for n in ALL_INGREDIENTS if _NAME_TO_GROUP.get(n, "Other") == group) def _filter_dropdown(group, current_value): new_choices = _choices_for_group(group) allowed = set(new_choices) cur = current_value or [] if isinstance(cur, str): kept = cur if cur in allowed else None else: kept = [v for v in cur if v in allowed] return gr.Dropdown(choices=new_choices, value=kept) # ===== math ===== def _unit(v, eps=1e-9): n = np.linalg.norm(v); return v / max(n, eps) def _basket_centroid(m, names): valid = [n for n in (names or []) if n in m.vocab] if not valid: return None return _unit(m.E[[m.vocab[n] for n in valid]].mean(axis=0)) def _stack_directions(m, keys, use_factor_pole=False): poles = [] for k in keys or []: if use_factor_pole: for mode in m.modes: if mode.mode_id == k: poles.append(_unit(mode.pole)); break else: if k in m.supervised_poles: poles.append(_unit(m.supervised_poles[k])) if not poles: return None return _unit(np.stack(poles, axis=0).sum(axis=0)) def _topk(m, q, k, exclude): sims = m.E @ q for n in exclude or []: if n in m.vocab: sims[m.vocab[n]] = -np.inf order = np.argsort(-sims) return [(m.itos[int(i)], float(sims[i])) for i in order[:k]] def _supervised_choices(sibling): return sorted(MODELS[sibling].supervised_poles.keys()) def _factor_mode_choices(sibling): return [(f"{m.label} ({m.mode_id})", m.mode_id) for m in MODELS[sibling].modes if m.kind == "factor"] def _slerp(v, d, theta_deg): d_perp = d - (d @ v) * v n = np.linalg.norm(d_perp) if n < 1e-9: return v d_perp = d_perp / n th = np.deg2rad(float(theta_deg)) return _unit(np.cos(th) * v + np.sin(th) * d_perp) # ===== Heatmap ===== def _basket_heatmap(m, basket): valid = [n for n in (basket or []) if n in m.vocab] fig, ax = plt.subplots(figsize=(5.5, 4.5)) if len(valid) < 2: ax.text(0.5, 0.5, "Add 2+ ingredients to see pairwise cosines", ha="center", va="center", fontsize=12, color="#888", transform=ax.transAxes) ax.axis("off"); 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"); ax.set_yticklabels(valid) for i in range(len(valid)): for j in range(len(valid)): v = float(sim[i, j]) ax.text(j, i, f"{v:.2f}", ha="center", va="center", fontsize=9, color=("white" if v < 0.55 else "black")) cb = plt.colorbar(im, ax=ax); cb.set_label("cosine") plt.tight_layout() return fig # ===== UMAP ===== 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) colors = [FG_COLORS.get(fg, "#cccccc") for fg in FOOD_GROUPS] hover_text = [f"{NAMES_BY_IDX[i]}
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() if show_neighbours and basket_idxs: centroid = _basket_centroid(m, basket) if centroid is not None: nb = _topk(m, centroid, k=int(k), exclude=basket) neighbour_set = {nm for nm, _ in nb} keep = lambda i: NAMES_BY_IDX[i] not in basket_set and NAMES_BY_IDX[i] not in neighbour_set bg_x = [float(coords2[i, 0]) for i in range(n) if keep(i)] bg_y = [float(coords2[i, 1]) for i in range(n) if keep(i)] bg_z = [float(z[i]) for i in range(n) if keep(i)] if three_d else None bg_c = [colors[i] for i in range(n) if keep(i)] bg_h = [hover_text[i] for i in range(n) if keep(i)] 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), text=bg_h, hovertemplate="%{text}", 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), text=bg_h, hovertemplate="%{text}", name="ingredients", showlegend=False)) 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 nl = [NAMES_BY_IDX[i] for i in ni] mk = dict(size=11 if not three_d else 6, color="#ff8800", opacity=0.95, line=dict(color="#ffffff", width=1.2)) TR = go.Scatter3d if three_d else go.Scatter kw = dict(mode="markers+text", marker=mk, text=nl, textposition="top center", textfont=dict(size=10), hovertemplate="%{text} (neighbour)", name=f"top-{k} neighbours") fig.add_trace(TR(x=nx, y=ny, z=nz, **kw) if three_d else TR(x=nx, y=ny, **kw)) 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 bl = [NAMES_BY_IDX[i] for i in basket_idxs] mk = dict(size=18 if not three_d else 9, color=KAIKAKU_ACCENT, symbol="star" if not three_d else "diamond", line=dict(color="#111111", width=1.5)) TR = go.Scatter3d if three_d else go.Scatter kw = dict(mode="markers+text", marker=mk, text=bl, textposition="top center", textfont=dict(size=13, color="#111111"), hovertemplate="%{text} (basket)", name="basket") fig.add_trace(TR(x=bx, y=by, z=bz, **kw) if three_d else TR(x=bx, y=by, **kw)) fig.update_layout( title=dict(text=f"UMAP - Epicure-{sibling.capitalize()}{' (3D)' if three_d else ''}", font=dict(size=14)), height=620, margin=dict(l=40, r=40, t=50, b=40), paper_bgcolor="#ffffff", plot_bgcolor="#ffffff", legend=dict(orientation="v", x=1.02, y=1, font=dict(size=11)), ) 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=dict(title="UMAP 1"), yaxis=dict(title="UMAP 2"), zaxis=dict(title="PC1 (z)"), bgcolor="#ffffff")) return fig # ===== Explore: side-by-side neighbours across siblings ===== def explore_all_siblings(basket, k): """Returns 3 dataframes (Cooc/Core/Chem neighbours), heatmap, and mode tables per sibling.""" out_nb = [] out_modes = [] for sib in ["cooc","core","chem"]: m = MODELS[sib] c = _basket_centroid(m, basket) if c is None: out_nb.append([]); out_modes.append([]); continue nb = _topk(m, c, int(k), exclude=basket or []) out_nb.append([[n, f"{s:.4f}"] for n, s in nb]) scored = [(mode.mode_id, mode.label, mode.kind, float(_unit(mode.pole) @ c)) for mode in m.modes] scored.sort(key=lambda x: -x[3]) out_modes.append([[mid, label, kind, f"{sim:.3f}"] for mid, label, kind, sim in scored[:5]]) heat = _basket_heatmap(MODELS["chem"], basket) return out_nb[0], out_nb[1], out_nb[2], heat, out_modes[0], out_modes[1], out_modes[2] # ===== Transform: unified operator ===== def transform(sibling, op, basket, directions, mode_labels, theta, negatives, k): m = MODELS[sibling] if op == "Rotate to supervised direction": v = _basket_centroid(m, basket) if v is None: return [], "_(empty basket)_" d = _stack_directions(m, directions, use_factor_pole=False) if d is None: return [[n, f"{s:.4f}"] for n, s in _topk(m, v, k, basket)], "_(no direction selected)_" q = _slerp(v, d, theta) rows = [[n, f"{s:.4f}"] for n, s in _topk(m, q, k, basket)] return rows, _explain_slerp(m, basket, directions or [], theta, q, v, d) if op == "Rotate to emergent mode": label_to_id = {f"{md.label} ({md.mode_id})": md.mode_id for md in m.modes if md.kind == "factor"} mode_ids = [label_to_id[lab] for lab in (mode_labels or []) if lab in label_to_id] v = _basket_centroid(m, basket) if v is None: return [], "_(empty basket)_" d = _stack_directions(m, mode_ids, use_factor_pole=True) if d is None: return [[n, f"{s:.4f}"] for n, s in _topk(m, v, k, basket)], "_(no mode selected)_" q = _slerp(v, d, theta) rows = [[n, f"{s:.4f}"] for n, s in _topk(m, q, k, basket)] return rows, _explain_slerp(m, basket, mode_ids, theta, q, v, d) # Arithmetic pos = _basket_centroid(m, basket) if pos is None: return [], "_(no positives)_" neg = _basket_centroid(m, negatives) if negatives else None q = _unit(pos - neg) if neg is not None else pos rows = [[n, f"{s:.4f}"] for n, s in _topk(m, q, k, (basket or []) + (negatives or []))] return rows, _explain_arithmetic(m, basket, negatives or [], q) def _explain_slerp(m, basket, dir_keys, theta, q, v, d): if q is None or v is None or d is None: return "" cos_theta = float(q @ v) travelled = min(max(float(theta) / 90.0, 0.0), 1.0) dir_nb = _topk(m, _unit(d), 5, exclude=basket or []) seed_nb = _topk(m, v, 3, exclude=basket or []) dirs_str = " + ".join(dir_keys) if dir_keys else "(none)" return ( f"**Why these results.** Rotated cos to seed = {cos_theta:.3f} " f"({travelled*100:.0f}% of the way to {dirs_str}). " f"Direction's own neighbourhood: {', '.join(n for n, _ in dir_nb[:5])}. " f"Seed basket's own top-3: {', '.join(n for n, _ in seed_nb)}." ) def _explain_arithmetic(m, positives, negatives, q): if q is None: return "" pos_sims = [(n, float(_unit(m.E[m.vocab[n]]) @ q)) for n in positives if n in m.vocab] neg_sims = [(n, float(_unit(m.E[m.vocab[n]]) @ q)) for n in negatives if n in m.vocab] pp = ", ".join(f"{n} ({s:+.2f})" for n, s in pos_sims) or "(none)" np_ = ", ".join(f"{n} ({s:+.2f})" for n, s in neg_sims) or "(none)" return f"**Why these results.** Result vs positives: {pp}. Result vs negatives: {np_}." # ===== From-text: combined fridge parser + recipe builder ===== _LINE_SPLIT = re.compile(r"[\n;]") _BRACKET = re.compile(r"\([^)]*\)") _QTY = r"(?:\d+(?:[\.,/]\d+)?|a|an|one|two|three|four|five|six|seven|eight|nine|ten|half|quarter)" _UNIT = (r"(?:cups?|tbsp\.?|tablespoons?|tsp\.?|teaspoons?|oz\.?|ounces?|lbs?\.?|pounds?|" r"grams?|kgs?|kilos?|ml|liters?|litres?|cloves?|bunches?|sprigs?|pinch(?:es)?|" r"slices?|pieces?|cans?|packets?|sticks?|leaves?|stalks?|heads?|inch(?:es)?|" r"splash(?:es)?|dash(?:es)?|drops?|handfuls?|large|small|medium)") _LEADING_QTY = re.compile(rf"^\s*{_QTY}\s+(?:{_UNIT}\b\s*)?(?:of\s+)?", re.IGNORECASE) _LEADING_UNIT_ONLY = re.compile(rf"^\s*{_UNIT}\b\s*(?:of\s+)?", re.IGNORECASE) _JUICE_OF = re.compile(rf"^\s*(?:juice|zest)\s+(?:of\s+)?(?:{_QTY}\s+)?", re.IGNORECASE) _LEADING_PREP = re.compile( r"^\s*(?:fresh|dried|cooked|frozen|raw|ripe|firm|boneless|skinless|smoked|low[- ]fat)\s+", re.IGNORECASE) _TRAILING_PREP = re.compile( r"\s*,\s*(?:chopped|minced|diced|sliced|grated|crushed|whole|ground|peeled|" r"to taste|optional|finely|coarsely|cubed|shredded|julienned|halved|quartered|warmed|" r"toasted|roasted|bruised|melted|softened|cooked|drained|rinsed|patted dry|trimmed|" r"deveined|seeded|stemmed|crumbled).*$", re.IGNORECASE) _KNOWN_PLURALS = {"tortillas":"tortilla","thighs":"thigh","leaves":"leaf","onions":"onion", "potatoes":"potato","tomatoes":"tomato","cloves":"clove"} def _clean_line(line): s = line.strip().lower() s = _BRACKET.sub(" ", s) if "juice" in s or "zest" in s: s = _JUICE_OF.sub("", s) s = _TRAILING_PREP.sub("", s) s = _LEADING_QTY.sub("", s) s = _LEADING_UNIT_ONLY.sub("", s) s = _LEADING_PREP.sub("", s) s = _LEADING_PREP.sub("", s) tokens = [_KNOWN_PLURALS.get(t, t) for t in s.split()] return re.sub(r"\s+", " ", " ".join(tokens)).strip() def _fuzzy_lookup(cleaned, vocab, vocab_sp, min_score): if not cleaned: return None, 0.0 candidates = [] for scorer in (fuzz_scorers.token_set_ratio, fuzz_scorers.WRatio, fuzz_scorers.partial_ratio): hits = fuzz_process.extract(cleaned, vocab_sp, scorer=scorer, score_cutoff=min_score, limit=10) for _sp, score, idx in hits: candidates.append((vocab[idx], float(score))) if not candidates: return None, 0.0 cleaned_tokens = set(cleaned.split()) def rank(c): name, score = c nt = set(name.replace("_", " ").split()) return (-score, 0 if nt.issubset(cleaned_tokens) else 1, -len(name)) candidates.sort(key=rank) return candidates[0] def parse_fridge(raw_text, sibling="chem", min_score=70): if not raw_text or not raw_text.strip(): return [], [] vocab = list(MODELS[sibling].vocab.keys()) vocab_sp = [v.replace("_", " ") for v in vocab] rows, matched = [], [] for line in _LINE_SPLIT.split(raw_text): if not line.strip(): continue cleaned = _clean_line(line) if not cleaned: rows.append([line.strip(), "(empty)", 0.0]); continue match, score = _fuzzy_lookup(cleaned, vocab, vocab_sp, int(min_score)) if match is None: tokens = cleaned.split() if len(tokens) > 1: match, score = _fuzzy_lookup(" ".join(tokens[:-1]), vocab, vocab_sp, int(min_score)) if match is None: rows.append([line.strip(), "(no match)", 0.0]); continue rows.append([line.strip(), match, round(score, 1)]) matched.append(match) seen, dedup = set(), [] for n in matched: if n not in seen: seen.add(n); dedup.append(n) return rows, dedup # Sentence-transformer for thematic queries _ST = None def _get_st(): global _ST if _ST is None: from sentence_transformers import SentenceTransformer _ST = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cpu") return _ST @lru_cache(maxsize=4) def _mode_label_matrix(sibling): m = MODELS[sibling] modes = [md for md in m.modes if md.kind == "factor"] if not modes: return [], [], np.zeros((0, 384), dtype=np.float32) labels = [md.label for md in modes] mids = [md.mode_id for md in modes] M = _get_st().encode(labels, normalize_embeddings=True, convert_to_numpy=True) return mids, labels, M.astype(np.float32) def _mode_quartile(mode): members = list(mode.members or []) n = max(4, min(12, (len(members) + 3) // 4)) return members[:n] _STOP = {"i","im","i'm","a","an","the","for","of","with","and","or","some","my","me","we", "make","making","cook","cooking","prepare","preparing","want","need","to","tonight", "people","person","servings","dinner","lunch","dish","recipe","quick","easy", "tasty","yummy","good","great","food","meal","style"} _TOK_RE = re.compile(r"[A-Za-z][A-Za-z\-']{1,}") def suggest_basket(prompt, sibling="chem", k=10): if not prompt or not prompt.strip(): return [], [], "_(empty prompt)_" vocab = list(MODELS[sibling].vocab.keys()) vocab_sp = [v.replace("_"," ") for v in vocab] tokens = [t for t in _TOK_RE.findall(prompt.lower()) if t not in _STOP and len(t) > 2] direct = {} direct_evidence = [] for tok in tokens: hits = fuzz_process.extract(tok, vocab_sp, scorer=fuzz_scorers.token_set_ratio, score_cutoff=88, limit=2) for _sp, score, idx in hits: name = vocab[idx] if score > direct.get(name, 0): direct[name] = float(score) direct_evidence.append((tok, name, float(score))) mids, labels, M = _mode_label_matrix(sibling) thematic = {} thematic_modes = [] if M.shape[0] > 0: q = _get_st().encode([prompt], normalize_embeddings=True, convert_to_numpy=True)[0] sims = M @ q order = np.argsort(-sims) picked = [(mids[i], labels[i], float(sims[i])) for i in order[:3] if sims[i] >= 0.25] thematic_modes = picked id_to_mode = {md.mode_id: md for md in MODELS[sibling].modes if md.kind == "factor"} for mid, lab, sim in picked: for name in _mode_quartile(id_to_mode[mid]): s_existing, _ = thematic.get(name, (0.0, "")) thematic[name] = (max(s_existing, sim * 100.0), lab) combined = {} for name, sc in direct.items(): combined[name] = (sc, "direct") for name, (sc, lab) in thematic.items(): prev = combined.get(name) if prev is None or sc > prev[0]: combined[name] = (sc, "both" if prev else "thematic") ranked = sorted(combined.items(), key=lambda kv: (-kv[1][0], 0 if kv[1][1] != "thematic" else 1, kv[0]))[:int(k)] rows = [[name, src, round(score, 1)] for name, (score, src) in ranked] names = [name for name, _ in ranked] lines = [] if direct_evidence: lines.append("**Direct mentions:** " + ", ".join(sorted({f"`{n}`" for _, n, _ in direct_evidence}))) if thematic_modes: lines.append("**Matched modes:** " + "; ".join(f"`{lab}` (cos {sim:.2f})" for _, lab, sim in thematic_modes)) return rows, names, "\n\n".join(lines) if lines else "_(no matches)_" def parse_or_suggest(text, sibling, mode_choice): """Auto-detect: fridge-list if mostly short lines with units; recipe-prompt otherwise.""" if not text or not text.strip(): return [], "_(empty)_", [] if mode_choice == "Recipe / dish description": rows, names, expl = suggest_basket(text, sibling, 10) return rows, expl, names rows, names = parse_fridge(text, sibling, 70) return rows, f"Matched {len(names)} ingredients.", names # ===== Mode atlas (used inside Explore Accordion) ===== def browse_modes(sibling, kind_filter, query): m = MODELS[sibling] rows, q = [], (query or "").strip().lower() for mode in m.modes: if kind_filter != "all" and mode.kind != kind_filter: continue if q and q not in mode.label.lower() and q not in mode.property.lower(): continue rows.append([mode.mode_id, mode.kind, mode.property, mode.label, mode.n_members, ", ".join(mode.members[:10])]) rows.sort(key=lambda r: (r[1], -r[4])) return rows # ===== Public API endpoint helpers ===== def _suggest(name, sibling, n=5): vocab = list(MODELS[sibling].vocab.keys()) hits = fuzz_process.extract((name or "").lower().replace(" ", "_"), vocab, scorer=fuzz_scorers.WRatio, limit=n) return [h[0] for h in hits] def api_neighbors(ingredient, sibling="chem", k=5): if sibling not in MODELS: return {"error": "bad sibling"} if ingredient not in MODELS[sibling].vocab: return {"error": f"'{ingredient}' not in vocab", "suggestions": _suggest(ingredient, sibling)} m = MODELS[sibling] q = _unit(m.E[m.vocab[ingredient]]) return [{"name": n, "cosine": round(float(s), 6)} for n, s in _topk(m, q, int(k), [ingredient])] def api_slerp(seed, direction, theta_deg=30, sibling="chem", k=5): if sibling not in MODELS: return {"error": "bad sibling"} m = MODELS[sibling] if seed not in m.vocab: return {"error": f"'{seed}' not in vocab", "suggestions": _suggest(seed, sibling)} if direction not in m.supervised_poles: return {"error": f"'{direction}' not a supervised pole"} v = _unit(m.E[m.vocab[seed]]) d = _unit(m.supervised_poles[direction]) q = _slerp(v, d, float(theta_deg)) return [{"name": n, "cosine": round(float(s), 6)} for n, s in _topk(m, q, int(k), [seed])] def api_arithmetic(positives, negatives, sibling="chem", k=5): if sibling not in MODELS: return {"error": "bad sibling"} positives = list(positives or []); negatives = list(negatives or []) if not positives: return {"error": "positives must be non-empty"} m = MODELS[sibling] unknown = [x for x in positives + negatives if x not in m.vocab] if unknown: return {"error": f"unknown: {unknown}"} pos = _basket_centroid(m, positives) neg = _basket_centroid(m, negatives) if negatives else None q = _unit(pos - neg) if neg is not None else pos return [{"name": n, "cosine": round(float(s), 6)} for n, s in _topk(m, q, int(k), positives + negatives)] def api_embed(ingredient, sibling="chem"): if sibling not in MODELS: return {"error": "bad sibling"} m = MODELS[sibling] if ingredient not in m.vocab: return {"error": f"'{ingredient}' not in vocab"} return [float(x) for x in _unit(m.E[m.vocab[ingredient]]).tolist()] # ===== Theme ===== THEME = gr.themes.Soft( primary_hue=gr.themes.Color( c50="#E8F4F1", c100="#C8E6DE", c200=KAIKAKU_ACCENT_LIGHT, c300="#7BBAA9", c400="#4DA08F", c500=KAIKAKU_ACCENT, c600=KAIKAKU_ACCENT_HOVER, c700="#155547", c800="#0F3B33", c900=KAIKAKU_DARK, c950=KAIKAKU_DEEP, ), neutral_hue="slate", font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"], ).set( block_label_text_color="#1f2937", block_label_text_weight="600", block_title_text_color="#0f172a", block_title_text_weight="700", body_text_color="#0f172a", body_text_color_subdued="#475569", button_primary_background_fill=KAIKAKU_ACCENT, button_primary_background_fill_hover=KAIKAKU_ACCENT_HOVER, button_primary_text_color="#ffffff", button_primary_border_color=KAIKAKU_ACCENT, button_secondary_background_fill="#f1f5f9", button_secondary_text_color=KAIKAKU_DARK, slider_color=KAIKAKU_ACCENT, color_accent=KAIKAKU_ACCENT, ) CUSTOM_CSS = f""" .gradio-container {{max-width: 1280px !important;}} footer {{visibility: hidden;}} .gradio-container label, .gradio-container .label, .gradio-container [data-testid="block-label"], .gradio-container .block-label {{ color: #0f172a !important; font-weight: 600 !important; background: transparent !important; }} .gradio-container button[role="tab"] {{ color: #334155 !important; font-weight: 500 !important; }} .gradio-container button[role="tab"][aria-selected="true"] {{ color: {KAIKAKU_ACCENT} !important; border-bottom-color: {KAIKAKU_ACCENT} !important; font-weight: 700 !important; }} .gradio-container button.primary, .gradio-container .primary > button {{ background: {KAIKAKU_ACCENT} !important; color: #ffffff !important; border-color: {KAIKAKU_ACCENT} !important; font-weight: 600 !important; }} .gradio-container table thead th {{ color: #0f172a !important; font-weight: 700 !important; background: #f8fafc !important; }} .gradio-container table tbody td {{ color: #0f172a !important; }} /* Spectrum bar */ .spectrum-bar {{ display: flex; align-items: stretch; margin: 12px 0 4px 0; height: 56px; border-radius: 8px; overflow: hidden; box-shadow: 0 1px 2px rgba(0,0,0,0.05); }} .spectrum-cell {{ flex: 1; display: flex; flex-direction: column; justify-content: center; padding: 6px 14px; color: #0f172a; }} .spectrum-cell-1 {{ background: #f0f9f6; }} .spectrum-cell-2 {{ background: #d8efe7; }} .spectrum-cell-3 {{ background: #b8dfd1; }} .spectrum-name {{ font-weight: 700; font-size: 0.95em; }} .spectrum-sub {{ font-size: 0.8em; color: #475569; }} .spectrum-arrow {{ width: 16px; background: transparent; display:flex; align-items:center; justify-content:center; color: #94a3b8; }} """ SPECTRUM_BAR = """
Cooc
recipe co-occurrence; neighbours = recipe companions
→
Core
blended; concentrated geometry; tightest emergent modes
→
Chem
FlavorDB compound metapaths; neighbours = flavour-profile peers
""" # ===== Pre-rendered killer demo on landing ===== _DEFAULT_BASKET = ["chicken","lemon","garlic"] _INIT_NB_COOC, _INIT_NB_CORE, _INIT_NB_CHEM, _INIT_HEATMAP, _INIT_MD_COOC, _INIT_MD_CORE, _INIT_MD_CHEM = explore_all_siblings(_DEFAULT_BASKET, 8) _INIT_UMAP = umap_view("chem", _DEFAULT_BASKET, True, 8) # ===== UI ===== with gr.Blocks(title="Epicure Explorer", theme=THEME, css=CUSTOM_CSS) as demo: gr.Markdown( """# Epicure Explorer Three sibling ingredient embeddings 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. """ ) gr.HTML(SPECTRUM_BAR) with gr.Tabs(): # ---------- Tab 1: EXPLORE ---------- with gr.Tab("Explore"): gr.Markdown("Pick ingredients. See nearest neighbours in **all three siblings side-by-side** so the spectrum shows in one screen.") with gr.Row(): ex_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=_DEFAULT_BASKET, label="Ingredient basket", multiselect=True, max_choices=10, scale=4) ex_k = gr.Slider(3, 15, value=8, step=1, label="K", scale=1) with gr.Row(): ex_fg = gr.Radio(choices=FOOD_GROUP_CHOICES, value="All", label="Filter dropdown by food group", interactive=True, scale=3) ex_btn = gr.Button("Find neighbours", variant="primary", scale=1) ex_fg.change(_filter_dropdown, inputs=[ex_fg, ex_basket], outputs=ex_basket, show_progress="hidden") gr.Examples( examples=[ [["chicken","lemon","garlic"], 8], [["miso","ginger","sesame_oil"], 8], [["tomato","basil","mozzarella_cheese"], 8], [["chocolate","strawberry","cream"], 8], [["cumin","coriander","turmeric"], 8], [["coconut_milk","lemongrass","fish_sauce"], 8], [["red_wine","beef","rosemary"], 8], ], inputs=[ex_basket, ex_k], label="Try a basket (one click)", ) with gr.Row(): ex_nb_cooc = gr.Dataframe(value=_INIT_NB_COOC, headers=["Cooc","cos"], label="Cooc (recipe-context)", interactive=False) ex_nb_core = gr.Dataframe(value=_INIT_NB_CORE, headers=["Core","cos"], label="Core (blended)", interactive=False) ex_nb_chem = gr.Dataframe(value=_INIT_NB_CHEM, headers=["Chem","cos"], label="Chem (chemistry)", interactive=False) with gr.Accordion("Closest modes (per sibling)", open=False): with gr.Row(): ex_md_cooc = gr.Dataframe(value=_INIT_MD_COOC, headers=["id","label","kind","cos"], label="Cooc top modes", interactive=False, wrap=True) ex_md_core = gr.Dataframe(value=_INIT_MD_CORE, headers=["id","label","kind","cos"], label="Core top modes", interactive=False, wrap=True) ex_md_chem = gr.Dataframe(value=_INIT_MD_CHEM, headers=["id","label","kind","cos"], label="Chem top modes", interactive=False, wrap=True) with gr.Accordion("Pairwise coherence (basket members)", open=False): ex_heat = gr.Plot(value=_INIT_HEATMAP, label="Heatmap") with gr.Accordion("Browse the mode atlas (150-200 modes per sibling)", open=False): with gr.Row(): atlas_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling") atlas_kind = gr.Radio(choices=["all","factor","continuous","binary"], value="all", label="Kind") atlas_q = gr.Textbox(label="Search labels", placeholder="e.g. South Asian, baking", scale=2) atlas_btn = gr.Button("Browse", variant="primary") atlas_table = gr.Dataframe( headers=["mode_id","kind","property","label","n_members","top members"], interactive=False, wrap=True, ) atlas_btn.click(browse_modes, inputs=[atlas_sib, atlas_kind, atlas_q], outputs=atlas_table) ex_btn.click( explore_all_siblings, inputs=[ex_basket, ex_k], outputs=[ex_nb_cooc, ex_nb_core, ex_nb_chem, ex_heat, ex_md_cooc, ex_md_core, ex_md_chem], show_progress="minimal", ) # ---------- Tab 2: TRANSFORM ---------- with gr.Tab("Transform"): gr.Markdown("Rotate the basket toward a direction, an emergent mode, or compute `basket - negatives`. **All three operators on one form.**") with gr.Row(): tx_sib = gr.Radio(choices=["cooc","core","chem"], value="core", label="Sibling") tx_op = gr.Radio( choices=["Rotate to supervised direction","Rotate to emergent mode","Arithmetic (basket - negatives)"], value="Arithmetic (basket - negatives)", label="Operation", ) with gr.Row(): tx_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=["miso"], label="Basket / positives", multiselect=True, max_choices=10, scale=3) tx_neg = gr.Dropdown(choices=ALL_INGREDIENTS, value=["salt"], label="Negatives (Arithmetic only)", multiselect=True, max_choices=10, scale=2) with gr.Row(): tx_dirs = gr.Dropdown(choices=_supervised_choices("core"), value=[], label="Supervised directions (for 'Rotate to supervised')", multiselect=True, max_choices=5, scale=3) tx_modes = gr.Dropdown(choices=[lab for lab, _ in _factor_mode_choices("core")], value=[], label="Factor modes (for 'Rotate to emergent')", multiselect=True, max_choices=5, scale=3) with gr.Row(): tx_theta = gr.Slider(0, 90, value=30, step=5, label="Rotation angle (deg, SLERP only)", scale=2) tx_k = gr.Slider(3, 15, value=8, step=1, label="K", scale=1) tx_btn = gr.Button("Run", variant="primary", scale=1) tx_sib.change(lambda s: gr.Dropdown(choices=_supervised_choices(s), value=[]), inputs=tx_sib, outputs=tx_dirs) tx_sib.change(lambda s: gr.Dropdown(choices=[lab for lab, _ in _factor_mode_choices(s)], value=[]), inputs=tx_sib, outputs=tx_modes) tx_table = gr.Dataframe(headers=["Ingredient","cos"], label="Top-K result", interactive=False) tx_why = gr.Markdown() tx_btn.click( transform, inputs=[tx_sib, tx_op, tx_basket, tx_dirs, tx_modes, tx_theta, tx_neg, tx_k], outputs=[tx_table, tx_why], show_progress="minimal", ) gr.Examples( examples=[ ["core", "Arithmetic (basket - negatives)", ["miso"], [], [], 30, ["salt"], 8], ["core", "Arithmetic (basket - negatives)", ["coffee"], [], [], 30, ["milk"], 8], ["chem", "Arithmetic (basket - negatives)", ["chocolate"], [], [], 30, ["sugar"], 8], ["chem", "Rotate to supervised direction", ["rice"], ["cuisine:South_Asian"], [], 30, [], 8], ["chem", "Rotate to supervised direction", ["corn"], ["cuisine:Latin_American"], [], 30, [], 8], ], inputs=[tx_sib, tx_op, tx_basket, tx_dirs, tx_modes, tx_theta, tx_neg, tx_k], label="Try one of these", ) # ---------- Tab 3: MAP ---------- with gr.Tab("Map"): gr.Markdown("UMAP of the 1,790-ingredient embedding (cosine, n_neighbors=30, min_dist=0.03; paper Fig 1).") with gr.Row(): map_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling", scale=1) map_basket = gr.Dropdown(choices=ALL_INGREDIENTS, value=_DEFAULT_BASKET, label="Highlight basket", multiselect=True, max_choices=10, scale=3) with gr.Row(): map_3d = gr.Checkbox(value=False, label="3-D") map_nb = gr.Checkbox(value=True, label="Show top-K neighbours") map_k = gr.Slider(3, 20, value=10, step=1, label="K", scale=1) map_btn = gr.Button("Update", variant="primary", scale=1) map_plot = gr.Plot(value=_INIT_UMAP, label="UMAP") map_btn.click(umap_view, inputs=[map_sib, map_basket, map_nb, map_k, map_3d], outputs=map_plot, show_progress="minimal") # ---------- Tab 4: FROM TEXT ---------- with gr.Tab("From text"): gr.Markdown("Paste a **shopping list / recipe ingredients** to get canonical matches, **or a dish description** to get thematic suggestions. Send the result into the Explore tab.") ft_text = gr.Textbox( label="Free text", lines=6, value="I'm making Thai green curry for 4 people", placeholder=("Either a dish description ('I'm making Thai green curry for 4'), or " "an ingredient list ('2 chicken thighs / 1 cup coconut milk / fish sauce / ...')"), ) ft_mode = gr.Radio( choices=["Recipe / dish description", "Ingredient list (shopping list / fridge)"], value="Recipe / dish description", label="Treat as", ) with gr.Row(): ft_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling") ft_btn = gr.Button("Match", variant="primary") ft_send = gr.Button("Send to Explore", variant="secondary") ft_table = gr.Dataframe(headers=["Input","Match","Score"], interactive=False, label="Matched ingredients") ft_expl = gr.Markdown() ft_matched = gr.State([]) ft_btn.click(parse_or_suggest, inputs=[ft_text, ft_sib, ft_mode], outputs=[ft_table, ft_expl, ft_matched], show_progress="full") ft_send.click(lambda names: gr.Dropdown(value=(names or [])[:10]), inputs=[ft_matched], outputs=[ex_basket]) gr.Examples( examples=[ ["I'm making Thai green curry for 4 people", "Recipe / dish description"], ["spicy vegetarian taco filling", "Recipe / dish description"], ["Japanese miso-glazed salmon and greens", "Recipe / dish description"], ["2 boneless chicken thighs\n1 cup coconut milk\n1 tbsp fish sauce\nfresh lemongrass\n3 cloves garlic\njuice of one lime", "Ingredient list (shopping list / fridge)"], ], inputs=[ft_text, ft_mode], label="Try one of these", ) # ---- Hidden API endpoints ---- with gr.Group(visible=False): api_in_s1 = gr.Textbox(visible=False) api_in_s2 = gr.Textbox(visible=False) api_in_n = gr.Number(visible=False, value=5) api_in_n2 = gr.Number(visible=False, value=30) api_in_l1 = gr.JSON(visible=False, value=[]) api_in_l2 = gr.JSON(visible=False, value=[]) api_out = gr.JSON(visible=False) gr.Button(visible=False).click(api_neighbors, inputs=[api_in_s1, api_in_s2, api_in_n], outputs=api_out, api_name="neighbors") gr.Button(visible=False).click(api_slerp, inputs=[api_in_s1, api_in_s2, api_in_n2, gr.Textbox(visible=False, value="chem"), api_in_n], outputs=api_out, api_name="slerp") gr.Button(visible=False).click(api_arithmetic, inputs=[api_in_l1, api_in_l2, api_in_s1, api_in_n], outputs=api_out, api_name="arithmetic") gr.Button(visible=False).click(api_embed, inputs=[api_in_s1, api_in_s2], outputs=api_out, api_name="embed") gr.Markdown( """--- **Cite:** Radzikowski and Chen, 2026, *Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings*, [arXiv:2605.22391](https://arxiv.org/abs/2605.22391). Models: [epicure-cooc](https://huggingface.co/Kaikaku/epicure-cooc) · [epicure-core](https://huggingface.co/Kaikaku/epicure-core) · [epicure-chem](https://huggingface.co/Kaikaku/epicure-chem) · [dataset](https://huggingface.co/datasets/Kaikaku/epicure-corpus-resources) · [API](/?view=api) """ ) if __name__ == "__main__": demo.launch()