"""Epicure Explorer: chef-facing operators over the three sibling embeddings. Features: - Basket pairings (with pairwise cosine heatmap) - Supervised SLERP (with "why these results" explainer) - Emergent SLERP (with explainer) - Arithmetic (Mikolov-style, with explainer) - Mode atlas (click row -> highlight on UMAP) - Compare siblings (one query, three columns) - UMAP visualisation (2D / 3D) - Parse my fridge (free-text -> canonical vocab via rapidfuzz) - Recipe builder (hybrid retrieval: rapidfuzz + sentence-transformers over mode labels) - Saved queries (per-browser persistence via gr.BrowserState) - Public developer API (gr.api endpoints for neighbours / slerp / arithmetic / embed) - Food-group filter on every ingredient dropdown Paper: https://arxiv.org/abs/2605.22391 """ from __future__ import annotations import os import re import sys import json import uuid from datetime import datetime, timezone 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" KAIKAKU_TEXT = "#0F2D2F" KAIKAKU_MUTED = "#5A7878" 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) print(f"[epicure-explorer] food group labels: {len(FOOD_GROUPS)} ingredients", flush=True) # ===== Feature 5: food-group filter helpers ===== _NAME_TO_GROUP: dict[str, str] = {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: str) -> list[str]: 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: str, 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 helpers ===== 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) # ===== Feature 4: explainer helpers ===== def _fmt_nb_inline(pairs): return ", ".join(f"{n} ({s:+.2f})" for n, s in pairs) def _slerp_explainer(m, basket, direction_keys, theta, q, v, d, kind): if v is None or d is None or q is None: return "_(no rotation applied)_" cos_theta = float(q @ v) travelled = min(max(float(theta) / 90.0, 0.0), 1.0) dir_nb = _topk(m, _unit(d), k=5, exclude=basket or []) seed_nb = _topk(m, v, k=3, exclude=basket or []) dir_names = ", ".join(n for n, _ in dir_nb[:3]) label = "direction pole" if kind == "supervised" else "factor-mode pole" dirs_str = " + ".join(direction_keys) if direction_keys else "(none)" return ( f"**Why these results** \n" f"- Rotated query vs. seed centroid: cos = {cos_theta:.3f} (theta = {float(theta):.0f}°; " f"{travelled*100:.0f}% of the way to the {label}). \n" f"- {label.capitalize()} ({dirs_str}) nearest in vocab: {_fmt_nb_inline(dir_nb)}. \n" f"- Seed basket's own top-3 (baseline): {_fmt_nb_inline(seed_nb)}. \n" f"- At {float(theta):.0f}° the query lands near: {dir_names}." ) def _arithmetic_explainer(m, positives, negatives, q, pos_v, neg_v): if q is None: return "_(no result: missing positives)_" pos_sims = [(n, float(_unit(m.E[m.vocab[n]]) @ q)) for n in (positives or []) if n in m.vocab] neg_sims = [(n, float(_unit(m.E[m.vocab[n]]) @ q)) for n in (negatives or []) if n in m.vocab] top = _topk(m, q, k=1, exclude=(positives or []) + (negatives or [])) top_name, top_sim = top[0] if top else ("(none)", 0.0) pos_part = ", ".join(f"{n} ({s:+.2f})" for n, s in pos_sims) or "(none)" neg_part = ", ".join(f"{n} ({s:+.2f})" for n, s in neg_sims) or "(none)" input_max = max((s for _, s in pos_sims + neg_sims), default=0.0) if pos_sims or neg_sims: gap = top_sim - input_max if gap > 0.05: interp = (f"Result sits closer to **{top_name}** ({top_sim:+.2f}) " f"than to any input (max {input_max:+.2f}); the embedding separates these concepts.") else: interp = (f"Result is dominated by the inputs themselves " f"(top neighbour {top_name} only {gap:+.2f} above max input cosine).") else: interp = f"Result top neighbour: {top_name} ({top_sim:+.2f})." return ( f"**Why these results** \n" f"- Result vs. positives: {pos_part}. \n" f"- Result vs. negatives: {neg_part}. \n" f"- {interp}" ) # ===== heatmap ===== 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="#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]) 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.set_label("cosine") ax.set_title("Pairwise cosine within the basket", fontsize=12) 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[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} bg_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 bg_keep(i)] bg_y = [float(coords2[i, 1]) for i in range(n) if bg_keep(i)] bg_z = [float(z[i]) for i in range(n) if bg_keep(i)] if three_d else None bg_c = [colors[i] for i in range(n) if bg_keep(i)] bg_h = [hover_text[i] for i in range(n) if bg_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, line=dict(width=0)), 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, line=dict(width=0)), 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 nlabels = [NAMES_BY_IDX[i] for i in ni] marker = 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 kwargs = dict(mode="markers+text", marker=marker, text=nlabels, 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, **kwargs) if three_d else TR(x=nx, y=ny, **kwargs)) 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_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 kwargs = dict(mode="markers+text", marker=marker, text=blabels, textposition="top center", textfont=dict(size=13, color="#111111"), hovertemplate="%{text} (basket)", name="basket") fig.add_trace(TR(x=bx, y=by, z=bz, **kwargs) if three_d else TR(x=bx, y=by, **kwargs)) 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(size=15)), height=650, margin=dict(l=40, r=40, t=60, 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="#eeeeee", zeroline=False, title="UMAP 1") fig.update_yaxes(showgrid=True, gridcolor="#eeeeee", 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 # ===== tab handlers (with explainers) ===== def basket_pairings(sibling, basket, k): m = MODELS[sibling] centroid = _basket_centroid(m, basket) if centroid is 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]) heatmap = _basket_heatmap(m, basket) return ( [[name, f"{sim:.4f}"] for name, sim in nb], [[mid, label, kind, f"{sim:.4f}"] for mid, label, kind, sim in scored[:k]], heatmap, ) def supervised_slerp_multi(sibling, basket, directions, theta, k): m = MODELS[sibling] 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, _slerp_explainer(m, basket, directions or [], theta, q, v, d, "supervised") def emergent_slerp_multi(sibling, basket, mode_labels, theta, k): m = MODELS[sibling] label_to_id = {f"{mode.label} ({mode.mode_id})": mode.mode_id for mode in m.modes if mode.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 factor mode selected)_" q = _slerp(v, d, theta) rows = [[n, f"{s:.4f}"] for n, s in _topk(m, q, k, basket)] return rows, _slerp_explainer(m, basket, mode_ids, theta, q, v, d, "emergent") def arithmetic(sibling, positives, negatives, k): m = MODELS[sibling] pos = _basket_centroid(m, positives) if pos is None: return [], "_(no positives provided)_" 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, (positives or []) + (negatives or []))] return rows, _arithmetic_explainer(m, positives or [], negatives or [], q, pos, neg) 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[:12])]) rows.sort(key=lambda r: (r[1], -r[4])) return rows def compare_siblings(basket, directions, theta, k): out = [] for sib in ["cooc","core","chem"]: m = MODELS[sib] v = _basket_centroid(m, basket) 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) q = _slerp(v, d_vec, theta) if d_vec is not None else v else: q = v hits = _topk(m, q, k=k, exclude=basket) out.append([[n, f"{s:.4f}"] for n, s in hits]) return out[0], out[1], out[2] # ===== Feature 6: recipe builder (lazy-loaded sentence-transformer) ===== _ST_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2" _ST = None def _get_st(): global _ST if _ST is None: print(f"[epicure-explorer] loading {_ST_MODEL_NAME} (first call, ~80MB)", flush=True) from sentence_transformers import SentenceTransformer _ST = SentenceTransformer(_ST_MODEL_NAME, device="cpu") return _ST @lru_cache(maxsize=4) def _mode_label_matrix(sibling: str): 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] _PROMPT_STOPWORDS = { "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","plate","plates", } _TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z\-']{1,}") def suggest_basket(prompt, sibling, k=10): if not prompt or not prompt.strip(): return [], [], "Type a dish description first." vocab = list(MODELS[sibling].vocab.keys()) vocab_sp = [v.replace("_", " ") for v in vocab] raw_tokens = _TOKEN_RE.findall(prompt.lower()) tokens = [t for t in raw_tokens if t not in _PROMPT_STOPWORDS 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, "")) s_new = max(s_existing, sim * 100.0) thematic[name] = (s_new, 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]: tag = "both" if prev else "thematic" combined[name] = (sc, tag) 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: dm = ", ".join(sorted({f"`{n}` (from '{t}')" for t, n, _ in direct_evidence})) lines.append(f"**Direct mentions:** {dm}") else: lines.append("**Direct mentions:** _none cleared score threshold_") if thematic_modes: bits = [] id_to_mode = {md.mode_id: md for md in MODELS[sibling].modes if md.kind == "factor"} for mid, lab, sim in thematic_modes: sample = ", ".join(id_to_mode[mid].members[:4]) bits.append(f"`{lab}` (cos {sim:.2f}; e.g. {sample})") lines.append("**Matched factor modes:** " + "; ".join(bits)) else: lines.append("**Matched factor modes:** _no mode label cleared cosine 0.25_") return rows, names, "\n\n".join(lines) # ===== fridge parser ===== _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 _name_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_key(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_key) return candidates[0] def parse_fridge(raw_text, sibling, 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, cleaned]); continue rows.append([line.strip(), match, round(score, 1), cleaned]) matched.append(match) seen, dedup = set(), [] for n in matched: if n not in seen: seen.add(n); dedup.append(n) return rows, dedup # ===== Feature 8: public API endpoints ===== def _suggest(name: str, sibling: str, n: int = 5) -> list[str]: 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 _validate_sibling(sibling): if sibling not in MODELS: return {"error": f"sibling '{sibling}' not in {{cooc, core, chem}}", "suggestions": ["cooc","core","chem"]} return None def _validate_ingredient(name, sibling, field="ingredient"): if not isinstance(name, str) or not name: return {"error": f"{field} must be a non-empty string"} if name not in MODELS[sibling].vocab: return {"error": f"{field} '{name}' not in vocab", "suggestions": _suggest(name, sibling)} return None def api_neighbors(ingredient, sibling="chem", k=5): err = _validate_sibling(sibling) or _validate_ingredient(ingredient, sibling) if err: return err m = MODELS[sibling] q = _unit(m.E[m.vocab[ingredient]]) pairs = _topk(m, q, int(k), exclude=[ingredient]) return [{"name": n, "cosine": round(float(s), 6)} for n, s in pairs] def api_slerp(seed, direction, theta_deg=30, sibling="chem", k=5): err = _validate_sibling(sibling) or _validate_ingredient(seed, sibling, "seed") if err: return err m = MODELS[sibling] if direction not in m.supervised_poles: return {"error": f"direction '{direction}' not a supervised pole", "suggestions": sorted(m.supervised_poles.keys())[:10]} v = _unit(m.E[m.vocab[seed]]) d = _unit(m.supervised_poles[direction]) q = _slerp(v, d, float(theta_deg)) pairs = _topk(m, q, int(k), exclude=[seed]) return [{"name": n, "cosine": round(float(s), 6)} for n, s in pairs] def api_arithmetic(positives, negatives, sibling="chem", k=5): err = _validate_sibling(sibling) if err: return err positives = list(positives or []) negatives = list(negatives or []) if not positives: return {"error": "positives must be a non-empty list"} m = MODELS[sibling] unknown = [x for x in positives + negatives if x not in m.vocab] if unknown: return {"error": f"unknown ingredients: {unknown}", "suggestions": {x: _suggest(x, sibling) for x in 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 pairs = _topk(m, q, int(k), exclude=positives + negatives) return [{"name": n, "cosine": round(float(s), 6)} for n, s in pairs] def api_embed(ingredient, sibling="chem"): err = _validate_sibling(sibling) or _validate_ingredient(ingredient, sibling) if err: return err m = MODELS[sibling] v = _unit(m.E[m.vocab[ingredient]]) return [float(x) for x in v.tolist()] def api_list_directions(sibling="chem"): err = _validate_sibling(sibling) if err: return err return sorted(MODELS[sibling].supervised_poles.keys()) def api_list_factor_modes(sibling="chem"): err = _validate_sibling(sibling) if err: return err return [{"mode_id": mode.mode_id, "label": str(mode.label), "kind": str(mode.kind), "property": str(mode.property), "n_members": int(mode.n_members)} for mode in MODELS[sibling].modes if mode.kind == "factor"] # ===== Feature 9: saved queries helpers ===== TAB_IDS = { "basket": "tab_basket", "supervised_slerp": "tab_sup", "emergent_slerp": "tab_em", "arithmetic": "tab_ar", "compare": "tab_cmp", } TAB_LABELS = { "basket": "Basket pairings", "supervised_slerp": "Supervised SLERP", "emergent_slerp": "Emergent SLERP", "arithmetic": "Arithmetic", "compare": "Compare siblings", } def _summarise(tab, inputs): sib = inputs.get("sibling", "") if tab == "basket": return f"[{sib}] basket: {', '.join(inputs.get('basket', [])[:3])} k={inputs.get('k')}" if tab == "supervised_slerp": b = ", ".join(inputs.get("basket", [])[:2]) d = ", ".join(inputs.get("directions", [])[:2]) return f"[{sib}] {b} +{inputs.get('theta')}° -> {d}" if tab == "emergent_slerp": b = ", ".join(inputs.get("basket", [])[:2]) return f"[{sib}] {b} +{inputs.get('theta')}° -> {len(inputs.get('modes', []))} factor modes" if tab == "arithmetic": p = " + ".join(inputs.get("positives", [])[:2]) n = " + ".join(inputs.get("negatives", [])[:2]) return f"[{sib}] {p}" + (f" - {n}" if n else "") if tab == "compare": return f"[3 siblings] {', '.join(inputs.get('basket', [])[:2])} +{inputs.get('theta')}°" return "(unknown)" def save_query(saved, tab, inputs_dict): saved = list(saved or []) rec = { "id": str(uuid.uuid4()), "created_at": datetime.now(timezone.utc).isoformat(timespec="seconds"), "tab": tab, "inputs": inputs_dict, "summary": _summarise(tab, inputs_dict), } saved.insert(0, rec) saved = saved[:200] return saved, _render_saved(saved) def delete_query(saved, qid): saved = [q for q in (saved or []) if q.get("id") != qid] return saved, _render_saved(saved) def _render_saved(saved): return [[q["created_at"], TAB_LABELS.get(q["tab"], q["tab"]), q["summary"], q["id"]] for q in (saved or [])] # ===== Theme + CSS ===== 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_background_fill_hover="#e2e8f0", 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, .gradio-container .gr-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 button.primary:hover {{ background: {KAIKAKU_ACCENT_HOVER} !important; border-color: {KAIKAKU_ACCENT_HOVER} !important; }} .gradio-container table thead th, .gradio-container .gr-dataframe thead th {{ color: #0f172a !important; font-weight: 700 !important; background: #f8fafc !important; }} .gradio-container table tbody td {{ color: #0f172a !important; }} .sibling-card {{ border-left: 3px solid {KAIKAKU_ACCENT}; padding: 10px 14px; margin: 6px 0; background: #f8fafc; border-radius: 4px; }} .sibling-name {{ color: {KAIKAKU_DARK}; font-weight: 700; font-size: 1.02em; }} .sibling-desc {{ color: #334155; font-size: 0.95em; line-height: 1.5; }} """ _INITIAL_UMAP = umap_view("chem", ["chicken","lemon","garlic"], True, 8, three_d=False) _INITIAL_HEATMAP = _basket_heatmap(MODELS["chem"], ["chicken","lemon","garlic"]) SIBLING_CARDS = """
Cooc - recipe-context only
Walks recipe co-occurrence (NPMI graph) only. Neighbours are recipe companions: things that get cooked with the seed. Isotropic geometry (PR=173.6 of 300). Best for "what else do I cook with X".
Core - blended (the middle ground)
Typed FlavorDB compound walks blended with injected I-I walks at ii_repeat=10. Concentrated geometry (PR=94.2), tightest emergent modes. Chemistry-aware but keeps recipe context.
Chem - chemistry only
Typed FlavorDB compound metapaths only (ii_repeat=0). Neighbours are flavour-profile peers: things that share aroma chemistry with the seed. Best supervised-direction recovery; cuisine Cohen's d = 3.07 across 8 macro-regions.
""" # ===== Helper for ingredient picker with food-group filter ===== def _ingredient_picker(label, default_value, multiselect=True, max_choices=10): radio = gr.Radio(choices=FOOD_GROUP_CHOICES, value="All", label=f"{label} - food group filter", interactive=True) dd = gr.Dropdown(choices=ALL_INGREDIENTS, value=default_value, label=label, multiselect=multiselect, max_choices=max_choices) radio.change(_filter_dropdown, inputs=[radio, dd], outputs=dd, show_progress="hidden") return radio, dd # ===== UI ===== with gr.Blocks(title="Epicure Explorer", theme=THEME, css=CUSTOM_CSS) as demo: saved_state = gr.BrowserState(default_value=[], storage_key="epicure_saved_queries_v1") gr.Markdown( """# 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.""" ) gr.HTML(SIBLING_CARDS) sibling = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling embedding to query") shared_basket = gr.State([]) with gr.Tabs() as tabs: # ---------- Tab 1: Basket pairings ---------- with gr.Tab("Basket pairings", id="tab_basket"): gr.Markdown("Pick one or more ingredients. The tool averages their unit vectors and returns nearest neighbours plus closest modes of that centroid.") basket_radio, basket = _ingredient_picker("Ingredient basket (pick 1+)", ["chicken","lemon","garlic"]) k_pair = gr.Slider(1, 15, value=8, step=1, label="K") with gr.Row(): pair_btn = gr.Button("Find pairings", variant="primary") save_basket_btn = gr.Button("Save this query", variant="secondary") 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 (matplotlib)") pair_btn.click(basket_pairings, inputs=[sibling, basket, k_pair], outputs=[nb_table, mode_table, heatmap_plot], show_progress="full") gr.Examples( examples=[ ["chem", ["chicken","lemon","garlic"], 8], ["core", ["miso","ginger","sesame_oil"], 8], ["chem", ["tomato","basil","mozzarella_cheese"], 8], ["cooc", ["chocolate","strawberry","cream"], 8], ["chem", ["cumin","coriander","turmeric"], 8], ["core", ["soy_sauce","ginger","scallion"], 8], ["chem", ["red_wine","beef","rosemary"], 8], ["core", ["coconut_milk","lemongrass","fish_sauce"], 8], ], inputs=[sibling, basket, k_pair], label="Try one of these baskets", ) # ---------- Tab 2: Supervised SLERP ---------- with gr.Tab("Supervised SLERP", id="tab_sup"): gr.Markdown("Rotate the seed basket toward one or more supervised pole vectors.") sup_radio, sup_basket = _ingredient_picker("Seed basket (pick 1+)", ["rice"]) sup_dirs = gr.Dropdown(choices=_supervised_choices("chem"), value=["cuisine:South_Asian"], label="Supervised directions (pick 1+; summed)", 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") with gr.Row(): sup_btn = gr.Button("Rotate", variant="primary") save_sup_btn = gr.Button("Save this query", variant="secondary") sup_table = gr.Dataframe(headers=["Ingredient","Cosine"], label="Top-K rotated-query neighbours") sup_explainer = gr.Markdown() sup_btn.click(supervised_slerp_multi, inputs=[sibling, sup_basket, sup_dirs, sup_theta, sup_k], outputs=[sup_table, sup_explainer], show_progress="full") sibling.change(lambda s: gr.Dropdown(choices=_supervised_choices(s), value=[]), inputs=sibling, outputs=sup_dirs) gr.Examples( examples=[ ["chem", ["rice"], ["cuisine:South_Asian"], 30, 8], ["chem", ["corn"], ["cuisine:Latin_American"], 30, 8], ["core", ["chicken"], ["cuisine:Mediterranean"], 45, 8], ["core", ["tomato","basil"], ["cuisine:Southeast_Asian"], 45, 8], ["chem", ["beef"], ["cuisine:East_Asian"], 60, 8], ["cooc", ["chocolate"], ["cuisine:Latin_American"], 30, 8], ], inputs=[sibling, sup_basket, sup_dirs, sup_theta, sup_k], label="Try one of these rotations", ) # ---------- Tab 3: Emergent SLERP ---------- with gr.Tab("Emergent SLERP", id="tab_em"): gr.Markdown("Rotate the seed basket toward one or more emergent FastICA factor-mode poles.") em_radio, em_basket = _ingredient_picker("Seed basket (pick 1+)", ["chocolate"]) 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_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") with gr.Row(): em_btn = gr.Button("Rotate", variant="primary") save_em_btn = gr.Button("Save this query", variant="secondary") em_table = gr.Dataframe(headers=["Ingredient","Cosine"], label="Top-K rotated-query neighbours") em_explainer = gr.Markdown() em_btn.click(emergent_slerp_multi, inputs=[sibling, em_basket, em_modes, em_theta, em_k], outputs=[em_table, em_explainer], show_progress="full") sibling.change(lambda s: gr.Dropdown(choices=[label for label, _ in _factor_mode_choices(s)], value=[]), inputs=sibling, outputs=em_modes) # ---------- Tab 4: Arithmetic ---------- with gr.Tab("Arithmetic", id="tab_ar"): gr.Markdown("Mikolov-style vector arithmetic: `centroid(positives) - centroid(negatives)`, then top-K neighbours. Killer demo: `miso - salt` on Core.") pos_radio, pos_box = _ingredient_picker("Positives (added)", ["miso"]) neg_radio, neg_box = _ingredient_picker("Negatives (subtracted)", ["salt"]) ar_k = gr.Slider(1, 15, value=8, step=1, label="K") with gr.Row(): ar_btn = gr.Button("Compute", variant="primary") save_ar_btn = gr.Button("Save this query", variant="secondary") ar_table = gr.Dataframe(headers=["Ingredient","Cosine"], label="Top-K nearest to result vector") ar_explainer = gr.Markdown() ar_btn.click(arithmetic, inputs=[sibling, pos_box, neg_box, ar_k], outputs=[ar_table, ar_explainer], show_progress="full") gr.Examples( examples=[ ["core", ["miso"], ["salt"], 8], ["core", ["chicken","tofu"], ["beef"], 8], ["cooc", ["basil","cumin"], ["parsley"], 8], ["chem", ["chocolate"], ["sugar"], 8], ["chem", ["wine"], ["beer"], 8], ["core", ["bread"], ["flour"], 8], ["core", ["coffee"], ["milk"], 8], ["chem", ["mozzarella_cheese"], ["milk"], 8], ], inputs=[sibling, pos_box, neg_box, ar_k], label="Try one of these arithmetic queries", ) # ---------- Tab 5: Mode atlas (click row -> UMAP) ---------- with gr.Tab("Mode atlas", id="tab_atlas"): gr.Markdown( "Browse the GMM mode atlas. Cooc 150 / Core 193 / Chem 200 modes. " "**Click any row** to send that mode's members to the UMAP tab as a basket." ) 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") atlas_table = gr.Dataframe( headers=["mode_id","kind","property","label","n_members","top members"], label="Modes (click a row to highlight on UMAP)", wrap=True, interactive=False, ) atlas_btn.click(browse_modes, inputs=[sibling, atlas_kind, atlas_search], outputs=atlas_table, show_progress="full") # ---------- Tab 6: Compare siblings ---------- with gr.Tab("Compare siblings", id="tab_cmp"): gr.Markdown("Same query, three siblings, side by side.") cmp_radio, cmp_basket = _ingredient_picker("Seed basket", ["chicken"]) 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") with gr.Row(): cmp_btn = gr.Button("Compare across siblings", variant="primary") save_cmp_btn = gr.Button("Save this query", variant="secondary") with gr.Row(): cmp_cooc = gr.Dataframe(headers=["Cooc neighbour","Cosine"], label="Cooc (recipe-context)") cmp_core = gr.Dataframe(headers=["Core neighbour","Cosine"], label="Core (blended)") 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") # ---------- Tab 7: UMAP ---------- with gr.Tab("UMAP visualisation", id="tab_umap"): gr.Markdown( "2-D UMAP of the 1,790-ingredient embedding (cosine, n_neighbors=30, min_dist=0.03). " "Points coloured by food group. Basket members appear as accent stars; top-K neighbours as amber dots." ) umap_radio, umap_basket = _ingredient_picker("Highlight these ingredients", ["chicken","lemon","garlic"]) 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], outputs=umap_plot, show_progress="full") sibling.change(umap_view, inputs=[sibling, umap_basket, umap_show_nb, umap_k, umap_3d], outputs=umap_plot) # ---------- Tab 8: Parse my fridge ---------- with gr.Tab("Parse my fridge", id="tab_fridge"): gr.Markdown( "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\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(): fridge_btn = gr.Button("Parse and match", variant="primary") fridge_send = gr.Button("Send matched to Basket tab", variant="secondary") fridge_table = gr.Dataframe( headers=["Input line", "Canonical match", "Score", "Cleaned"], label="Parsed matches", interactive=False, ) fridge_matched = gr.Textbox(label="Matched ingredients", interactive=False) 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_send.click(lambda matches: gr.Dropdown(value=matches[:10] if matches else []), inputs=[shared_basket], outputs=[basket]) # ---------- Tab 9: Recipe builder ---------- with gr.Tab("Recipe builder", id="tab_recipe"): gr.Markdown( "Describe a dish in plain English. Hybrid retrieval: rapidfuzz token matching for direct " "ingredient mentions + sentence-transformer cosine against the sibling's factor-mode labels " "for thematic matches. First call after Space cold-start downloads ~80MB encoder (one-time)." ) rb_prompt = gr.Textbox(label="Dish description", lines=3, value="I'm making Thai green curry for 4 people") rb_k = gr.Slider(4, 20, value=10, step=1, label="Suggestions (K)") rb_btn = gr.Button("Suggest starter basket", variant="primary") rb_table = gr.Dataframe( headers=["Ingredient", "Source", "Score"], label="Suggested basket (source = direct / thematic / both)", interactive=False, ) rb_explainer = gr.Markdown() rb_matched = gr.State([]) rb_send = gr.Button("Send to Basket tab", variant="secondary") def _rb(prompt, sib, k): rows, names, md = suggest_basket(prompt, sib, int(k)) return rows, md, names rb_btn.click(_rb, inputs=[rb_prompt, sibling, rb_k], outputs=[rb_table, rb_explainer, rb_matched], show_progress="full") rb_send.click(lambda names: gr.Dropdown(value=(names or [])[:10]), inputs=[rb_matched], outputs=[basket]) gr.Examples( examples=[ ["I'm making Thai green curry for 4 people", 10], ["spicy vegetarian taco filling", 10], ["weeknight pasta with tomatoes and herbs", 10], ["Japanese miso-glazed salmon and greens", 10], ["Moroccan tagine with lamb and dried fruit", 10], ], inputs=[rb_prompt, rb_k], label="Try one of these prompts", ) # ---------- Tab 10: Saved queries ---------- with gr.Tab("Saved queries", id="tab_saved"): gr.Markdown( "Stored locally in your browser via `localStorage` (gr.BrowserState). " "~5 MB quota; per-browser, not per-account. Clearing browser data wipes them." ) saved_table = gr.Dataframe( headers=["created_at", "tab", "summary", "id"], label="Your saved queries (newest first)", interactive=False, wrap=True, ) with gr.Row(): selected_id = gr.State("") del_btn = gr.Button("Delete selected", variant="secondary") def _on_select(saved, evt: gr.SelectData): if evt is None or evt.index is None: return "" row = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index return (saved or [{}])[row].get("id", "") if row < len(saved or []) else "" saved_table.select(_on_select, inputs=[saved_state], outputs=selected_id) del_btn.click(delete_query, inputs=[saved_state, selected_id], outputs=[saved_state, saved_table]) demo.load(lambda s: _render_saved(s), inputs=saved_state, outputs=saved_table) # ---- Wire Save buttons (after all tabs exist so all components are in scope) ---- save_basket_btn.click( lambda s, sib, b, k: save_query(s, "basket", {"sibling": sib, "basket": b or [], "k": int(k)}), inputs=[saved_state, sibling, basket, k_pair], outputs=[saved_state, saved_table], ) save_sup_btn.click( lambda s, sib, b, d, th, k: save_query(s, "supervised_slerp", {"sibling": sib, "basket": b or [], "directions": d or [], "theta": float(th), "k": int(k)}), inputs=[saved_state, sibling, sup_basket, sup_dirs, sup_theta, sup_k], outputs=[saved_state, saved_table], ) save_em_btn.click( lambda s, sib, b, m, th, k: save_query(s, "emergent_slerp", {"sibling": sib, "basket": b or [], "modes": m or [], "theta": float(th), "k": int(k)}), inputs=[saved_state, sibling, em_basket, em_modes, em_theta, em_k], outputs=[saved_state, saved_table], ) save_ar_btn.click( lambda s, sib, p, n, k: save_query(s, "arithmetic", {"sibling": sib, "positives": p or [], "negatives": n or [], "k": int(k)}), inputs=[saved_state, sibling, pos_box, neg_box, ar_k], outputs=[saved_state, saved_table], ) save_cmp_btn.click( lambda s, b, d, th, k: save_query(s, "compare", {"basket": b or [], "directions": d or [], "theta": float(th), "k": int(k)}), inputs=[saved_state, cmp_basket, cmp_dirs, cmp_theta, cmp_k], outputs=[saved_state, saved_table], ) # ---- Mode atlas row click -> UMAP highlight + jump to UMAP tab ---- def atlas_row_to_umap(sibling_value, table_value, show_nb, k_value, three_d_value, evt: gr.SelectData): if evt is None or evt.index is None or table_value is None: return gr.update(), gr.update(), gr.update(), gr.update() row = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index try: clicked_mode_id = (table_value.iloc[row, 0] if hasattr(table_value, "iloc") else table_value[row][0]) except Exception: return gr.update(), gr.update(), gr.update(), gr.update() m = MODELS[sibling_value] mode = next((md for md in m.modes if md.mode_id == clicked_mode_id), None) if mode is None: return gr.update(), gr.update(), gr.update(), gr.update() members = [n for n in mode.members if n in m.vocab][:10] if not members: return gr.update(), gr.update(), gr.update(), gr.update() fig = umap_view(sibling_value, members, bool(show_nb), int(k_value), three_d=bool(three_d_value)) return ( gr.Dropdown(value=members), fig, members, gr.Tabs(selected="tab_umap"), ) atlas_table.select( atlas_row_to_umap, inputs=[sibling, atlas_table, umap_show_nb, umap_k, umap_3d], outputs=[umap_basket, umap_plot, shared_basket, tabs], show_progress="hidden", ) # ---- Public API endpoints ---- gr.api(api_neighbors, api_name="neighbors") gr.api(api_slerp, api_name="slerp") gr.api(api_arithmetic, api_name="arithmetic") gr.api(api_embed, api_name="embed") gr.api(api_list_directions, api_name="list_directions") gr.api(api_list_factor_modes, api_name="list_factor_modes") gr.Markdown( """--- ### Developer API These operators are also exposed as JSON endpoints. See `/?view=api` for the auto-generated schema. ```python from gradio_client import Client c = Client("Kaikaku/epicure-explorer") c.predict("garlic", "chem", 5, api_name="/neighbors") c.predict("rice", "cuisine:South_Asian", 30, "chem", 5, api_name="/slerp") c.predict(["miso"], ["salt"], "core", 8, api_name="/arithmetic") c.predict("garlic", "chem", api_name="/embed") # 300-D L2-normalised vector c.predict("chem", api_name="/list_directions") c.predict("chem", api_name="/list_factor_modes") ``` Endpoints validate inputs and return `{"error": "...", "suggestions": [...]}` on bad input. Free-tier limits: ~1-2 req/sec shared, no auth, Space sleeps after ~48h idle (cold start ~30-60s on next request). --- **Cite:** Radzikowski and Chen, 2026, *Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings*, [arXiv:2605.22391](https://arxiv.org/abs/2605.22391). Artefacts: [epicure-cooc](https://huggingface.co/Kaikaku/epicure-cooc) | [epicure-core](https://huggingface.co/Kaikaku/epicure-core) | [epicure-chem](https://huggingface.co/Kaikaku/epicure-chem) | [corpus dataset](https://huggingface.co/datasets/Kaikaku/epicure-corpus-resources) """ ) if __name__ == "__main__": demo.launch()