From ce7977a690e89281d66f2c0bd54e74142dc369f6 Mon Sep 17 00:00:00 2001
From: "Austin L." <86896075+rvnminers-A-and-N@users.noreply.github.com>
Date: Tue, 30 Jun 2026 18:44:23 +0000
Subject: [PATCH] feat(demo): mixture per-ingredient reads + single-molecule
taste-palette match
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Mixture mode now shows, in addition to the documented-hazard screen:
- each INGREDIENT read individually (structure + name + tastes + GRAS + any
structural-alert/tox flags);
- SINGLE MOLECULES with a similar taste palette to the blend — predict.palette_match()
unions the ingredients' tastes and finds labeled molecules whose known taste-label
set best matches (Jaccard), each clickable to analyze. Framed honestly: a blend's
real palette isn't the union of its parts (suppression/synergy needs their data) —
this is a structural-label approximation.
Verified: glucose + caffeine -> ingredients [Hexose sweet/bitter, Caffeine bitter];
palette {sweet,bitter} -> isoleucine / a flavanone / ... at 100% label match.
Signed-off-by: Austin L. <86896075+rvnminers-A-and-N@users.noreply.github.com>
---
training/app.py | 28 ++++++++++++++++++++++++++--
training/predict.py | 25 +++++++++++++++++++++++++
training/workbench.html | 31 +++++++++++++++++++++++++++++--
3 files changed, 80 insertions(+), 4 deletions(-)
diff --git a/training/app.py b/training/app.py
index 0c9f4ef..c868588 100644
--- a/training/app.py
+++ b/training/app.py
@@ -125,9 +125,33 @@ class MixtureQuery(BaseModel):
@app.post("/api/mixture")
def api_mixture(m: MixtureQuery):
- """Documented dangerous-mixture screen over 2..n ingredients (predict.check_mixture)."""
+ """Per-ingredient reads + documented-hazard screen + a single-molecule palette match."""
smis = [s for s in (_resolve(x) for x in m.ingredients) if s]
- return P.check_mixture(smis, m.processes)
+ out = P.check_mixture(smis, m.processes)
+ reads, palette = [], set()
+ for s in smis:
+ r = P.predict(s)
+ tp = r.get("taste_profile", [])
+ tastes = [t for t in ("sweet", "bitter", "umami") if isinstance(r.get(t), (int, float)) and r[t] >= 0.5]
+ if r.get("sour"):
+ tastes.append("sour")
+ if r.get("salty") is True:
+ tastes.append("salty")
+ for t in (r.get("known_tastes") or []):
+ if t not in tastes:
+ tastes.append(t)
+ palette.update(tastes)
+ reads.append({"smiles": r["smiles"], "name": _names(s)[0], "svg": _svg(s, 110, 80),
+ "top_taste": tp[0]["taste"] if tp else None, "tastes": tastes,
+ "gras": r["safety"]["gras_status"], "alerts": r["safety"]["structural_alerts"],
+ "tox_flags": r["safety"]["tox_screen"].get("flags", []) if r["applicability"]["in_domain"] else []})
+ pal = P.palette_match(sorted(palette), k=5)
+ for mt in pal.get("matches", []):
+ mt["svg"] = _svg(mt["smiles"], 110, 80)
+ mt["name"] = _names(mt["smiles"])[0]
+ out["ingredients"] = reads
+ out["palette"] = pal
+ return out
def _load_suggest():
diff --git a/training/predict.py b/training/predict.py
index 8fc6733..8c025f6 100644
--- a/training/predict.py
+++ b/training/predict.py
@@ -710,6 +710,31 @@ def substitute(smiles: str, k: int = 8, min_similarity: float = 0.0) -> dict:
"basis": "Tanimoto / Morgan r2 2048-bit over labeled molecules"}
+def palette_match(tastes, k=5):
+ """Single molecules whose KNOWN taste-label set best matches a target taste set
+ (Jaccard over sweet/bitter/umami/sour/salty). NOT a blend-perception model — a
+ label-set similarity over the labeled molecules, for the 'one molecule like this
+ mixture' view. A blend's actual palette isn't the union of its parts (suppression /
+ synergy); this is an honest structural-label approximation."""
+ if _SUB_INDEX is None:
+ _build_sub_index()
+ _, smis, tlist = _SUB_INDEX
+ target = set(tastes)
+ if not target or not smis:
+ return {"target": sorted(target), "matches": []}
+ scored = []
+ for smi, ts in zip(smis, tlist):
+ s = set(ts)
+ if not s:
+ continue
+ j = len(target & s) / len(target | s)
+ if j > 0:
+ scored.append((j, smi, sorted(s)))
+ scored.sort(key=lambda e: -e[0])
+ return {"target": sorted(target),
+ "matches": [{"smiles": sm, "tastes": ts, "match": round(j, 2)} for j, sm, ts in scored[:k]]}
+
+
def predict(smiles: str, include_aroma: bool = False) -> dict:
mol = Chem.MolFromSmiles(smiles)
if mol is None:
diff --git a/training/workbench.html b/training/workbench.html
index ead416c..f271c5d 100644
--- a/training/workbench.html
+++ b/training/workbench.html
@@ -93,6 +93,9 @@
.mix-btn{font-family:var(--ui);font-weight:600;font-size:14px;padding:9px 18px;border:none;border-radius:8px;background:var(--accent);color:#fff;cursor:pointer}
.mix-btn:disabled{opacity:.5}
#mixResults{margin-top:14px}
+ .mix-ings{display:flex;flex-direction:column;gap:6px;margin-bottom:8px}
+ .mix-ing{display:flex;gap:10px;align-items:center;padding:8px;border:1px solid var(--line);border-radius:8px;background:var(--surface)}
+ .mix-ing:hover{background:var(--accent-soft)}
.hazard{border:1px solid #E7C4B8;background:#FBEDE9;color:#8A3A22;border-radius:8px;padding:10px 13px;margin-bottom:8px;font-size:13px;line-height:1.5}
.hazard.cond{border-color:#E8D38A;background:#FFF6E6;color:#7A5A12}
.suggest-dd{position:absolute;z-index:60;background:var(--panel);border:1px solid var(--line);border-radius:9px;box-shadow:0 8px 24px #00000022;max-height:340px;overflow:auto}
@@ -396,6 +399,10 @@ Aroma
['results','behaviorCard','aromaCard','footnote','err'].forEach(id=>$(id).style.display='none');
if(on && !$('mixRows').children.length){ mixAddRow(); mixAddRow(); }
});
+$('mixResults').addEventListener('click', e=>{
+ const card=e.target.closest('.mix-ing[data-smi]');
+ if(card && card.dataset.smi){ $('mixToggle').checked=false; $('mixToggle').dispatchEvent(new Event('change')); q.value=card.dataset.smi; window.scrollTo({top:0,behavior:'smooth'}); run(); }
+});
$('mixCheck').addEventListener('click', async ()=>{
const ings=[...$('mixRows').querySelectorAll('input')].map(i=>i.value.trim()).filter(Boolean);
const procs=[...document.querySelectorAll('.proc:checked')].map(c=>c.value);
@@ -403,11 +410,31 @@ Aroma
$('mixCheck').disabled=true; $('mixCheck').textContent='Checking…';
try{
const r=await post('/api/mixture',{ingredients:ings, processes:procs});
- const A=r.active_hazards||[], C=r.conditional_hazards||[];
let html='';
- if(!A.length && !C.length) html='No documented hazards for these ingredients/processes. (Absence of a flag is not a safety clearance.)
';
+ const ing=r.ingredients||[];
+ if(ing.length){
+ html+='Ingredients (read individually)
'+ing.map(it=>`
+
+
${it.svg||''}
+
${it.name||it.smiles}
+
${(it.tastes||[]).join(' · ')||'—'}${(it.gras||'').indexOf('in GRAS')===0?' · GRAS':''}
+ ${((it.alerts||[]).length||(it.tox_flags||[]).length)?`
⚠ ${[...(it.alerts||[]),...(it.tox_flags||[]).map(f=>'tox:'+f)].join(', ')}
`:''}
+
`).join('')+'
Documented mixture hazards
';
+ if(!A.length && !C.length) html+='None documented for these ingredients/processes. (Not a safety clearance.)
';
A.forEach(h=>html+=`⚠ ${h.possible_product} — from ${h.precursors.join(' + ')}. ${h.note}
`);
C.forEach(h=>html+=`○ ${h.possible_product} — from ${h.precursors.join(' + ')}; would form with: ${(h.requires_process||[]).join(', ')}. ${h.note}
`);
+ const pal=r.palette||{}, M=pal.matches||[];
+ if(M.length){
+ html+=`Single molecules with a similar taste palette (${(pal.target||[]).join(' · ')||'—'})
`+M.map(it=>`
+
+
${it.svg||''}
+
${it.name||it.smiles}
${(it.tastes||[]).join(' · ')}
+
${Math.round(it.match*100)}% match
`).join('')+'
Each ingredient is read individually and screened for documented hazards. How the blend actually tastes together (suppression / synergy) needs your formulation data — not predictable from public data.
';
$('mixResults').innerHTML=html;
}catch(e){ $('mixResults').innerHTML=''+e.message+'
'; }
finally{ $('mixCheck').disabled=false; $('mixCheck').textContent='Check mixture'; }