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app.py

@@ -175,29 +175,58 @@ def _pyramid_for_ingredient(model, heads, engine, smiles, cas, objective_dim):
     return pyr
 
 
+def _row_pair(r):
+    """Extract (label, conc) from a Dataframe row regardless of shape.
+
+    Gradio 6 may deliver rows as lists, tuples, numpy arrays, or dicts keyed by
+    header name. Normalise to (str label, float conc); return ("", 0.0) on junk.
+    """
+    try:
+        if isinstance(r, dict):
+            vals = list(r.values())
+            label = vals[0] if vals else ""
+            conc = vals[1] if len(vals) > 1 else 0
+        else:
+            label = r[0]
+            conc = r[1] if len(r) > 1 else 0
+        label = "" if label is None else str(label)
+        if hasattr(conc, "item"):  # numpy scalar
+            conc = conc.item()
+        conc = float(conc) if conc not in (None, "") else 0.0
+        return label, conc
+    except (TypeError, ValueError, IndexError):
+        return "", 0.0
+
+
 def predict(arm_label, rows, progress=gr.Progress()):
-    rows = [r for r in rows if r and r[0]]
-    if not rows:
-        return None, "Add at least one ingredient."
+    pairs = [_row_pair(r) for r in (rows or [])]
+    pairs = [(l, c) for l, c in pairs if l and c > 0]
+    if not pairs:
+        return None, "Add at least one ingredient (with a concentration > 0)."
     try:
         model, heads, engine, resolved, objective_dim = _load_arm(arm_label)
     except Exception as e:
         return None, f"Model load failed: {e}"
 
     mats = {label: (sm, cas) for label, sm, cas in _materials()}
-    total_w = sum(float(r[1] or 0) for r in rows) or 1.0
+    total_w = sum(c for _, c in pairs) or 1.0
     blend = np.zeros(objective_dim, dtype=np.float64)   # (objective_dim,) tier-summed
     tier_blend = np.zeros((3, objective_dim), dtype=np.float64)
     used = []
-    for r in progress.tqdm(rows, desc="Encoding"):
-        label, conc = r[0], float(r[1] or 0)
-        if label not in mats or conc <= 0:
+    skipped = []
+    for label, conc in progress.tqdm(pairs, desc="Encoding"):
+        if label not in mats:
+            skipped.append(label.split("  [")[0])
             continue
         sm, cas = mats[label]
         pyr = _pyramid_for_ingredient(model, heads, engine, sm, cas, objective_dim)  # (3, D)
         w = conc / total_w
         tier_blend += w * pyr
         used.append(label.split("  [")[0])
+    if not used:
+        return None, ("None of the selected ingredients matched the catalog. "
+                      "Pick ingredients from the Quick-add list." +
+                      (f" Unmatched: {', '.join(skipped[:5])}" if skipped else ""))
 
     # Tier masses = perceived intensity over dry-down
     tier_mass = tier_blend.sum(axis=1)  # (3,)