Refresh catalog and harden consumer inference

app.py

@@ -477,7 +477,7 @@ def consumer_product_view(product_name: str, selected_aspect: str) -> Tuple[str,
     store = product.get("store") or "Amazon"
     asin = product.get("parent_asin") or "-"
     detail = (
-        f'<div class="product-card product-layout">{image_html}<div>'
+        f'<div class="product-card"><div class="product-layout">{image_html}<div>'
         f'<h3>{_esc(product["name"])}</h3>'
         f'<p class="muted">{_esc(product["categories"])}</p>'
         f'<div class="meta-pills"><span>Store: {_esc(store)}</span>'
@@ -486,8 +486,9 @@ def consumer_product_view(product_name: str, selected_aspect: str) -> Tuple[str,
         f'<span>Price: ${_safe_float(product["price"]):.2f}</span>'
         f'<span>Rating: {_safe_float(product["average_rating"]):.1f}</span>'
         f'<span>Reviews: {int(_safe_float(product["rating_number"]))}</span></div>'
-        f'<p class="metadata-summary"><b>Metadata:</b> {_esc(_short_text(product["features"], 190))}</p>'
-        f'{_recommendation_html(result, product["name"])}{_overall_html(result)}</div></div>'
+        f'<p class="metadata-summary"><b>Metadata:</b> {_esc(_short_text(product["features"], 130))}</p>'
+        f'</div></div><div class="decision-layout">{_overall_html(result)}'
+        f'{_recommendation_html(result, product["name"])}</div></div>'
     )
     evidence = f'<h4>Key review evidence</h4>{_highlight_review(product["review"], selected_aspect)}'
     return detail, _aspect_cards(result, product["review"], selected_aspect), evidence, _aspect_rows(result, product["review"])
@@ -706,11 +707,15 @@ button.primary, .gradio-button.primary { background:var(--accent) !important; bo
 .status.ok { background:#ecfdf5; border-color:#86efac; color:#065f46; }
 .status.bad { background:#fff1f2; border-color:#fda4af; color:#991b1b; }
 .product-card, .overall-card, .note-card { border:1px solid var(--line); border-radius:8px; padding:16px; background:white; }
-.product-layout { display:grid; grid-template-columns:104px 1fr; gap:14px; align-items:start; }
-.product-img { width:104px; height:132px; object-fit:cover; border-radius:8px; border:1px solid var(--line); background:#f8fafc; }
+.product-layout { display:grid; grid-template-columns:96px 1fr; gap:12px; align-items:start; }
+.product-img { width:96px; height:118px; object-fit:cover; border-radius:8px; border:1px solid var(--line); background:#f8fafc; }
 .product-card h3 { margin:0 0 6px; font-size:18px; line-height:1.25; }
 .product-card p { margin:7px 0; }
 .metadata-summary { color:#334155; font-size:13px; line-height:1.45; }
+.decision-layout { display:grid; grid-template-columns:1fr 1fr; gap:10px; margin-top:10px; align-items:stretch; }
+.decision-layout .overall-card, .decision-layout .recommendation-card { margin:0; padding:12px; }
+.decision-layout .recommendation-card .small-label { display:none; }
+.decision-layout .recommendation-card p { display:none; }
 .recommendation-card { border:1px solid #fed7aa; border-left:5px solid var(--accent); background:#fff7ed; border-radius:8px; padding:13px 14px; margin:12px 0; }
 .recommendation-card p { margin:7px 0 0; }
 .muted { color:var(--muted); }
@@ -743,7 +748,7 @@ mark { background:#fde68a; color:#111827; border-radius:4px; padding:1px 3px; }
 .kv-table td { border-bottom:1px solid #e2e8f0; padding:9px 12px; }
 .kv-table td:first-child { width:220px; color:#334155; font-weight:700; background:#f8fafc; }
 .compact-note { color:#475569; font-size:13px; margin:4px 0 10px; }
-@media (max-width:860px) { .aspect-grid, .metric-grid, .product-layout { grid-template-columns:1fr; } .product-img { width:100%; height:180px; } }
+@media (max-width:860px) { .aspect-grid, .metric-grid, .product-layout, .decision-layout { grid-template-columns:1fr; } .product-img { width:100%; height:180px; } }
 """
 
 

src/inference.py

@@ -174,6 +174,18 @@ class AspectPredictor:
             checkpoint_dir=checkpoint_dir, device=device,
         )
 
+    def _prepare_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        bert = getattr(self.model, "bert", None)
+        embeddings = getattr(bert, "embeddings", None)
+        word_embeddings = getattr(embeddings, "word_embeddings", None)
+        if word_embeddings is None:
+            return input_ids
+        vocab_size = int(word_embeddings.num_embeddings)
+        if vocab_size <= 0:
+            return input_ids
+        unk_id = int(getattr(self.tokenizer, "unk_token_id", 0) or 0)
+        return torch.where(input_ids >= vocab_size, torch.full_like(input_ids, unk_id), input_ids)
+
     def predict(self, review_text: str, product_meta: Dict,
                 return_attention: bool = True) -> Dict:
         enc = self.tokenizer(
@@ -183,7 +195,7 @@ class AspectPredictor:
             padding="max_length",
             return_tensors="pt",
         )
-        input_ids = enc["input_ids"].to(self.device)
+        input_ids = self._prepare_input_ids(enc["input_ids"]).to(self.device)
         attn_mask = enc["attention_mask"].to(self.device)
 
         meta_df = _meta_dict_to_df(product_meta)
@@ -227,7 +239,7 @@ class AspectPredictor:
                 padding="max_length",
                 return_tensors="pt",
             )
-            input_ids = enc["input_ids"].to(self.device)
+            input_ids = self._prepare_input_ids(enc["input_ids"]).to(self.device)
             attn_mask = enc["attention_mask"].to(self.device)
 
             meta_df = pd.concat(
@@ -261,4 +273,4 @@ class AspectPredictor:
                 item.update(attn_payload)
                 item.update(_attention_insights(attn_payload))
                 results.append(item)
-        return results
+        return results