Diversify merchant negative spotlight

app.py

@@ -1,4 +1,4 @@
-"""Dual-interface Hugging Face Space for clothing aspect-level sentiment analysis."""
+"""Dual-interface Hugging Face Space for clothing aspect-level sentiment analysis."""
 from __future__ import annotations
 
 import csv
@@ -765,8 +765,9 @@ def export_merchant_scores(rows):
 
 def negative_product_spotlight(limit: int = 6) -> str:
     cards = []
+    used_products = set()
     for aspect in ASPECTS:
-        best = None
+        candidates = []
         for product in PRODUCTS:
             try:
                 result = _predict_product(product["name"])
@@ -778,8 +779,11 @@ def negative_product_spotlight(limit: int = 6) -> str:
             conf = _safe_float(d.get("confidence"))
             hits = _keyword_hits(product.get("review", ""), aspect)
             reason = ", ".join(hits[:3]) if hits else _short_text(product.get("review") or product.get("features"), 48)
-            if best is None or conf > best[0]:
-                best = (conf, product, reason)
+            candidates.append((conf, product, reason))
+        candidates.sort(key=lambda x: x[0], reverse=True)
+        best = next((item for item in candidates if item[1]["name"] not in used_products), None)
+        if best is None and candidates:
+            best = candidates[0]
         if best is None:
             cards.append(
                 f'<div class="negative-card compact-negative"><h4>{aspect}</h4>'
@@ -787,6 +791,7 @@ def negative_product_spotlight(limit: int = 6) -> str:
             )
             continue
         conf, product, reason = best
+        used_products.add(product["name"])
         img = f'<img class="mini-product-img" src="{_esc(_product_image_url(product))}" alt="{_esc(product["name"])}">'
         cards.append(
             f'<div class="negative-card compact-negative"><h4>{aspect}</h4>'
@@ -796,33 +801,6 @@ def negative_product_spotlight(limit: int = 6) -> str:
         )
     return '<div class="negative-grid">' + "".join(cards[:limit]) + '</div>'
 
-    items = []
-    for product in PRODUCTS:
-        try:
-            result = _predict_product(product["name"])
-        except Exception:
-            continue
-        for aspect in ASPECTS:
-            d = result.get("aspect_details", {}).get(aspect, {})
-            if d.get("label") != "Negative":
-                continue
-            hits = _keyword_hits(product.get("review", ""), aspect)
-            reason = ", ".join(hits) if hits else _short_text(product.get("review") or product.get("features"), 90)
-            items.append((_safe_float(d.get("confidence")), aspect, product, reason))
-    items.sort(key=lambda x: x[0], reverse=True)
-    cards = []
-    for conf, aspect, product, reason in items[:limit]:
-        img = f'<img class="mini-product-img" src="{_esc(_product_image_url(product))}" alt="{_esc(product["name"])}">'
-        cards.append(
-            f'<div class="negative-card">{img}<div><b>{_esc(product["name"])}</b>'
-            f'<div class="small-label">{_esc(product["category"])} · {aspect} Negative · conf {conf:.2f}</div>'
-            f'<p>{_esc(reason)}</p></div></div>'
-        )
-    if not cards:
-        return '<div class="note-card">No highly negative product found in the current catalog sample.</div>'
-    return '<div class="negative-grid">' + "".join(cards) + '</div>'
-
-
 def _metadata_risks(features: str, categories: str, price: Any, rating: Any, count: Any) -> Dict[str, str]:
     text = f"{features} {categories}".lower()
     risks = {}
@@ -1274,3 +1252,4 @@ demo = build_app()
 if __name__ == "__main__":
     demo.launch(ssr_mode=False)
 
+