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app.py

@@ -2814,6 +2814,65 @@ def _select_grid_candidates_with_text_ai(
         return candidates[:limit]
 
 
+def _filter_garments_for_wedding(items: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Use a single LLM call to select only wedding-appropriate garments
+    (ethnic wear, blazers, sherwanis, formal suits, sarees, lehengas, etc.)
+    from the full wardrobe using metadata only — no images sent."""
+
+    if not items:
+        return items
+
+    garment_summaries = []
+    for item in items:
+        garment_summaries.append({
+            "id": str(item.get("id") or ""),
+            "type": str(item.get("type") or ""),
+            "category": str(item.get("category") or "Unknown"),
+            "color": str(item.get("color") or "Unknown"),
+            "pattern": str(item.get("pattern") or "Unknown"),
+            "fabric": str(item.get("fabric") or "Unknown"),
+            "fit": str(item.get("fit") or "Unknown"),
+            "style": str(item.get("style") or "Unknown"),
+            "occasion": str(item.get("occasion") or "Unknown"),
+        })
+
+    prompt = (
+        "You are a fashion expert selecting garments suitable for a WEDDING occasion.\n\n"
+        "Wedding-appropriate garments include:\n"
+        "- Ethnic wear: kurtas, sherwanis, nehru jackets, sarees, lehengas, churidars, dhotis, salwar kameez\n"
+        "- Formal/semi-formal: blazers, suit jackets, dress shirts, formal trousers, dress pants, waistcoats\n"
+        "- Elegant pieces: silk fabrics, embroidered items, brocade, velvet\n"
+        "- Accessories that work for weddings: formal shoes, stoles, dupattas\n\n"
+        "Garments to EXCLUDE:\n"
+        "- Casual everyday items: plain t-shirts, basic tees, hoodies, sweatshirts, joggers, denim jeans, gym shorts\n"
+        "- Sportswear, athleisure, distressed or ripped clothing\n"
+        "- Very casual items like graphic tees, cargo shorts, flip-flops\n\n"
+        "If a garment is borderline (e.g. a smart chino, a dark polo), include it only if it could realistically "
+        "be part of a wedding guest outfit.\n\n"
+        "From the garments below, return ONLY the IDs of those appropriate for a wedding.\n"
+        "Return strictly valid JSON and nothing else.\n\n"
+        "Return EXACT shape:\n"
+        "{\"selected_ids\":[\"id1\",\"id2\",...]}\n\n"
+        f"Garments JSON:\n{json.dumps(garment_summaries, ensure_ascii=True)}"
+    )
+
+    try:
+        response_text = run_nvidia_text_inference(prompt, max_tokens=OUTFIT_TEXT_SELECTOR_MAX_TOKENS)
+        parsed = parse_json_from_text(response_text)
+        selected_ids_raw = parsed.get("selected_ids") if isinstance(parsed, dict) else None
+        if not isinstance(selected_ids_raw, list):
+            print("[wedding-filter] LLM returned no selected_ids, using all items")
+            return items
+
+        selected_ids = {str(v).strip() for v in selected_ids_raw if str(v).strip()}
+        filtered = [i for i in items if str(i.get("id") or "").strip() in selected_ids]
+        print(f"[wedding-filter] in={len(items)} kept={len(filtered)} ids={selected_ids}")
+        return filtered if filtered else items
+    except Exception as exc:
+        print(f"[wedding-filter] LLM call failed reason={exc!r}, using all items")
+        return items
+
+
 def _resolve_outfit_grid_sources(
     wardrobe_items: list[dict[str, Any]],
     occasion: str,
@@ -2821,6 +2880,9 @@ def _resolve_outfit_grid_sources(
     bottom_selected: dict[str, Any] | None,
     other_selected: dict[str, Any] | None,
 ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], str, dict[str, Any] | None]:
+    if occasion == "wedding":
+        wardrobe_items = _filter_garments_for_wedding(wardrobe_items)
+
     all_tops = [i for i in wardrobe_items if i.get("type") == "topwear"]
     all_bottoms = [i for i in wardrobe_items if i.get("type") == "bottomwear"]
     all_others = [