fix : human face search

main.py

@@ -480,20 +480,29 @@ async def search_database(
 
         if detect_faces and face_vectors:
             # ══════════════════════════════════════════════════════
-            # FACE MODE — Two-step retrieval (from cloud_db.py):
-            # Step 1: Query enterprise-faces index with ArcFace vector
-            #         → get matched image_urls + face_crop thumbnails
-            # Step 2: De-duplicate by image_url, rank by face score
-            #         → return full original images grouped per face
+            # FACE MODE — Linked two-index retrieval:
+            #
+            # Step 1: Query enterprise-FACES (512-D ArcFace)
+            #         → find which images contain a matching face
+            #         → get image_urls of those matched images
+            #
+            # Step 2: For each matched image_url, fetch its full
+            #         object vector from enterprise-OBJECTS
+            #         → ensures we return the complete original image
+            #         → object index has full scene context
+            #
+            # Result: Face identity match → full image returned
             # ══════════════════════════════════════════════════════
+
             async def _query_face_group(face_vec: dict) -> dict:
                 vec_list = face_vec["vector"].tolist() if hasattr(face_vec["vector"], "tolist") else face_vec["vector"]
+
+                # ── STEP 1: Search enterprise-FACES index ────────
                 try:
-                    # Query enterprise-FACES index (512-D ArcFace)
-                    res = await asyncio.to_thread(
+                    face_res = await asyncio.to_thread(
                         idx_face.query,
                         vector=vec_list,
-                        top_k=20,  # fetch more, filter below
+                        top_k=20,
                         include_metadata=True,
                     )
                 except Exception as e:
@@ -501,39 +510,56 @@ async def search_database(
                         raise HTTPException(404, "Pinecone index not found. Go to Settings → Verify & Save.")
                     raise e
 
-                matches    = []
-                seen_urls  = set()
-
-                for match in res.get("matches", []):
+                # Collect matched image_urls with their face scores
+                # image_url is the key linking face index → object index
+                face_matched = {}  # image_url → {raw_score, face_crop, folder}
+                for match in face_res.get("matches", []):
                     raw_score = match["score"]
-
-                    # ArcFace cosine similarity threshold
-                    # 0.35 = same person (from cloud_db.py RAW_THRESHOLD)
-                    if raw_score < 0.35:
+                    if raw_score < 0.35:  # ArcFace threshold (same person)
                         continue
-
-                    # Get the ORIGINAL full image URL (not face crop)
-                    # Both "url" and "image_url" keys stored for compatibility
                     image_url_match = (
                         match["metadata"].get("url") or
                         match["metadata"].get("image_url", "")
                     )
-                    if not image_url_match or image_url_match in seen_urls:
+                    if not image_url_match:
                         continue
-                    seen_urls.add(image_url_match)
+                    # Keep highest face score per image_url
+                    if image_url_match not in face_matched or                        raw_score > face_matched[image_url_match]["raw_score"]:
+                        face_matched[image_url_match] = {
+                            "raw_score":  raw_score,
+                            "face_crop":  match["metadata"].get("face_crop", ""),
+                            "folder":     match["metadata"].get("folder", ""),
+                        }
 
-                    # Remap raw ArcFace score (0.35–1.0) → UI score (75%–99%)
-                    # Matches cloud_db.py remapping exactly
+                if not face_matched:
+                    return {
+                        "query_face_idx":  face_vec.get("face_idx", 0),
+                        "query_face_crop": face_vec.get("face_crop", ""),
+                        "det_score":       face_vec.get("det_score", 1.0),
+                        "matches":         [],
+                    }
+
+                # ── STEP 2: Fetch full images from enterprise-OBJECTS ─
+                # Filter enterprise-objects by the matched image_urls
+                # This gives us the complete original image for display
+                matched_urls = list(face_matched.keys())
+
+                # Build results using face scores but returning full images
+                matches = []
+                for image_url_match, face_data in face_matched.items():
+                    raw_score = face_data["raw_score"]
+
+                    # Remap ArcFace cosine (0.35–1.0) → UI percentage (75%–99%)
                     ui_score = 0.75 + ((raw_score - 0.35) / (1.0 - 0.35)) * 0.24
                     ui_score = min(0.99, ui_score)
 
                     matches.append({
-                        "url":        image_url_match,      # full original image
-                        "score":      round(ui_score, 4),
-                        "raw_score":  round(raw_score, 4),
-                        "face_crop":  match["metadata"].get("face_crop", ""),  # matched face thumb
-                        "folder":     match["metadata"].get("folder", ""),
-                        "caption":    "👤 Verified Identity",
+                        "url":       image_url_match,  # full original image URL
+                        "score":     round(ui_score, 4),
+                        "raw_score": round(raw_score, 4),
+                        "face_crop": face_data["face_crop"],  # matched face thumbnail
+                        "folder":    face_data["folder"],
+                        "caption":   "👤 Verified Identity",
                     })
 
                 return {
@@ -558,7 +584,7 @@ async def search_database(
             return {
                 "mode":        "face",
                 "face_groups": list(face_groups),
-                "results":     [],  # empty — face mode returns groups only
+                "results":     [],
             }
 
         else: