Update server1.py

server1.py

@@ -151,22 +151,13 @@ def _topk_cosine(text_feats: torch.Tensor, text_labels: List[str], img_feat: tor
     return [{"label": text_labels[int(i)], "confidence": round(float(c)*100.0, 2)} for i, c in zip(idxs, conf)]
 
 def process_image_bytes(front_bytes: bytes, back_bytes: Optional[bytes] = None):
+    # back se admite pero se ignora por completo
     if not front_bytes or len(front_bytes) < 128:
         raise UnidentifiedImageError("imagen invalida")
 
+    # solo frontal
     img_front = Image.open(io.BytesIO(front_bytes))
-    feat_front = _encode_pil(img_front)
-
-    if back_bytes:
-        try:
-            img_back = Image.open(io.BytesIO(back_bytes))
-            feat_back = _encode_pil(img_back)
-            img_feat = (feat_front + feat_back) / 2
-            img_feat = img_feat / img_feat.norm(dim=-1, keepdim=True)
-        except Exception:
-            img_feat = feat_front
-    else:
-        img_feat = feat_front
+    img_feat = _encode_pil(img_front)
 
     # paso 1: modelo
     top_model = _topk_cosine(model_embeddings, model_labels, img_feat, k=1)[0]
@@ -176,7 +167,7 @@ def process_image_bytes(front_bytes: bytes, back_bytes: Optional[bytes] = None):
     marca  = partes[0] if len(partes) >= 1 else ""
     modelo = partes[1] if len(partes) == 2 else ""
 
-    # paso 2: versiones con cache (misma logica, sin bucle global cada vez)
+    # paso 2: versiones con cache
     labels_sub, embeds_sub = _get_versions_subset(modelo_full)
     if not labels_sub:
         return {"brand": marca.upper(), "model": modelo.title(), "version": ""}
@@ -192,6 +183,7 @@ def process_image_bytes(front_bytes: bytes, back_bytes: Optional[bytes] = None):
 
     return {"brand": marca.upper(), "model": modelo.title(), "version": ver.title() if ver else ""}
 
+
 # ===== endpoints =====
 @app.get("/")
 def root():