Update server1.py

server1.py

@@ -1,5 +1,4 @@
 # app.py
-# comentarios sin tildes / sin enye
 
 import os, io, traceback
 from typing import Optional, List, Tuple
@@ -12,7 +11,6 @@ from functools import lru_cache
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
-# ===== caches (usar ruta propia, escribible en runtime) =====
 CACHE_ROOT = os.environ.get("APP_CACHE", "/tmp/appcache")
 os.environ["XDG_CACHE_HOME"] = CACHE_ROOT
 os.environ["HF_HOME"] = os.path.join(CACHE_ROOT, "hf")
@@ -27,8 +25,6 @@ os.makedirs(os.environ["TORCH_HOME"], exist_ok=True)
 import open_clip  # importar despues de ajustar caches
 
 # ===== limites basicos =====
-# por defecto conservamos 1 hilo (tu baseline). Para probar mas:
-# export NUM_THREADS=4 (o el valor que quieras) sin tocar codigo
 NUM_THREADS = int(os.environ.get("NUM_THREADS", "1"))
 torch.set_num_threads(NUM_THREADS)
 os.environ["OMP_NUM_THREADS"] = str(NUM_THREADS)
@@ -65,7 +61,6 @@ clip_model = clip_model.to(device=DEVICE, dtype=DTYPE).eval()
 for p in clip_model.parameters():
     p.requires_grad = False
 
-# extraer normalizacion y size desde el preprocess devuelto
 normalize = next(t for t in getattr(preprocess, "transforms", []) if isinstance(t, T.Normalize))
 SIZE = next((getattr(t, "size", None) for t in getattr(preprocess, "transforms", []) if hasattr(t, "size")), None)
 if isinstance(SIZE, (tuple, list)):
@@ -117,8 +112,6 @@ if model_embeddings.shape[1] != img_dim or version_embeddings.shape[1] != img_di
         f"versiones={version_embeddings.shape[1]}. Recalcula embeddings con {MODEL_NAME}."
     )
 
-# ===== cache perezosa de sub-embeddings por modelo_full =====
-# no cambia precision; solo evita escanear version_labels en cada request
 _versions_cache: dict[str, Tuple[List[str], torch.Tensor]] = {}
 
 def _get_versions_subset(modelo_full: str) -> Tuple[List[str], Optional[torch.Tensor]]:
@@ -151,11 +144,9 @@ 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))
     img_feat = _encode_pil(img_front)