update

app.py

@@ -12,20 +12,25 @@ dataset = load_dataset("Segizu/dataset_faces", download_config=download_config)
 if "train" in dataset:
     dataset = dataset["train"]
 
+# 🔄 Preprocesar imagen para Facenet
+def preprocess_image(img):
+    img_rgb = img.convert("RGB")
+    img_resized = img_rgb.resize((160, 160), Image.Resampling.LANCZOS)
+    return np.array(img_resized)
+
 # 📦 Construir base de datos de embeddings
 def build_database():
     database = []
     for i, item in enumerate(dataset):
         try:
             img = item["image"]
-            img_rgb = img.convert("RGB")
-            img_np = np.array(img_rgb)
+            img_processed = preprocess_image(img)
             embedding = DeepFace.represent(
-                img_path=img_np,
+                img_path=img_processed,
                 model_name="Facenet",
                 enforce_detection=False
             )[0]["embedding"]
-            database.append((f"image_{i}", img_rgb, embedding))
+            database.append((f"image_{i}", img, embedding))
         except Exception as e:
             print(f"❌ No se pudo procesar imagen {i}: {e}")
     return database
@@ -33,9 +38,9 @@ def build_database():
 # 🔍 Buscar rostros similares
 def find_similar_faces(uploaded_image):
     try:
-        img_np = np.array(uploaded_image.convert("RGB"))
+        img_processed = preprocess_image(uploaded_image)
         query_embedding = DeepFace.represent(
-            img_path=img_np,
+            img_path=img_processed,
             model_name="Facenet",
             enforce_detection=False
         )[0]["embedding"]