metadata v12

app.py

@@ -2,11 +2,13 @@ import numpy as np
 from PIL import Image, UnidentifiedImageError
 import gradio as gr
 from deepface import DeepFace
-from datasets import load_dataset, Image as HfImage
+from datasets import load_dataset
 import os
 import pickle
 from pathlib import Path
 import gc
+import requests
+from io import BytesIO
 
 # 📁 Directorio para almacenar embeddings
 EMBEDDINGS_DIR = Path("embeddings")
@@ -22,9 +24,6 @@ dataset = load_dataset(
 
 print("✅ Primer item:", dataset[0])
 
-# 🖼️ Convertir columna a imágenes usando HfImage (PIL)
-dataset = dataset.cast_column("image", HfImage())
-
 # 🔄 Preprocesar imagen para DeepFace
 def preprocess_image(img: Image.Image) -> np.ndarray:
     img_rgb = img.convert("RGB")
@@ -46,12 +45,18 @@ def build_database():
         batch = dataset[i:i + batch_size]
         print(f"📦 Procesando lote {i // batch_size + 1}/{(len(dataset) + batch_size - 1) // batch_size}")
 
-        for j, img in enumerate(batch):
+        for j, item in enumerate(batch):
             try:
-                if not isinstance(img, Image.Image):
-                    print(f"⚠️ Saltando item {i + j} - no es imagen: {type(img)}")
+                image_url = item["image"]
+                if not isinstance(image_url, str) or not image_url.startswith("http"):
+                    print(f"⚠️ Saltando item {i + j} - URL inválida: {image_url}")
                     continue
 
+                # Descargar imagen desde URL
+                response = requests.get(image_url, timeout=10)
+                response.raise_for_status()
+                img = Image.open(BytesIO(response.content)).convert("RGB")
+
                 img_processed = preprocess_image(img)
                 embedding = DeepFace.represent(
                     img_path=img_processed,