update

app.py

@@ -2,45 +2,49 @@ import numpy as np
 from PIL import Image
 import gradio as gr
 from deepface import DeepFace
-from datasets import load_dataset
+from datasets import load_dataset, DownloadConfig
 
-# Cargar el dataset de Hugging Face
-dataset = load_dataset("Segizu/dataset_faces")
+# ✅ Cargar el dataset de Hugging Face forzando la descarga limpia
+download_config = DownloadConfig(force_download=True)
+dataset = load_dataset("Segizu/dataset_faces", download_config=download_config)
 if "train" in dataset:
     dataset = dataset["train"]
 
-# Cargar embeddings de todas las imágenes del dataset
+# 📦 Construir base de datos de embeddings
 def build_database():
     database = []
     for i, item in enumerate(dataset):
         try:
             img = item["image"]
-            # Convertir a RGB y np.array
             img_rgb = img.convert("RGB")
             img_np = np.array(img_rgb)
-            # Obtener representación (embedding)
-            representation = DeepFace.represent(img_path=img_np, model_name="Facenet", enforce_detection=False)[0]["embedding"]
-            database.append((f"image_{i}", img_rgb, representation))
+            embedding = DeepFace.represent(
+                img_path=img_np,
+                model_name="Facenet",
+                enforce_detection=False
+            )[0]["embedding"]
+            database.append((f"image_{i}", img_rgb, embedding))
         except Exception as e:
             print(f"❌ No se pudo procesar imagen {i}: {e}")
     return database
 
-# Inicializamos base de datos de embeddings
-database = build_database()
-
-# Comparar imagen cargada con la base
+# 🔍 Buscar rostros similares
 def find_similar_faces(uploaded_image):
     try:
         img_np = np.array(uploaded_image.convert("RGB"))
-        query_representation = DeepFace.represent(img_path=img_np, model_name="Facenet", enforce_detection=False)[0]["embedding"]
+        query_embedding = DeepFace.represent(
+            img_path=img_np,
+            model_name="Facenet",
+            enforce_detection=False
+        )[0]["embedding"]
     except:
         return [], "⚠ No se detectó un rostro válido en la imagen."
 
     similarities = []
-    for name, db_img, rep in database:
-        distance = np.linalg.norm(np.array(query_representation) - np.array(rep))
-        similarity = 1 / (1 + distance)  # Normalizado
-        similarities.append((similarity, name, db_img))
+    for name, db_img, embedding in database:
+        dist = np.linalg.norm(np.array(query_embedding) - np.array(embedding))
+        sim_score = 1 / (1 + dist)
+        similarities.append((sim_score, name, db_img))
 
     similarities.sort(reverse=True)
     top_matches = similarities[:5]
@@ -54,7 +58,10 @@ def find_similar_faces(uploaded_image):
 
     return gallery_items, text_summary
 
-# Interfaz Gradio
+# ⚙️ Inicializar base
+database = build_database()
+
+# 🎛️ Interfaz Gradio
 demo = gr.Interface(
     fn=find_similar_faces,
     inputs=gr.Image(label="📤 Sube una imagen", type="pil"),