diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..c957848293c654a36ed7309c83d1f5d1a02b9997 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/app.py b/app.py
index 04cc31aa8d0e06aeaac3b59bb361ed71d831e43f..223aab68d982be3b81dd2dc6e02d77e89203b16b 100644
--- a/app.py
+++ b/app.py
@@ -1,7 +1,85 @@
 import gradio as gr
+import face_recognition
+import numpy as np
+import os
+from PIL import Image
+import dlib
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Verificar si CUDA está disponible y seleccionar el modelo adecuado
+if dlib.DLIB_USE_CUDA:
+    print("✅ CUDA está disponible. Se usará GPU para reconocimiento facial.")
+    model_used = "cnn"  # Modelo optimizado para GPU
+else:
+    print("⚠ CUDA no está disponible. Se usará CPU para reconocimiento facial.")
+    model_used = "hog"  # Modelo más adecuado para CPU
+
+# 📂 Directorio donde se encuentran las imágenes
+IMAGE_DIRECTORY = "dataset_faces/"
+
+def load_images_and_encodings(directory):
+    """
+    Carga las imágenes y extrae sus embeddings.
+    """
+    known_encodings = []
+    known_images = []
+    known_names = []
+
+    for filename in os.listdir(directory):
+        if filename.lower().endswith((".jpg", ".png", ".jpeg")):
+            path = os.path.join(directory, filename)
+            image = face_recognition.load_image_file(path)
+            encodings = face_recognition.face_encodings(image, model=model_used)
+            if encodings:  # Si se detecta al menos una cara
+                known_encodings.append(encodings[0])
+                known_images.append(path)
+                known_names.append(filename)
+    return known_encodings, known_images, known_names
+
+# Cargar los datos de la carpeta de imágenes
+known_encodings, known_images, known_names = load_images_and_encodings(IMAGE_DIRECTORY)
+
+def find_similar_faces_gradio(uploaded_image):
+    """
+    Dada una imagen subida, busca las imágenes similares del dataset.
+    Devuelve una lista de diccionarios para la galería y un texto con detalles.
+    """
+    if uploaded_image is None:
+        return [], "No se subió ninguna imagen."
+    
+    # Convertir la imagen subida a array de NumPy
+    image_np = np.array(uploaded_image)
+    face_encodings = face_recognition.face_encodings(image_np, model=model_used)
+    if not face_encodings:
+        return [], "⚠ No se detectó ningún rostro en la imagen subida."
+    
+    query_encoding = face_encodings[0]
+    distances = face_recognition.face_distance(known_encodings, query_encoding)
+    sorted_indices = np.argsort(distances)  # Ordenar por similitud (menor distancia = mayor similitud)
+    
+    # Mostrar las 5 imágenes más similares
+    top_n = 5
+    gallery_items = []
+    details = ""
+    for idx in sorted_indices[:top_n]:
+        # Abrir la imagen del dataset
+        img = Image.open(known_images[idx])
+        similarity = 1 - distances[idx]  # Definir similitud (valor entre 0 y 1)
+        caption = f"{os.path.basename(known_images[idx])}: Similitud: {similarity:.2f}"
+        gallery_items.append({"image": img, "caption": caption})
+        details += caption + "\n"
+    
+    return gallery_items, details
+
+# Definir la interfaz con Gradio
+demo = gr.Interface(
+    fn=find_similar_faces_gradio,
+    inputs=gr.Image(label="Sube una imagen", type="pil"),
+    outputs=[
+        gr.Gallery(label="Imágenes similares").style(grid=[2], height="auto"),
+        gr.Textbox(label="Detalles de similitud", lines=5)
+    ],
+    title="🔍 Buscador de Rostros en un Directorio",
+    description="Sube una imagen y se mostrarán las fotos más similares del directorio."
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()
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