Update app.py

app.py

@@ -6,6 +6,8 @@ from typing import Dict, List, Optional, Tuple, Union
 import gradio as gr
 import spaces
 import torch
+import face_recognition
+import numpy as np
 from PIL import Image
 from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 
@@ -91,12 +93,18 @@ def describe_batch(images: List[Image.Image], context_json: str,
     return outputs
 
 
-def image_size_str(image: Image.Image) -> str:
-    """Devuelve el tamaño de la imagen en formato 'ancho x alto'."""
-    if image is None:
-        raise ValueError("Debes proporcionar una imagen.")
-    width, height = image.size
-    return f"{width}x{height}"
+def face_image_embedding(image: Image.Image) -> List[float]:
+    try:
+        face_model = face_recognition
+        encs = face_model.face_encodings(image)
+        if encs:
+            embeddings = [(e / np.linalg.norm(e)).astype(float).tolist() for e in encs]
+            return embeddings
+        return None
+    except Exception as e:
+        print(f"Fallo embedding cara: {e}")
+    
+    return None
 
 
 # ----------------------------- UI & Endpoints --------------------------------
@@ -140,11 +148,6 @@ with gr.Blocks(title="Salamandra Vision 7B · ZeroGPU") as demo:
     batch_btn.click(describe_batch, [batch_in_images, batch_context, batch_max, batch_temp], batch_out,
                     api_name="predict", concurrency_limit=1)
 
-    # Endpoint utilitario: devolver tamaño de imagen como string
-    size_img = gr.Image(label="Imagen para tamaño", type="pil")
-    size_btn = gr.Button("Obtener tamaño")
-    size_out = gr.Textbox(label="Tamaño (ancho x alto)")
-    size_btn.click(image_size_str, [size_img], size_out, api_name="image_size", concurrency_limit=4)
 
 demo.queue(max_size=16).launch()