Update app.py

app.py

@@ -2,26 +2,27 @@ import gradio as gr
 from transformers import AutoImageProcessor, AutoModel
 import torch
 from PIL import Image
+import numpy as np
 
 # Cargar el modelo solo una vez
 processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
 model = AutoModel.from_pretrained("facebook/dinov2-base")
 model.eval()
 
-def get_embedding(image_file):
-    image = Image.open(image_file).convert("RGB")
+def get_embedding(image_np):
+    image = Image.fromarray(image_np).convert("RGB")
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         embeddings = model(**inputs).last_hidden_state[:, 0]  # CLS token
     return embeddings.squeeze().tolist()
 
-# Interfaz Gradio para uso visual o programático (API)
 iface = gr.Interface(
     fn=get_embedding,
-    inputs=gr.Image(type="filepath"),  # << cambia aquí
+    inputs=gr.Image(type="numpy"),  # CAMBIO CLAVE AQUÍ
     outputs="json",
     description="Microservicio para extraer embeddings de imágenes usando DINOv2."
 )
 
-iface.launch()
 iface.queue()
+iface.launch()
+