Fix #23 app.py

app.py

@@ -11,6 +11,7 @@ from torchvision.utils import save_image
 import gradio as gr
 import numpy as np
 import io
+import tempfile  # Importar tempfile
 
 # Asegúrate de que las funciones necesarias estén definidas (si no lo están ya)
 def resize(img, size):
@@ -265,11 +266,15 @@ class Solver(object):
                 transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
             ])
             # Convertir a PIL image antes de la transformación
-            source_image = Image.open(io.BytesIO(source_image))  # Use BytesIO
-            reference_image = Image.open(io.BytesIO(reference_image))
+            with tempfile.NamedTemporaryFile(suffix=".jpg") as source_temp_file, \
+                    tempfile.NamedTemporaryFile(suffix=".jpg") as reference_temp_file:
+                source_temp_file.write(source_image)
+                reference_temp_file.write(reference_image)
+                source_image_pil = Image.open(source_temp_file.name)
+                reference_image_pil = Image.open(reference_temp_file.name)
 
-            source_image = transform(source_image).unsqueeze(0).to(self.device)
-            reference_image = transform(reference_image).unsqueeze(0).to(self.device)
+            source_image = transform(source_image_pil).unsqueeze(0).to(self.device)
+            reference_image = transform(reference_image_pil).unsqueeze(0).to(self.device)
 
             # Codificar el estilo de la imagen de referencia
             s_ref = self.S(reference_image, torch.tensor([0]).to(self.device))
@@ -307,7 +312,7 @@ def gradio_interface():
     )
 
     # Ruta al checkpoint
-    checkpoint_path = "iter/20500_nets_ema.ckpt"
+    checkpoint_path = "iter/10500_nets_ema.ckpt"
 
     # Crear la interfaz de Gradio
     inputs = [
@@ -330,4 +335,4 @@ def gradio_interface():
 
 if __name__ == '__main__':
     iface = gradio_interface()
-    iface.launch(share=True)
+    iface.launch(share=True)