Update app.py

app.py

@@ -28,8 +28,12 @@ os.system('pip install -U gradio')
 
 import gradio as gr
 
-#Primero definimos todas las funciones, clases y variables que sopn necesarias para que esto funcione
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def get_y_fn (x):
+    return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
+    
 def transform_image(image):
     my_transforms = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize(
@@ -72,8 +76,6 @@ from albumentations import (
     ShiftScaleRotate
 )
 
-def get_y_fn (x):
-    return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
 
 class SegmentationAlbumentationsTransform(ItemTransform):
     split_idx = 0
@@ -86,7 +88,6 @@ class SegmentationAlbumentationsTransform(ItemTransform):
         aug = self.aug(image=np.array(img), mask=np.array(mask))
         return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
 
-#Cargamos el modelo
 repo_id = "maviced/practica3"
 learn = from_pretrained_fastai(repo_id)
 model = learn.model
@@ -121,10 +122,3 @@ def predict(img):
 gr.Interface(fn=predict, inputs=["image"], outputs=["image"],
              examples=['color_154.jpg','color_155.jpg']).launch(share=True)
 
-    
-# Creamos la interfaz y la lanzamos. 
-#gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.inputs.Image(shape=(480, 640))).launch(share=False) #,examples=['color_155.jpg','color_154 (1).jpg']
-
-    
-# Creamos la interfaz y la lanzamos. 
-#gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.inputs.Image(shape=(480, 640)),examples=['color_155.jpg','color_154.jpg']).launch(share=False)