Update app.py

app.py

@@ -1,48 +1,46 @@
-from fastai.vision.all import *
 import gradio as gr
-import torchvision.transforms as transforms
+from fastai.vision.all import *
+
 from pathlib import Path
 import PIL
+import torchvision.transforms as transforms
+
+
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
 model = torch.jit.load("unet.pth")
 model = model.cpu()
 model.eval()
 
-
 def transform_image(image):
+    my_transforms = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(
+                                            [0.485, 0.456, 0.406],
+                                            [0.229, 0.224, 0.225])])
+    image_aux = image
+    
+image = transforms.Resize((480,640))(Image.fromarray(image))
+tensor = my_transforms(image_aux).unsqueeze(0).to(device)
+
 
-  #mask = PILMask.create(Path(str(image).replace("Images","Labels").replace("color","gt").replace(".jpg",".png")))
-  #image = PIL.Image.open(image)
-  my_transforms = transforms.Compose([transforms.ToTensor(),
-                                      transforms.Normalize(
-                                          [0.485, 0.456, 0.406],
-                                          [0.229, 0.224, 0.225])])
-  image_aux = image
-  #my_transforms(image_aux).unsqueeze(0).to(device)
-  image = transforms.Resize((480,640))(Image.fromarray(image))
-  tensor = my_transforms(image_aux).unsqueeze(0).to(device)
-  #tensor = transform_image(image=image)
-     
-  
-  
-  model.to(device)
-  with torch.no_grad():
+model.to(device)
+with torch.no_grad():
     outputs = model(tensor)
-  
-  outputs = torch.argmax(outputs,1)
-  
-  mask = np.array(outputs.cpu())
-  mask[mask==0]=255
-  mask[mask==1]=150
-  mask[mask==2]=76
-  mask[mask==3]=25
-  mask[mask==4]=0
-  
-  mask=np.reshape(mask,(480,640))
-  return Image.fromarray(mask.astype('uint8'))
+
+outputs = torch.argmax(outputs,1)
+
+
+mask = np.array(outputs.cpu())
+mask[mask==0]=255
+mask[mask==1]=150
+mask[mask==2]=76
+mask[mask==3]=25
+mask[mask==4]=0
+
+mask=np.reshape(mask,(480,640))
+return Image.fromarray(mask.astype('uint8'))
+
 
     
 # Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_188.jpg','color_189.jpg']).launch(share=False)
-
+gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_154.jpg','color_189.jpg']).launch(share=False)