Update app.py

app.py

@@ -99,18 +99,24 @@ class model:
  
  
  nucleus_image = image['image'].convert('L')
- protein_image = image['mask'].convert('L')
+ protein_image = image['mask'].split()[3]
 
  to_tensor = T.ToTensor()
  nucleus_image = to_tensor(nucleus_image)
  protein_image = to_tensor(protein_image)
- stacked_images = torch.stack([nucleus_image, protein_image], dim=0)
- processed_images = process_image(stacked_images, dataset)
-
- nucleus_image = processed_images[0].unsqueeze(0)
- protein_image = processed_images[1].unsqueeze(0)
- protein_image = protein_image/torch.max(protein_image)
- protein_image = 1 - protein_image 
+ #protein_image = protein_image
+ #stacked_images = torch.stack([nucleus_image, protein_image], dim=0)
+ #processed_images = process_image(stacked_images, dataset)
+
+ #nucleus_image = processed_images[0].unsqueeze(0)
+ #protein_image = processed_images[1].unsqueeze(0)
+ #protein_image = protein_image/torch.max(protein_image)
+ #protein_image = 1 - protein_image 
+
+ nucleus_image = nucleus_image.unsqueeze(0)
+ nucleus_image = process_image(nucleus_image, dataset, 'nucleus')
+ protein_image = 1.0*(protein_image > .01)
+ 
 
  formatted_predicted_sequence = run_sequence_prediction(
  sequence_input=sequence_input,
@@ -164,10 +170,10 @@ with gr.Blocks(theme='gradio/soft') as demo:
  with gr.Row().style(equal_height=True):
  nucleus_image = gr.Image(
  source="upload", 
- tool="sketch",
+ tool="color-sketch",
  label="Nucleus Image", 
  interactive=True,
- image_mode="L",
+ image_mode="RGBA",
  type="pil"
  )