Update app.py

app.py

@@ -149,46 +149,6 @@ def generate_input_points(image, grid_size=10):
     input_points = torch.tensor(input_points).view(1, 1, grid_size*grid_size, 2)
     
     return input_points
-    
-def process_image():
-    """Processes the uploaded image, loads the model, and evaluates to get predictions"""
-
-    """ Get Image """
-    img_src = uploaded_image_path()
-    
-    # Read the image bytes from the file
-    with open(img_src, 'rb') as f:
-        image_bytes = f.read()
-    
-    # Convert the image bytes to a PIL Image
-    image = bytes_to_pil_image(image_bytes)
-
-    """ Prepare Inputs """
-    # get input points prompt (grid of points)
-    input_points = generate_input_points(image)
-    
-    # prepare image and prompt for the model
-    inputs = processor(image, input_points=input_points, return_tensors="pt")
-    
-    # # remove batch dimension which the processor adds by default
-    # inputs = {k:v.squeeze(0) for k,v in inputs.items()}
-
-    # Move the input tensor to the GPU if it's not already there
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    """ Get Predictions """
-    # forward pass
-    with torch.no_grad():
-        outputs = model(**inputs, multimask_output=False)
-    
-    # apply sigmoid
-    prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
-    # convert soft mask to hard mask
-    prob = prob.cpu().numpy().squeeze()
-    prediction = (prob > 0.5).astype(np.uint8)
-    
-    # Return the processed result 
-    return image, prob, prediction
 
 ### SERVER ###
 def server(input: Inputs, output: Outputs, session: Session):
@@ -218,6 +178,46 @@ def server(input: Inputs, output: Outputs, session: Session):
     #     else:
     #         return None  # Return an empty string if no image is uploaded
 
+    def process_image():
+        """Processes the uploaded image, loads the model, and evaluates to get predictions"""
+    
+        """ Get Image """
+        img_src = uploaded_image_path()
+        
+        # Read the image bytes from the file
+        with open(img_src, 'rb') as f:
+            image_bytes = f.read()
+        
+        # Convert the image bytes to a PIL Image
+        image = bytes_to_pil_image(image_bytes)
+    
+        """ Prepare Inputs """
+        # get input points prompt (grid of points)
+        input_points = generate_input_points(image)
+        
+        # prepare image and prompt for the model
+        inputs = processor(image, input_points=input_points, return_tensors="pt")
+        
+        # # remove batch dimension which the processor adds by default
+        # inputs = {k:v.squeeze(0) for k,v in inputs.items()}
+    
+        # Move the input tensor to the GPU if it's not already there
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        
+        """ Get Predictions """
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, multimask_output=False)
+        
+        # apply sigmoid
+        prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
+        # convert soft mask to hard mask
+        prob = prob.cpu().numpy().squeeze()
+        prediction = (prob > 0.5).astype(np.uint8)
+        
+        # Return the processed result 
+        return image, prob, prediction
+
     @reactive.Calc
     def get_predictions():
         """Processes the image when uploaded to get predictions"""