validate

app.py

@@ -7,6 +7,8 @@ import seaborn as sns
 import shinyswatch
 
 from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+from transformers import SamModel, SamConfig, SamProcessor
+import torch
 
 sns.set_theme()
 
@@ -42,6 +44,30 @@ def server(input: Inputs, output: Outputs, session: Session):
         if input.image_input():
             src = input.image_input()[0]['datapath']
             img = {"src": src, "width": "500px"} 
+
+            # Load the model configuration
+            model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
+            processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+            # Create an instance of the model architecture with the loaded configuration
+            my_sam_model = SamModel(config=model_config)
+            #Update the model by loading the weights from saved file.
+            my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu')))
+
+            new_image = np.array(Image.open(src))
+            inputs = processor(new_image, return_tensors="pt")
+            inputs = {k: v.to(device) for k, v in inputs.items()}
+            my_sam_model.eval()
+            # forward pass
+            with torch.no_grad():
+                outputs = my_sam_model(**inputs, multimask_output=False)
+
+            # apply sigmoid
+            single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
+            # convert soft mask to hard mask
+            single_patch_prob = single_patch_prob.cpu().numpy().squeeze()
+            single_patch_prediction = (single_patch_prob > 0.5).astype(np.uint8)
+
             return img
         return None