updare requirements

requirements.txt

@@ -3,4 +3,6 @@ shinyswatch==0.6.0
 seaborn==0.12.2
 matplotlib==3.7.1
 transformers
-torch
+torch
+patchify
+PIL

run.py

@@ -61,8 +61,21 @@ def pred(src):
 
     single_patch = Image.fromarray(random_array)
     inputs = processor(single_patch, input_points=input_points, return_tensors="pt")
-    
+
     inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    my_mito_model.eval()
+
+    # forward pass
+    with torch.no_grad():
+        outputs = my_mito_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)
+    
     x = 1
     # my_sam_model.eval()
     # # forward pass