trial version ready

app.py

@@ -10,7 +10,7 @@ import tempfile
 from pathlib import Path
 from skimage.filters import threshold_otsu
 import torchio as tio
-import psutil
+# import psutil
 
 def infer_full_vol(tensor, model):
     tensor = tensor.unsqueeze(0).unsqueeze(0)  # Shape: [1, 1, D, H, W] - adding batch and channel dims
@@ -61,7 +61,7 @@ def infer_patch_based(tensor, model, patch_size=64, stride_length=32, stride_wid
         total_batches = len(patch_loader)
         progress_bar = st.progress(0)
         for i, patches_batch in enumerate(patch_loader):
-            st.text(f"Processing batch {i + 1} of {total_batches}... ({((i + 1) / total_batches) * 100:.2f}% complete)")
+            st.text(f"Processing batch {i + 1} of {total_batches}...")
             
             local_batch = patches_batch['img'][tio.DATA].float()
             local_batch = local_batch / local_batch.max()
@@ -78,7 +78,7 @@ def infer_patch_based(tensor, model, patch_size=64, stride_length=32, stride_wid
             aggregator.add_batch(output, locations)
 
             progress_bar.progress((i + 1) / total_batches)
-            st.text(f"CPU usage: {psutil.cpu_percent()}% | RAM usage: {psutil.virtual_memory().percent}%")
+            # st.text(f"CPU usage: {psutil.cpu_percent()}% | RAM usage: {psutil.virtual_memory().percent}%")
 
         predicted = aggregator.get_output_tensor().squeeze().numpy()
         
@@ -130,11 +130,11 @@ if selected_mode == "Patch-based inference [Default for DS6]":
         patch_size = st.number_input("Patch size:", min_value=1, value=64)
         stride_length = st.number_input("Stride length:", min_value=1, value=32)
     with col2:
+        batch_size = st.number_input("Batch size:", min_value=1, value=14)
         stride_width = st.number_input("Stride width:", min_value=1, value=32)
-        stride_depth = st.number_input("Stride depth:", min_value=1, value=16)
     with col3:
-        batch_size = st.number_input("Batch size:", min_value=1, value=14)
         num_worker = st.number_input("Number of workers:", min_value=1, value=3)
+        stride_depth = st.number_input("Stride depth:", min_value=1, value=16)
 
 # Process button
 process_button = st.button("Process")