Update app.py

app.py

@@ -61,6 +61,8 @@ 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)")
+            
             local_batch = patches_batch['img'][tio.DATA].float()
             local_batch = local_batch / local_batch.max()
             locations = patches_batch[tio.LOCATION]
@@ -76,7 +78,6 @@ 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"Processing batch {i + 1} of {total_batches}... ({((i + 1) / total_batches) * 100:.2f}% complete)")
             st.text(f"CPU usage: {psutil.cpu_percent()}% | RAM usage: {psutil.virtual_memory().percent}%")
 
         predicted = aggregator.get_output_tensor().squeeze().numpy()
@@ -124,12 +125,16 @@ selected_mode = st.selectbox("Select the running mode:", mode_options)
 
 # Parameters for patch-based inference
 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)
-    stride_width = st.number_input("Stride width:", min_value=1, value=32)
-    stride_depth = st.number_input("Stride depth:", min_value=1, value=16)
-    batch_size = st.number_input("Batch size:", min_value=1, value=10)
-    num_worker = st.number_input("Number of workers:", min_value=1, value=2)
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        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:
+        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)
 
 # Process button
 process_button = st.button("Process")