Light cleanup of unused code and comments

app.py

@@ -62,17 +62,6 @@ MULTI_DEMO_PATHS = [
 
 torch.classes.__path__ = [os.path.join(torch.__path__[0], torch.classes.__file__)]
 
-isGPU  = torch.cuda.is_available()
-device = torch.device("cuda" if isGPU else "cpu")
-print(f"Using {device}")
-
-if ENABLE_DENOISE:
-    from df import init_df
-    dfModel, dfState, _ = init_df(model_base_dir="DeepFilterNet3")
-    dfModel.to(device)
-else:
-    dfModel = dfState = None
-
 pipeline = Sonogram()
 st.session_state.pipeline = pipeline
 
@@ -157,17 +146,22 @@ uploaded_file_paths = st.file_uploader(
     key=f"uploader_{st.session_state.uploader_key}",
 )
 
+# Make temp directory
 temp_dir = tempfile.mkdtemp()
 
+# If files have been uploaded
 if uploaded_file_paths:
     for uploaded_file in uploaded_file_paths:
+        # Check for supported file types via extension
         if not uploaded_file.name.lower().endswith(SUPPORTED_FILE_TYPES):
             st.error(f"File must be of type: {SUPPORTED_FILE_TYPES}")
             continue
+        # Save file locally to server
         fname = uploaded_file.name
         path  = os.path.join(temp_dir, fname)
         with open(path, "wb") as f:
             f.write(uploaded_file.getvalue())
+        # Add file to list of known files
         if fname not in st.session_state.file_names:
             register_file(fname)
         st.session_state.file_paths[fname] = path