made classify as a submodule under modeling package.

app.py

@@ -12,10 +12,9 @@
 
 import yaml
 from clams import ClamsApp, Restifier
-from mmif import Mmif, View, Annotation, Document, AnnotationTypes, DocumentTypes
-
-import classify
+from mmif import Mmif, View, AnnotationTypes, DocumentTypes
 
+from modeling import classify
 
 logging.basicConfig(filename='swt.log', level=logging.DEBUG)
 
@@ -41,13 +40,16 @@ def _annotate(self, mmif: Union[str, dict, Mmif], **parameters) -> Mmif:
             return mmif
         vd = vds[0]
 
+        # aad the timeframes to a new view and return the updated Mmif object
+        new_view: View = mmif.new_view()
+        self.sign_view(new_view, parameters)
+        parameters = self.get_configuration(parameters)
+
         # calculate the frame predictions and extract the timeframes
+        # use `parameters` as needed as runtime configuration
         predictions = self.classifier.process_video(vd.location)
         timeframes = self.classifier.extract_timeframes(predictions)
 
-        # aad the timeframes to a new view and return the updated Mmif object
-        new_view: View = mmif.new_view()
-        self.sign_view(new_view, parameters)
         new_view.new_contain(AnnotationTypes.TimeFrame, document=vd.id)
         for tf in timeframes:
             start, end, score, label = tf

classify.py → modeling/classify.py

@@ -62,7 +62,6 @@ def process_video(self, mp4_file: str):
         an instance of numpy.ndarray."""
         print(f'Processing {mp4_file}...')
         logging.info(f'processing {mp4_file}...')
-        basename = os.path.splitext(os.path.basename(mp4_file))[0]
         predictions = []
         vidcap = cv2.VideoCapture(mp4_file)
         fps = round(vidcap.get(cv2.CAP_PROP_FPS), 2)