cleanup

tiger.py

@@ -161,7 +161,7 @@ def prediction_transform(predictions: np.array, **params):
         raise NotImplementedError
 
 
-def get_on_target_predictions(transcripts: pd.DataFrame, model: tf.keras.Model, status=None, progress_bar=None):
+def get_on_target_predictions(transcripts: pd.DataFrame, model: tf.keras.Model, status_update_fn=None):
 
     # loop over transcripts
     predictions = pd.DataFrame()
@@ -183,10 +183,8 @@ def get_on_target_predictions(transcripts: pd.DataFrame, model: tf.keras.Model,
         percent_complete = 100 * min((i + 1) / len(transcripts), 1)
         update_text = 'Evaluating on-target guides for each transcript: {:.2f}%'.format(percent_complete)
         print('\r' + update_text, end='')
-        if status is not None:
-            status.text(update_text)
-        if progress_bar is not None:
-            progress_bar.progress(percent_complete / 100)
+        if status_update_fn is not None:
+            status_update_fn(update_text, percent_complete)
     print('')
 
     return predictions
@@ -204,7 +202,7 @@ def top_guides_per_transcript(predictions: pd.DataFrame):
     return top_guides.reset_index(drop=True)
 
 
-def find_off_targets(top_guides: pd.DataFrame, status=None):
+def find_off_targets(top_guides: pd.DataFrame, status_update_fn=None):
 
     # load reference transcripts
     reference_transcripts = load_transcripts([os.path.join('transcripts', f) for f in REFERENCE_TRANSCRIPTS])
@@ -261,10 +259,8 @@ def find_off_targets(top_guides: pd.DataFrame, status=None):
         percent_complete = 100 * min((i + 1) / len(reference_transcripts), 1)
         update_text = 'Scanning for off-targets: {:.2f}%'.format(percent_complete)
         print('\r' + update_text, end='')
-        if status is not None:
-            with status:
-                st.text(update_text)
-                st.progress(percent_complete / 100)
+        if status_update_fn is not None:
+            status_update_fn(update_text, percent_complete)
     print('')
 
     return off_targets
@@ -288,7 +284,7 @@ def predict_off_target(off_targets: pd.DataFrame, model: tf.keras.Model):
     return off_targets.sort_values(SCORE_COL, ascending=False).reset_index(drop=True)
 
 
-def tiger_exhibit(transcripts: pd.DataFrame, mode: str, check_off_targets: bool, status=None, progress_bar=None):
+def tiger_exhibit(transcripts: pd.DataFrame, mode: str, check_off_targets: bool, status_update_fn=None):
 
     # load model
     if os.path.exists('model'):
@@ -298,7 +294,7 @@ def tiger_exhibit(transcripts: pd.DataFrame, mode: str, check_off_targets: bool,
         exit()
 
     # evaluate all on-target guides per transcript
-    on_target_predictions = get_on_target_predictions(transcripts, tiger, status, progress_bar)
+    on_target_predictions = get_on_target_predictions(transcripts, tiger, status_update_fn)
 
     # initialize other outputs
     off_target_predictions = None
@@ -316,7 +312,7 @@ def tiger_exhibit(transcripts: pd.DataFrame, mode: str, check_off_targets: bool,
 
     # check off-target effects for top guides
     if check_off_targets:
-        off_targets = find_off_targets(on_target_predictions, status, progress_bar)
+        off_targets = find_off_targets(on_target_predictions, status_update_fn)
         off_target_predictions = predict_off_target(off_targets, model=tiger)
 
     # reverse guide sequences