enable status bar

app.py

@@ -16,7 +16,9 @@ st.session_state['userInput'] = st.text_input(
     label='Enter a target transcript:',
     # value='ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC',
     placeholder='Upper or lower case')
-
+#status bar
+status_text = st.empty()
+status_bar = st.progress(0)
 # input is too short
 if len(st.session_state['userInput']) < TARGET_LEN:
     transcript_len = len(st.session_state['userInput'])
@@ -24,7 +26,8 @@ if len(st.session_state['userInput']) < TARGET_LEN:
 
 # valid input
 elif all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in st.session_state['userInput']]):
-    on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[st.session_state['userInput']])))
+    on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[st.session_state['userInput']])),
+            status_bar, status_text)
     st.write('On-target predictions: ', on_target)
     st.download_button(label='Download', data=convert_df(on_target), file_name='on_target.csv', mime='text/csv')
     if len(off_target) > 0:

tiger.py

@@ -112,7 +112,7 @@ def predict_on_target(transcript_seq: str, model: tf.keras.Model):
     return predictions
 
 
-def find_off_targets(top_guides: pd.DataFrame):
+def find_off_targets(top_guides: pd.DataFrame, status_bar, status_text):
 
     # load reference transcripts
     reference_transcripts = load_transcripts([os.path.join('transcripts', f) for f in REFERENCE_TRANSCRIPTS])
@@ -166,6 +166,8 @@ def find_off_targets(top_guides: pd.DataFrame):
             off_targets = pd.concat([off_targets, pd.DataFrame(dict_off_targets)])
 
         # progress update
+        status_text.text("Scanning for off-targets Percent complete: {:.2f}%".format(int(100 * min(i / len(reference_transcripts), 1))))
+        status_bar.progress(int(100 * min(i / len(reference_transcripts), 1)))
         print('\rPercent complete: {:.2f}%'.format(100 * min(i / len(reference_transcripts), 1)), end='')
     print('')
 
@@ -186,7 +188,7 @@ def predict_off_target(off_targets: pd.DataFrame, model: tf.keras.Model):
     return off_targets.sort_values('Normalized LFC')
 
 
-def tiger_exhibit(transcripts: pd.DataFrame):
+def tiger_exhibit(transcripts: pd.DataFrame, status_bar, status_text):
 
     # load model
     if os.path.exists('model'):
@@ -204,11 +206,13 @@ def tiger_exhibit(transcripts: pd.DataFrame):
         on_target_predictions = pd.concat([on_target_predictions, df.iloc[:NUM_TOP_GUIDES]])
 
         # progress update
+        status_text.text("Scanning for on-targets Percent complete: {:.2f}%".format(100 * min((i + 1) / len(transcripts), 1)))
+        status_bar.progress(int(100 * min((i + 1) / len(transcripts), 1)))
         print('\rPercent complete: {:.2f}%'.format(100 * min((i + 1) / len(transcripts), 1)), end='')
     print('')
 
     # predict off-target effects for top guides
-    off_targets = find_off_targets(on_target_predictions)
+    off_targets = find_off_targets(on_target_predictions,  status_bar, status_text)
     off_target_predictions = predict_off_target(off_targets, model=tiger)
 
     return on_target_predictions.reset_index(drop=True), off_target_predictions.reset_index(drop=True)