streamlit titration support

app.py

@@ -35,6 +35,7 @@ def initiate_run():
     st.session_state.transcripts = None
     st.session_state.input_error = None
     st.session_state.on_target = None
+    st.session_state.titration = None
     st.session_state.off_target = None
 
     # initialize transcript DataFrame
@@ -90,6 +91,8 @@ if __name__ == '__main__':
         st.session_state.input_error = None
     if 'on_target' not in st.session_state:
         st.session_state.on_target = None
+    if 'titration' not in st.session_state:
+        st.session_state.titration = None
     if 'off_target' not in st.session_state:
         st.session_state.off_target = None
 
@@ -159,6 +162,20 @@ if __name__ == '__main__':
     else:
         on_target_results.empty()
 
+    # titration results
+    titration_results = st.empty()
+    if st.session_state.titration is not None:
+        with titration_results.container():
+            st.write('Titration predictions:', st.session_state.titration)
+            st.download_button(
+                label='Download titration predictions',
+                data=convert_df(st.session_state.titration),
+                file_name='titration.csv',
+                mime='text/csv'
+            )
+    else:
+        titration_results.empty()
+
     # off-target results
     off_target_results = st.empty()
     if st.session_state.off_target is not None:
@@ -178,7 +195,7 @@ if __name__ == '__main__':
 
     # keep trying to run model until we clear inputs (streamlit UI changes can induce race-condition reruns)
     if st.session_state.transcripts is not None:
-        st.session_state.on_target, st.session_state.off_target = tiger.tiger_exhibit(
+        st.session_state.on_target, st.session_state.titration, st.session_state.off_target = tiger.tiger_exhibit(
             transcripts=st.session_state.transcripts,
             mode={v: k for k, v in tiger.RUN_MODES.items()}[st.session_state.mode],
             check_off_targets=st.session_state.check_off_targets,