input errors

app.py

@@ -37,7 +37,7 @@ def progress_update(update_text, percent_complete):
 
 def initiate_run():
     st.session_state.running = True
-    st.session_state.on_target = st.session_state.off_target = None
+    st.session_state.input_error = st.session_state.on_target = st.session_state.off_target = None
 
 
 def run():
@@ -59,24 +59,20 @@ def run():
                 f.write(st.session_state.fasta_entry.getvalue().decode('utf-8'))
             transcripts = tiger.load_transcripts([fasta_path], enforce_unique_ids=False)
 
-    # make sure all transcripts have unique identifiers
-    if transcripts.index.has_duplicates:
-        with TRANSCRIPT_ENTRY:
-            st.write("Duplicate transcript ID's detected in fasta file")
-        return
-
     # convert to upper case as used by tokenizer
     transcripts[tiger.SEQ_COL] = transcripts[tiger.SEQ_COL].apply(lambda s: s.upper())
 
-    # check that all transcripts only contain nucleotides A, C, G, T, and wildcard N
-    if not all(transcripts[tiger.SEQ_COL].apply(lambda s: set(s).issubset(tiger.NUCLEOTIDE_TOKENS.keys()))):
-        with TRANSCRIPT_ENTRY:
-            st.write('Transcript(s) must only contain upper or lower case A, C, G, and Ts')
+    # ensure all transcripts have unique identifiers
+    if transcripts.index.has_duplicates:
+        st.session_state.input_error = "Duplicate transcript ID's detected in fasta file"
 
-    # check that all transcripts satisfy length requirements
+    # ensure all transcripts only contain nucleotides A, C, G, T, and wildcard N
+    elif not all(transcripts[tiger.SEQ_COL].apply(lambda s: set(s).issubset(tiger.NUCLEOTIDE_TOKENS.keys()))):
+        st.session_state.input_error = 'Transcript(s) must only contain upper or lower case A, C, G, and Ts'
+
+    # ensure all transcripts satisfy length requirements
     elif any(transcripts[tiger.SEQ_COL].apply(lambda s: len(s) < tiger.TARGET_LEN)):
-        with TRANSCRIPT_ENTRY:
-            st.write('Transcript(s) must be at least {:d} bases.'.format(tiger.TARGET_LEN))
+        st.session_state.input_error = 'Transcript(s) must be at least {:d} bases.'.format(tiger.TARGET_LEN)
 
     # run model if we have any transcripts
     elif len(transcripts) > 0:
@@ -98,6 +94,8 @@ if __name__ == '__main__':
         st.session_state.entry_method = ENTRY_METHODS['manual']
     if 'running' not in st.session_state:
         st.session_state.running = False
+    if 'input_error' not in st.session_state:
+        st.session_state.input_error = None
     if 'on_target' not in st.session_state:
         st.session_state.on_target = None
     if 'off_target' not in st.session_state:
@@ -128,7 +126,7 @@ if __name__ == '__main__':
     # transcript entry
     with TRANSCRIPT_ENTRY:
         st.selectbox(
-            label='How would you like to provide transcripts of interest?',
+            label='How would you like to provide transcript(s) of interest?',
             options=ENTRY_METHODS.values(),
             key='entry_method',
             disabled=st.session_state.running
@@ -155,6 +153,10 @@ if __name__ == '__main__':
     # results
     with RESULTS:
 
+        # input error
+        if st.session_state.input_error is not None:
+            st.error(st.session_state.input_error, icon="🚨")
+
         # on-target results
         if st.session_state.on_target is not None:
             st.write('On-target predictions:', st.session_state.on_target)