app.py overhaul progress

app.py

@@ -1,8 +1,17 @@
-import os
-import shutil
+import tiger
 import pandas as pd
 import streamlit as st
-from tiger import tiger_exhibit, load_transcripts, TARGET_LEN, NUCLEOTIDE_TOKENS
+
+ENTRY_METHODS = dict(
+    manual='Manual entry of single transcript',
+    fasta="Fasta file upload (supports multiple transcripts if they have unique ID's)"
+)
+
+# containers
+DOCUMENTATION = st.container()
+MODE_SELECTION = st.container()
+TRANSCRIPT_ENTRY = st.container()
+RESULTS = st.container()
 
 
 @st.cache_data
@@ -11,77 +20,146 @@ def convert_df(df):
     return df.to_csv().encode('utf-8')
 
 
-# title and initialization
-st.title('TIGER Cas13 Efficacy Prediction')
-st.session_state['manual_seq'] = ''
-st.session_state['fasta_seq'] = ''
-
-# run mode selection
-with st.form(key='calc_options'):
-    c1_names = ['On-target', 'On- and off-target']
-    option = st.radio('Select mode:', c1_names, index=0)
-    submitButton = st.form_submit_button(label='Choose options')
-
-# text input
-manual_entry = st.form('text')
-manual_input = manual_entry.text_input(
-    label='Enter a target transcript:',
-    # value='ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC',
-    placeholder='Upper or lower case')
-if manual_input:
-    if len(manual_input) < TARGET_LEN:
-        manual_entry.write('Transcript must be at least {:d} bases.'.format(TARGET_LEN))
+def mode_change_callback():
+    if st.session_state.mode == tiger.RUN_MODES['all']:
+        st.session_state.check_off_targets = False
+        st.session_state.disable_off_target_checkbox = True
     else:
-        st.session_state['manual_seq'] = manual_input
-manual_mode = manual_entry.form_submit_button(label='calculate')
-
-# fasta input
-fasta_form = st.form('fasta')
-fasta_input = fasta_form.file_uploader(label='Upload a fasta file:')
-if fasta_input:
-    if os.path.exists('temp'):
-        shutil.rmtree('temp')
-    os.makedirs('temp')
-    st.write(fasta_input.name)
-    fpath = os.path.join('temp', fasta_input.name)
-    with open(fpath, 'w') as f:
-        f.write(fasta_input.getvalue().decode('utf-8'))
-    transcript_tbl = load_transcripts([fpath])
-    fasta_form.text('fasta file contents')
-    fasta_form.write(transcript_tbl)
-    seq = transcript_tbl['seq'][0]
-    st.session_state['fasta_seq'] = seq
-fasta_mode = fasta_form.form_submit_button(label='Calculate')
-
-# input-specific configuration
-if manual_mode:
-    src_seq = st.session_state['manual_seq']
-    status_text = manual_entry.empty()
-    status_bar = manual_entry.progress(0)
-elif fasta_mode:
-    src_seq = st.session_state['fasta_seq']
-    status_text = fasta_form.empty()
-    status_bar = fasta_form.progress(0)
-else:
-    src_seq = status_bar = status_text = None
-
-# valid input
-if src_seq and all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in src_seq]):
-    on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[src_seq])),
-                                          status_bar, status_text, check_off_targets=option == 'On and Off Target')
-    on_target.rename(columns={'Guide': '23 nt guide sequence'}, inplace=True)
-    if len(on_target) > 0:
-        if on_target.iloc[0]['On-target ID'] == 0:
-            on_target.drop(['On-target ID'], axis=1, inplace=True)
-    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 option == 'On and Off Target' and len(off_target) > 0:
-        off_target.rename(columns={'Guide': '23 nt guide sequence'}, inplace=True)
-        st.write('Off-target predictions: ', off_target)
-        st.download_button(label='Download', data=convert_df(off_target), file_name='off_target.csv', mime='text/csv')
-    elif option == 'On and Off Target' and len(off_target) == 0:
-        st.write('We did not find any off-target effects!')
-
-# invalid input
-elif src_seq is not None:
-    st.write('Invalid input!')
+        st.session_state.disable_off_target_checkbox = False
+
+
+def entry_method_change_callback():
+    if st.session_state.entry_method == ENTRY_METHODS['manual']:
+        st.session_state.manual_entry_disabled = False
+        st.session_state.fasta_entry_disabled = True
+    elif st.session_state.entry_method == ENTRY_METHODS['fasta']:
+        st.session_state.manual_entry_disabled = True
+        st.session_state.fasta_entry_disabled = False
+
+
+def process_input():
+
+    # initialize transcript DataFrame
+    st.session_state.transcripts = pd.DataFrame()
+
+    # manual entry
+    if st.session_state.entry_method == ENTRY_METHODS['manual']:
+        sequence = st.session_state.manual_entry
+        if len(sequence) < tiger.TARGET_LEN:
+            with TRANSCRIPT_ENTRY:
+                st.write('Transcript must be at least {:d} bases.'.format(tiger.TARGET_LEN))
+        else:
+            st.session_state.transcripts = pd.DataFrame({tiger.ID_COL: ['ManualEntry'], tiger.SEQ_COL: [sequence]})
+
+    # fasta file upload
+    elif st.session_state.entry_method == ENTRY_METHODS['fasta']:
+        if st.session_state.fasta_entry is not None:
+            fasta_path = st.session_state.fasta_entry.name
+            with open(fasta_path, 'w') as f:
+                f.write(st.session_state.fasta_entry.getvalue().decode('utf-8'))
+            df = tiger.load_transcripts([fasta_path], enforce_unique_ids=False)
+            if df.index.has_duplicates:
+                with TRANSCRIPT_ENTRY:
+                    st.write("Duplicate transcript ID's detected in fasta file")
+            else:
+                st.session_state.transcripts = df
+
+    # TODO: convert to upper and check for ACGT only
+    print(st.session_state.transcripts)
+
+
+if __name__ == '__main__':
+
+    # app initialization
+    if 'mode' not in st.session_state:
+        st.session_state.mode = tiger.RUN_MODES['all']
+        st.session_state.disable_off_target_checkbox = True
+    if 'entry_method' not in st.session_state:
+        st.session_state.entry_method = ENTRY_METHODS['manual']
+        st.session_state.manual_entry_disabled = False
+        st.session_state.fasta_entry_disabled = True
+
+    # title and documentation
+    with DOCUMENTATION:
+        st.title('TIGER Cas13 Efficacy Prediction')
+
+    # mode selection
+    with MODE_SELECTION:
+        col1, col2 = st.columns([0.65, 0.35])
+        with col1:
+            st.radio(
+                label='What do you want to predict?',
+                options=tuple(tiger.RUN_MODES.values()),
+                key='mode',
+                on_change=mode_change_callback
+            )
+        with col2:
+            st.checkbox(
+                label='Find off-target effects (slow)',
+                key='check_off_targets',
+                disabled=st.session_state.disable_off_target_checkbox
+            )
+
+    # transcript entry
+    with TRANSCRIPT_ENTRY:
+        st.selectbox(
+            label='How would you like to provide transcripts of interest?',
+            options=ENTRY_METHODS.values(),
+            key='entry_method',
+            on_change=entry_method_change_callback
+        )
+        st.text_input(
+            label='Enter a target transcript:',
+            key='manual_entry',
+            placeholder='Upper or lower case',
+            disabled=st.session_state.manual_entry_disabled
+        )
+        st.file_uploader(
+            label='Upload a fasta file:',
+            key='fasta_entry',
+            disabled=st.session_state.fasta_entry_disabled
+        )
+        run = st.button(label='Get predictions!', on_click=process_input)
+
+    # with RESULTS:
+    #     if run:
+    #         on_target, off_target = tiger.tiger_exhibit(
+    #             transcripts=st.session_state.transcripts,
+    #             mode=st.session_state.mode,
+    #             status=st.empty(),
+    #             progress_bar=st.progress(0),
+    #             check_off_targets=st.session_state.check_off_targets
+    #         )
+
+    # # input-specific configuration
+    # if manual_mode:
+    #     src_seq = st.session_state['manual_seq']
+    #     status_text = manual_entry.empty()
+    #     status_bar = manual_entry.progress(0)
+    # elif fasta_mode:
+    #     src_seq = st.session_state['fasta_seq']
+    #     status_text = fasta_form.empty()
+    #     status_bar = fasta_form.progress(0)
+    # else:
+    #     src_seq = status_bar = status_text = None
+    #
+    # # valid input
+    # if src_seq and all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in src_seq]):
+    #     on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[src_seq])),
+    #                                           status_bar, status_text, check_off_targets=option == 'On and Off Target')
+    #     on_target.rename(columns={'Guide': '23 nt guide sequence'}, inplace=True)
+    #     if len(on_target) > 0:
+    #         if on_target.iloc[0]['On-target ID'] == 0:
+    #             on_target.drop(['On-target ID'], axis=1, inplace=True)
+    #     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 option == 'On and Off Target' and len(off_target) > 0:
+    #         off_target.rename(columns={'Guide': '23 nt guide sequence'}, inplace=True)
+    #         st.write('Off-target predictions: ', off_target)
+    #         st.download_button(label='Download', data=convert_df(off_target), file_name='off_target.csv', mime='text/csv')
+    #     elif option == 'On and Off Target' and len(off_target) == 0:
+    #         st.write('We did not find any off-target effects!')
+    #
+    # # invalid input
+    # elif src_seq is not None:
+    #     st.write('Invalid input!')