app.py

import streamlit as st
from tiger import tiger_exhibit, TARGET_LEN, NUCLEOTIDE_TOKENS


@st.cache
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode('utf-8')


# title and instructions
st.title('TIGER Cas13 Efficacy Prediction')
st.session_state['userInput'] = ''
st.session_state['userInput'] = st.text_input(
    label='Enter a target transcript:',
    # value='ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC',
    placeholder='Upper or lower case')

# input is too short
if len(st.session_state['userInput']) < TARGET_LEN:
    transcript_len = len(st.session_state['userInput'])
    st.write('Transcript length ({:d}) must be at least {:d} bases.'.format(transcript_len, 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(st.session_state['userInput'])
    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:
        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')
    else:
        st.write('We did not find any off-target effects!')

# invalid input
else:
    st.write('Nucleotides other than ACGT detected!')