app.py

import streamlit as st
import pandas as pd
from run import run, GUIDE_LEN, NUCLEOTIDE_TOKENS


# def run_with_input(reset=False):
#     if reset:
#         st.write("")
#         return 0
#     returned_x = run(st.session_state["userInput"])
#     csv_x = returned_x.to_csv()
#     st.write("model prediction: ", returned_x)
#     return csv_x

@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('Enter target transcript (or substring):')


if len(st.session_state['userInput']) < GUIDE_LEN:
    st.write('Transcript length must be >= 23 bases. It is {:d} chars'.format(len(st.session_state['userInput'])))
    st.write("")
elif all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in st.session_state['userInput']]):
    st.write('This is your sequence', st.session_state['userInput'])
    predictions = run(st.session_state['userInput'])
    st.write('Model predictions: ', predictions)
    csv = convert_df(predictions)
    st.download_button(label='Download CSV file', data=csv, file_name='tiger_predictions.csv', mime='text/csv')
else:
    st.write('only ACTG is allowed')