import streamlit as st
from tiger import tiger_predict, 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']]):
    predictions = tiger_predict(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')

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