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import streamlit as st
import pandas as pd
from os import path
import sys
import streamlit.components.v1 as components
sys.path.append('code/')
import pdb_featureVector
import alphafold_featureVector
import argparse
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode,GridUpdateMode
import base64
from huggingface_hub import hf_hub_download
import streamlit as st
import gzip
showWarningOnDirectExecution = False
def convert_df(df):
return df.to_csv(index=False, sep= '\t').encode('utf-8')
if 'visibility' not in st.session_state:
st.session_state['visibility'] = 'visible'
st.session_state.disabled = False
original_title = '<p style="font-family:Trebuchet MS; color:#000000; font-size: 25px; font-weight:bold; text-align:center">ASCARIS</p>'
st.markdown(original_title, unsafe_allow_html=True)
original_title = '<p style="font-family:Trebuchet MS; color:#000000; font-size: 25px; font-weight:bold; text-align:center">(Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations)</p>'
st.markdown(original_title, unsafe_allow_html=True)
st.write('')
st.write('')
st.write('')
st.write('')
with st.form('mform', clear_on_submit=False):
source = st.selectbox('Select the protein structure resource (1: PDB-SwissModel-Modbase, 2: AlphaFold)',[1,2])
impute = st.selectbox('Missing value imputation (mostly for the cases where the corresponding annotation does not exist in the protein)',[True, False])
input_data = st.text_input('Enter SAV data points (format: "UniProt/Swiss-Prot human protein accession" – "wild type a.a." – "position on the sequence" – "mutated a.a."). Example: P04217-E-20-A or O43556-I-40-A,P57737-W-372-A')
parser = argparse.ArgumentParser(description='ASCARIS')
input_set = input_data
impute = impute
submitted = st.form_submit_button(label="Submit", help=None, on_click=None, args=None, kwargs=None, type="secondary", disabled=False, use_container_width=False)
print('*****************************************')
print('Feature vector generation is in progress. \nPlease check log file for updates..')
print('*****************************************')
mode = int(source)
selected_df = pd.DataFrame()
st.write('The online tool may be slow, especially while processing multiple SAVs. To address this, please consider using the programmatic version at https://github.com/HUBioDataLab/ASCARIS/')
if submitted:
with st.spinner('In progress...This may take a while...'):
try:
if mode == 1:
selected_df = pdb_featureVector.pdb(input_set, mode, impute)
elif mode == 2:
selected_df = alphafold_featureVector.alphafold(input_set, mode, impute)
else:
selected_df = pd.DataFrame()
except:
selected_df = pd.DataFrame()
pass
if selected_df is None:
st.success('Feature vector failed. Check the log file.')
else:
if len(selected_df) != 0 :
st.write(selected_df)
st.success('Feature vector is successfully created.')
csv = convert_df(selected_df)
st.download_button("Press to Download the Feature Vector", csv,f"ASCARIS_SAV_rep_{input_set}.csv","text/csv",key='download-csv')
else:
st.success('Feature vector failed. Check the log file.')
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