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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.') | |