Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| from os import path | |
| import sys | |
| import streamlit.components.v1 as components | |
| sys.path.append('code/') | |
| #sys.path.append('ASCARIS/code/') | |
| import pdb_featureVector | |
| import alphafold_featureVector | |
| import argparse | |
| from st_aggrid import AgGrid, GridOptionsBuilder, JsCode,GridUpdateMode | |
| import base64 | |
| showWarningOnDirectExecution = False | |
| def convert_df(df): | |
| return df.to_csv(index=False).encode('utf-8') | |
| # Check if 'key' already exists in session_state | |
| # If not, then initialize it | |
| 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:#FD7456; 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:#FD7456; 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('') | |
| selected_df = pd.DataFrame() | |
| with st.form('mform', clear_on_submit=True): | |
| source = st.selectbox('Select the protein structure resource (1: PDB-SwissModel-Modbase, 2: AlphaFold)',[1,2]) | |
| impute = st.selectbox('Imputation',[True, False]) | |
| input_data = st.text_input('Enter SAV data points (Format Provided Below)', "Q9Y4W6-N-432-T", | |
| label_visibility=st.session_state.visibility, | |
| disabled=st.session_state.disabled, | |
| ) | |
| parser = argparse.ArgumentParser(description='ASCARIS') | |
| parser.add_argument('-s', '--source_option', | |
| help='Selection of input structure data.\n 1: PDB Structures (default), 2: AlphaFold Structures', | |
| default=1) | |
| parser.add_argument('-i', '--input_datapoint', | |
| help='Input file or query datapoint\n Option 1: Comma-separated list of identifiers (UniProt ID-wt residue-position-mutated residue (e.g. Q9Y4W6-N-432-T or Q9Y4W6-N-432-T, Q9Y4W6-N-432-T)) \n Option 2: Enter comma-separated file path') | |
| parser.add_argument('-impute', '--imputation_state', default='True', | |
| help='Whether resulting feature vector should be imputed or not. Default True.') | |
| args = parser.parse_args() | |
| input_set = input_data | |
| mode = source | |
| 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(mode) | |
| 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 != None: | |
| if len(selected_df) != 0 : | |
| st.success('Feature vector successfully created.') | |
| csv = convert_df(selected_df) | |
| st.download_button("Press to Download the Feature Vector", csv,"ASCARIS_SAV_rep.csv","text/csv",key='download-csv') | |
| else: | |
| st.success('Feature vector failed.') | |
| else: | |
| st.success('Feature vector failed. Check log file.') | |