Spaces:
Sleeping
Sleeping
File size: 3,829 Bytes
c2a02c6 8a2e1bf c2a02c6 c8b993f ec06c4b c8b993f 0d7f3a7 b68774d 9abc2e6 c2a02c6 8a2e1bf c2a02c6 8a2e1bf c2a02c6 e5bafbe dd50af6 43efedb dd50af6 e515e54 b7a9a10 43efedb b7a9a10 c2a02c6 b27ef4c b68774d b27ef4c 9e2f96b c8b993f 806931d c8b993f 8f90700 7da329c 8f90700 efb78a7 8f90700 4ed9060 c4235e2 4ed9060 a94e895 d14545c 4ed9060 a0f4539 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
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.')
|