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import pdb_featureVector
import alphafold_featureVector
import argparse
from huggingface_hub import hf_hub_download
import streamlit as st
print('I AM HERE GOD DAMN IT')
st.write('I AM HERE GOD DAMN IT')
path = hf_hub_download(repo_id="HuBioDataLab/AlphafoldStructures", filename="AF-A0A075B6Y9-F1-model_v4.pdb.gz",repo_type = 'dataset')
st.write(path)
pdb_path= hf_hub_download(repo_id="HuBioDataLab/AlphafoldStructures",repo_type = 'dataset', filename="AF-A0A075B6Y9-F1-model_v4.pdb.gz")
import gzip
st.write('HER')
with gzip.open(pdb_path, mode="rt") as f:
file_content = f.read()
st.write(file_content)
st.write('REH')
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 idenfiers (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 = args.input_datapoint
mode = args.source_option
impute = args.imputation_state
def run_featureVector(input_set, mode, impute):
print('*****************************************')
print('Feature vector generation is in progress. \nPlease check log file for updates..')
print('*****************************************')
mode = int(mode)
if mode == 1:
pdb_featureVector.pdb(input_set, mode, impute)
elif mode == 2:
alphafold_featureVector.alphafold(input_set, mode, impute)
if __name__ == '__main__':
run_featureVector(input_set, mode, impute)
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