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.')