app.py

# -*- coding: utf-8 -*-
"""
Created on Sun Nov  3 08:48:11 2024

@author: BM109X32G-10GPU-02
"""

import streamlit as st
import pandas as pd
import rdkit
import streamlit_ketcher
from streamlit_ketcher import st_ketcher
import run
import screen

# Page setup
st.set_page_config(page_title="BiBERTa", page_icon="🔋", layout="wide")
st.title("🔋BiBERTa")
st.subheader('',divider='rainbow')
# Connect to the Google Sheet

url1= r"https://docs.google.com/spreadsheets/d/1AKkZS04VF3osFT36aNHIb4iUbV8D1uNfsldcpHXogj0/gviz/tq?tqx=out:csv&sheet=dap"
df1 = pd.read_csv(url1, dtype=str, encoding='utf-8')
col1, col2 = st.columns(2)
st.subheader("💗**The donor and acceptor database**")
with col1:
	st.caption("🔍**Search papers or molecules**")
	text_search = st.text_input(label="_", value="",label_visibility="hidden" )
	m1 = df1["Donor_Name"].str.contains(text_search)
	m2 = df1["reference"].str.contains(text_search)
	m3 = df1["Acceptor_Name"].str.contains(text_search)
	df_search = df1[m1 | m2|m3]
with col2:
	st.subheader("  ")
	st.link_button(":black[📝**DATABASE**]",  r"https://docs.google.com/spreadsheets/d/1AKkZS04VF3osFT36aNHIb4iUbV8D1uNfsldcpHXogj0")
	st.caption(':black[👆If you want to update the origin database, click the button.]')
if text_search:
    st.write(df_search)
    st.download_button( "⬇️Download edited files as .csv", df_search.to_csv(), "df_search.csv", use_container_width=True)
edited_df = st.data_editor(df1, num_rows="dynamic")

st.download_button(
    "⬇️ Download edited files as .csv", edited_df.to_csv(), "edited_df.csv", use_container_width=True
)
#st.subheader("👇 :red[***Select the type of active layer...***]")
st.subheader("💗**Molecular editor**")
col3, col4 = st.columns(2)
with col3:
	
	option = st.selectbox( ' 👇Select the type of active layer materials to be edited...', 	
 	  ("🎈Donor", "🎈Acceptor"), placeholder="👇Select the type of active layer materials...",	)
with col4:
	st.subheader("  ")
	st.markdown('👇An example of PM6 : Y6.')
	if st.button("🙋‍♂️**Example**"):
		option ="example"
		molecule = 'O=C(C(C=C(F)C(F)=C1)=C1C/2=C(C#N)/C#N)C2=C/C3=C(CCCCCCCCCCC)C(S4)=C(S3)C5=C4C6=C(N5CC(CC)CCCC)C7=C(C(SC8=C9SC(/C=C%10C(C(C=C(F)C(F)=C%11)=C%11C\%10=C(C#N)C#N)=O)=C8CCCCCCCCCCC)=C9N7CC(CC)CCCC)C%12=NSN=C6%12'
		do = 'CCCCC(CC)CC1=C(F)C=C(C2=C3C=C(C4=CC=C(C5=C6C(=O)C7=C(CC(CC)CCCC)SC(CC(CC)CCCC)=C7C(=O)C6=C(C6=CC=C(C)S6)S5)S4)SC3=C(C3=CC(F)=C(CC(CC)CCCC)S3)C3=C2SC(C)=C3)S1'

if option =="🎈Acceptor":
	st.subheader("👨‍🔬**Input the SMILES of Acceptor Molecule**")
	molecule = st.text_input("👨‍🔬**Input the SMILES of Acceptor Molecule**", label_visibility="hidden" )
	acceptor= st_ketcher(molecule )
    
	st.subheader("💗**PCE prediction**")
    
	st.subheader(f"🏆**New SMILES of edited acceptor molecules**: {acceptor}")
 
	st.subheader(":black[**🧡Input the SMILES of Donor Molecule**]")
	donor= st.text_input(":black[**🧡Input the SMILES of Donor Molecule**]", label_visibility="hidden")
if option =="🎈Donor":
	st.subheader("👨‍🔬**Input the SMILES of Donor Molecule**" )
	do= st.text_input("👨‍🔬**Input the SMILES of Donor Molecule**" , label_visibility="hidden")
	donor = st_ketcher(do)
	st.subheader("💗**PCE prediction**")
	st.subheader(f"🏆**New SMILES of edited donor molecules**: {donor}")
    
	st.subheader(":black[**🧡Input the SMILES of Acceptor Molecule**]")
	acceptor = st.text_input(":black[**🧡Input the SMILES of Acceptor Molecule**]", label_visibility="hidden")
if option =="example":
	
	st.subheader("👨‍🔬**Input the SMILES of Acceptor**")
	st.markdown(molecule)
	acceptor= st_ketcher(molecule )
	st.subheader(f"🏆**New SMILES of edited acceptor molecules**: {acceptor}")
	st.subheader(":black[**🧡Input the SMILES of Donor**]")
	st.markdown(do)
	donor= do
try:
 
    pce = run.smiles_aas_test( str(acceptor ), str(donor) )
    st.subheader(f"⚡**PCE**: ``{pce}``")
except:
    st.subheader(f"⚡**PCE**:  None  ")
st.subheader(":black[**🧡 High-throughput screening for high-performance D/A pairs**]")

col5, col6 = st.columns(2)
with col5:
	uploaded_files = st.file_uploader("Choose a CSV file")
	st.write( "🎈upload a csv file containing ['donor' ] and ['acceptor']")
with col6:
	url2= r"https://docs.google.com/spreadsheets/d/1jPfHM21IjksNn_80fdakS1ofDNIagwMXBWAjoZBr-YY/gviz/tq?tqx=out:csv"
	df2 = pd.read_csv(url2)
	st.markdown('👇The example of  input files for high-throughput screening.')
	st.download_button(
    		"⬇️ Download example files", df2.to_csv(), "example.csv"	)

if st.button("📑PREDICT"):
	if uploaded_files is not None:
		text = st.markdown(":red[Predictions are being made... Please wait...]")
		st.progress(100, text=None)
		x = screen.smiles_aas_test(uploaded_files )
		fx = pd.DataFrame(list(x))
		st.markdown(":red[Prediction finished! ]")	
		st.download_button( "⬇️Download  the predicted files as .csv", fx.to_csv(), "predict results.csv", use_container_width=True)
	else:
		st.markdown(":red[Please upload the file first!]")