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="DeepDAP", page_icon="🔋", layout="wide")
st.title("🔋DeepDAP")
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)
with col1:
	st.header("🔍**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.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...***]")
option = st.radio(
 "👇 :red[**Select the type of active layer...**]",
   [":black[**Donor**]", ":black[**Acceptor**]"], label_visibility="hidden"
)
if option ==":black[**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(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 ==":black[**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(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")
try:
    pce = run.smiles_aas_test( str(acceptor ), str(donor) )
    st.subheader(f"⚡**PCE**: ``{pce}``")
except:
    st.subheader(f"⚡**PCE**:  None  ")
st.subheader(":black[**🧡Batch screening for high-performance D/A pairs**]")
uploaded_files = st.file_uploader("Choose a CSV file")
st.write( "🎈upload a csv file containing ['donor' ] and ['acceptor']")
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 )	
		x = pd.DataFrame(x)
		
		st.download_button( "⬇️Download  the predicted files as .csv", x.to_csv(), "predict results.csv", use_container_width=True)
	else:
		st.markdown(":red[Please upload the file first!]")