Upload app.py

app.py

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+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)
+st.subheader("🔍**Search papers or molecules**")
+with col1:
+	
+	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...***]")
+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(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(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!]")