Delete app.py

app.py

@@ -1,76 +0,0 @@
-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:
-    mola = Chem.MolFromSmiles(acceptor)
-    acceptor = Chem.MolToSmiles(mola)
-    mold = Chem.MolFromSmiles(donor)
-    donor = Chem.MolToSmiles(mold)
-    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!]")