Upload app.py

app.py

@@ -6,43 +6,44 @@ import streamlit_ketcher
 from streamlit_ketcher import st_ketcher
 import abcBERT
 import RF
- 
+from streamlit_gsheets import GSheetsConnection
+
 # Page setup
 st.set_page_config(page_title="DeepAcceptor", page_icon="🔋", layout="wide")
 st.title("🔋DeepAcceptor")
 
 # Connect to the Google Sheet
-
-url1 = r"https://docs.google.com/spreadsheets/d/1HfCTquggWtXMevB-DenMKDs-q8JUya949mf0XPDRmtM/gviz/tq?tqx=out:csv&sheet=nfa"
+url1 = r"https://docs.google.com/spreadsheets/d/1YOEIg0nMTSPkAOr8wkqxQRLuUhys3-J0I-KPEpmzPLw/gviz/tq?tqx=out:csv&sheet=accept"
+url = r"https://docs.google.com/spreadsheets/d/1YOEIg0nMTSPkAOr8wkqxQRLuUhys3-J0I-KPEpmzPLw/gviz/tq?tqx=out:csv&sheet=111"
 df1 = pd.read_csv(url1, dtype=str, encoding='utf-8')
-
-text_search = st.text_input("🔍Search papers or molecules", value="")
-
-m1 = df1["name"].str.contains(text_search)
-m2 = df1["reference"].str.contains(text_search)
-df_search = df1[m1 | m2]
+col1, col2 = st.columns(2)
+with col1:
+
+	text_search = st.text_input("🔍**Search papers or molecules**", value="")
+	m1 = df1["name"].str.contains(text_search)
+	m2 = df1["reference"].str.contains(text_search)
+	df_search = df1[m1 | m2]
+with col2:
 if text_search:
+	st.link_button("📝**DATABASE**",  r"https://docs.google.com/spreadsheets/d/1YOEIg0nMTSPkAOr8wkqxQRLuUhys3-J0I-KPEpmzPLw")
+	st.caption('👆If you want to update the database, click the button.')
     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")
-
+edited_df.to_csv(url)
 st.download_button(
     "⬇️ Download edited files as .csv", edited_df.to_csv(), "edited_df.csv", use_container_width=True
 )
 
-molecule = st.text_input("🎄Molecule")
+molecule = st.text_input("📋**Molecule**")
 smile_code = st_ketcher(molecule)
-st.markdown(f"✨Smiles code: {smile_code}")
+st.subheader(f"✨**Smiles code**: {smile_code}")
+P = RF.main( str(smile_code ) )
+st.header(f":blue[⚡**PCE predicted by RF**]: {P}")
 
-try:
-    P = RF.main( str(smile_code ) )
-    st.markdown(f"⚡PCE predicted by RF: {P}")
-except:
-    st.markdown(f"⚡PCE predicted by RF: None")
-    
 try:
     pce = abcBERT.main( str(smile_code ) )
-    st.markdown(f"⚡PCE predicted by abcBERT: {pce}")
+    st.header(f":blue[⚡**PCE predicted by abcBERT**]: {pce}")
 except:
-    st.markdown(f"⚡PCE predicted by abcBERT: None")
+    st.header(f":blue[⚡**PCE predicted by abcBERT**]:  :red["Running"]")