Spaces:
Running
Running
File size: 1,519 Bytes
2ba59d0 36c5570 2ba59d0 6f8c1dc 36c5570 2ba59d0 36c5570 3e2a6d6 cecc846 36c5570 2ba59d0 36c5570 2ba59d0 36c5570 6f8c1dc 36c5570 2ba59d0 36c5570 2ba59d0 66041c1 36c5570 2ba59d0 36c5570 66041c1 2ba59d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
# -*- coding: utf-8 -*-
import streamlit as st
import pandas as pd
import rdkit
import streamlit_ketcher
from streamlit_ketcher import st_ketcher
import abcBERT
import RF
# 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"
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]
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
)
molecule = st.text_input("📋Molecule")
smile_code = st_ketcher(molecule)
st.markdown(f"✨Smiles code: {smile_code}")
P = RF.main( str(smile_code ) )
try:
pce = abcBERT.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}")
except:
st.markdown(f"⚡PCE predicted by abcBERT: None")
|