| import streamlit as st | |
| from PIL import Image | |
| import numpy as np | |
| import pubchempy as pcp | |
| from rdkit import Chem | |
| from rdkit.Chem import Draw | |
| import time | |
| st.title("3D2SMILES") | |
| col1, col2 = st.columns(2) | |
| gen_strategy = col1.selectbox("Select a generative strategy", ("Beam Search", "Sampling", "Greedy Search")) | |
| model = col2.selectbox("Select a model", ("V1", "V2")) | |
| uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) | |
| if uploaded_file: | |
| start_time = time.time() | |
| image = Image.open(uploaded_file) | |
| options = ["CC(=O)OC1=CC=CC=C1C(=C)C(=O)O", "CC(=O)", "CC(=O)O", "CC(=O)C", "CC(=O)C1=CC=CC=C1"] | |
| grid = [st.columns(2) for _ in range(len(options) // 3 + 1)] | |
| cols = [col for row in grid for col in row] | |
| for i, (smiles, col) in enumerate(zip(options, cols)): | |
| cid = pcp.get_compounds(smiles, 'smiles') | |
| name = cid[0].synonyms[0] | |
| col.markdown("## Option {}: {}".format(i + 1, name)) | |
| m = Chem.MolFromSmiles(smiles) | |
| img = Draw.MolToImage(m) | |
| col.image(img, use_container_width=False) | |
| pubchem_url = "https://pubchem.ncbi.nlm.nih.gov/compound/{}".format(cid[0].cid) | |
| col.markdown("[PubChem Link]({})".format(pubchem_url)) | |
| st.markdown("---") | |
| st.markdown("Taken {} seconds".format(round(time.time() - start_time, 2))) |