app.py

import os
import gc
import random
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import torch
import tokenizers
import transformers
from transformers import AutoTokenizer, EncoderDecoderModel, AutoModelForSeq2SeqLM
import sentencepiece
from rdkit import Chem
import rdkit
import streamlit as st

st.title('predictproduct-t5')
st.text('At this space, you can predict the products of reactions from their inputs.')
st.text('The format of the string is like "REACTANT:{reactants of the reaction}CATALYST:{catalysts of the reaction}REAGENT:{reagents of the reaction}SOLVENT:{solvent of the reaction}".')
st.text('If there are no catalyst or reagent, fill the blank with a space. And if there are multiple reactants, concatenate them with "."')
display_text = 'input the reaction smiles (e.g. REACTANT:CNc1nc(SC)ncc1CO.O.O=[Cr](=O)([O-])O[Cr](=O)(=O)[O-].[Na+]CATALYST: REAGENT: SOLVENT:CC(=O)O)'

class CFG():
    input_data = st.text_area(display_text)
    model_name_or_path = 'sagawa/ZINC-t5-productpredicition'
    model = 't5'
    num_beams = 5
    num_return_sequences = 5
    seed = 42


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
seed_everything(seed=CFG.seed) 


tokenizer = AutoTokenizer.from_pretrained(CFG.model_name_or_path, return_tensors='pt')
    
if CFG.model == 't5':
    model = AutoModelForSeq2SeqLM.from_pretrained(CFG.model_name_or_path).to(device)
elif CFG.model == 'deberta':
    model = EncoderDecoderModel.from_pretrained(CFG.model_name_or_path).to(device)

input_compound = CFG.input_data
min_length = min(input_compound.find('CATALYST') - input_compound.find(':') - 10, 0)
inp = tokenizer(input_compound, return_tensors='pt').to(device)
output = model.generate(**inp, min_length=min_length, max_length=min_length+50, num_beams=CFG.num_beams, num_return_sequences=CFG.num_return_sequences, return_dict_in_generate=True, output_scores=True)
scores = output['sequences_scores'].tolist()
output = [tokenizer.decode(i, skip_special_tokens=True).replace('. ', '.').rstrip('.') for i in output['sequences']]
for ith, out in enumerate(output):
    mol = Chem.MolFromSmiles(out.rstrip('.'))
    if type(mol) == rdkit.Chem.rdchem.Mol:
        output.append(out.rstrip('.'))
        scores.append(scores[ith])
        break
if type(mol) == None:
    output.append(None)
    scores.append(None)
output += scores
output = [input_compound] + output
output_df = pd.DataFrame(np.array(output).reshape(1, -1), columns=['input'] + [f'{i}th' for i in range(CFG.num_beams)] + ['valid compound'] + [f'{i}th score' for i in range(CFG.num_beams)] + ['valid compound score'])
st.table(output_df)