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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.markdown('##### At this space, you can predict the products of reactions from their inputs.')
st.markdown('##### The code expects input_data as a string or CSV file that contains an "input" column. The format of the string or contents of the column are like "REACTANT:{reactants of the reaction}CATALYST:{catalysts of the reaction}REAGENT:{reagents of the reaction}SOLVENT:{solvent of the reaction}".')
st.markdown('##### If there are no catalyst or reagent, fill the blank with a space. And if there are multiple reactants, concatenate them with "."')
st.markdown('##### The output contains smiles of predicted products and sum of log-likelihood for each prediction. Predictions are ordered by their log-likelihood.(0th is the most probable product.) "valid compound" is the most probable and valid(can be recognized by RDKit) prediction.')
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():
num_beams = st.number_input(label='num beams', min_value=1, max_value=10, value=5, step=1)
num_return_sequences = num_beams
uploaded_file = st.file_uploader("Choose a CSV file")
input_data = st.text_area(display_text)
model_name_or_path = 'sagawa/ZINC-t5-productpredicition'
model = 't5'
seed = 42
if st.button('predict'):
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)
if CFG.uploaded_file is not None:
input_data = pd.read_csv(CFG.uploaded_file)
outputs = []
for idx, row in input_data.iterrows():
input_compound = row['input']
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)
if CFG.num_beams > 1:
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
outputs.append(output)
else:
output = [tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace('. ', '.').rstrip('.')]
mol = Chem.MolFromSmiles(output[0])
if type(mol) == rdkit.Chem.rdchem.Mol:
output.append(output[0])
else:
output.append(None)
output = [input_compound] + output
outputs.append(output)
if CFG.num_beams > 1:
output_df = pd.DataFrame(outputs, 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'])
else:
output_df = pd.DataFrame(outputs, columns=['input', '0th', 'valid compound'])
@st.cache
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv(index=False)
csv = convert_df(output_df)
st.download_button(
label="Download data as CSV",
data=csv,
file_name='output.csv',
mime='text/csv',
)
else:
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)
if CFG.num_beams > 1:
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
else:
output = [tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace('. ', '.').rstrip('.')]
mol = Chem.MolFromSmiles(output[0])
if type(mol) == rdkit.Chem.rdchem.Mol:
output.append(output[0])
else:
output.append(None)
if CFG.num_beams > 1:
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'])
else:
output_df = pd.DataFrame(np.array([input_compound]+output).reshape(1, -1), columns=['input', '0th', 'valid compound'])
st.table(output_df)
@st.cache
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv(index=False)
csv = convert_df(output_df)
st.download_button(
label="Download data as CSV",
data=csv,
file_name='output.csv',
mime='text/csv',
)
except:
pass |