app.py

import streamlit as st
from rdkit.Chem import MACCSkeys
from rdkit import Chem
import numpy as np
import pandas as pd
import xgboost as xgb
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# from torch.autograd import Variable
# from torch.utils.data import Dataset
# import torch.utils.data 
# from torch_geometric.data import DataLoader
# from torch_geometric.data import Data

# from torch_geometric.nn import GATConv, RGCNConv, GCNConv, global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
from sklearn.metrics import f1_score, accuracy_score, average_precision_score, roc_auc_score

import rdkit
from rdkit.Chem.Scaffolds import MurckoScaffold

# from itertools import compress
# import random
# from collections import defaultdict
import pickle
device = 'cpu'
model_path = 'model/'

st.set_page_config(
    page_title='Hello'
)
st.write('# JAK inhibiition prediction app')
st.sidebar.success('Select a page above')

st.markdown(
    """
        * This is an open-source app framework built specifically for JAK inhibition of a certain drug with its SMILES as input.
        * Suitable model(s) could be chosen for prediction based on your need (in JAK page). 
        * Simple machine learning models, tree models, graph-based models and bert models are trained ane evaluated (results in Model Evaluation page). 
        * Area uder the curve could also be drawn based on our test set results (in Plot AUC page). 
        Prediction should be used with caution and just for reference.
""")