Upload hello.py

hello.py

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+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.
+""")