app.py

import streamlit as st
import os
import torch
#import math
import numpy as np
#import matplotlib.pyplot as plt
#import pathlib
#from AtomLenz import *
#from utils_graph import *
#from Object_Smiles import Objects_Smiles 

#from robust_detection import wandb_config
from robust_detection import utils
from robust_detection.models.rcnn import RCNN
from robust_detection.data_utils.rcnn_data_utils import Objects_RCNN, COCO_RCNN

import pytorch_lightning as pl
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.loggers import CSVLogger
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import LearningRateMonitor
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import DataStructs
from PIL import Image

model_cls = RCNN
experiment_path_atoms="./models/atoms_model/"
dir_list = os.listdir(experiment_path_atoms)
dir_list = [os.path.join(experiment_path_atoms,f) for f in dir_list]
dir_list.sort(key=os.path.getctime, reverse=True)
checkpoint_file_atoms = [f for f in dir_list if "ckpt" in f][0]
model_atom = model_cls.load_from_checkpoint(checkpoint_file_atoms)
model_atom.model.roi_heads.score_thresh = 0.65 


st.title("Atom Level Entity Detector")

image_file = st.file_uploader("Upload a chemical structure candidate image",type=['png','jpeg','jpg'])
#st.write('filename is', file_name)
if image_file is not None:
   col1, col2 = st.columns(2)

   image = Image.open(image_file)
   col1.image(image, use_column_width=True)
   with open(os.path.join("uploads",image_file.name),"wb") as f: 
        f.write(image_file.getbuffer())         
   st.success("Saved File")
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)