implementing app

app.py

@@ -86,12 +86,12 @@ if image_file is not None:
    #st.success("Saved File")
    dataset.prepare_data()
    trainer = pl.Trainer(logger=False)
-   st.success('Predicting atoms,bonds,charges,..., please wait')
+   st.toast('Predicting atoms,bonds,charges,..., please wait')
    atom_preds = trainer.predict(model_atom, dataset.test_dataloader())
    bond_preds = trainer.predict(model_bond, dataset.test_dataloader())
    stereo_preds = trainer.predict(model_stereo, dataset.test_dataloader())
    charge_preds = trainer.predict(model_charge, dataset.test_dataloader())
-   st.success('Done')
+   st.toast('Done')
    #st.write(atom_preds)
    plt.imshow(image, cmap="gray")
    for bbox, label in zip(atom_preds[0]['boxes'][0], atom_preds[0]['preds'][0]):