implementing app

app.py

@@ -51,10 +51,12 @@ st.title("Atom Level Entity Detector")
 image_file = st.file_uploader("Upload a chemical structure candidate image",type=['png'])
 #st.write('filename is', file_name)
 if image_file is not None:
-   col1, col2 = st.columns(2)
+   #col1, col2 = st.columns(2)
 
    image = Image.open(image_file)
-   col1.image(image, use_column_width=True)
+   #col1.image(image, use_column_width=True)
+   st.image(image, use_column_width=True)
+   col1, col2 = st.columns(2)
    if not os.path.exists("uploads/images"):
        os.makedirs("uploads/images")
    with open(os.path.join("uploads/images/","0.png"),"wb") as f: 
@@ -63,14 +65,15 @@ if image_file is not None:
    dataset.prepare_data()
    trainer = pl.Trainer(logger=False)
    atom_preds = trainer.predict(model_atom, dataset.test_dataloader())
-   st.write(atom_preds)
+   #st.write(atom_preds)
    plt.imshow(image, cmap="gray")
    for bbox, label in zip(atom_preds[0]['boxes'][0], atom_preds[0]['preds'][0]):
-       st.write(bbox)
-       st.write(label)
+      # st.write(bbox)
+      # st.write(label)
        plot_bbox(bbox, label)
+   plt.axis('off')
    plt.savefig("example_image.png",bbox_inches='tight', pad_inches=0)
    image_vis = Image.open("example_image.png")
-   col2.image(image_vis, use_column_width=True)
+   col1.image(image_vis, use_column_width=True)
 #x = st.slider('Select a value')
 #st.write(x, 'squared is', x * x)