Updated interface

app.py

@@ -109,8 +109,6 @@ def calculate_dice_coefficient(image1, image2):
     false_negatives = np.sum(np.logical_and(np_image1_flat == 255, np_image2_flat != 255))
     dice_coefficient = (2 * true_positives) / (2 * true_positives + false_positives + false_negatives)
     return dice_coefficient
-
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 st.set_page_config(layout='wide')
 ds = load_dataset('ahishamm/combined_masks',split='train')
@@ -132,34 +130,22 @@ img = image_select(
     captions=["sample 1","sample 2","sample 3","sample 4"],
     return_value='index'
 )
-#testing with an uploaded image 
 processor = AutoProcessor.from_pretrained('ahishamm/skinsam')
 model = AutoModelForMaskGeneration.from_pretrained('ahishamm/skinsam_focalloss_base_combined')
 model.to(device)
-#uploaded_file = st.file_uploader("Choose a file",type=['jpg','jpeg','png'])
-#p = get_bounding_box(np.array(ds[img]['label'])) 
 p = get_bounding_box(np.array(label_arr[img])) 
-#predicted_mask_array = get_output(ds[img]['image'],p)
 predicted_mask_array = get_output(image_arr[img],p)
-#predicted_mask = generate_image(predicted_mask_array)
 predicted_mask = generate_image(predicted_mask_array)
-#result_image = show_mask(ds[img]['image'],predicted_mask_array)
 result_image = show_mask(image_arr[img],predicted_mask_array)
 with st.container(): 
     tab1, tab2 = st.tabs(['Visualizations','Metrics'])
     with tab1: 
-        col1, col2, col3 = st.columns(3) 
+        col1, col2 = st.columns(2) 
         with col1: 
-            #st.image(ds[img]['image'],caption='Original Skin Lesion Image',use_column_width=True)
             st.image(image_arr[img],caption='Original Skin Lesion Image',use_column_width=True)
-        with col2: 
-           st.image(predicted_mask,caption='Predicted Mask',use_column_width=True)
-        with  col3:
+        with col2:
                 st.image(result_image,caption='Mask Overlay',use_column_width=True)
     with tab2: 
-            #st.write(f'The IOU Score: {iou_calculation(ds[img]["label"],predicted_mask)}')
-            #st.write(f'The Pixel Accuracy: {calculate_pixel_accuracy(ds[img]["label"],predicted_mask)}')
-            #st.write(f'The Dice Coefficient Score: {calculate_dice_coefficient(ds[img]["label"],predicted_mask)}')
             st.write(f'The IOU Score: {iou_calculation(label_arr[img],predicted_mask)}')
             st.write(f'The Pixel Accuracy: {calculate_pixel_accuracy(label_arr[img],predicted_mask)}')
             st.write(f'The Dice Coefficient Score: {calculate_dice_coefficient(label_arr[img],predicted_mask)}')