fix repo_id name

app.py

@@ -80,7 +80,7 @@ with st.container():
         if st.session_state.clicked_pp:
             if st.session_state.clicked_pred == False:
                 with st.container():
-                    pred_image = nib.load(hf_hub_download(repo_id= "rootstrap/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
+                    pred_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
 
                     bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
                     
@@ -94,7 +94,7 @@ with st.container():
 
             else:
                 with st.container():
-                    pred_image = nib.load(hf_hub_download(repo_id= "rootstrap/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
+                    pred_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
 
                     bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
                     
@@ -103,7 +103,7 @@ with st.container():
                     x_value_pred = st.sidebar.slider('Move the slider to adjust the sagittal cut ', bounds_pred[0][0], bounds_pred[0][1], mean([bounds_pred[0][0], bounds_pred[0][1]]))
                     z_value_pred = st.sidebar.slider('Move the slider to adjust the axial cut ', bounds_pred[2][0], bounds_pred[2][1], mean([bounds_pred[2][0], bounds_pred[2][1]]))
 
-                    img_array = load_img(hf_hub_download(repo_id= "rootstrap/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
+                    img_array = load_img(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
                     new_data = transforms(img_array)
                     new_data_tensor = torch.from_numpy(np.array([new_data]))
 
@@ -145,7 +145,7 @@ with st.container():
                     st.pyplot()
     
         else:
-            raw_image = nib.load(hf_hub_download(repo_id= "rootstrap/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="raw", filename = img_path + ".nii"))
+            raw_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="raw", filename = img_path + ".nii"))
 
             bounds_raw = plotting.find_cuts._get_auto_mask_bounds(raw_image)