Add files via upload

app.py

@@ -18,7 +18,7 @@ from statistics import mean
 
 from constants import CLASSES
 from model.download_model import load_model
-from download_pictures import download_images
+from huggingface_hub import hf_hub_download
 
 #SET PAGE TITLE
 st.set_page_config(page_title = "Alzheimer Classifier", page_icon = ":brain:", layout = "wide")
@@ -26,9 +26,6 @@ st.set_page_config(page_title = "Alzheimer Classifier", page_icon = ":brain:", l
 #LOAD MODEL
 model = load_model()
 
-#LOAD IMAGES
-download_images()
-
 #SET NIFTI FILE LOADING AND PROCESSING CONFIGURATIONS
 transforms = Compose([
     ScaleIntensity(),
@@ -91,7 +88,7 @@ with st.container():
         if st.session_state.clicked_pp:
             if st.session_state.clicked_pred == False:
                 with st.container():
-                    pred_image = nib.load(os.path.join("images/preprocessed", img_path + ".nii.gz"))
+                    pred_image = nib.load(hf_hub_download(repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
 
                     bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
                     
@@ -105,7 +102,7 @@ with st.container():
 
             else:
                 with st.container():
-                    pred_image = nib.load(os.path.join("images/preprocessed", img_path + ".nii.gz"))
+                    pred_image = nib.load(hf_hub_download(repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
 
                     bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
                     
@@ -114,7 +111,7 @@ 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(os.path.join("images/preprocessed", img_path + ".nii.gz"))
+                    img_array = load_img(hf_hub_download(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]))
 
@@ -156,7 +153,7 @@ with st.container():
                     st.pyplot()
     
         else:
-            raw_image = nib.load(os.path.join("images/raw", img_path + ".nii"))
+            raw_image = nib.load(hf_hub_download(repo_type="dataset", subfolder="raw", filename = img_path + ".nii"))
 
             bounds_raw = plotting.find_cuts._get_auto_mask_bounds(raw_image)