this work

app.py

@@ -10,14 +10,7 @@ from streamlit_drawable_canvas import st_canvas
 from modules.streamlit_utils import *
 from glob import glob
 
-import os
-import subprocess
-
-# Run the setup script if the package directory does not exist
-if not os.path.exists('Streamlit-Image-Annotation'):
-    subprocess.run(['bash', 'setup.sh'], check=True)
-
-from streamlit_image_annotation import detection
+#from streamlit_image_annotation import detection
 from modules.toXML import create_XML
 
 def configure_page():
@@ -122,39 +115,7 @@ def launch_prediction(cropped_image, score_threshold, is_mobile, screen_width):
 
 from modules.eval import develop_prediction, generate_data
 from modules.utils import class_dict
-def modify_results(percentage_text_dist_thresh=0.5):
-    with st.expander("Method and Style modification (beta version)"):
-        label_list = list(class_dict.values())
-        bboxes = [[int(coord) for coord in box] for box in st.session_state.prediction['boxes']]
-        for i in range(len(bboxes)):
-            bboxes[i][2] = bboxes[i][2] - bboxes[i][0]
-            bboxes[i][3] = bboxes[i][3] - bboxes[i][1]
-        labels = [int(label) for label in st.session_state.prediction['labels']]
-        uploaded_image = prepare_image(st.session_state.crop_image, new_size=(1333, 1333), pad=False)
-        scale = 2000 / uploaded_image.size[0]
-        new_labels = detection(
-            image=uploaded_image, bboxes=bboxes, labels=labels, 
-            label_list=label_list, line_width=3, width=2000, use_space=False
-        )
-
-        if new_labels is not None:
-            new_lab = np.array([label['label_id'] for label in new_labels])
-  
-            # Convert back to original format
-            bboxes = np.array([label['bbox'] for label in new_labels])
-            for i in range(len(bboxes)):
-                bboxes[i][2] = bboxes[i][2] + bboxes[i][0]
-                bboxes[i][3] = bboxes[i][3] + bboxes[i][1]
 
-            scores = st.session_state.prediction['scores']
-            keypoints = st.session_state.prediction['keypoints']
-            #print('Old prediction:', st.session_state.prediction['keypoints'])
-            boxes, labels, scores, keypoints, flow_links, best_points, pool_dict = develop_prediction(bboxes, new_lab, scores, keypoints, class_dict, correction=False)
-
-            st.session_state.prediction = generate_data(st.session_state.prediction['image'], boxes, labels, scores, keypoints, flow_links, best_points, pool_dict, class_dict)
-            st.session_state.text_mapping = mapping_text(st.session_state.prediction, st.session_state.text_pred, print_sentences=False, percentage_thresh=percentage_text_dist_thresh)
-
-            #print('New prediction:', st.session_state.prediction['keypoints'])
 
 
 def display_bpmn_modeler(is_mobile, screen_width):
@@ -199,8 +160,8 @@ def main():
             with st.spinner('Waiting for result display...'):
                 display_options(st.session_state.crop_image, st.session_state.score_threshold, is_mobile, int(5/6 * screen_width))
 
-        if not is_mobile:
-            modify_results()
+        #if not is_mobile:
+            #modify_results()
 
         with st.expander("Options for BPMN modeler"):
             modeler_options(is_mobile)