change folders

app.py

@@ -1,33 +1,29 @@
 import streamlit as st
 import streamlit.components.v1 as components
-from PIL import Image
+from PIL import Image, ImageEnhance
 import torch
 from torchvision.transforms import functional as F
-from PIL import Image, ImageEnhance
-from htlm_webpage import display_bpmn_xml
 import gc
 import psutil
 import copy
-
-
-from OCR import text_prediction, filter_text, mapping_text, rescale
-from train import prepare_model
-from utils import draw_annotations, create_loader, class_dict, arrow_dict, object_dict
-from toXML import calculate_pool_bounds, add_diagram_elements
-from pathlib import Path
-from toXML import create_bpmn_object, create_flow_element
 import xml.etree.ElementTree as ET
 import numpy as np
-from display import draw_stream
-from eval import full_prediction
-from streamlit_image_comparison import image_comparison
 from xml.dom import minidom
+from pathlib import Path
+import gdown
+
+from modules.htlm_webpage import display_bpmn_xml
+from modules.OCR import text_prediction, filter_text, mapping_text, rescale
+from modules.utils import class_dict, arrow_dict, object_dict, find_closest_object
+from modules.toXML import calculate_pool_bounds, add_diagram_elements, create_bpmn_object, create_flow_element
+from modules.display import draw_stream
+from modules.eval import full_prediction
+from modules.train import get_faster_rcnn_model, get_arrow_model
+from streamlit_image_comparison import image_comparison
 from streamlit_cropper import st_cropper
 from streamlit_drawable_canvas import st_canvas
 from streamlit_image_select import image_select
-from utils import find_closest_object
-from train import get_faster_rcnn_model, get_arrow_model
-import gdown
+
 
 def get_memory_usage():
     process = psutil.Process()

flask.py

@@ -1,6 +0,0 @@
-from flask import Flask
-app = Flask(__name__)
-
-@app.route("/")
-def hello():
-    return "Hello World!\n"

OCR.py → modules/OCR.py

@@ -6,15 +6,21 @@ from azure.core.credentials import AzureKeyCredential
 import time
 import numpy as np
 import networkx as nx
-from eval import iou
-from utils import class_dict, proportion_inside
+from modules.utils import class_dict, proportion_inside
 import json
-from utils import rescale_boxes as rescale 
+from modules.utils import rescale_boxes as rescale 
+import streamlit as st
 
 
 VISION_KEY = os.getenv("VISION_KEY")
 VISION_ENDPOINT = os.getenv("VISION_ENDPOINT")
 
+#If local execution
+"""with open("VISION_KEY.json", "r") as json_file:
+    json_data = json.load(json_file)
+    
+VISION_KEY = json_data["VISION_KEY"]
+VISION_ENDPOINT = json_data["VISION_ENDPOINT"]"""
 
 def sample_ocr_image_file(image_data):
     # Set the values of your computer vision endpoint and computer vision key

display.py → modules/display.py

@@ -1,9 +1,9 @@
-from utils import draw_annotations, create_loader, class_dict, resize_boxes, resize_keypoints, find_other_keypoint
+from modules.utils import draw_annotations, create_loader, class_dict, resize_boxes, resize_keypoints, find_other_keypoint
 import cv2
 import numpy as np
 import torch
 import matplotlib.pyplot as plt
-from OCR import group_texts
+from modules.OCR import group_texts
 
 
 

eval.py → modules/eval.py

@@ -1,8 +1,8 @@
 import numpy as np
 import torch
-from utils import class_dict, object_dict, arrow_dict, find_closest_object, find_other_keypoint, filter_overlap_boxes, iou
+from modules.utils import class_dict, object_dict, arrow_dict, find_closest_object, find_other_keypoint, filter_overlap_boxes, iou
 from tqdm import tqdm
-from toXML import create_BPMN_id
+from modules.toXML import create_BPMN_id
 
 
 

toXML.py → modules/toXML.py

@@ -1,5 +1,5 @@
 import xml.etree.ElementTree as ET
-from utils import class_dict
+from modules.utils import class_dict
 
 def rescale(scale, boxes):
     for i in range(len(boxes)):

train.py → modules/train.py

@@ -7,7 +7,7 @@ import torch
 import torchvision.transforms.functional as F
 import matplotlib.pyplot as plt
 
-from eval import main_evaluation
+from modules.eval import main_evaluation
 from torch.optim import SGD, AdamW
 from torch.utils.data import DataLoader, Dataset, Subset, ConcatDataset
 from torch.utils.data.dataloader import default_collate
@@ -15,7 +15,7 @@ from torchvision.models.detection import keypointrcnn_resnet50_fpn, KeypointRCNN
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 from torchvision.models.detection.keypoint_rcnn import KeypointRCNNPredictor
 from tqdm import tqdm
-from utils import write_results
+from modules.utils import write_results