refactoring, improved string cleansing preprocessing

test-notebooks/dashboard/app.py

@@ -1,5 +1,8 @@
-from typing import cast
+import time
+import webbrowser
 from pathlib import Path
+from threading import Thread
+from typing import cast
 
 import pandas as pd
 import plotly.express as px
@@ -13,17 +16,20 @@ from dash import (
     dcc,
     html,
 )
-from lang_main import load_pickle
+from lang_main import CALLER_PATH
+from lang_main.io import load_pickle
 from lang_main.types import ObjectID, TimelineCandidates
 from pandas import DataFrame
 
 # df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
 
 # ** data
-p_df = Path(r'.\test-notebooks\dashboard\Pipe-TargetFeature_Step-3_remove_NA.pkl')
-p_tl = Path(
-    r'.\test-notebooks\dashboard\Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl'
-)
+# p_df = Path(r'.\test-notebooks\dashboard\Pipe-TargetFeature_Step-3_remove_NA.pkl')
+p_df = CALLER_PATH.joinpath('./Pipe-TargetFeature_Step-3_remove_NA.pkl')
+# p_tl = Path(
+#     r'.\test-notebooks\dashboard\Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl'
+# )
+p_tl = CALLER_PATH.joinpath('./Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl')
 ret = cast(DataFrame, load_pickle(p_df))
 data = ret[0]
 ret = cast(tuple[TimelineCandidates, dict[ObjectID, str]], load_pickle(p_tl))
@@ -171,5 +177,19 @@ def update_table_candidates(index, obj_id):
     return table_data, cols
 
 
-if __name__ == '__main__':
+def _start_webbrowser():
+    host = '127.0.0.1'
+    port = '8050'
+    adress = f'http://{host}:{port}/'
+    time.sleep(2)
+    webbrowser.open_new(adress)
+
+
+def main():
+    webbrowser_thread = Thread(target=_start_webbrowser, daemon=True)
+    webbrowser_thread.start()
     app.run(debug=True)
+
+
+if __name__ == '__main__':
+    main()