model creation

src/pages/2-model_creation.py

-from utils.data_handling import st_var
 from common import *
 st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
 
@@ -8,13 +7,11 @@ UiComponents(pagespath=pages_folder, csspath=css_file, imgpath=image_path,
              header=True, sidebar=True, bgimg=False, colborders=True)
 
 
-st_var(variable='counter', initialize=True, update=False)
+st_var(variable='counter', initialize=True, update=False, type='increment')
+
 ################ clean the results dir #############
 HandleItems.delete_files(keep=['.py', '.pyc', '.bib'])
-for i in ['model', 'dataset', 'figures']:
-    dirpath = Path('./report/results/')
-    if not dirpath.exists():
-        dirpath.mkdir(parents=True, exist_ok=True)
+Path('./report/results/model').mkdir(parents=True, exist_ok=True)
 # ####################################### page preamble #######################################
 st.header("Calibration Model Development")  # page title
 st.markdown("Create a predictive model, then use it for predicting your target variable (chemical data) from NIRS spectra")
@@ -277,7 +274,7 @@ if not x_block.empty and not y.empty:
 ################################################### END : visualize and split the data #######################################################
 
 
-#     ###################################################     BEGIN : Create Model     ####################################################
+    ###################################################     BEGIN : Create Model     ####################################################
 model_type = None  # initialize the selected regression algorithm
 model = None        # initialize the regression model object
 intervalls_with_cols = DataFrame()
@@ -348,9 +345,6 @@ if not x_block.empty and not y.empty:
                     '##### https://journals.sagepub.com/doi/abs/10.1366/0003702001949500')
         st.markdown("-------------")
 
-#     # if  model_type != st.session_state.model_type:
-#     #     st.session_state.model_type = model_type
-#     #     increment()
 
     # Training set preparation for cross-validation(CV)
     with c5:  # Model columns
@@ -492,7 +486,7 @@ if not x_block.empty and not y.empty:
                                       max_value=500, value=10 if model_type == 'TPE-iPLS' else None, disabled=False if model_type == 'TPE-iPLS' else True)
         remodel_button = st.button('re-model the data', type="primary", use_container_width=True,
                                    disabled=False if model_type else True,
-                                   on_click=lambda: st_var(variable='counter', initialize=False, update=True))
+                                   on_click=lambda: st_var(variable='counter', initialize=False, update=True, type = 'increment'))
 
         hash_ = ObjectHash(current=hash_, add=[
             iternum, internum, st.session_state.counter, model_type])