added automated simple interface with default values

src/pages/1-samples_selection.py

 from Packages import *
 st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
 from Modules import *
-repertoire_a_vider = 'D:/Mouhcine/nirs_workflow/src/Report/figures'
 
+# empty temp figures
+repertoire_a_vider = 'D:/Mouhcine/nirs_workflow/src/Report/figures'
 if os.path.exists(repertoire_a_vider):
     for fichier in os.listdir(repertoire_a_vider):
         chemin_fichier = os.path.join(repertoire_a_vider, fichier)
@@ -11,27 +12,40 @@ if os.path.exists(repertoire_a_vider):
         elif os.path.isdir(chemin_fichier):
             shutil.rmtree(chemin_fichier)
 # HTML pour le bandeau "CEFE - CNRS"
-# bandeau_html = """
-# <div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
-#   <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
-# </div>
-# """
-# # Injecter le code HTML du bandeau
-# st.markdown(bandeau_html, unsafe_allow_html=True)
 add_header()
+#load specific model page css
+local_css(css_file / "style_model.css")
+
+#define some variables
 tcr=pd.DataFrame()
 sam=pd.DataFrame()
 sam1=pd.DataFrame()
+dim_red_methods=['', 'PCA','UMAP', 'NMF']  # List of dimensionality reduction algos
+cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP'] # List of clustering algos
+selec_strategy = ['center','random']
 
-# path = os.path.dirname(os.path.abspath(__file__)).replace('\\','/')
-# css_file = path[:path.find('/pages')]+'/style'
-# local_css(css_file +"/style_model.css")
-local_css(css_file / "style_model.css")
-
+# check session state and define default values if simple interface to automate processing
 st.session_state["interface"] = st.session_state.get('interface')
 if st.session_state["interface"] == 'simple':
+    st.write(':red[Automated Simple Interface]')
     hide_pages("Predictions")
-    
+    if 37 not in st.session_state:
+        default_reduction_option = 1
+    else:
+        default_reduction_option = dim_red_methods.index(st.session_state.get(37))
+    if 38 not in st.session_state:
+        default_clustering_option = 1
+    else:
+        default_clustering_option = cluster_methods.index(st.session_state.get(38))
+    if 102 not in st.session_state:
+        default_sample_selection_option = 1
+    else:
+        default_sample_selection_option = selec_strategy.index(st.session_state.get(102))
+
+if st.session_state["interface"] == 'advanced':
+    default_reduction_option = 0
+    default_clustering_option = 0
+    default_sample_selection_option = 0
 
 ################################### I - Data Loading and Visualization ########################################
 st.header("I - Spectral Data Visualization", divider='blue')
@@ -111,9 +125,6 @@ scores, loadings, pc = st.columns([2, 3, 0.5])
 influence, hotelling, qexp = st.columns([2, 2, 1])
 st.header('III - Selected samples for chemical analysis', divider='blue')
 
-dim_red_methods=['', 'PCA','UMAP', 'NMF']  # List of dimensionality reduction algos
-cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP'] # List of clustering algos
-
 dr_model = None # dimensionality reduction model
 cl_model = None # clustering model
 
@@ -122,8 +133,8 @@ t = pd.DataFrame # scores
 p = pd.DataFrame # loadings
 labels = []
 if not spectra.empty:
-    dim_red_method = pc.selectbox("Dimensionality reduction techniques: ", options = dim_red_methods, key = 37)
-    clus_method = pc.selectbox("Clustering techniques: ", options = cluster_methods, key = 38)
+    dim_red_method = pc.selectbox("Dimensionality reduction techniques: ", options = dim_red_methods, index = default_reduction_option, key = 37)
+    clus_method = pc.selectbox("Clustering techniques: ", options = cluster_methods, index = default_clustering_option, key = 38)
     xc = standardize(spectra, center=True, scale=False)
 
 
@@ -196,7 +207,6 @@ if not t.empty:
     
 
 #################################################### III - Samples selection using the reduced data preentation ######
-selec_strategy = ['center','random']
 samples_df_chem = pd.DataFrame
 selected_samples = []
 selected_samples_idx = []
@@ -205,7 +215,7 @@ selected_samples_idx = []
 if labels:
     if clus_method:
         selection = scores.radio('Select samples selection strategy:',
-                                    options = selec_strategy)
+                                    options = selec_strategy, index = default_sample_selection_option, key=102)
     # Strategy 0
     if selection == selec_strategy[0]:
         # list samples at clusters centers - Use sklearn.metrics.pairwise_distances_argmin if you want more than 1 sample per cluster
@@ -499,8 +509,8 @@ Ac_Km = ['Spectra_Plot.png', 'Elbow.png', 'graphe_loadings.png', 'plot_axe1_axe2
 
 # Streamlit container
 with st.container():
-    header3, header4 = st.columns(2)
-    if header3.button("Exporter le rapport"):
+    header3, = st.columns(1)
+    if header3.button("Export report"):
         if test == '.csv':
             if dim_red_method == dim_red_methods[1] and clus_method == cluster_methods[1]:
                 latex_report = report.report(sam, tcr, Nb_ech, nb_clu, 'sample', Ac_Km, 'csv', 'kmeans')