borders modif

src/pages/1-samples_selection.py

@@ -12,15 +12,16 @@ from Modules import *
 # st.markdown(bandeau_html, unsafe_allow_html=True)
 add_header()
 
+local_css(css_file / "style_model.css")
 
 st.session_state["interface"] = st.session_state.get('interface')
 if st.session_state["interface"] == 'simple':
     hide_pages("Predictions")
+    
 
 ################################### I - Data Loading and Visualization ########################################
+st.header("I - Spectral Data Visualization", divider='blue')
 col2, col1 = st.columns([3, 1])
-col1.header("Data Loading", divider='blue')
-col2.header("Spectral Data Visualization", divider='blue')
 
 
 ## Preallocation of data structure
@@ -90,12 +91,10 @@ if not spectra.empty:
 
 
 ############################## Exploratory data analysis ###############################
-container2 = st.container(border=True)
-container2.header("Exploratory Data Analysis-Multivariable Data Analysis", divider='blue')
+st.header("II - Exploratory Data Analysis-Multivariable Data Analysis", divider='blue')
 scores, loadings, pc = st.columns([2, 3, 0.5])
 influence, hotelling, qexp = st.columns([2, 2, 1])
-st.header('Selected samples for chemical analysis', divider='blue')
-selected_s, selected_samples_metd = st.columns([3, 3])
+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
@@ -221,9 +220,9 @@ if labels:
             sam1.insert(loc=0, column='index', value=selected_samples_idx)
             sam1.insert(loc=1, column='cluster', value=np.array(labels)[selected_samples_idx])
         sam1.index = np.arange(len(selected_samples_idx))+1
-        selected_s.write(f' - The total number of samples:{tcr.shape[0]}.\n- The number of selected samples for chemical analysis: {sam1.shape[0]}.')
+        st.write(f' - The total number of samples:{tcr.shape[0]}.\n- The number of selected samples for chemical analysis: {sam1.shape[0]}.')
         sam = sam1
-        unclus = selected_s.checkbox("Include non clustered samples (for HDBSCAN clustering)", value=True)
+        unclus = st.checkbox("Include non clustered samples (for HDBSCAN clustering)", value=True)
 
         if clus_method == cluster_methods[2]:
             if selected_samples_idx:
@@ -239,7 +238,7 @@ if labels:
                     
                     sam = pd.concat([sam1, sam2], axis = 0)
                     sam.index = np.arange(sam.shape[0])+1
-                    selected_s.write(f' The number of Non-clustered samples is {sam2.shape[0]} samples')
+                    st.write(f' The number of Non-clustered samples is {sam2.shape[0]} samples')
         else:
             sam = sam1
         st.write(sam)

src/pages/3-prediction.py

@@ -13,6 +13,7 @@ from Class_Mod.DATA_HANDLING import *
 add_header()
 
 st.session_state["interface"] = st.session_state.get('interface')
+local_css(css_file / "style_model.css")
 
 
 st.header("Data loading", divider='blue')