server.R

	library(Rautoml)
options(shiny.maxRequestSize=3000000*1024^2)

function(input, output, session){
  # ---- Progress overlay for cold-start feedback ----
  update_progress <- function(msg) {
    waiter::waiter_show(
      html = tagList(
        spin_loaders(id = 2, style="width:56px;height:56px;color:#7BC148;"),
        tags$br(),
        tags$span(msg, style="color:#555;font-size:14px;margin-top:12px;display:block;")
      ),
      color = "#FFF"
    )
  }
  update_progress("Loading packages...")
  
  source("server/auth.R")
  source("Server/homepage_translation_labels.R", local = TRUE)
  register_homepage_labels(output, session, get_rv_labels)
  
  USER = user_auth(input, output, session)
  
  authed_started = reactiveVal(FALSE)

  observeEvent(USER$logged_in, {
	 req(isTRUE(USER$logged_in))
	 waiter_show(
		html = spin_loaders(id = 3, style="width:56px;height:56px;color:#7BC148;"),
		color = "#FFF"
	 )

	 if (authed_started()) return()
	 authed_started(TRUE)

	 app_username = USER$username
  
	  model_training_caret_pb = Attendant$new("model_training_caret_pb", hide_on_max = TRUE)
	  data_upload_id_pb = Attendant$new("data_upload_id_pb", hide_on_max = TRUE)
	  model_metrics_caret_pb = Attendant$new("model_metrics_caret_pb", hide_on_max = TRUE)
	  deploy_models_caret_pb = Attendant$new("deploy_models_caret_pb", hide_on_max = TRUE)
	  predict_models_caret_pb = Attendant$new("predict_models_caret_pb", hide_on_max = TRUE)
	  predict_endpoint_models_caret_pb = Attendant$new("predict_endpoint_models_caret_pb", hide_on_max = TRUE)
	  generate_research_questions_outcome_pb = Attendant$new("generate_research_questions_outcome_pb", hide_on_max = TRUE)
	  generate_research_questions_additional_analysis_pb = Attendant$new("generate_research_questions_additional_analysis_pb", hide_on_max = TRUE)
	  feature_engineering_perform_preprocess_pb = Attendant$new("feature_engineering_perform_preprocess_pb", hide_on_max = TRUE)
	  model_training_caret_metrics_download_all_zip_pb = Attendant$new("model_training_caret_metrics_download_all_zip_pb", hide_on_max = TRUE)
	  
	  #### ---- Input validators ---------------------------------------------------
	  source("server/input_validators.R")
	  #### ---- Create needed folders for datasets and logs ------------------------
	  source("server/create_dirs.R", local=TRUE)
	  
	  #### ---- FastAPI base URL réactif (lié au champ fastapi_base) ----
	  source("R/utils_logging.R")

	  DEFAULT_API_BASE <- Sys.getenv("FASTAPI_BASE", "http://api:8000")

	  source("server/automl_controls_server.R")
	  source("server/train_model_server.R")
	  source("R/utils_api.R")
	  source("server/deploy_model_server.R", local=TRUE)
	  source("ui/deploy_model_ui.R", local=TRUE)
	  source("server/predict_pycaret_server.R", local = TRUE)
	  
	  source("server/history_actions.R", local = TRUE)
	  source("server/history_transform_actions.R", local = TRUE)
	  source("server/history_visualize_auto_actions.R", local = TRUE)
	  source("server/history_visualize_custom_actions.R", local = TRUE)
	  
	  api_base <- reactive({
		 val <- input$fastapi_base
			 if (is.null(val) || !nzchar(trimws(val))) {
				DEFAULT_API_BASE
			 } else {
				trimws(val)
			 }
	  })
	  observe({
	    
	    expand_label   <- get_rv_labels("sidebar_toggle_expand_menu")
	    collapse_label <- get_rv_labels("sidebar_toggle_menu_aria")
	    
	    session$sendCustomMessage(
	      type = "sidebarLabels",
	      message = list(
	        expand   = expand_label,
	        collapse = collapse_label
	      )
	    )
	    
	  })
	  
	  # Send sidebar toggle labels to JS (reactive-safe)
	  sent_sidebar_labels <- reactiveVal(FALSE)
	  
	  observe({
	    req(rv_lang$labelling_file_df)    
	    req(!sent_sidebar_labels())       
	    
	    expand_label   <- get_rv_labels("sidebar_toggle_expand_menu")
	    collapse_label <- get_rv_labels("sidebar_toggle_menu_aria")
	    
	    session$sendCustomMessage(
	      "sidebarLabels",
	      list(expand = expand_label, collapse = collapse_label)
	    )
	    
	    sent_sidebar_labels(TRUE)
	  })

	  #### ---- Placeholder for reactive values ------------------------------------
	  ##### -------- Currently selected dataset ------------------------------------
	  rv_current = reactiveValues(
		 dataset_id = NULL
		 , metadata_id = NULL
		 , data = NULL
		 , selected_vars = NULL
		 , selected_var = NULL
		 , working_df = NULL
		 , current_filter = NULL
		 , current_filter_reset = NULL
		 , manage_data_title_explore = NULL
		 , missing_prop = NULL
		 , has_missing_data_check=FALSE
		 , manage_data_title_transform = NULL
		 , merge_data_title_merge = NULL
		 , transform_data_select_vars = NULL
		 , vartype = NULL
		 , changed_variable_type_log = NULL
		 , transform_data_plot_df = NULL
		 , renamed_variable_log = NULL
		 , transform_data_quick_plot_out = NULL
		 , recoded_variable_labels_log = NULL
		 , missing_prop_df = NULL
		 , created_missing_values_log = NULL
		 , outlier_values = NULL
		 , handle_missing_values_log = NULL
		 , handle_outlier_values_log = NULL
		 , transform_data_plot_missing_data_out = NULL
		 , quick_explore_summary = NULL
		 , max_tockens = 10000
		 , seed = 9991
		 , outcome = NULL
		 , vartype_all = NULL
	  )
	  
	  ##### ---- Sidebar history actions -----------------------------------
	  history_actions_server(input, output, session, rv_current, get_rv_labels)
	  history_transform_actions_server(input, output, session, rv_current, get_rv_labels)
	  history_visualize_auto_actions_server(input, output, session, rv_current, plots_sec_rv, get_rv_labels)
	  history_visualize_custom_actions_server(input, output, session, rv_current, plots_sec_rv, get_rv_labels)
	  #####------------------Plots Reactive-------------------

	  plots_sec_rv <- reactiveValues(
		 plot_rv=NULL
		 ,tab_rv=NULL
		 ,plot_bivariate_auto=NULL
		 ,plot_corr = NULL
	  )
	  

	  ##### --------- Meta data ---------------------------------------------
	  rv_metadata = reactiveValues(
		 upload_logs = NULL
		 , dataset_ids = NULL
		 , data_summary_str = NULL
		 , data_summary_skim = NULL
		 , data_summary_summary = NULL
		 , data_summary_summarytools = NULL
	  )

		rv_database <- reactiveValues(schema_list = NULL
			, table_list = NULL
			, conn = NULL
			, schema_selected = NULL
			, table_selected = NULL
			, df_table = data.frame()
			, df_table_str = NULL
			, query_table_name = NULL
			, database_host = NULL
			, database_name = NULL
			, database_user = NULL
			, database_pass = NULL
			, details = NULL
		)

		## ---
		
		rv_omop<- reactiveValues(
		  url = NULL )
		
		
		## LLM/GAI
		rv_generative_ai = reactiveValues(
			history = NULL
		)

		## Reactive values for ML/AI module
		rv_ml_ai = reactiveValues(
			session_id = NULL
			, seed_value = NULL
			, dataset_id = NULL 
			, analysis_type = NULL
			, task = NULL
			, outcome = NULL
			, model_formula = NULL
			, partition_ratio = NULL
			, predictors = NULL
			, excluded_predictors = NULL
			, ml_ai_setup_result = NULL
			, history = NULL
			, split = NULL
			, train_df = NULL
			, test_df = NULL
			, preprocessed = NULL
			, feature_engineering_preprocessed_log = NULL
			, at_least_one_model = FALSE
		)

		## RV to hold UIs
		rv_ui_models = reactiveValues(
			model_training_caret_models_ols_check = NULL
			, model_training_caret_models_ols_advance_control = NULL
		)
			
		## Train control caret
		rv_train_control_caret = reactiveValues(
			method = "cv"
			, number = 5
			, repeats = NA
			, search = "grid"
			, verboseIter = FALSE
			, savePredictions = FALSE
			, classProbs = TRUE
		)
		
		## Trained models
		rv_training_models = reactiveValues(
			ols_model = NULL
			, ols_param = FALSE
			, ols_name = NULL
			, ols_trained_model = NULL
			
			, rf_model = NULL
			, rf_param = FALSE
			, rf_name = NULL
			, rf_trained_model = NULL
			
			, gbm_model = NULL
			, gbm_param = FALSE
			, gbm_name = NULL
			, gbm_trained_model = NULL
			
			, xgbTree_model = NULL
			, xgbTree_param = FALSE
			, xgbTree_name = NULL
			, xgbTree_trained_model = NULL
			
			, xgbLinear_model = NULL
			, xgbLinear_param = FALSE
			, xgbLinear_name = NULL
			, xgbLinear_trained_model = NULL
			
			, svmRadial_model = NULL
			, svmRadial_param = FALSE
			, svmRadial_name = NULL
			, svmRadial_trained_model = NULL
			
			, svmLinear_model = NULL
			, svmLinear_param = FALSE
			, svmLinear_name = NULL
			, svmLinear_trained_model = NULL
			
			, svmPoly_model = NULL
			, svmPoly_param = FALSE
			, svmPoly_name = NULL
			, svmPoly_trained_model = NULL
			
			, glmnet_model = NULL
			, glmnet_param = FALSE
			, glmnet_name = NULL
			, glmnet_trained_model = NULL
			
			, lasso_model = NULL
			, lasso_param = FALSE
			, lasso_name = NULL
			, lasso_trained_model = NULL
			
			, ridge_model = NULL
			, ridge_param = FALSE
			, ridge_name = NULL
			, ridge_trained_model = NULL
			
			, knn_model = NULL
			, knn_param = FALSE
			, knn_name = NULL
			, knn_trained_model = NULL
			
			, nnet_model = NULL
			, nnet_param = FALSE
			, nnet_name = NULL
			, nnet_trained_model = NULL
			
			, avNNet_model = NULL
			, avNNet_param = FALSE
			, avNNet_name = NULL
			, avNNet_trained_model = NULL
			
			, pls_model = NULL
			, pls_param = FALSE
			, pls_name = NULL
			, pls_trained_model = NULL
			
			, rpart_model = NULL
			, rpart_param = FALSE
			, rpart_name = NULL
			, rpart_trained_model = NULL
			
			, mlpWeightDecayML_model = NULL
			, mlpWeightDecayML_param = FALSE
			, mlpWeightDecayML_name = NULL
			, mlpWeightDecayML_trained_model = NULL
			
			, naive_bayes_model = NULL
			, naive_bayes_param = FALSE
			, naive_bayes_name = NULL
			, naive_bayes_trained_model = NULL
			
			, all_trained_models = NULL
		)
		
		rv_training_results = reactiveValues(
			models = NULL
			, train_metrics_df = NULL
			, test_metrics_objs = NULL
			, post_model_metrics_objs = NULL
			, control_parameters = NULL
			, tuned_parameters = NULL
		)

	  # Update training results when a new model is trained
	  automl_controls_server(
		 id         = "automl_controls",
		 rv_current = rv_current,
		 rv_ml_ai   = rv_ml_ai,
		 api_base   = api_base,
		 app_username = app_username
	  )

	  train_model_server(
		 id         = "train_model",
		 rv_current = rv_current,
		 rv_ml_ai   = rv_ml_ai,
		 api_base   = api_base
	  )

	  # End update training
	  # ---- (A) Detect if a complete PyCaret run is available (leaderboard displayed) ----
	  .can_show_by_train <- reactive({
		 curr_ds <- rv_current$dataset_id %||% rv_ml_ai$dataset_id
		 isTRUE(rv_ml_ai$status %in% c("Finished","Finished_NoPlots")) &&
		 !is.null(rv_ml_ai$leaderboard) && NROW(rv_ml_ai$leaderboard) > 0 &&
		 isTRUE(nzchar(rv_ml_ai$trained_dataset_id)) &&
		 identical(rv_ml_ai$trained_dataset_id, curr_ds)
	  })

	  observeEvent(rv_current$dataset_id, {
	  # If we change the dataset, we clean up the transient state linked to the previous train.
	  if (!identical(rv_current$dataset_id, rv_ml_ai$trained_dataset_id)) {
		 rv_ml_ai$leaderboard <- NULL
		 rv_ml_ai$leaderboard_full <- NULL
		 rv_ml_ai$test_leaderboard <- NULL
		 rv_ml_ai$test_leaderboard_full <- NULL
		 rv_ml_ai$models <- NULL
		 rv_ml_ai$eval_metrics <- NULL
		 rv_ml_ai$eval_plots <- NULL
		 rv_ml_ai$status <- NULL
	  }
	}, ignoreInit = FALSE)



	# ---- (B) Datasets with pre-trained models (historical) ----
	  .get_models_index_csv <- function() file.path(getwd(), app_username, "logs", "models", "index.csv")
	  dataset_has_history <- reactive({
		 idx <- .get_models_index_csv()
		 if (!file.exists(idx)) return(FALSE)
		 df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL)
		 if (is.null(df) || !"dataset_id" %in% names(df)) return(FALSE)
		 ds <- rv_ml_ai$dataset_id %||% rv_current$dataset_id
		 if (is.null(ds) || !nzchar(ds)) return(FALSE)
		 any(df$dataset_id == ds & (df$framework %in% c("PyCaret","pycaret","Pycaret")))
	  })

	  # Expose known datasets for the Deploy module (used in its selector)
	  observe({
		 idx <- .get_models_index_csv()
		 if (!file.exists(idx)) return(invisible(NULL))
		 df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL)
		 if (is.null(df) || !"dataset_id" %in% names(df)) return(invisible(NULL))
		 rv_current$known_datasets <- sort(unique(df$dataset_id))
	  })
	  .can_show_deploy <- reactive({
		 isTRUE(.can_show_by_train()) || isTRUE(dataset_has_history())
	  })
	  # (keep this if you still use it on the JS side)
	  output$can_show_deploy <- reactive({ .can_show_deploy() })
	  outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE)

	  # ---- Deploy tab UI container ----
	  output$deploy_container <- renderUI({
		 if (!isTRUE(.can_show_deploy())) return(NULL)  # => onglet totalement vide
		 column(width = 12, deployment_ui("deploy"))
	  })

	  outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE)
	  ## Deployed models table
		rv_deploy_models = reactiveValues(
			trained_models_table = NULL
		)

		## Deployed models
		rv_deployed_models = reactiveValues()
	  
		## Reactive values to stock AutoML leaderboard
		rv_automl <- reactiveValues(
		  leaderboard = NULL
		)

		#### ---- App title ----------------------------------------------------
	  source("server/header_footer_configs.R", local=TRUE)

	  app_title()
	  
	  ###-------App Footer--------------------------
	  
	  footer_language_translation()
	  ###-------Menu Translate---------
	  
	  menu_translation()

	  #### ---- Change language ----------------------------------------------------
	  output$change_language = change_language

	  source("server/change_language_update.R", local = TRUE)
	  change_language_update() 
	  
	  #### ---- Upload data UI --------------------------------------------
	  source("ui/upload_data.R", local = TRUE)
	  output$upload_type = upload_type
	  
	  #### ---- Upload dataset/files UI --------------------------------------------
	  source("server/input_files.R", local = TRUE)
	  output$input_files = input_files
	  
	  #### ---- Show uploaded datasets UI --------------------------------------------
	  output$show_uploaded = show_uploaded
	  
	  #### ---- Data upload form -----------------------------------------------
	  source("ui/upload_form.R", local = TRUE)
	  output$study_name = study_name
	  output$study_country = study_country
	  output$additional_info = additional_info
	  output$submit_upload = submit_upload
	 
	  #### ---- Databse and API connection warning ---------------------
	  db_api_con_future
	  
	  #### ---- Upload datasets ----------------------------------------
	  source("server/upload_data.R", local = TRUE)
	  upload_data_server()
	  
	  #### ---- Database integration ----------------------------------------
	  source("server/database_integration.R", local = TRUE)
	  database_integration_server()
	 
	  #### --- Database related form elements ---###
	  output$db_type = db_type
	  output$db_host = db_host
	  output$db_name = db_name
	  output$db_user = db_user
	  output$db_pwd = db_pwd
	  output$db_connect = db_connect
	  output$db_schema_list = db_schema_list
	  output$db_table_list = db_table_list
	  output$db_custom_query = db_custom_query
	  output$db_run_query = db_run_query
	  output$db_port = db_port
	  output$db_disconnect = db_disconnect
	  output$db_tab_query = db_tab_query
	  output$existing_connection = existing_connection
	  
	  source("server/omop_analysis.R", local = TRUE)
	  omop_analysis_server()
	  
	  stderr_file_path <- file.path(getwd(), app_username, "output", "dq_stderr.txt")
	  
	  stderr_content<-create_log_reader(stderr_file_path)
	  

	  #### ---- Collect logs ----------------------------------------
	  source("server/collect_logs.R", local = TRUE)
	  collect_logs_server()
	  
	  #### ---- Display uploaded datasets ----------------------------------------
	  source("server/display_uploaded_data.R", local = TRUE)
	  display_uploaded_data_server()

	  #### ---- Delete uploaded dadatsets ----------------------------------------
	  source("server/delete_uploaded_data.R", local = TRUE)
	  delete_uploaded_data_server()
	  
	  #### ---- Update logfiles based on existing datasets -------------------####
	  source("server/update_logs.R", local = TRUE)
	  update_logs_server()
	  
	  #### ---- Manage data ----------------------------------------------
	  
	  ##### ---- Select data ---------------------------------------------
	  source("server/select_data.R", local = TRUE)
	  select_data_server()
	  manage_data_show_server()
	  
	  ##### ---- Display meta data for the selected dataset ---------------------------------------------
	  source("server/display_metadata.R", local = TRUE)
	  display_selected_metadata_server()
	  reset_display_selected_metadata_server()

	  ##### ---- Currently selected data ---------------------------------------------
	  source("server/selected_data.R", local = TRUE)
	  currently_selected_data_server()

	  ##### ----Generate summary stats for the row data -------------------
	  source("server/manage_data_summary.R", local = TRUE)
	  generate_data_summary_server()
	  display_data_summary_server()

	  #### ----- Explore data -----------------------------------------------
	  source("server/explore_data.R", local = TRUE)
	  explore_data_server()
	  explore_data_subactions_server()
	  
	  ##----User Defined Visualization section-----------------------
	  source("ui/user_defined_visualization_header.R", local = TRUE)
	  output$user_output_type = user_output_type
	  output$user_tab_options = user_tab_options
	  output$user_calc_var = user_calc_var
	  #output$user_strata_var = user_strata_var
	  output$user_row_var = user_row_var
	  output$usr_create_cross_tab = usr_create_cross_tab
	  output$user_download_table = user_download_table
	  
	  output$user_table_options = user_table_options
	  output$user_report_numeric = user_report_numeric
	  output$user_add_p_value = user_add_p_value
	  output$user_add_confidence_interval = user_add_confidence_interval
	  output$user_drop_missing_values = user_drop_missing_values
	  output$user_table_caption = user_table_caption
	  
	  output$user_plot_options = user_plot_options
	  output$user_select_variable_on_x_axis = user_select_variable_on_x_axis
	  output$user_select_variable_on_y_axis = user_select_variable_on_y_axis
	  output$user_plot_title = user_plot_title
	  output$user_x_axis_label = user_x_axis_label
	  output$user_y_axis_label = user_y_axis_label
	  output$user_create = user_create
	  output$user_download = user_download
	  
	  output$user_more_plot_options = user_more_plot_options
	  output$user_transform_to_doughnut = user_transform_to_doughnut
	  output$user_select_color_variable = user_select_color_variable
	  output$user_select_group_variable = user_select_group_variable
	  output$user_visual_orientation = user_visual_orientation
	  output$user_bar_width = user_bar_width
	  output$user_line_size = user_line_size
	  output$user_select_line_type = user_select_line_type
	  output$user_add_shapes = user_add_shapes
	  
	  output$user_select_shape = user_select_shape
	  output$user_add_smooth = user_add_smooth
	  output$user_display_confidence_interval = user_display_confidence_interval
	  output$user_level_of_confidence_interval = user_level_of_confidence_interval
	  output$user_select_line_join = user_select_line_join
	  output$user_add_line_type = user_add_line_type
	  output$user_add_points = user_add_points
	  output$user_y_variable_summary_type = user_y_variable_summary_type
	  output$user_title_position = user_title_position
	  
	  output$user_size_of_plot_title = user_size_of_plot_title
	  output$user_axis_title_size = user_axis_title_size
	  output$user_facet_title_size = user_facet_title_size
	  output$user_axis_text_size = user_axis_text_size
	  output$user_data_label_size = user_data_label_size
	  output$user_x_axis_text_angle = user_x_axis_text_angle
	  output$user_legend_title = user_legend_title
	  output$user_stacked = user_stacked
	  output$user_add_density = user_add_density
	  output$user_remove_histogram = user_remove_histogram
	  output$user_select_color_variable_single = user_select_color_variable_single
	  output$user_select_color_parlet = user_select_color_parlet
	  output$user_numeric_summary = user_numeric_summary
	  output$user_tab_more_out = user_tab_more_out
	  output$user_graph_more_out = user_tab_more_out
	  
	  output$bivariate_header_label = bivariate_header_label
	  output$corrplot_header_label = corrplot_header_label
	  
	  output$user_select_bivariate_single_color = user_select_bivariate_single_color
	  output$user_select_color_parlet_bivariate = user_select_color_parlet_bivariate
	  output$user_select_color_parlet_corrplot = user_select_color_parlet_corrplot
	  output$bivariate_plot_title = bivariate_plot_title
	  output$corrplot_title = corrplot_title
	  output$user_download_autoreport = user_download_autoreport
	  output$user_generatebivriate = user_generatebivriate

	  

	  ##### ---- Explore data actions ----------------------------------
	  explore_data_actions_server()
	  
	  ##### ---- Filter data --------------------------------------------
	  explore_data_filter_server()
	  explore_data_apply_filter_server()
	  explore_data_current_filter_server()
	  
	  ##### ---- Show/display -------------------------------------------------------
	  explore_show_data_server()
	  explore_data_reset_current_filter_server()
	  
	  ##### ---- Compute proportion of missing data ---------------------------
	  explore_missing_data_server()
	  
	  ##### ---- Select variables ---------------------------------------------
	  explore_data_select_variables_server()
	  explore_data_selected_variables_server()
	  
	  ##### ---- Update data -----------------------------------------------
	  explore_data_update_data_server()

	  
	  #### ---- Transform variables -------------------------------------- ####
	  source("server/transform_data.R", local = TRUE)

	  ##### ---- Select variables to transform ------------------------------------###
	  transform_data_select_variables_server()

	  ##### ---- Change type  -----------------------------------------------###
	  transform_data_change_type_server()

	  ##### ---- Rename variables  -----------------------------------------------###
	  transform_data_rename_variables_server()

	  ##### ---- Recode/change value labels ---------------------------------------###
	  transform_data_quick_explore_recode_server()
	  
	  ##### ---- Handle missing data ---------------------------------------###
	  transform_data_create_missing_values_server()
	  
	  ##### ---- Identify outliers ---------------------------------------###
	  transform_data_identify_outliers_server()
	  
	  ##### ---- Handle missing values ---------------------------------------###
	  transform_data_handle_missing_values_server()
	  
	  ##### ---- Plot transform data ----------------------------------------------###
	  transform_data_quick_explore_plot_server()
	  
	  ##### ---- Plot missing data (LAZY-LOADED) --------------------------------###
	  .missing_loaded <- FALSE
	  observeEvent(input$dynamic_meinu_aphrc, {
		if (!.missing_loaded && input$dynamic_meinu_aphrc %in% c("manageData", "transformData")) {
		  .missing_loaded <<- TRUE
		  transform_data_plot_missing_data_server()
		}
	  }, ignoreInit = TRUE)
	  
	  #### ---- Combine datasets with the existing one --------------------------------------####
	  source("server/combine_data.R", local = TRUE)
	  
	  ##### ---- List of internal data ------------------------------------------####
	  combine_data_list_datasets()
	  
	  ##### ---- Combine data options ------------------------------------------####
	  combine_data_type()

	  ##### ---- Combine data mtch type ------------------------------------------####
	  combine_data_match_type()

	  ##### ---- Combine data variables matched --------------------####
	  combine_data_variable_matching()
	  
	  #### ----- Perform matching ---------------------------------####
	  combine_data_perform_variable_match()

	  ##### ---- Combine data perform merging  --------------------####
	  combine_data_perform_merging()

	  #### ---- Reset combine data --------------------------------####
	  combine_data_reset()

	  ##### ---- Custom visualizations (LAZY-LOADED) ------------------ #####
	  .viz_loaded <- FALSE
	  observeEvent(input$dynamic_meinu_aphrc, {
		if (!.viz_loaded && input$dynamic_meinu_aphrc %in% c("summarizeCustom", "visualizeData")) {
		  .viz_loaded <<- TRUE
		  source("server/user_defined_visualization.R", local = TRUE)
		  user_defined_server()
		}
	  }, ignoreInit = TRUE)
	  
	  ### ------- OMOP ------------------------------------------ #####
	  
	  #### ----- Cohort Constructor ---------#####
	  source("server/run_cohort_pipeline.R", local = TRUE)
	  run_cohort_pipeline()
	  
	  #### ----- Feature Extraction ---------#####
	  source("server/feature_extraction_pipeline.R", local = TRUE)
	  feature_extraction_pipeline()
	  
	  #### ---- Achilles Integration -------------------####
	  
	  source("server/run_achilles.R", local = TRUE)
	  achilles_integration_server()
	  
	  ### ---- OMOP CDM Summaries---------------------------####
	  source("server/omop_summaries.R", local = TRUE)
	  omopVizServer()

	  ### ---- Data Anonymization ---------------------------####
     source("server/anon_server_module_quant.R", local = TRUE)
	  mod_quant_anon_server("quant_anon", rv_current = rv_current)

	  #### ---- Generate Research Questions --------------------------------------####
	  source("server/research_questions.R", local = TRUE)
	  generate_research_questions_choices()
	  

	  ##### ---- API Token ------------------ ####
	  generate_research_questions_api_token()
	  generate_research_questions_api_store()

	  #### ---- Addional prompts --------------- ####
	  generate_research_questions_additional()

	  #### ---- Generate insights using Gemini --------------- ####
	  generate_research_questions_gemini()

	  #### ---- Machine learning and AI --------------- ####
	  
	  ##### ----- Set ML/AI UI ------------------- ####
	  update_progress("Loading ML modules...")
	  source("server/setup_models.R", local=TRUE)
	  setup_models_ui()
	  
	  ##### ----- Preprocessing ------------------- ####
	  source("server/feature_engineering.R", local=TRUE)
	  
	  #### Preprocessing ------------------------------------------- ####
	  feature_engineering_perform_preprocess_server()

	  #### ------ Missing value imputation -------------------------- ####
	  feature_engineering_recipe_server()
	  feature_engineering_impute_missing_server()
	  
	  #### ----- Modelling framework --------------------------------- ####

	  source("server/modelling_framework.R", local=TRUE)
	  modelling_framework_choices()
	  
	  #### ----- Model setup ----------------------------------------- ####
	  source("server/model_training_setup.R", local=TRUE)
	  model_training_setup_server()

	  #### ----- Caret models --------------------------------------- ####
	  source("server/model_training_caret_models.R", local=TRUE)
	  
	  ## LM/GLM
	  model_training_caret_models_ols_server()

	  ## RF
	  model_training_caret_models_rf_server()

	  ## GBM
	  model_training_caret_models_gbm_server()

	  ## xgbTree
	  model_training_caret_models_xgbTree_server()

	  ## xgbLinear
	  model_training_caret_models_xgbLinear_server()

	  ## svmRadial
	  model_training_caret_models_svmRadial_server()
	  
	  ## svmLinear
	  model_training_caret_models_svmLinear_server()

	  ## svmPoly
	  model_training_caret_models_svmPoly_server()

	  ## glmnet
	  model_training_caret_models_glmnet_server()
	 
	  ## LASSO
	  model_training_caret_models_lasso_server()

	  ## Ridge
	  model_training_caret_models_ridge_server()

	  ## KNN
	  model_training_caret_models_knn_server()

	  ## NNET
	  model_training_caret_models_nnet_server()

	  ## TREEBAG
	  model_training_caret_models_treebag_server()

	  ## avNNet
	  model_training_caret_models_avNNet_server()

	  ## PLS
	  model_training_caret_models_pls_server()

	  ## GAM
	  model_training_caret_models_gam_server()

	  ## DT (RPART)
	  model_training_caret_models_rpart_server()

	  ## mlpWeightDecayML (MLP)
	  model_training_caret_models_mlpWeightDecayML_server()
	  
	  ## Naive Bayes
	  model_training_caret_models_naive_bayes_server()
	  
	  #### ----- Train all models ----------------------------------- ####
	  source("server/train_caret_models.R", local=TRUE)
	  model_training_caret_train_all_server()

	  #### ----- Compare trained models (LAZY-LOADED) --------------------- ####
	  .compare_loaded <- FALSE
	  observeEvent(input$dynamic_meinu_aphrc, {
		if (!.compare_loaded && input$dynamic_meinu_aphrc %in% c("validateDeployModel", "trainModel")) {
		  .compare_loaded <<- TRUE
		  source("server/compare_trained_caret_models.R", local = TRUE)
		  model_training_caret_train_metrics_server()
		}
	  }, ignoreInit = TRUE)

	  #### ----- Deploy trained models ------------------------------- ####
	  source("server/deploy_trained_caret_models.R", local=TRUE)
	  deploy_trained_caret_models()

	  #### ---- Predict using no-code models ------------------------ ####
	  source("server/predict_trained_caret_models.R", local=TRUE)
	  predict_trained_caret_models()


	  #### ---- PyCaret Integration (API) ----------------------------------------------------

	  # New ADD
	  rv_ml_ai   <- rv_ml_ai   %||% reactiveValues(target = NULL, outcome = NULL)
	  rv_current <- rv_current %||% reactiveValues(target = NULL)

	  deployment_server(id="deploy",rv_ml_ai=rv_ml_ai,rv_current = rv_current,api_base=api_base)
	  predict_pycaret_server("predict_pycaret", api_base , rv_current, rv_ml_ai)

	  # END NEW ADD
	  #### ---- Call current dataset for FastAPI ---------------------------------------------------  
	  source("server/automl_server.R", local=TRUE)
	  automl_server("automl_module", rv_current, rv_ml_ai)

	  observe({
		 req(!is.null(rv_ml_ai$modelling_framework))  # Check if value exist
		 
		 if (tolower(rv_ml_ai$modelling_framework) == "pycaret") {
			output$automl_module_ui <- renderUI({
			  automl_ui("automl_module")
			})
		 } else {
			output$automl_module_ui <- renderUI({
			  h4("")
			})
		 }
	  })
	  
	  observeEvent(input$modelling_framework_choices, {
		 rv_ml_ai$framework <- tolower(input$modelling_framework_choices %||% "")
	  }, ignoreInit = FALSE)
	  
	  #### ---- Deep Learning Server (LAZY-LOADED) ----- ###
	  .dl_loaded <- FALSE
	  observeEvent(input$dynamic_meinu_aphrc, {
		if (!.dl_loaded && input$dynamic_meinu_aphrc %in% c("deeplearning", "cnndeep")) {
		  .dl_loaded <<- TRUE
		  source("server/deep_learning.R", local = TRUE)
		  deep_learning()
		}
	  }, ignoreInit = TRUE)
	  
	  #### ---- Reset various components --------------------------------------####
	  ## Various components come before this
	  source("server/resets.R", local = TRUE)

	  ##### ---- Reset on delete or language change ------------------- ####
	  reset_data_server()

	  #### ---- Activate required fields --------------------------------------####
	  iv$enable()
	  iv_url$enable()
	  iv_ml$enable()

	  update_progress("Ready!")
	  waiter::waiter_hide()
  }, ignoreInit = FALSE)

  waiter::waiter_hide()
  
}