src/config/optimization_config.py

import pandas as pd
import src.etl.transform as transformed_data
import datetime
from datetime import timedelta
import src.etl.extract as extract
from src.config.constants import ShiftType, LineType, KitLevel, DefaultConfig

# Re-import all the packages
import importlib

# Reload modules to get latest changes
importlib.reload(extract)
importlib.reload(transformed_data)  # Uncomment if needed


def get_date_span():
    try:
        # Try to get from streamlit session state (from config page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'start_date' in st.session_state:
            from datetime import datetime, timedelta
            start_date = datetime.combine(st.session_state.start_date, datetime.min.time())
            
            # Check if we have calculated planning_days, otherwise determine from data
            if 'planning_days' in st.session_state and st.session_state.planning_days:
                planning_days = st.session_state.planning_days
                end_date = start_date + timedelta(days=planning_days - 1)
            else:
                # Determine date range from actual demand data for the exact start date
                try:
                    demand_data = extract.read_orders_data(start_date=start_date)
                    if not demand_data.empty:
                        import pandas as pd
                        # Get unique finish dates for this exact start date
                        finish_dates = pd.to_datetime(demand_data["Basic finish date"]).dt.date.unique()
                        finish_dates = sorted(finish_dates)
                        if finish_dates:
                            end_date = datetime.combine(max(finish_dates), datetime.min.time())
                            planning_days = (end_date - start_date).days + 1
                        else:
                            end_date = start_date
                            planning_days = 1
                    else:
                        end_date = start_date + timedelta(days=4)  # Default 5 days
                        planning_days = 5
                except Exception as e:
                    print(f"Could not determine date range from data: {e}")
                    end_date = start_date + timedelta(days=4)  # Default 5 days
                    planning_days = 5
            
            date_span = list(range(1, planning_days + 1))
            print(f"Using dates from config page: {start_date} to {end_date} ({planning_days} days)")
            print("date span", date_span)
            return date_span, start_date, end_date
    except Exception as e:
        print(f"Could not get dates from streamlit session: {e}")
    
    print(f"Loading default date values")
    # Default to match the user's data in COOIS_Released_Prod_Orders.csv
    from datetime import datetime
    return list(range(1, 6)), datetime(2025, 7, 7), datetime(2025, 7, 11)  # Default 5 days


#fetch date from streamlit or default value. The streamlit and default references the demand data (COOIS_Planned_and_Released.csv)
DATE_SPAN, start_date, end_date = get_date_span() 

# Update global dates in extract module BEFORE any data loading
extract.set_global_dates(start_date, end_date)

print(f"\n📅 DATE RANGE: {start_date} to {end_date}")
print(f"📁 PRODUCT SOURCE: COOIS_Released_Prod_Orders.csv")

def get_product_list():
    """
    Get filtered product list.
    IMPORTANT: This dynamically loads data to reflect current Streamlit configs/dates.
    """
    try:
        # Always get fresh filtered products to reflect current configs
        from src.demand_filtering import get_shared_filter_instance
        filter_instance = get_shared_filter_instance()
        
        # Force reload data to pick up new dates/configs
        filter_instance.load_data(force_reload=True)
        
        product_list = filter_instance.get_filtered_product_list()
        print(f"📦 FRESH FILTERED PRODUCTS: {len(product_list)} products ready for optimization")
        print(f"🎯 Products: {product_list}")
        return product_list
    except Exception as e:
        print(f"Error loading dynamic product list: {e}")
        # Fallback to unfiltered list
        product_list = transformed_data.get_released_product_list(start_date)
        print(f"📦 FALLBACK UNFILTERED PRODUCTS: {len(product_list)} products -> {product_list}")
        return product_list

# DO NOT load at import time - always call get_product_list() dynamically
# PRODUCT_LIST = get_product_list()  # REMOVED - was causing stale data!


def get_employee_type_list():
    """Get employee type list - try from streamlit session state first, then from data files"""
    try:
        # Try to get from streamlit session state (from Dataset Metadata page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'selected_employee_types' in st.session_state:
            print(f"Using employee types from Dataset Metadata page: {st.session_state.selected_employee_types}")
            return st.session_state.selected_employee_types
    except Exception as e:
        print(f"Could not get employee types from streamlit session: {e}")
    
    # Default: load from data files
    print(f"Loading employee type list from data files")
    employee_type_list = extract.read_employee_data()
    emp_type_list = employee_type_list["employment_type"].unique()
    return emp_type_list
        
# DO NOT load at import time - always call get_employee_type_list() dynamically
# EMPLOYEE_TYPE_LIST = get_employee_type_list()  # REMOVED - was causing stale data!

def get_shift_list():
    """Get shift list - try from streamlit session state first, then from data files"""
    try:
        # Try to get from streamlit session state (from Dataset Metadata page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'selected_shifts' in st.session_state:
            print(f"Using shifts from Dataset Metadata page: {st.session_state.selected_shifts}")
            return st.session_state.selected_shifts
    except Exception as e:
        print(f"Could not get shifts from streamlit session: {e}")
    
    # Default: load from data files
    print(f"Loading shift list from data files")
    shift_list = extract.get_shift_info()
    shift_list = shift_list["id"].unique()
    return shift_list

# Evening shift activation mode - define early to avoid circular dependency
# Options:
#   "normal" - Only use regular shift (1) and overtime shift (3) - NO evening shift
#   "activate_evening" - Allow evening shift (2) when demand is too high or cost-effective
#   "always_available" - Evening shift always available as option
EVENING_SHIFT_MODE = "normal"  # Default: only regular + overtime

# Evening shift activation threshold
# If demand cannot be met with regular + overtime, suggest evening shift activation
EVENING_SHIFT_DEMAND_THRESHOLD = 0.9  # Activate if regular+overtime capacity < 90% of demand

def get_active_shift_list():
    """
    Get the list of active shifts based on EVENING_SHIFT_MODE setting.
    """
    all_shifts = get_shift_list()
    
    if EVENING_SHIFT_MODE == "normal":
        # Only regular and overtime shifts - NO evening shift
        active_shifts = [s for s in all_shifts if s in ShiftType.REGULAR_AND_OVERTIME]
        print(f"[SHIFT MODE] Normal mode: Using shifts {active_shifts} (Regular + Overtime only, NO evening)")
        
    elif EVENING_SHIFT_MODE == "activate_evening":
        # All shifts including evening (2)
        active_shifts = list(all_shifts)
        print(f"[SHIFT MODE] Evening activated: Using all shifts {active_shifts}")
        
    elif EVENING_SHIFT_MODE == "always_available":
        # All shifts always available
        active_shifts = list(all_shifts)
        print(f"[SHIFT MODE] Always available: Using all shifts {active_shifts}")
        
    else:
        # Default to normal mode
        active_shifts = [s for s in all_shifts if s in ShiftType.REGULAR_AND_OVERTIME]
        print(f"[SHIFT MODE] Unknown mode '{EVENING_SHIFT_MODE}', defaulting to normal: {active_shifts}")
    
    return active_shifts

# DO NOT load at import time - always call get_active_shift_list() dynamically
# SHIFT_LIST = get_active_shift_list()  # REMOVED - was causing stale data!


def get_line_list():
    """Get line list - try from streamlit session state first, then from data files"""
    try:
        # Try to get from streamlit session state (from Dataset Metadata page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'selected_lines' in st.session_state:
            print(f"Using lines from Dataset Metadata page: {st.session_state.selected_lines}")
            return st.session_state.selected_lines
    except Exception as e:
        print(f"Could not get lines from streamlit session: {e}")
    
    # Default: load from data files
    print(f"Loading line list from data files")
    line_df = extract.read_packaging_line_data()
    line_list = line_df["id"].unique().tolist()
    return line_list

# DO NOT load at import time - always call get_line_list() dynamically
# LINE_LIST = get_line_list()  # REMOVED - was causing stale data!


def get_kit_line_match():
    kit_line_match = extract.read_kit_line_match_data()
    kit_line_match_dict = kit_line_match.set_index("kit_name")["line_type"].to_dict()
    
    # Create line name to ID mapping
    line_name_to_id = {
        "long line": LineType.LONG_LINE,
        "mini load": LineType.MINI_LOAD,
        "miniload": LineType.MINI_LOAD,     # Alternative naming (no space)
        "Long_line": LineType.LONG_LINE,    # Alternative naming
        "Mini_load": LineType.MINI_LOAD,    # Alternative naming
    }
    
    # Convert string line names to numeric IDs
    converted_dict = {}
    for kit, line_name in kit_line_match_dict.items():
        if isinstance(line_name, str) and line_name.strip():
            # Convert string names to numeric IDs
            line_id = line_name_to_id.get(line_name.strip(), None)
            if line_id is not None:
                converted_dict[kit] = line_id
            else:
                print(f"Warning: Unknown line type '{line_name}' for kit {kit}")
                # Default to long line if unknown
                converted_dict[kit] = LineType.LONG_LINE
        elif isinstance(line_name, (int, float)) and not pd.isna(line_name):
            # Already numeric
            converted_dict[kit] = int(line_name)
        else:
            # Missing or empty line type - skip (no production needed for non-standalone masters)
            pass  # Don't add to converted_dict - these kits won't have line assignments
            
    return converted_dict

KIT_LINE_MATCH_DICT = get_kit_line_match()


def get_line_cnt_per_type():
    try:
        # Try to get from streamlit session state (from config page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'line_counts' in st.session_state:
            print(f"Using line counts from config page: {st.session_state.line_counts}")
            return st.session_state.line_counts
    except Exception as e:
        print(f"Could not get line counts from streamlit session: {e}")
    
    print(f"Loading default line count values from data files")
    line_df = extract.read_packaging_line_data()
    line_cnt_per_type = line_df.set_index("id")["line_count"].to_dict()
    print("line cnt per type", line_cnt_per_type)
    return line_cnt_per_type

# DO NOT load at import time - always call get_line_cnt_per_type() dynamically
# LINE_CNT_PER_TYPE = get_line_cnt_per_type()  # REMOVED - was causing stale data!

def get_demand_dictionary(force_reload=False):
    """
    Get filtered demand dictionary. 
    IMPORTANT: This dynamically loads data to reflect current Streamlit configs/dates.
    """
    try:
        # Always get fresh filtered demand to reflect current configs
        from src.demand_filtering import get_shared_filter_instance
        filter_instance = get_shared_filter_instance()
        
        # Force reload data to pick up new dates/configs
        filter_instance.load_data(force_reload=True)
        
        demand_dictionary = filter_instance.get_filtered_demand_dictionary()
        print(f"📈 FRESH FILTERED DEMAND: {len(demand_dictionary)} products with total demand {sum(demand_dictionary.values())}")
        print(f"🔄 LOADED DYNAMICALLY: Reflects current Streamlit configs")
        return demand_dictionary
    except Exception as e:
        print(f"Error loading dynamic demand dictionary: {e}")
        raise Exception("Demand dictionary not found with error:"+str(e))

# DO NOT load at import time - always call get_demand_dictionary() dynamically
# DEMAND_DICTIONARY = get_demand_dictionary()  # REMOVED - was causing stale data!

def get_cost_list_per_emp_shift():
    try:
        # Try to get from streamlit session state (from config page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'cost_list_per_emp_shift' in st.session_state:
            print(f"Using cost list from config page: {st.session_state.cost_list_per_emp_shift}")
            return st.session_state.cost_list_per_emp_shift
    except Exception as e:
        print(f"Could not get cost list from streamlit session: {e}")
    
    print(f"Loading default cost values")
    # Default hourly rates - Important: multiple employment types with different costs
    return DefaultConfig.DEFAULT_COST_RATES

def shift_code_to_name():
    return ShiftType.get_all_names()

def line_code_to_name():
    """Convert line type IDs to readable names"""
    return LineType.get_all_names()

# DO NOT load at import time - always call get_cost_list_per_emp_shift() dynamically
# COST_LIST_PER_EMP_SHIFT = get_cost_list_per_emp_shift()  # REMOVED - was causing stale data!
        


# COST_LIST_PER_EMP_SHIFT = {  # WH_Workforce_Hourly_Pay_Scale
#     "Fixed": {1: 0, 2: 22, 3: 18},
#     "Humanizer": {1: 10, 2: 10, 3: 10},
# }







def get_team_requirements(product_list=None):
    """
    Extract team requirements from Kits Calculation CSV.
    Returns dictionary with employee type as key and product requirements as nested dict.
    """
    if product_list is None:
        product_list = get_product_list()  # Get fresh product list
        
    try:
        # Check if streamlit has this data (for future extension)
        # streamlit_team_req = dashboard.team_requirements
        # return streamlit_team_req
        pass
    except Exception as e:
        print(f"Using default value for team requirements, extracting from CSV: {e}")
        
    # Read the kits calculation data directly
    kits_df = extract.read_personnel_requirement_data()
    # kits_path = "data/real_data_excel/converted_csv/Kits__Calculation.csv"
    # kits_df = pd.read_csv(kits_path)
    print("kits_df columns:", kits_df.columns.tolist())
    print("kits_df head:", kits_df.head())
    # Initialize the team requirements dictionary
    team_req_dict = {
        "UNICEF Fixed term": {},
        "Humanizer": {}
    }
    
    # Process each product in the product list
    for product in product_list:
        print("product",product)
        print(f"Processing team requirements for product: {product}")
        product_data = kits_df[kits_df['Kit'] == product]
        print("product_data",product_data)
        if not product_data.empty:
            # Extract Humanizer and UNICEF staff requirements
            humanizer_req = product_data["Humanizer"].iloc[0]
            unicef_req = product_data["UNICEF staff"].iloc[0]
            
            # Convert to int (data is already cleaned in extract function)
            team_req_dict["Humanizer"][product] = int(humanizer_req)
            team_req_dict["UNICEF Fixed term"][product] = int(unicef_req)
        else:
            print(f"Warning: Product {product} not found in Kits Calculation data, setting requirements to 0")
            
    
    return team_req_dict

# DO NOT load at import time - always call get_team_requirements() dynamically
# TEAM_REQ_PER_PRODUCT = get_team_requirements(PRODUCT_LIST)  # REMOVED - was causing stale data!


def get_max_employee_per_type_on_day():
    try:
        # Try to get from streamlit session state (from config page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'max_employee_per_type_on_day' in st.session_state:
            print(f"Using max employee counts from config page: {st.session_state.max_employee_per_type_on_day}")
            return st.session_state.max_employee_per_type_on_day
    except Exception as e:
        print(f"Could not get max employee counts from streamlit session: {e}")
    
    print(f"Loading default max employee values")
    max_employee_per_type_on_day = {
        "UNICEF Fixed term": {
            t: 8 for t in DATE_SPAN
        },
        "Humanizer": {
            t: 10 for t in DATE_SPAN
        }
    }
    return max_employee_per_type_on_day

# DO NOT load at import time - always call get_max_employee_per_type_on_day() dynamically
# MAX_EMPLOYEE_PER_TYPE_ON_DAY = get_max_employee_per_type_on_day()  # REMOVED - was causing stale data!

# available employee but for fixed in shift 1, it is mandatory employment

MAX_HOUR_PER_PERSON_PER_DAY = 14  # legal standard
def get_max_hour_per_shift_per_person():
    """Get max hours per shift per person - checks Streamlit session state first"""
    try:
        import streamlit as st
        if hasattr(st, 'session_state') and 'max_hour_per_shift_per_person' in st.session_state:
            return st.session_state.max_hour_per_shift_per_person
    except Exception as e:
        print(f"Could not get max hours per shift from session: {e}")
    
    # Fallback to default only if not configured by user
    return DefaultConfig.MAX_HOUR_PER_SHIFT_PER_PERSON

# DO NOT load at import time - always call get_max_hour_per_shift_per_person() dynamically
# MAX_HOUR_PER_SHIFT_PER_PERSON = get_max_hour_per_shift_per_person()  # REMOVED - was causing stale data!

# Removed unnecessary getter functions - use direct imports instead:
# - MAX_HOUR_PER_PERSON_PER_DAY
# - MAX_HOUR_PER_SHIFT_PER_PERSON  
# - KIT_LINE_MATCH_DICT
# - MAX_PARALLEL_WORKERS
# - EVENING_SHIFT_MODE

# Keep these complex getters that access DefaultConfig or have complex logic:
def get_evening_shift_demand_threshold():
    """Get evening shift demand threshold - checks Streamlit session state first"""
    try:
        import streamlit as st
        if hasattr(st, 'session_state') and 'evening_shift_demand_threshold' in st.session_state:
            return st.session_state.evening_shift_demand_threshold
    except Exception as e:
        print(f"Could not get evening shift threshold from session: {e}")
    
    # Fallback to default only if not configured by user
    return getattr(DefaultConfig, 'EVENING_SHIFT_DEMAND_THRESHOLD', 10000)

def get_fixed_min_unicef_per_day():
    """Get fixed minimum UNICEF staff per day - checks Streamlit session state first"""
    try:
        import streamlit as st
        if hasattr(st, 'session_state') and 'fixed_min_unicef_per_day' in st.session_state:
            return st.session_state.fixed_min_unicef_per_day
    except Exception as e:
        print(f"Could not get fixed min UNICEF from session: {e}")
    
    # Fallback to default only if not configured by user
    return getattr(DefaultConfig, 'FIXED_MIN_UNICEF_PER_DAY', {1: 1, 2: 1, 3: 1, 4: 1, 5: 1})
def get_per_product_speed():
    try:
        # Try to get from streamlit session state (from config page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'per_product_speed' in st.session_state:
            print(f"Using per product speed from config page: {st.session_state.per_product_speed}")
            return st.session_state.per_product_speed
    except Exception as e:
        print(f"Could not get per product speed from streamlit session: {e}")
    
    print(f"Loading default per product speed from data files")
    per_product_speed = extract.read_package_speed_data()
    return per_product_speed


# ============================================================================
# BETTER APPROACH: Explicit module-level variables with clear documentation
# These variables provide backward compatibility while being explicit and clear
# ============================================================================

def _ensure_fresh_config():
    """
    Helper function to refresh module-level variables when configuration changes.
    Call this after updating Streamlit session state to ensure fresh values.
    """
    global PER_PRODUCT_SPEED, LINE_LIST, EMPLOYEE_TYPE_LIST, SHIFT_LIST
    global LINE_CNT_PER_TYPE, COST_LIST_PER_EMP_SHIFT, MAX_EMPLOYEE_PER_TYPE_ON_DAY
    global MAX_HOUR_PER_SHIFT_PER_PERSON, MAX_PARALLEL_WORKERS, FIXED_MIN_UNICEF_PER_DAY
    global PAYMENT_MODE_CONFIG
    
    # Refresh all cached values
    PER_PRODUCT_SPEED = get_per_product_speed()
    LINE_LIST = get_line_list()
    EMPLOYEE_TYPE_LIST = get_employee_type_list()
    SHIFT_LIST = get_active_shift_list()
    LINE_CNT_PER_TYPE = get_line_cnt_per_type()
    COST_LIST_PER_EMP_SHIFT = get_cost_list_per_emp_shift()
    MAX_EMPLOYEE_PER_TYPE_ON_DAY = get_max_employee_per_type_on_day()
    MAX_HOUR_PER_SHIFT_PER_PERSON = get_max_hour_per_shift_per_person()
    MAX_PARALLEL_WORKERS = get_max_parallel_workers()
    FIXED_MIN_UNICEF_PER_DAY = get_fixed_min_unicef_per_day()
    PAYMENT_MODE_CONFIG = get_payment_mode_config()

# Note: Module-level variables will be initialized at the end of this file
# after all functions are defined. This ensures all getter functions are available.

# ---- Kit Hierarchy for Production Ordering ----
def get_kit_hierarchy_data():
    try:
        # Try to get from streamlit first (future extension)
        # streamlit_hierarchy = dashboard.kit_hierarchy_data
        # return streamlit_hierarchy
        pass
    except Exception as e:
        print(f"Using default hierarchy data from extract: {e}")
        
    # Get hierarchy data from extract functions
    kit_levels, dependencies, priority_order = extract.get_production_order_data()
    
    return kit_levels, dependencies, priority_order

KIT_LEVELS, KIT_DEPENDENCIES, PRODUCTION_PRIORITY_ORDER = get_kit_hierarchy_data()
print(f"Kit Hierarchy loaded: {len(KIT_LEVELS)} kits, Priority order: {len(PRODUCTION_PRIORITY_ORDER)} items")

def get_max_parallel_workers():
    """Get max parallel workers - checks Streamlit session state first"""
    try:
        import streamlit as st
        if hasattr(st, 'session_state') and 'max_parallel_workers' in st.session_state:
            return st.session_state.max_parallel_workers
    except Exception as e:
        print(f"Could not get max parallel workers from session: {e}")
    
    # Fallback to default only if not configured by user
    return DefaultConfig.MAX_PARALLEL_WORKERS

# DO NOT load at import time - always call get_max_parallel_workers() dynamically
# MAX_PARALLEL_WORKERS = get_max_parallel_workers()  # REMOVED - was causing stale data!
# maximum number of workers that can work on a line at the same time


# Fixed staff constraint mode
# Options:
#   "mandatory" - Forces all fixed staff to work full hours every day (expensive, 99.7% waste)
#   "available" - Staff available up to limits but not forced (balanced approach)
#   "priority"  - Fixed staff used first, then temporary staff (realistic business model)
#   "none"      - Purely demand-driven scheduling (cost-efficient)
FIXED_STAFF_CONSTRAINT_MODE = "priority"  # Recommended: "priority" for realistic business model

def get_fixed_min_unicef_per_day():
    """
    Get fixed minimum UNICEF employees per day - try from streamlit session state first, then default
    This ensures a minimum number of UNICEF fixed-term staff are present every working day
    """
    try:
        import streamlit as st
        if hasattr(st, 'session_state') and 'fixed_min_unicef_per_day' in st.session_state:
            print(f"Using fixed minimum UNICEF per day from config page: {st.session_state.fixed_min_unicef_per_day}")
            return st.session_state.fixed_min_unicef_per_day
    except ImportError:
        pass  # Streamlit not available in CLI mode
    
    # Default value - minimum UNICEF Fixed term employees required per day
    return 2

# Set the constant for backward compatibility
# DO NOT load at import time - always call get_fixed_min_unicef_per_day() dynamically
# FIXED_MIN_UNICEF_PER_DAY = get_fixed_min_unicef_per_day()  # REMOVED - was causing stale data!


def get_payment_mode_config():
    """
    Get payment mode configuration - try from streamlit session state first, then default values
    Payment modes:
    - "bulk": If employee works any hours in shift, pay for full shift hours
    - "partial": Pay only for actual hours worked
    """
    try:
        # Try to get from streamlit session state (from Dataset Metadata page)
        import streamlit as st
        if hasattr(st, 'session_state') and 'payment_mode_config' in st.session_state:
            print(f"Using payment mode config from streamlit session: {st.session_state.payment_mode_config}")
            return st.session_state.payment_mode_config
    except Exception as e:
        print(f"Could not get payment mode config from streamlit session: {e}")
    
    # Default payment mode configuration
    print(f"Loading default payment mode configuration")
    payment_mode_config = DefaultConfig.PAYMENT_MODE_CONFIG
    
    return payment_mode_config

# DO NOT load at import time - always call get_payment_mode_config() dynamically
# PAYMENT_MODE_CONFIG = get_payment_mode_config()  # REMOVED - was causing stale data!

# ============================================================================
# INITIALIZE MODULE-LEVEL VARIABLES
# This section is at the end to ensure all functions are defined first
# ============================================================================

# Initialize with default values (will use fallback data when no Streamlit session)
PER_PRODUCT_SPEED = get_per_product_speed()
LINE_LIST = get_line_list()
EMPLOYEE_TYPE_LIST = get_employee_type_list()
SHIFT_LIST = get_active_shift_list()
LINE_CNT_PER_TYPE = get_line_cnt_per_type()
COST_LIST_PER_EMP_SHIFT = get_cost_list_per_emp_shift()
MAX_EMPLOYEE_PER_TYPE_ON_DAY = get_max_employee_per_type_on_day()
MAX_HOUR_PER_SHIFT_PER_PERSON = get_max_hour_per_shift_per_person()
MAX_PARALLEL_WORKERS = get_max_parallel_workers()
FIXED_MIN_UNICEF_PER_DAY = get_fixed_min_unicef_per_day()
PAYMENT_MODE_CONFIG = get_payment_mode_config()

print("✅ Module-level configuration variables initialized")

# Note: These variables are initialized once at import time with default/fallback values.
# To get fresh values after changing Streamlit configuration, either:
# 1. Call the get_*() functions directly (RECOMMENDED for dynamic use)
# 2. Call _ensure_fresh_config() to refresh all module-level variables
# 3. Use importlib.reload() to reload the entire module