"""
Utility functions and demo data for the Wildberries Analytics Dashboard
Includes fallback data for when API is not available
"""

import pandas as pd
import numpy as np
import json
from datetime import datetime, timedelta
from typing import Dict, List, Any
import random
from config import get_config, DEMO_SETTINGS

def load_demo_sales_data(period: str = "week") -> pd.DataFrame:
    """Generate realistic demo sales data for testing"""
    
    # Set random seed for reproducible results
    np.random.seed(42)
    random.seed(42)
    
    config = get_config()
    demo_config = DEMO_SETTINGS
    
    # Calculate date range
    if period == "week":
        days = 7
        num_sales = random.randint(50, 200)
    elif period == "month":
        days = 30
        num_sales = random.randint(200, 800)
    else:
        days = 7
        num_sales = random.randint(50, 200)
    
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)
    
    # Generate product list
    products = []
    for i in range(demo_config["demo_products_count"]):
        products.append({
            'product_id': 1000000 + i,
            'product_name': f'Товар {i+1}',
            'article': f'ART{1000+i}',
            'category': random.choice(demo_config["demo_categories"]),
            'brand': f'Бренд {chr(65 + i % 26)}',
            'base_price': random.randint(500, 5000)
        })
    
    # Generate sales data
    sales_data = []
    
    for _ in range(num_sales):
        product = random.choice(products)
        sale_date = start_date + timedelta(
            days=random.random() * days,
            hours=random.randint(0, 23),
            minutes=random.randint(0, 59)
        )
        
        # Generate realistic pricing with discounts
        base_price = product['base_price']
        discount_percent = random.choice([0, 5, 10, 15, 20, 25, 30]) if random.random() < 0.6 else 0
        price_with_discount = base_price * (1 - discount_percent / 100)
        
        # Generate additional pricing fields
        spp_discount = random.randint(0, 10) if random.random() < 0.3 else 0
        finished_price = price_with_discount * (1 - spp_discount / 100)
        
        # Determine if this is a return (10% chance)
        is_return = random.random() < 0.1
        sale_id = f'R{random.randint(100000, 999999)}' if is_return else f'S{random.randint(100000, 999999)}'
        
        # Calculate amount_for_pay based on priceWithDisc (what seller receives from forPay field)
        # This comes directly from the forPay API field, excluding returns
        if is_return:
            amount_for_pay = 0  # Returns don't generate payout for seller
        else:
            amount_for_pay = price_with_discount * 0.75  # What seller receives (forPay equivalent)
        
        sales_data.append({
            'sale_id': sale_id,
            'product_id': product['product_id'],
            'product_name': product['product_name'],
            'article': product['article'],
            'sale_date': sale_date,
            'last_change_date': sale_date,
            'warehouse': random.choice(['Коледино', 'Электросталь', 'Тула', 'Казань']),
            'country': 'Россия',
            'region': random.choice(['Московская', 'Санкт-Петербургская', 'Свердловская', 'Татарстан']),
            'city': random.choice(['Москва', 'Санкт-Петербург', 'Екатеринбург', 'Казань']),
            'total_price': price_with_discount,  # Use priceWithDisc for revenue
            'original_price': base_price,  # totalPrice
            'finished_price': finished_price,  # finishedPrice
            'discount_percent': discount_percent,
            'spp_discount': spp_discount,
            'price_with_discount': price_with_discount,
            'sale_amount': finished_price,
            'amount_for_pay': amount_for_pay,  # From forPay field, 0 for returns
            'sales_commission': price_with_discount - amount_for_pay,  # Commission = total_price - amount_for_pay
            'quantity': 1,
            'category': product['category'],
            'brand': product['brand'],
            'is_supply': True,
            'is_realization': True,
            'is_return': is_return,
            'order_type': 'Возвратный' if is_return else 'Клиентский'
        })
    
    df = pd.DataFrame(sales_data)
    df['sale_date'] = pd.to_datetime(df['sale_date'])
    df['last_change_date'] = pd.to_datetime(df['last_change_date'])
    
    # Ensure sales_commission is never negative and is 0 for returns
    df['sales_commission'] = df['sales_commission'].clip(lower=0)
    df.loc[df['is_return'], 'sales_commission'] = 0
    
    return df

def load_demo_inventory_data() -> pd.DataFrame:
    """Generate realistic demo inventory data"""
    
    np.random.seed(42)
    random.seed(42)
    
    demo_config = DEMO_SETTINGS
    
    # Generate inventory data
    inventory_data = []
    
    for i in range(demo_config["demo_products_count"]):
        # Generate realistic stock levels
        stock_level = random.randint(*demo_config["demo_stock_range"])
        
        # Some products should be low stock for demonstration
        if i < 3:  # First 3 products are low stock
            stock_level = random.randint(0, 10)
        elif i < 6:  # Next 3 are medium stock
            stock_level = random.randint(10, 50)
        
        inventory_data.append({
            'product_id': 1000000 + i,
            'product_name': f'Товар {i+1}',
            'article': f'ART{1000+i}',
            'current_stock': stock_level,
            'in_way_to_client': random.randint(0, 20),
            'in_way_from_client': random.randint(0, 5),
            'warehouse': random.choice(['Коледино', 'Электросталь', 'Тула', 'Казань']),
            'category': random.choice(demo_config["demo_categories"]),
            'brand': f'Бренд {chr(65 + i % 26)}',
            'price': random.randint(500, 5000),
            'last_change_date': datetime.now() - timedelta(days=random.randint(0, 3))
        })
    
    df = pd.DataFrame(inventory_data)
    df['last_change_date'] = pd.to_datetime(df['last_change_date'])
    
    return df

def process_sales_data(data: pd.DataFrame) -> pd.DataFrame:
    """Process and validate sales data from API or demo"""
    
    if data.empty:
        return data
    
    # Ensure required columns exist
    required_columns = ['product_id', 'product_name', 'sale_date', 'total_price', 'quantity']
    
    for col in required_columns:
        if col not in data.columns:
            if col == 'quantity':
                data[col] = 1  # Default quantity
            elif col == 'product_name' and 'article' in data.columns:
                data[col] = data['article']
            else:
                data[col] = f'Unknown {col}'
    
    # Data validation and cleaning
    data = data.copy()
    
    # Remove rows with missing critical data
    data = data.dropna(subset=['product_id', 'total_price'])
    
    # Ensure numeric columns are numeric
    numeric_columns = ['total_price', 'quantity', 'sale_amount', 'finished_price']
    for col in numeric_columns:
        if col in data.columns:
            data[col] = pd.to_numeric(data[col], errors='coerce')
            data[col] = data[col].fillna(0)
    
    # Ensure positive values
    for col in ['total_price', 'quantity']:
        if col in data.columns:
            data[col] = data[col].abs()
    
    # Sort by date
    if 'sale_date' in data.columns:
        data = data.sort_values('sale_date')
    
    return data

def calculate_daily_sales(sales_data: pd.DataFrame, product_id: int = None) -> pd.Series:
    """Calculate daily sales for a product or all products"""
    
    if sales_data.empty:
        return pd.Series()
    
    if product_id:
        sales_data = sales_data[sales_data['product_id'] == product_id]
    
    if 'sale_date' not in sales_data.columns:
        return pd.Series()
    
    # Group by date and sum quantities
    daily_sales = sales_data.groupby(sales_data['sale_date'].dt.date)['quantity'].sum()
    
    return daily_sales

def get_product_performance_metrics(sales_data: pd.DataFrame) -> pd.DataFrame:
    """Calculate performance metrics for each product"""
    
    if sales_data.empty:
        return pd.DataFrame()
    
    # Group by product
    product_metrics = sales_data.groupby(['product_id', 'product_name']).agg({
        'quantity': 'sum',
        'total_price': 'sum',
        'sale_date': ['count', 'min', 'max']
    }).round(2)
    
    # Flatten column names
    product_metrics.columns = ['total_quantity', 'total_revenue', 'total_orders', 'first_sale', 'last_sale']
    
    # Calculate additional metrics
    product_metrics['avg_order_value'] = (product_metrics['total_revenue'] / product_metrics['total_orders']).round(2)
    product_metrics['avg_daily_sales'] = product_metrics['total_quantity'] / 30  # Assuming 30-day period
    
    # Reset index to make product info regular columns
    product_metrics = product_metrics.reset_index()
    
    return product_metrics

def validate_api_response(response: Dict[str, Any], required_fields: List[str] = None) -> bool:
    """Validate API response structure"""
    
    if not isinstance(response, dict):
        return False
    
    if required_fields:
        for field in required_fields:
            if field not in response:
                return False
    
    return True

def format_currency(amount: float, currency: str = "₽") -> str:
    """Format currency amounts for display"""
    if pd.isna(amount) or amount is None:
        return f"0 {currency}"
    
    return f"{amount:,.2f} {currency}"

def format_number(number: float, decimals: int = 0) -> str:
    """Format numbers with thousand separators"""
    if pd.isna(number) or number is None:
        return "0"
    
    if decimals > 0:
        return f"{number:,.{decimals}f}"
    else:
        return f"{number:,.0f}"

def get_risk_color(days_until_stockout: float) -> str:
    """Get color code for risk level"""
    if days_until_stockout < 7:
        return "#ff4444"  # Red
    elif days_until_stockout < 14:
        return "#ffaa00"  # Orange
    else:
        return "#44aa44"  # Green

def export_to_csv(data: pd.DataFrame, filename: str = None) -> str:
    """Export DataFrame to CSV and return filename"""
    
    if filename is None:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"wildberries_data_{timestamp}.csv"
    
    # Clean data for export
    export_data = data.copy()
    
    # Convert datetime columns to strings
    for col in export_data.columns:
        if export_data[col].dtype == 'datetime64[ns]':
            export_data[col] = export_data[col].dt.strftime('%Y-%m-%d %H:%M:%S')
    
    # Save to CSV
    export_data.to_csv(filename, index=False, encoding='utf-8')
    
    return filename

def create_sample_data_file():
    """Create sample data JSON file for the examples directory"""
    
    sample_sales = load_demo_sales_data("week")
    sample_inventory = load_demo_inventory_data()
    
    sample_data = {
        "sales_data": sample_sales.head(10).to_dict('records'),
        "inventory_data": sample_inventory.head(10).to_dict('records'),
        "metadata": {
            "generated_at": datetime.now().isoformat(),
            "description": "Sample data for Wildberries Analytics Dashboard",
            "note": "This is demo data for testing purposes only"
        }
    }
    
    # Convert datetime objects to strings for JSON serialization
    for item in sample_data["sales_data"]:
        for key, value in item.items():
            if isinstance(value, (datetime, pd.Timestamp)):
                item[key] = value.isoformat()
    
    for item in sample_data["inventory_data"]:
        for key, value in item.items():
            if isinstance(value, (datetime, pd.Timestamp)):
                item[key] = value.isoformat()
    
    return sample_data

# Cache for demo data to avoid regenerating it multiple times
_demo_sales_cache = {}
_demo_inventory_cache = None

def get_cached_demo_sales(period: str) -> pd.DataFrame:
    """Get cached demo sales data to ensure consistency across calls"""
    global _demo_sales_cache
    
    if period not in _demo_sales_cache:
        _demo_sales_cache[period] = load_demo_sales_data(period)
    
    return _demo_sales_cache[period].copy()

def get_cached_demo_inventory() -> pd.DataFrame:
    """Get cached demo inventory data to ensure consistency across calls"""
    global _demo_inventory_cache
    
    if _demo_inventory_cache is None:
        _demo_inventory_cache = load_demo_inventory_data()
    
    return _demo_inventory_cache.copy()

# Update the main functions to use cached data
def load_demo_sales_data_cached(period: str = "week") -> pd.DataFrame:
    """Load demo sales data with caching"""
    return get_cached_demo_sales(period)

def load_demo_inventory_data_cached() -> pd.DataFrame:
    """Load demo inventory data with caching"""
    return get_cached_demo_inventory()