routes/dashboard_home.py

import os
import stripe
import requests
import logging
import pytz
import asyncio
import aiohttp
from fastapi import APIRouter, HTTPException, Header, Query, Depends
from datetime import datetime, timedelta
from dateutil.relativedelta import relativedelta
from typing import Dict, Any, Optional, List, Tuple
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor

router = APIRouter()

# Configuração das chaves do Stripe e Supabase
stripe.api_key = os.getenv("STRIPE_KEY")
stripe.api_version = "2023-10-16"
SUPABASE_URL = "https://ussxqnifefkgkaumjann.supabase.co"
SUPABASE_KEY = os.getenv("SUPA_KEY")

if not stripe.api_key or not SUPABASE_KEY:
    raise ValueError("❌ STRIPE_KEY ou SUPA_KEY não foram definidos no ambiente!")

SUPABASE_HEADERS = {
    "apikey": SUPABASE_KEY,
    "Authorization": f"Bearer {SUPABASE_KEY}",
    "Content-Type": "application/json"
}

# Configuração do logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Pool de threads para chamadas paralelas
thread_pool = ThreadPoolExecutor(max_workers=10)

# Cache para reduzir chamadas repetidas
@lru_cache(maxsize=128)
def get_cached_admin_status(user_id: str) -> bool:
    """Obtém e armazena em cache se um usuário é admin"""
    user_data_url = f"{SUPABASE_URL}/rest/v1/User?id=eq.{user_id}"
    response = requests.get(user_data_url, headers=SUPABASE_HEADERS)
    
    if response.status_code != 200 or not response.json():
        return False
    
    user_info = response.json()[0]
    return user_info.get("is_admin", False)

async def verify_admin_token(user_token: str) -> str:
    """Verifica se o token pertence a um administrador de forma assíncrona"""
    headers = {
        "Authorization": f"Bearer {user_token}",
        "apikey": SUPABASE_KEY,
        "Content-Type": "application/json"
    }
    
    async with aiohttp.ClientSession() as session:
        # Verificar se o token é válido
        async with session.get(f"{SUPABASE_URL}/auth/v1/user", headers=headers) as response:
            if response.status != 200:
                raise HTTPException(status_code=401, detail="Token inválido ou expirado")
            
            user_data = await response.json()
            user_id = user_data.get("id")
            if not user_id:
                raise HTTPException(status_code=400, detail="ID do usuário não encontrado")
    
    # Usar cache para verificar se é admin
    is_admin = await asyncio.to_thread(get_cached_admin_status, user_id)
    
    if not is_admin:
        raise HTTPException(status_code=403, detail="Acesso negado: privilégios de administrador necessários")
    
    return user_id

async def fetch_stripe_data(fetch_func, **params):
    """Executa chamadas ao Stripe em thread separada para não bloquear"""
    return await asyncio.to_thread(fetch_func, **params)

async def get_total_platform_revenue(start_timestamp: Optional[int] = None, end_timestamp: Optional[int] = None) -> Dict[str, Any]:
    """Obtém o faturamento total da plataforma no Stripe de forma otimizada"""
    try:
        query_params = {"limit": 100}
        
        # Adicionar filtros de data se fornecidos
        if start_timestamp and end_timestamp:
            query_params["created"] = {"gte": start_timestamp, "lte": end_timestamp}
        elif start_timestamp:
            query_params["created"] = {"gte": start_timestamp}
        elif end_timestamp:
            query_params["created"] = {"lte": end_timestamp}
        
        payments = []
        has_more = True
        last_id = None
        
        # Usar batch para reduzir chamadas
        while has_more:
            if last_id:
                query_params["starting_after"] = last_id
            
            # Usar thread para não bloquear o evento loop
            payment_list = await fetch_stripe_data(stripe.Charge.list, **query_params)
            payments.extend(payment_list.data)
            
            has_more = payment_list.has_more
            if payment_list.data:
                last_id = payment_list.data[-1].id
            else:
                has_more = False
        
        # Calcular totais em uma única passagem
        totals = {"total": 0, "succeeded": 0, "failed": 0}
        
        for payment in payments:
            amount = payment.amount
            totals["total"] += amount
            
            if payment.status == 'succeeded':
                totals["succeeded"] += amount
            elif payment.status == 'failed':
                totals["failed"] += amount
        
        return {
            "total_revenue": totals["total"],
            "successful_revenue": totals["succeeded"],
            "failed_revenue": totals["failed"],
            "currency": "BRL",
            "payment_count": len(payments)
        }
        
    except Exception as e:
        logger.error(f"❌ Erro ao obter faturamento total: {str(e)}")
        return {
            "total_revenue": 0,
            "successful_revenue": 0,
            "failed_revenue": 0,
            "currency": "BRL",
            "payment_count": 0,
            "error": str(e)
        }

async def get_platform_transfers(start_timestamp: Optional[int] = None, end_timestamp: Optional[int] = None) -> Dict[str, Any]:
    """Obtém o total transferido para estilistas de forma otimizada"""
    try:
        query_params = {"limit": 100}
        
        # Adicionar filtros de data se fornecidos
        if start_timestamp and end_timestamp:
            query_params["created"] = {"gte": start_timestamp, "lte": end_timestamp}
        elif start_timestamp:
            query_params["created"] = {"gte": start_timestamp}
        elif end_timestamp:
            query_params["created"] = {"lte": end_timestamp}
        
        transfers = []
        has_more = True
        last_id = None
        
        # Usar batch para reduzir chamadas
        while has_more:
            if last_id:
                query_params["starting_after"] = last_id
            
            # Usar thread para não bloquear o evento loop
            transfer_list = await fetch_stripe_data(stripe.Transfer.list, **query_params)
            transfers.extend(transfer_list.data)
            
            has_more = transfer_list.has_more
            if transfer_list.data:
                last_id = transfer_list.data[-1].id
            else:
                has_more = False
        
        # Cálculo otimizado
        total_transferred = sum(transfer.amount for transfer in transfers)
        
        return {
            "total_transferred_to_stylists": total_transferred,
            "currency": "BRL",
            "transfer_count": len(transfers)
        }
        
    except Exception as e:
        logger.error(f"❌ Erro ao obter transferências: {str(e)}")
        return {
            "total_transferred_to_stylists": 0,
            "currency": "BRL",
            "transfer_count": 0,
            "error": str(e)
        }

def get_app_revenue_share(total_revenue: int, total_transferred: int) -> Dict[str, Any]:
    """Calcula a parte do faturamento que ficou com o aplicativo"""
    app_revenue = total_revenue - total_transferred
    
    # Calcular percentuais
    if total_revenue > 0:
        app_percentage = (app_revenue / total_revenue) * 100
        stylists_percentage = (total_transferred / total_revenue) * 100
    else:
        app_percentage = 0
        stylists_percentage = 0
    
    return {
        "app_revenue": app_revenue,
        "app_percentage": round(app_percentage, 2),
        "stylists_percentage": round(stylists_percentage, 2),
        "currency": "BRL"
    }

async def get_platform_users() -> Dict[str, Any]:
    """Obtém informações sobre usuários, estilistas e assinaturas da plataforma de forma assíncrona"""
    try:
        async with aiohttp.ClientSession() as session:
            # Executar chamadas em paralelo para obter usuários, estilistas e assinaturas
            tasks = [
                session.get(f"{SUPABASE_URL}/rest/v1/User?select=id", headers=SUPABASE_HEADERS),
                session.get(f"{SUPABASE_URL}/rest/v1/User?role=eq.stylist&select=id", headers=SUPABASE_HEADERS),
                session.get(f"{SUPABASE_URL}/rest/v1/Subscriptions?select=id,active", headers=SUPABASE_HEADERS)
            ]
            
            responses = await asyncio.gather(*tasks)
            users_response, stylists_response, subscriptions_response = responses
            
            total_users = 0
            if users_response.status == 200:
                users_data = await users_response.json()
                total_users = len(users_data)
            
            total_stylists = 0
            if stylists_response.status == 200:
                stylists_data = await stylists_response.json()
                total_stylists = len(stylists_data)
                
            # Processar dados de assinaturas
            total_subscriptions = 0
            active_subscriptions = 0
            if subscriptions_response.status == 200:
                subscriptions_data = await subscriptions_response.json()
                total_subscriptions = len(subscriptions_data)
                active_subscriptions = sum(1 for sub in subscriptions_data if sub.get("active", False))
        
        logger.info(f"Total de usuários: {total_users}, Total de estilistas: {total_stylists}, " 
                   f"Assinaturas ativas: {active_subscriptions}, Total de assinaturas: {total_subscriptions}")
        
        return {
            "total_users": total_users,
            "total_stylists": total_stylists,
            "active_subscriptions": active_subscriptions,
            "total_subscriptions": total_subscriptions
        }
        
    except Exception as e:
        logger.error(f"❌ Erro ao obter informações de usuários e assinaturas: {str(e)}")
        return {
            "total_users": 0,
            "total_stylists": 0,
            "active_subscriptions": 0,
            "total_subscriptions": 0,
            "error": str(e)
        }

async def get_top_stylists(start_timestamp: Optional[int] = None, end_timestamp: Optional[int] = None, limit: int = 10) -> List[Dict[str, Any]]:
    """Obtém os estilistas com mais assinantes ativos e totais de forma otimizada"""
    try:
        # Obter todas as assinaturas
        base_url = f"{SUPABASE_URL}/rest/v1/Subscriptions?select=stylist_id,active,created_at"
        
        # Adicionar filtros de data se fornecidos
        if start_timestamp or end_timestamp:
            date_filters = []
            if start_timestamp:
                start_date = datetime.fromtimestamp(start_timestamp).strftime("%Y-%m-%d")
                date_filters.append(f"created_at=gte.{start_date}")
            if end_timestamp:
                end_date = datetime.fromtimestamp(end_timestamp).strftime("%Y-%m-%d")
                date_filters.append(f"created_at=lte.{end_date}")
            
            if date_filters:
                date_query = "&".join(date_filters)
                base_url = f"{base_url}&{date_query}"
        
        # Fazer a requisição para obter todas as assinaturas
        async with aiohttp.ClientSession() as session:
            async with session.get(base_url, headers=SUPABASE_HEADERS) as response:
                if response.status != 200:
                    logger.error(f"❌ Erro ao obter assinaturas: {response.status}")
                    return []
                
                subscriptions_data = await response.json()
        
        # Contadores para assinaturas ativas e totais por estilista
        stylists_stats = {}
        for subscription in subscriptions_data:
            stylist_id = subscription.get("stylist_id")
            is_active = subscription.get("active", False)
            
            if stylist_id not in stylists_stats:
                stylists_stats[stylist_id] = {
                    "active_count": 0, 
                    "total_count": 0
                }
            
            # Incrementar contadores
            stylists_stats[stylist_id]["total_count"] += 1
            if is_active:
                stylists_stats[stylist_id]["active_count"] += 1
        
        # Se não houver estilistas, retornar lista vazia
        if not stylists_stats:
            return []
        
        # Ordenar estilistas por número de assinaturas ativas (decrescente)
        top_stylist_ids = sorted(
            stylists_stats.keys(), 
            key=lambda x: stylists_stats[x]["active_count"], 
            reverse=True
        )[:limit]
        
        # Sem estilistas encontrados
        if not top_stylist_ids:
            return []
        
        # Obter detalhes dos estilistas em uma única chamada
        stylists_url = f"{SUPABASE_URL}/rest/v1/User?id=in.({','.join(top_stylist_ids)})&select=id,name,avatar"
        
        # Modificar os headers para garantir que a resposta seja tratada como UTF-8
        headers = SUPABASE_HEADERS.copy()
        headers["Accept"] = "application/json; charset=utf-8"
        
        # Obter detalhes dos estilistas
        async with aiohttp.ClientSession() as session:
            async with session.get(stylists_url, headers=headers) as response:
                if response.status != 200:
                    logger.error(f"❌ Erro ao obter detalhes dos estilistas: {response.status}")
                    return []
                
                # Forçar a decodificação como UTF-8
                text = await response.text(encoding='utf-8')
                import json
                stylists_data = json.loads(text)
        
        # Mapear os IDs para facilitar o acesso
        stylists_map = {stylist["id"]: stylist for stylist in stylists_data}
        
        # Combinar estatísticas com detalhes dos estilistas
        result = []
        for stylist_id in top_stylist_ids:
            if stylist_id in stylists_map:
                result.append({
                    "id": stylist_id,
                    "name": stylists_map[stylist_id].get("name", "Nome não disponível"),
                    "avatar": stylists_map[stylist_id].get("avatar", ""),
                    "active_subscriptions": stylists_stats[stylist_id]["active_count"],
                    "total_subscriptions": stylists_stats[stylist_id]["total_count"]
                })
        
        return result
        
    except Exception as e:
        logger.error(f"❌ Erro ao obter top estilistas: {str(e)}")
        return []
        
async def get_monthly_revenue_data(target_date) -> Dict[str, Any]:
    """Obtém o faturamento de um mês específico de forma otimizada"""
    ny_timezone = pytz.timezone('America/New_York')
    if not isinstance(target_date, datetime):
        target_date = datetime.now(ny_timezone)
    
    # Calcular início e fim do mês
    month_start = datetime(target_date.year, target_date.month, 1, tzinfo=ny_timezone)
    if target_date.month == 12:
        month_end = datetime(target_date.year + 1, 1, 1, tzinfo=ny_timezone) - timedelta(seconds=1)
    else:
        month_end = datetime(target_date.year, target_date.month + 1, 1, tzinfo=ny_timezone) - timedelta(seconds=1)
    
    # Converter para timestamps
    start_timestamp = int(month_start.timestamp())
    end_timestamp = int(month_end.timestamp())
    
    # Obter dados do mês em paralelo
    revenue_data, transfer_data = await asyncio.gather(
        get_total_platform_revenue(start_timestamp, end_timestamp),
        get_platform_transfers(start_timestamp, end_timestamp)
    )
    
    # Calcular divisão app/estilistas
    total_revenue = revenue_data["successful_revenue"]
    total_transferred = transfer_data["total_transferred_to_stylists"]
    app_revenue = total_revenue - total_transferred
    
    return {
        "year": target_date.year,
        "month": target_date.month,
        "name": target_date.strftime("%b"),
        "total_revenue": total_revenue,
        "app_revenue": app_revenue,
        "stylists_revenue": total_transferred,
        "payment_count": revenue_data["payment_count"],
        "transfer_count": transfer_data["transfer_count"]
    }

# Função auxiliar para lidar com os períodos específicos
def get_period_timestamps(period: str) -> Tuple[Optional[int], Optional[int]]:
    """Retorna timestamps inicial e final baseados no período especificado"""
    ny_timezone = pytz.timezone('America/New_York')
    now_ny = datetime.now(ny_timezone)
    current_year = now_ny.year
    
    # Inicializar timestamps
    start_timestamp = None
    end_timestamp = int(now_ny.timestamp())
    
    # Lidar com os meses específicos
    months = {
        'January': 1, 'February': 2, 'March': 3, 'April': 4,
        'May': 5, 'June': 6, 'July': 7, 'August': 8,
        'September': 9, 'October': 10, 'November': 11, 'December': 12
    }
    
    if period in months:
        # Período para um mês específico do ano atual
        month_num = months[period]
        
        # Se o mês solicitado ainda não chegou neste ano, use o ano anterior
        if month_num > now_ny.month:
            year = current_year - 1
        else:
            year = current_year
            
        # Definir início e fim do mês
        month_start = datetime(year, month_num, 1, tzinfo=ny_timezone)
        if month_num == 12:
            month_end = datetime(year + 1, 1, 1, tzinfo=ny_timezone) - timedelta(seconds=1)
        else:
            month_end = datetime(year, month_num + 1, 1, tzinfo=ny_timezone) - timedelta(seconds=1)
        
        start_timestamp = int(month_start.timestamp())
        end_timestamp = int(month_end.timestamp())
    
    elif period == "All Year":
        # Período para o ano atual inteiro
        year_start = datetime(current_year, 1, 1, tzinfo=ny_timezone)
        year_end = datetime(current_year + 1, 1, 1, tzinfo=ny_timezone) - timedelta(seconds=1)
        
        start_timestamp = int(year_start.timestamp())
        end_timestamp = int(year_end.timestamp())
        
        # Se estamos no início do ano, pode ser útil mostrar dados do ano anterior também
        if now_ny.month <= 2:
            year_start = datetime(current_year - 1, 1, 1, tzinfo=ny_timezone)
            start_timestamp = int(year_start.timestamp())
    
    # Períodos legados (manter para compatibilidade)
    elif period == "last_month":
        start_date = now_ny - relativedelta(months=1)
        start_timestamp = int(start_date.timestamp())
    elif period == "last_year":
        start_date = now_ny - relativedelta(years=1)
        start_timestamp = int(start_date.timestamp())
    
    return start_timestamp, end_timestamp

# Helper function to identify if we need specific month data
def is_specific_month(period: str) -> bool:
    """Verifica se o período solicitado é um mês específico"""
    months = [
        'January', 'February', 'March', 'April', 'May', 'June',
        'July', 'August', 'September', 'October', 'November', 'December'
    ]
    return period in months

@router.get("/admin/dashboard")
async def get_admin_dashboard(
    user_token: str = Header(None, alias="User-key"),
    period: str = Query("All Year", description="Período: All Year, January, February, etc.")
):
    """
    Endpoint para dashboard administrativo com métricas de faturamento
    e divisão entre app e estilistas - versão otimizada com seleção de meses
    """
    try:
        # Verificar se é um administrador
        user_id = await verify_admin_token(user_token)
        
        # Obter timestamps baseados no período
        start_timestamp, end_timestamp = get_period_timestamps(period)
        
        # Executar todas as chamadas em paralelo
        tasks = [
            get_total_platform_revenue(start_timestamp, end_timestamp),
            get_platform_transfers(start_timestamp, end_timestamp),
            get_platform_users(),
            get_top_stylists(start_timestamp, end_timestamp, 10)
        ]
        
        # Adicionar task para dados mensais se for um mês específico
        monthly_task = None
        monthly_data = None
        
        if is_specific_month(period):
            # Obter dados mensais para o mês solicitado
            ny_timezone = pytz.timezone('America/New_York')
            now_ny = datetime.now(ny_timezone)
            current_year = now_ny.year
            
            # Obter número do mês
            months = {
                'January': 1, 'February': 2, 'March': 3, 'April': 4,
                'May': 5, 'June': 6, 'July': 7, 'August': 8,
                'September': 9, 'October': 10, 'November': 11, 'December': 12
            }
            month_num = months[period]
            
            # Se o mês ainda não chegou neste ano, use o ano anterior
            if month_num > now_ny.month:
                year = current_year - 1
            else:
                year = current_year
                
            target_date = datetime(year, month_num, 1, tzinfo=ny_timezone)
            monthly_task = get_monthly_revenue_data(target_date)
            tasks.append(monthly_task)
        
        # Esperar por todas as chamadas completarem
        results = await asyncio.gather(*tasks)
        
        # Extrair resultados
        if is_specific_month(period):
            revenue_data, transfer_data, user_data, top_stylists, monthly_data = results
        else:
            revenue_data, transfer_data, user_data, top_stylists = results
        
        # Calcular divisão de receita
        revenue_share = get_app_revenue_share(
            revenue_data["successful_revenue"],
            transfer_data["total_transferred_to_stylists"]
        )
        
        # Preparar resposta
        response = {
            "total_revenue": revenue_data["successful_revenue"],
            "failed_revenue": revenue_data["failed_revenue"],
            "total_transferred_to_stylists": transfer_data["total_transferred_to_stylists"],
            "app_revenue": revenue_share["app_revenue"],
            "app_revenue_percentage": revenue_share["app_percentage"],
            "stylists_revenue_percentage": revenue_share["stylists_percentage"],
            "payment_count": revenue_data["payment_count"],
            "transfer_count": transfer_data["transfer_count"],
            "currency": "BRL",
            "period": period,
            "users": user_data,
            "top_stylists": top_stylists
        }
        
        # Adicionar dados específicos de mês
        if is_specific_month(period) and monthly_data:
            response["monthly_data"] = monthly_data
            
        # Para "All Year", adicionar dados mensais para visualização
        if period == "All Year":
            # Obter dados para cada mês do ano atual
            ny_timezone = pytz.timezone('America/New_York')
            now_ny = datetime.now(ny_timezone)
            current_year = now_ny.year
            
            monthly_tasks = []
            for month in range(1, 13):
                # Só incluir meses que já passaram ou o mês atual
                if month <= now_ny.month:
                    target_date = datetime(current_year, month, 1, tzinfo=ny_timezone)
                    monthly_tasks.append(get_monthly_revenue_data(target_date))
            
            if monthly_tasks:
                monthly_results = await asyncio.gather(*monthly_tasks)
                response["year_data"] = monthly_results
        
        return response
        
    except HTTPException as he:
        raise he
        
    except Exception as e:
        logger.error(f"❌ Erro ao gerar dashboard administrativo: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))