"""
CFO Executive Dashboard
======================

Executive-level financial dashboard for Chief Financial Officers with
strategic financial oversight, AI cost management, and board-ready reporting.
"""

# Copyright (c) 2025 Infinidatum
# Author: Duraimurugan Rajamanickam
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
import json
import logging
from dataclasses import dataclass

# Import VizlyChart for professional visualizations
try:
    import finopsmetrics.vizlychart as vc
    from finopsmetrics.vizlychart.enterprise.charts import ExecutiveDashboard, FinancialAnalytics
    from finopsmetrics.vizlychart.enterprise.themes import EnterpriseTheme
    VIZLYCHART_AVAILABLE = True
except ImportError:
    VIZLYCHART_AVAILABLE = False
    # VizlyChart is optional - dashboards work with fallback visualizations

from .iam_system import get_iam_manager, DashboardType, DataClassification

logger = logging.getLogger(__name__)


@dataclass
class FinancialKPI:
    """Financial KPI with trend and target information."""
    name: str
    current_value: float
    target_value: Optional[float]
    previous_value: Optional[float]
    unit: str
    trend: str  # 'up', 'down', 'stable'
    status: str  # 'good', 'warning', 'critical'
    variance_percent: Optional[float] = None

    def __post_init__(self):
        if self.previous_value and self.current_value:
            self.variance_percent = ((self.current_value - self.previous_value) / self.previous_value) * 100


@dataclass
class CostBreakdown:
    """Cost breakdown by category with detailed analytics."""
    category: str
    current_cost: float
    budget: float
    previous_period: float
    forecast: float
    variance_percent: float
    trend: str
    subcategories: Dict[str, float]


class CFODashboard:
    """CFO Executive Dashboard with financial analytics and AI cost management."""

    def __init__(self):
        self.iam_manager = get_iam_manager()
        self.theme = self._get_executive_theme()
        self.data_sources = self._initialize_data_sources()

    def _get_executive_theme(self) -> Dict[str, Any]:
        """Get executive theme configuration."""
        return {
            "name": "CFO Executive Theme",
            "colors": {
                "primary": "#1e3a8a",      # Deep blue
                "secondary": "#059669",    # Green for positive
                "warning": "#d97706",      # Orange for warnings
                "danger": "#dc2626",       # Red for critical
                "success": "#059669",      # Green for success
                "neutral": "#6b7280",      # Gray for neutral
                "background": "#f8fafc",   # Light background
                "text": "#1f2937",         # Dark text
                "accent": "#7c3aed"        # Purple accent
            },
            "fonts": {
                "primary": "Inter, -apple-system, sans-serif",
                "headers": "Inter, -apple-system, sans-serif",
                "monospace": "SF Mono, Consolas, monospace"
            },
            "watermark": {
                "enabled": True,
                "text": "OpenFinOps Confidential - CFO Dashboard",
                "position": "bottom_right",
                "opacity": 0.1
            },
            "branding": {
                "logo_url": "/assets/finopsmetrics-logo.svg",
                "company_name": "OpenFinOps",
                "dashboard_title": "Chief Financial Officer Dashboard"
            }
        }

    def _initialize_data_sources(self) -> Dict[str, Any]:
        """Initialize data sources for financial metrics."""
        return {
            "financial_db": "postgresql://finopsmetrics:password@postgres:5432/financial_data",
            "cost_management": "https://api.finopsmetrics.com/cost-management",
            "ai_platforms": {
                "openai": {"api_key": "sk-...", "cost_tracking": True},
                "anthropic": {"api_key": "sk-ant-...", "cost_tracking": True},
                "aws": {"cost_explorer": True, "detailed_billing": True},
                "azure": {"cost_management": True},
                "gcp": {"billing_api": True}
            },
            "erp_system": "https://api.company.com/erp",
            "treasury_system": "https://api.company.com/treasury"
        }

    def generate_dashboard(self, user_id: str, time_period: str = "current_month") -> Dict[str, Any]:
        """Generate comprehensive CFO dashboard."""

        # Verify user access
        if not self.iam_manager.can_access_dashboard(user_id, DashboardType.CFO_EXECUTIVE):
            raise PermissionError("User does not have access to CFO dashboard")

        user_access_level = self.iam_manager.get_user_data_access_level(user_id)
        if user_access_level != DataClassification.RESTRICTED:
            raise PermissionError("Insufficient data access level for CFO dashboard")

        logger.info(f"Generating CFO dashboard for user {user_id}, period: {time_period}")

        # Generate dashboard components
        dashboard_data = {
            "metadata": self._get_dashboard_metadata(user_id, time_period),
            "executive_summary": self._generate_executive_summary(time_period),
            "financial_kpis": self._generate_financial_kpis(time_period),
            "ai_cost_analytics": self._generate_ai_cost_analytics(time_period),
            "cost_breakdown": self._generate_cost_breakdown(time_period),
            "budget_performance": self._generate_budget_performance(time_period),
            "financial_forecasts": self._generate_financial_forecasts(time_period),
            "risk_indicators": self._generate_risk_indicators(time_period),
            "board_highlights": self._generate_board_highlights(time_period),
            "compliance_status": self._generate_compliance_status(),
            "visualizations": self._generate_visualizations(time_period)
        }

        # Log dashboard access
        self.iam_manager._log_audit_event("dashboard_access", user_id, {
            "dashboard_type": "cfo_executive",
            "time_period": time_period,
            "components_generated": list(dashboard_data.keys())
        })

        return dashboard_data

    def _get_dashboard_metadata(self, user_id: str, time_period: str) -> Dict[str, Any]:
        """Get dashboard metadata and user context."""
        user = self.iam_manager.users.get(user_id)

        return {
            "dashboard_id": f"cfo_exec_{user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "generated_at": datetime.utcnow().isoformat(),
            "generated_for": {
                "user_id": user_id,
                "name": user.full_name if user else "Unknown",
                "email": user.email if user else "Unknown",
                "department": user.department if user else "Unknown"
            },
            "time_period": time_period,
            "data_classification": "RESTRICTED",
            "refresh_interval": 300,  # 5 minutes
            "expires_at": (datetime.utcnow() + timedelta(hours=4)).isoformat(),
            "version": "2.1.0",
            "theme": self.theme["name"]
        }

    def _generate_executive_summary(self, time_period: str) -> Dict[str, Any]:
        """Generate executive summary with key insights."""

        # Simulate real financial data - in production, fetch from actual sources
        current_revenue = 125000000  # $125M
        previous_revenue = 118000000  # $118M
        ai_investment = 8500000     # $8.5M
        ai_savings = 12000000       # $12M savings from AI

        revenue_growth = ((current_revenue - previous_revenue) / previous_revenue) * 100
        ai_roi = ((ai_savings - ai_investment) / ai_investment) * 100

        return {
            "headline_metrics": {
                "total_revenue": {
                    "value": current_revenue,
                    "unit": "USD",
                    "growth_rate": revenue_growth,
                    "target_achievement": 104.2  # 104.2% of target
                },
                "ai_investment_roi": {
                    "value": ai_roi,
                    "unit": "percent",
                    "investment": ai_investment,
                    "savings": ai_savings,
                    "payback_months": 8.5
                },
                "cost_optimization": {
                    "value": 15.2,  # 15.2% cost reduction
                    "unit": "percent",
                    "absolute_savings": 18500000
                }
            },
            "key_insights": [
                {
                    "type": "positive",
                    "title": "AI Investments Driving Strong ROI",
                    "description": f"AI initiatives have generated ${ai_roi:.1f}% ROI with ${ai_savings/1000000:.1f}M in operational savings",
                    "impact": "high",
                    "action_required": False
                },
                {
                    "type": "attention",
                    "title": "Cloud Infrastructure Costs Above Budget",
                    "description": "Cloud costs are 12% above budget due to increased AI training workloads",
                    "impact": "medium",
                    "action_required": True,
                    "recommendation": "Implement automated scaling and cost optimization policies"
                },
                {
                    "type": "positive",
                    "title": "Revenue Target Exceeded",
                    "description": f"Q4 revenue of ${current_revenue/1000000:.1f}M exceeds target by {revenue_growth:.1f}%",
                    "impact": "high",
                    "action_required": False
                }
            ],
            "strategic_priorities": [
                {
                    "priority": "Optimize AI Infrastructure Costs",
                    "status": "in_progress",
                    "budget_impact": 2500000,
                    "timeline": "Q1 2024"
                },
                {
                    "priority": "Scale AI Revenue Initiatives",
                    "status": "planning",
                    "revenue_potential": 15000000,
                    "timeline": "H1 2024"
                }
            ]
        }

    def _generate_financial_kpis(self, time_period: str) -> List[FinancialKPI]:
        """Generate key financial KPIs for executive oversight."""

        kpis = [
            FinancialKPI(
                name="Total Revenue",
                current_value=125000000,
                target_value=120000000,
                previous_value=118000000,
                unit="USD",
                trend="up",
                status="good"
            ),
            FinancialKPI(
                name="AI ROI",
                current_value=41.2,
                target_value=35.0,
                previous_value=28.5,
                unit="percent",
                trend="up",
                status="good"
            ),
            FinancialKPI(
                name="Operating Margin",
                current_value=18.4,
                target_value=20.0,
                previous_value=17.8,
                unit="percent",
                trend="up",
                status="warning"
            ),
            FinancialKPI(
                name="AI Infrastructure Costs",
                current_value=8500000,
                target_value=7500000,
                previous_value=7200000,
                unit="USD",
                trend="up",
                status="warning"
            ),
            FinancialKPI(
                name="Cash Flow",
                current_value=23000000,
                target_value=22000000,
                previous_value=21500000,
                unit="USD",
                trend="up",
                status="good"
            ),
            FinancialKPI(
                name="Cost Per AI Inference",
                current_value=0.0034,
                target_value=0.0040,
                previous_value=0.0045,
                unit="USD",
                trend="down",
                status="good"
            ),
            FinancialKPI(
                name="R&D Investment",
                current_value=15600000,
                target_value=15000000,
                previous_value=14200000,
                unit="USD",
                trend="up",
                status="good"
            ),
            FinancialKPI(
                name="AI Training Efficiency",
                current_value=156.2,
                target_value=150.0,
                previous_value=142.8,
                unit="models_per_dollar",
                trend="up",
                status="good"
            )
        ]

        return kpis

    def _generate_ai_cost_analytics(self, time_period: str) -> Dict[str, Any]:
        """Generate detailed AI cost analytics and optimization insights."""

        # Simulate AI cost data
        ai_costs = {
            "training_costs": {
                "total": 4200000,
                "by_provider": {
                    "aws_ec2": 2800000,
                    "azure_ml": 950000,
                    "gcp_tpu": 450000
                },
                "by_model_type": {
                    "llm_training": 3100000,
                    "computer_vision": 750000,
                    "nlp_models": 350000
                },
                "optimization_potential": 680000
            },
            "inference_costs": {
                "total": 2100000,
                "by_provider": {
                    "openai_api": 1200000,
                    "anthropic_api": 400000,
                    "internal_hosting": 500000
                },
                "by_application": {
                    "customer_service": 850000,
                    "content_generation": 650000,
                    "analytics": 400000,
                    "other": 200000
                },
                "monthly_trend": [1800000, 1950000, 2100000]
            },
            "storage_costs": {
                "total": 180000,
                "data_lakes": 120000,
                "model_artifacts": 60000
            },
            "networking_costs": {
                "total": 95000,
                "data_transfer": 65000,
                "api_calls": 30000
            }
        }

        # Calculate cost optimization opportunities
        optimization_opportunities = [
            {
                "category": "Training Infrastructure",
                "opportunity": "Switch to Spot Instances",
                "potential_savings": 450000,
                "implementation_effort": "medium",
                "risk_level": "low",
                "timeline": "1 month"
            },
            {
                "category": "Inference Optimization",
                "opportunity": "Model Quantization",
                "potential_savings": 280000,
                "implementation_effort": "high",
                "risk_level": "medium",
                "timeline": "3 months"
            },
            {
                "category": "API Cost Management",
                "opportunity": "Batch Processing",
                "potential_savings": 150000,
                "implementation_effort": "low",
                "risk_level": "low",
                "timeline": "2 weeks"
            }
        ]

        return {
            "ai_costs": ai_costs,
            "total_ai_spend": sum([
                ai_costs["training_costs"]["total"],
                ai_costs["inference_costs"]["total"],
                ai_costs["storage_costs"]["total"],
                ai_costs["networking_costs"]["total"]
            ]),
            "cost_trends": {
                "monthly_growth_rate": 8.5,
                "quarterly_forecast": 7200000,
                "annual_projection": 28800000
            },
            "optimization_opportunities": optimization_opportunities,
            "total_optimization_potential": sum(opp["potential_savings"] for opp in optimization_opportunities),
            "cost_per_business_metric": {
                "cost_per_customer": 12.45,
                "cost_per_transaction": 0.34,
                "cost_per_model_prediction": 0.0028
            }
        }

    def _generate_cost_breakdown(self, time_period: str) -> List[CostBreakdown]:
        """Generate detailed cost breakdown by category."""

        cost_categories = [
            CostBreakdown(
                category="AI & Machine Learning",
                current_cost=6575000,
                budget=6000000,
                previous_period=5800000,
                forecast=7200000,
                variance_percent=9.6,
                trend="increasing",
                subcategories={
                    "Model Training": 4200000,
                    "Inference APIs": 2100000,
                    "Data Storage": 180000,
                    "Networking": 95000
                }
            ),
            CostBreakdown(
                category="Infrastructure & Cloud",
                current_cost=12500000,
                budget=13000000,
                previous_period=11800000,
                forecast=13200000,
                variance_percent=-3.8,
                trend="stable",
                subcategories={
                    "Compute Resources": 7500000,
                    "Storage Systems": 2800000,
                    "Networking": 1200000,
                    "Security & Monitoring": 1000000
                }
            ),
            CostBreakdown(
                category="Personnel & Operations",
                current_cost=45000000,
                budget=46000000,
                previous_period=43500000,
                forecast=47000000,
                variance_percent=-2.2,
                trend="stable",
                subcategories={
                    "Engineering": 28000000,
                    "Data Science": 12000000,
                    "Operations": 3500000,
                    "Management": 1500000
                }
            ),
            CostBreakdown(
                category="Software & Licensing",
                current_cost=8900000,
                budget=9200000,
                previous_period=8400000,
                forecast=9500000,
                variance_percent=-3.3,
                trend="increasing",
                subcategories={
                    "Enterprise Software": 4200000,
                    "AI/ML Platforms": 2800000,
                    "Security Tools": 1200000,
                    "Productivity Tools": 700000
                }
            ),
            CostBreakdown(
                category="Research & Development",
                current_cost=15600000,
                budget=15000000,
                previous_period=14200000,
                forecast=16800000,
                variance_percent=4.0,
                trend="increasing",
                subcategories={
                    "AI Research": 8900000,
                    "Product Development": 4200000,
                    "Innovation Labs": 1800000,
                    "External Partnerships": 700000
                }
            )
        ]

        return cost_categories

    def _generate_budget_performance(self, time_period: str) -> Dict[str, Any]:
        """Generate budget vs actual performance analysis."""

        budget_data = {
            "quarterly_performance": {
                "Q1": {"budget": 85000000, "actual": 83200000, "variance": -2.1},
                "Q2": {"budget": 88000000, "actual": 89500000, "variance": 1.7},
                "Q3": {"budget": 91000000, "actual": 92800000, "variance": 2.0},
                "Q4": {"budget": 95000000, "actual": 98200000, "variance": 3.4}
            },
            "annual_summary": {
                "total_budget": 359000000,
                "total_actual": 363700000,
                "variance_amount": 4700000,
                "variance_percent": 1.3,
                "trend": "over_budget"
            },
            "budget_alerts": [
                {
                    "category": "AI Infrastructure",
                    "severity": "medium",
                    "message": "AI infrastructure costs 12% over budget",
                    "impact": 720000,
                    "recommendation": "Implement cost optimization measures"
                },
                {
                    "category": "R&D Investment",
                    "severity": "low",
                    "message": "R&D spending 4% over budget due to accelerated AI initiatives",
                    "impact": 600000,
                    "recommendation": "Approved overrun for strategic initiatives"
                }
            ],
            "forecast_accuracy": {
                "current_quarter": 96.8,
                "rolling_average": 94.2,
                "improvement_trend": "stable"
            }
        }

        return budget_data

    def _generate_financial_forecasts(self, time_period: str) -> Dict[str, Any]:
        """Generate financial forecasts and projections."""

        # Generate forecast data for next 12 months
        base_revenue = 125000000
        monthly_forecasts = []

        for month in range(12):
            growth_factor = 1 + (0.08 / 12)  # 8% annual growth
            seasonal_factor = 1 + 0.1 * np.sin((month + 3) * np.pi / 6)  # Seasonal variation

            forecast_revenue = base_revenue * (growth_factor ** month) * seasonal_factor

            monthly_forecasts.append({
                "month": month + 1,
                "revenue": forecast_revenue,
                "costs": forecast_revenue * 0.78,  # 78% cost ratio
                "margin": forecast_revenue * 0.22,
                "ai_investment": forecast_revenue * 0.08  # 8% of revenue in AI
            })

        return {
            "monthly_forecasts": monthly_forecasts,
            "annual_projections": {
                "revenue": {
                    "conservative": 1450000000,
                    "likely": 1580000000,
                    "optimistic": 1720000000
                },
                "ai_roi": {
                    "conservative": 35.0,
                    "likely": 42.5,
                    "optimistic": 55.0
                },
                "market_scenarios": [
                    {
                        "scenario": "AI Market Expansion",
                        "probability": 65,
                        "revenue_impact": 180000000,
                        "investment_required": 45000000
                    },
                    {
                        "scenario": "Economic Downturn",
                        "probability": 25,
                        "revenue_impact": -95000000,
                        "cost_savings": 35000000
                    },
                    {
                        "scenario": "Regulatory Changes",
                        "probability": 10,
                        "revenue_impact": -25000000,
                        "compliance_costs": 15000000
                    }
                ]
            },
            "confidence_intervals": {
                "revenue_forecast": {"lower": 0.92, "upper": 1.15},
                "cost_forecast": {"lower": 0.95, "upper": 1.08},
                "ai_roi_forecast": {"lower": 0.85, "upper": 1.25}
            }
        }

    def _generate_risk_indicators(self, time_period: str) -> Dict[str, Any]:
        """Generate financial risk indicators and early warning systems."""

        return {
            "liquidity_risks": {
                "cash_position": {
                    "current": 95000000,
                    "minimum_required": 75000000,
                    "runway_months": 18.5,
                    "status": "healthy"
                },
                "credit_facilities": {
                    "available": 150000000,
                    "utilized": 25000000,
                    "utilization_ratio": 16.7,
                    "status": "good"
                }
            },
            "operational_risks": {
                "ai_dependency": {
                    "revenue_at_risk": 45000000,  # Revenue dependent on AI systems
                    "mitigation_score": 78,
                    "backup_systems": True,
                    "status": "monitored"
                },
                "cost_volatility": {
                    "cloud_cost_variance": 15.2,
                    "ai_training_costs": "high_volatility",
                    "hedging_strategies": ["reserved_instances", "budget_controls"],
                    "status": "managed"
                }
            },
            "market_risks": {
                "competitive_pressure": {
                    "market_share_risk": 8.5,
                    "ai_innovation_pace": "accelerating",
                    "investment_requirements": 25000000,
                    "status": "attention_required"
                },
                "regulatory_risks": {
                    "ai_regulation_impact": "medium",
                    "compliance_costs": 5000000,
                    "preparation_status": "in_progress",
                    "status": "monitored"
                }
            },
            "early_warning_indicators": [
                {
                    "indicator": "AI Cost Growth Rate",
                    "current_value": 15.2,
                    "warning_threshold": 20.0,
                    "critical_threshold": 30.0,
                    "status": "normal",
                    "trend": "increasing"
                },
                {
                    "indicator": "Customer Acquisition Cost",
                    "current_value": 145.80,
                    "warning_threshold": 180.00,
                    "critical_threshold": 220.00,
                    "status": "normal",
                    "trend": "stable"
                },
                {
                    "indicator": "AI Model Performance Degradation",
                    "current_value": 2.1,
                    "warning_threshold": 5.0,
                    "critical_threshold": 10.0,
                    "status": "normal",
                    "trend": "stable"
                }
            ]
        }

    def _generate_board_highlights(self, time_period: str) -> Dict[str, Any]:
        """Generate board-ready highlights and executive summary."""

        return {
            "executive_achievements": [
                {
                    "achievement": "AI ROI Exceeds 40%",
                    "details": "AI initiatives delivered 41.2% ROI, significantly above industry average of 28%",
                    "financial_impact": 12000000,
                    "strategic_importance": "high"
                },
                {
                    "achievement": "Revenue Growth Above Target",
                    "details": "Q4 revenue of $125M exceeded target by 4.2%, driven by AI-powered products",
                    "financial_impact": 5000000,
                    "strategic_importance": "high"
                },
                {
                    "achievement": "Cost Optimization Success",
                    "details": "Implemented AI-driven cost optimization reducing infrastructure costs by 15.2%",
                    "financial_impact": 18500000,
                    "strategic_importance": "medium"
                }
            ],
            "strategic_initiatives": [
                {
                    "initiative": "AI Center of Excellence",
                    "status": "on_track",
                    "investment": 25000000,
                    "expected_roi": 180000000,
                    "timeline": "18 months",
                    "key_milestones": [
                        "Q1: Infrastructure setup complete",
                        "Q2: First models in production",
                        "Q3: Customer deployment begins",
                        "Q4: Full revenue impact"
                    ]
                },
                {
                    "initiative": "Global AI Ethics Framework",
                    "status": "planning",
                    "investment": 5000000,
                    "expected_roi": "risk_mitigation",
                    "timeline": "12 months",
                    "compliance_value": "regulatory_readiness"
                }
            ],
            "market_position": {
                "ai_innovation_ranking": 3,  # 3rd in industry
                "market_share": 12.5,
                "competitive_advantages": [
                    "Proprietary AI models",
                    "Integrated platform approach",
                    "Strong financial performance",
                    "Experienced AI team"
                ],
                "growth_opportunities": [
                    "International expansion",
                    "Enterprise AI solutions",
                    "AI-as-a-Service offerings",
                    "Industry-specific AI products"
                ]
            },
            "recommendations": [
                {
                    "recommendation": "Accelerate AI Infrastructure Investment",
                    "rationale": "High ROI and competitive advantage opportunity",
                    "investment_required": 45000000,
                    "expected_return": 180000000,
                    "timeline": "6 months",
                    "risk_level": "medium"
                },
                {
                    "recommendation": "Establish AI Risk Management Framework",
                    "rationale": "Proactive regulatory compliance and risk mitigation",
                    "investment_required": 8000000,
                    "expected_return": "risk_reduction",
                    "timeline": "12 months",
                    "risk_level": "low"
                }
            ]
        }

    def _generate_compliance_status(self) -> Dict[str, Any]:
        """Generate compliance status and regulatory readiness."""

        return {
            "financial_compliance": {
                "sox_compliance": {
                    "status": "compliant",
                    "last_audit": "2024-01-15",
                    "next_review": "2024-07-15",
                    "issues": 0,
                    "score": 98.5
                },
                "gaap_compliance": {
                    "status": "compliant",
                    "ai_revenue_recognition": "compliant",
                    "r&d_capitalization": "reviewed",
                    "score": 97.2
                },
                "international_standards": {
                    "ifrs_readiness": 85,
                    "transfer_pricing": "compliant",
                    "tax_optimization": "under_review"
                }
            },
            "ai_governance": {
                "ai_ethics_framework": {
                    "status": "in_development",
                    "completion": 75,
                    "board_approval": "pending"
                },
                "data_privacy": {
                    "gdpr_compliance": 92,
                    "ccpa_compliance": 88,
                    "data_governance": "established"
                },
                "ai_risk_management": {
                    "bias_monitoring": "implemented",
                    "model_validation": "ongoing",
                    "explainability": "in_progress"
                }
            },
            "regulatory_readiness": {
                "ai_regulation_preparedness": 78,
                "emerging_requirements": [
                    "EU AI Act compliance",
                    "US AI disclosure requirements",
                    "Industry-specific AI standards"
                ],
                "investment_required": 12000000,
                "timeline": "18 months"
            }
        }

    def _generate_visualizations(self, time_period: str) -> Dict[str, Any]:
        """Generate professional visualizations for executive presentation."""

        visualizations = {}

        if VIZLYCHART_AVAILABLE:
            # Executive KPI Dashboard
            visualizations["executive_kpis"] = self._create_executive_kpi_chart()

            # AI Cost Waterfall
            visualizations["ai_cost_waterfall"] = self._create_ai_cost_waterfall()

            # Revenue vs Target Gauge
            visualizations["revenue_gauge"] = self._create_revenue_gauge()

            # Financial Forecast
            visualizations["financial_forecast"] = self._create_forecast_chart()

            # Risk Heatmap
            visualizations["risk_heatmap"] = self._create_risk_heatmap()

            # Board Summary Dashboard
            visualizations["board_summary"] = self._create_board_summary()

        return visualizations

    def _create_executive_kpi_chart(self) -> Dict[str, Any]:
        """Create executive KPI visualization."""

        kpis = self._generate_financial_kpis("current_month")

        kpi_data = []
        for kpi in kpis:
            kpi_data.append({
                "name": kpi.name,
                "value": kpi.current_value,
                "target": kpi.target_value,
                "status": kpi.status,
                "trend": kpi.trend,
                "variance": kpi.variance_percent
            })

        if VIZLYCHART_AVAILABLE:
            chart = ExecutiveDashboard()

            return chart.create_kpi_dashboard(
                kpis=kpi_data,
                title="CFO Executive KPIs",
                theme="executive_professional",
                watermark="OpenFinOps Confidential",
                export_formats=["png", "pdf"]
            )
        else:
            return {
                "chart_type": "executive_kpis",
                "data": kpi_data,
                "config": {"title": "CFO Executive KPIs"}
            }

    def _create_ai_cost_waterfall(self) -> Dict[str, Any]:
        """Create AI cost waterfall chart."""

        waterfall_data = [
            {"category": "Previous Period", "value": 5800000, "type": "base"},
            {"category": "Training Increase", "value": 420000, "type": "increase"},
            {"category": "Inference Growth", "value": 280000, "type": "increase"},
            {"category": "Optimization Savings", "value": -150000, "type": "decrease"},
            {"category": "New Initiatives", "value": 225000, "type": "increase"},
            {"category": "Current Period", "value": 6575000, "type": "total"}
        ]

        if VIZLYCHART_AVAILABLE:
            chart = FinancialAnalytics()

            return chart.create_waterfall_chart(
                data=waterfall_data,
                title="AI Cost Analysis - Period over Period",
                subtitle="Breakdown of AI cost changes",
                theme="financial_professional",
                currency="USD",
                watermark="OpenFinOps Confidential"
            )
        else:
            return {
                "chart_type": "waterfall",
                "data": waterfall_data,
                "config": {"title": "AI Cost Analysis"}
            }

    def _create_revenue_gauge(self) -> Dict[str, Any]:
        """Create revenue performance gauge."""

        gauge_data = {
            "current": 125000000,
            "target": 120000000,
            "previous": 118000000,
            "max_scale": 140000000,
            "ranges": [
                {"min": 0, "max": 100000000, "color": "#dc2626", "label": "Below Target"},
                {"min": 100000000, "max": 120000000, "color": "#d97706", "label": "Approaching Target"},
                {"min": 120000000, "max": 140000000, "color": "#059669", "label": "Above Target"}
            ]
        }

        if VIZLYCHART_AVAILABLE:
            chart = ExecutiveDashboard()

            return chart.create_performance_gauge(
                current_value=gauge_data["current"],
                target_value=gauge_data["target"],
                title="Revenue Performance",
                subtitle="Current vs Target",
                theme="executive_gauge",
                unit="USD",
                format="currency"
            )
        else:
            return {
                "chart_type": "gauge",
                "data": gauge_data,
                "config": {"title": "Revenue Performance"}
            }

    def _create_forecast_chart(self) -> Dict[str, Any]:
        """Create financial forecast visualization."""

        forecast_data = self._generate_financial_forecasts("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = FinancialAnalytics()

            return chart.create_forecast_chart(
                forecasts=forecast_data["monthly_forecasts"],
                title="12-Month Financial Forecast",
                confidence_intervals=forecast_data["confidence_intervals"],
                theme="financial_forecast",
                currency="USD"
            )
        else:
            return {
                "chart_type": "forecast",
                "data": forecast_data,
                "config": {"title": "Financial Forecast"}
            }

    def _create_risk_heatmap(self) -> Dict[str, Any]:
        """Create risk assessment heatmap."""

        risk_data = [
            {"category": "Liquidity", "subcategory": "Cash Position", "probability": 15, "impact": 25, "score": 3.75},
            {"category": "Operational", "subcategory": "AI Dependency", "probability": 35, "impact": 80, "score": 28},
            {"category": "Market", "subcategory": "Competition", "probability": 60, "impact": 70, "score": 42},
            {"category": "Regulatory", "subcategory": "AI Compliance", "probability": 40, "impact": 60, "score": 24},
            {"category": "Technology", "subcategory": "Platform Risk", "probability": 25, "impact": 85, "score": 21.25},
            {"category": "Financial", "subcategory": "Cost Volatility", "probability": 55, "impact": 45, "score": 24.75}
        ]

        if VIZLYCHART_AVAILABLE:
            chart = ExecutiveDashboard()

            return chart.create_risk_heatmap(
                risks=risk_data,
                title="Enterprise Risk Assessment",
                theme="risk_management",
                color_scale="risk_gradient"
            )
        else:
            return {
                "chart_type": "heatmap",
                "data": risk_data,
                "config": {"title": "Risk Assessment"}
            }

    def _create_board_summary(self) -> Dict[str, Any]:
        """Create board-ready summary visualization."""

        board_data = self._generate_board_highlights("current_month")

        if VIZLYCHART_AVAILABLE:
            chart = ExecutiveDashboard()

            return chart.create_board_summary(
                achievements=board_data["executive_achievements"],
                initiatives=board_data["strategic_initiatives"],
                recommendations=board_data["recommendations"],
                title="Board Executive Summary",
                theme="board_presentation",
                layout="executive"
            )
        else:
            return {
                "chart_type": "board_summary",
                "data": board_data,
                "config": {"title": "Board Summary"}
            }

    def export_dashboard_report(self, dashboard_data: Dict[str, Any], format: str = "pdf") -> str:
        """Export dashboard as comprehensive report."""

        report_id = f"cfo_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

        if format == "pdf":
            # Generate PDF report with executive summary
            report_path = f"/exports/{report_id}.pdf"

            # In production, use proper PDF generation library
            logger.info(f"Generated CFO executive report: {report_path}")

        elif format == "excel":
            # Generate Excel workbook with multiple sheets
            report_path = f"/exports/{report_id}.xlsx"

            # In production, use openpyxl or similar
            logger.info(f"Generated CFO Excel report: {report_path}")

        elif format == "powerpoint":
            # Generate PowerPoint presentation
            report_path = f"/exports/{report_id}.pptx"

            # In production, use python-pptx
            logger.info(f"Generated CFO PowerPoint report: {report_path}")

        return report_path


# Initialize CFO Dashboard
cfo_dashboard = CFODashboard()


def get_cfo_dashboard() -> CFODashboard:
    """Get CFO dashboard instance."""
    return cfo_dashboard