#!/usr/bin/env python3
"""
Data Analysis Tool with Realistic Mock Data Generation
"""

import logging
import random
from datetime import datetime
from typing import Any, Dict, Optional, List

from ..core.bedrock_client import BedrockClient
from ..core.knowledge_base import KnowledgeBase
from ..utils.json_parser import parse_json

logger = logging.getLogger(__name__)


class DataAnalyzer:
    """Data analysis with realistic mock data generation for optimization problems"""
    
    def __init__(self, bedrock_client: BedrockClient, knowledge_base: KnowledgeBase):
        self.bedrock = bedrock_client
        self.kb = knowledge_base
    
    async def analyze_data(self, problem_description: str, intent_data: Optional[Dict] = None) -> Dict[str, Any]:
        """Analyze data readiness and generate realistic mock data"""
        try:
            intent = intent_data.get('intent', 'unknown') if intent_data else 'unknown'
            industry = intent_data.get('industry', 'unknown') if intent_data else 'unknown'
            optimization_type = intent_data.get('optimization_type', 'linear_programming') if intent_data else 'linear_programming'
            
            # Generate realistic mock data based on problem type
            mock_data = self._generate_mock_data(problem_description, intent, industry, optimization_type)
            
            # Analyze data readiness
            readiness_score = self._calculate_readiness_score(mock_data)
            
            result = {
                "readiness_score": readiness_score,
                "data_quality": "high",  # Mock data is always high quality
                "entities": len(mock_data.get('variables', [])),
                "variables_identified": [var.get('name', '') for var in mock_data.get('variables', [])],
                "constraints_identified": [const.get('type', '') for const in mock_data.get('constraints', [])],
                "mock_data": mock_data,
                "data_source": "realistic_mock_data",
                "assumptions": mock_data.get('assumptions', []),
                "industry_benchmarks": mock_data.get('benchmarks', {})
            }
            
            return {
                "status": "success",
                "step": "data_analysis",
                "timestamp": datetime.now().isoformat(),
                "result": result,
                "message": f"Mock data generated with {readiness_score:.1%} readiness"
            }
        except Exception as e:
            logger.error(f"Data analysis error: {e}")
            return {"status": "error", "step": "data_analysis", "error": str(e)}
    
    def _generate_mock_data(self, problem_description: str, intent: str, industry: str, optimization_type: str) -> Dict[str, Any]:
        """Generate realistic mock data based on problem characteristics"""
        problem_lower = problem_description.lower()
        
        # Manufacturing/Production Planning
        if any(word in problem_lower for word in ['manufacturing', 'production', 'factory', 'plant', 'furniture', 'chairs', 'tables', 'cabinets']):
            return self._generate_manufacturing_data(problem_description)
        
        # Portfolio Optimization
        elif any(word in problem_lower for word in ['portfolio', 'investment', 'stocks', 'assets', 'returns', 'risk']):
            return self._generate_portfolio_data(problem_description)
        
        # Scheduling Problems
        elif any(word in problem_lower for word in ['schedule', 'nurse', 'employee', 'shift', 'staffing', 'roster']):
            return self._generate_scheduling_data(problem_description)
        
        # Supply Chain/Logistics
        elif any(word in problem_lower for word in ['supply', 'logistics', 'transportation', 'warehouse', 'inventory']):
            return self._generate_supply_chain_data(problem_description)
        
        # Resource Allocation
        elif any(word in problem_lower for word in ['resource', 'allocation', 'budget', 'capacity', 'assignment']):
            return self._generate_resource_allocation_data(problem_description)
        
        # Default generic optimization
        else:
            return self._generate_generic_data(problem_description)
    
    def _generate_manufacturing_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate realistic manufacturing data"""
        # Extract product types from problem description
        products = []
        if 'chair' in problem_description.lower():
            products.append('chairs')
        if 'table' in problem_description.lower():
            products.append('tables')
        if 'cabinet' in problem_description.lower():
            products.append('cabinets')
        
        if not products:
            products = ['product_a', 'product_b', 'product_c']
        
        # Realistic manufacturing parameters
        variables = []
        constraints = []
        assumptions = []
        
        for i, product in enumerate(products):
            # Realistic production capacity (units per day)
            daily_capacity = random.randint(50, 300)
            
            # Realistic labor hours per unit (based on complexity)
            labor_hours = random.uniform(1.5, 8.0)
            
            # Realistic profit margins ($10-150 per unit)
            profit_per_unit = random.uniform(10, 150)
            
            # Realistic demand (monthly)
            monthly_demand_min = random.randint(500, 2000)
            monthly_demand_max = monthly_demand_min + random.randint(500, 1500)
            
            variables.append({
                "name": f"{product}_daily_production",
                "type": "continuous",
                "bounds": f"0 to {daily_capacity}",
                "description": f"Daily production quantity of {product}",
                "realistic_value": daily_capacity * 0.7  # 70% utilization
            })
            
            constraints.append({
                "type": "capacity",
                "expression": f"{product}_daily_production <= {daily_capacity}",
                "description": f"Daily production capacity limit for {product}",
                "realistic_bound": daily_capacity
            })
            
            constraints.append({
                "type": "demand",
                "expression": f"{monthly_demand_min} <= {product}_daily_production * 30 <= {monthly_demand_max}",
                "description": f"Monthly demand range for {product}",
                "realistic_range": [monthly_demand_min, monthly_demand_max]
            })
            
            assumptions.append(f"{product.title()}: {labor_hours:.1f} labor hours/unit, ${profit_per_unit:.2f} profit/unit")
        
        # Labor constraints
        total_labor_hours = random.randint(800, 2000)
        constraints.append({
            "type": "labor",
            "expression": f"sum(labor_hours * production) <= {total_labor_hours}",
            "description": "Daily labor hour availability",
            "realistic_bound": total_labor_hours
        })
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "maximize",
                "expression": "sum(profit_per_unit * production)",
                "description": "Maximize total profit"
            },
            "assumptions": assumptions,
            "benchmarks": {
                "typical_utilization": "70-85%",
                "profit_margin_range": "15-35%",
                "labor_efficiency": "80-95%"
            },
            "industry_standards": {
                "setup_time": "5-15% of production time",
                "quality_rate": "95-99%",
                "maintenance_downtime": "2-5%"
            }
        }
    
    def _generate_portfolio_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate realistic portfolio optimization data"""
        # Extract number of assets
        num_assets = 10  # Default
        if 'stocks' in problem_description.lower():
            num_assets = random.randint(8, 15)
        
        variables = []
        constraints = []
        assumptions = []
        
        # Realistic asset data
        asset_names = [f"Asset_{i+1}" for i in range(num_assets)]
        
        for i, asset in enumerate(asset_names):
            # Realistic expected returns (annual, 5-15%)
            expected_return = random.uniform(0.05, 0.15)
            
            # Realistic volatility (15-35%)
            volatility = random.uniform(0.15, 0.35)
            
            # Realistic correlation with market
            beta = random.uniform(0.7, 1.4)
            
            variables.append({
                "name": f"weight_{asset}",
                "type": "continuous",
                "bounds": "0 to 1",
                "description": f"Portfolio weight for {asset}",
                "expected_return": expected_return,
                "volatility": volatility,
                "beta": beta
            })
            
            assumptions.append(f"{asset}: {expected_return:.1%} expected return, {volatility:.1%} volatility")
        
        # Portfolio constraints
        constraints.append({
            "type": "budget",
            "expression": "sum(weights) = 1",
            "description": "Portfolio weights must sum to 100%"
        })
        
        constraints.append({
            "type": "risk",
            "expression": "portfolio_variance <= max_variance",
            "description": "Portfolio risk constraint",
            "max_variance": 0.04  # 20% volatility limit
        })
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "maximize",
                "expression": "sum(expected_return * weight)",
                "description": "Maximize expected portfolio return"
            },
            "assumptions": assumptions,
            "benchmarks": {
                "market_return": "8-12% annually",
                "risk_free_rate": "2-4%",
                "typical_sharpe_ratio": "0.5-1.2"
            },
            "market_data": {
                "correlation_matrix": "Generated based on realistic correlations",
                "risk_free_rate": 0.03,
                "market_volatility": 0.20
            }
        }
    
    def _generate_scheduling_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate realistic scheduling data"""
        # Extract scheduling parameters
        num_employees = random.randint(10, 50)
        num_shifts = 3  # Day, Evening, Night
        num_days = 7   # Weekly schedule
        
        variables = []
        constraints = []
        assumptions = []
        
        for emp in range(num_employees):
            for day in range(num_days):
                for shift in range(num_shifts):
                    variables.append({
                        "name": f"emp_{emp}_day_{day}_shift_{shift}",
                        "type": "binary",
                        "bounds": "binary",
                        "description": f"Employee {emp} assigned to shift {shift} on day {day}"
                    })
        
        # Coverage constraints
        for day in range(num_days):
            for shift in range(num_shifts):
                min_coverage = random.randint(3, 8)
                constraints.append({
                    "type": "coverage",
                    "expression": f"sum(emp_*_day_{day}_shift_{shift}) >= {min_coverage}",
                    "description": f"Minimum coverage for day {day}, shift {shift}",
                    "min_coverage": min_coverage
                })
        
        # Employee constraints
        for emp in range(num_employees):
            max_shifts_per_week = random.randint(4, 6)
            constraints.append({
                "type": "employee_limit",
                "expression": f"sum(emp_{emp}_day_*_shift_*) <= {max_shifts_per_week}",
                "description": f"Employee {emp} maximum shifts per week",
                "max_shifts": max_shifts_per_week
            })
        
        assumptions.append(f"{num_employees} employees, {num_shifts} shifts/day, {num_days} days/week")
        assumptions.append("Minimum 8 hours between shifts")
        assumptions.append("Maximum 5 consecutive days")
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "minimize",
                "expression": "sum(assignment_costs)",
                "description": "Minimize total scheduling costs"
            },
            "assumptions": assumptions,
            "benchmarks": {
                "typical_utilization": "75-90%",
                "overtime_threshold": "40 hours/week",
                "shift_premium": "10-25%"
            }
        }
    
    def _generate_supply_chain_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate realistic supply chain data"""
        num_suppliers = random.randint(3, 8)
        num_products = random.randint(5, 15)
        num_customers = random.randint(10, 30)
        
        variables = []
        constraints = []
        assumptions = []
        
        # Supplier capacity variables
        for supplier in range(num_suppliers):
            capacity = random.randint(1000, 10000)
            variables.append({
                "name": f"supplier_{supplier}_capacity",
                "type": "continuous",
                "bounds": f"0 to {capacity}",
                "description": f"Production capacity at supplier {supplier}",
                "realistic_capacity": capacity
            })
        
        # Demand variables
        for customer in range(num_customers):
            for product in range(num_products):
                demand = random.randint(50, 500)
                variables.append({
                    "name": f"demand_customer_{customer}_product_{product}",
                    "type": "continuous",
                    "bounds": f"0 to {demand}",
                    "description": f"Demand for product {product} from customer {customer}",
                    "realistic_demand": demand
                })
        
        assumptions.append(f"{num_suppliers} suppliers, {num_products} products, {num_customers} customers")
        assumptions.append("Lead times: 1-5 days")
        assumptions.append("Transportation costs: $0.50-$2.00 per unit")
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "minimize",
                "expression": "sum(production_costs + transportation_costs)",
                "description": "Minimize total supply chain costs"
            },
            "assumptions": assumptions,
            "benchmarks": {
                "typical_fill_rate": "95-98%",
                "inventory_turnover": "6-12 times/year",
                "on_time_delivery": "90-95%"
            }
        }
    
    def _generate_resource_allocation_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate realistic resource allocation data"""
        num_projects = random.randint(5, 20)
        num_resources = random.randint(3, 10)
        
        variables = []
        constraints = []
        assumptions = []
        
        for project in range(num_projects):
            for resource in range(num_resources):
                variables.append({
                    "name": f"project_{project}_resource_{resource}",
                    "type": "continuous",
                    "bounds": "0 to 1",
                    "description": f"Allocation of resource {resource} to project {project}",
                    "realistic_allocation": random.uniform(0.1, 0.8)
                })
        
        # Resource capacity constraints
        for resource in range(num_resources):
            capacity = random.uniform(0.8, 1.2)
            constraints.append({
                "type": "resource_capacity",
                "expression": f"sum(project_*_resource_{resource}) <= {capacity}",
                "description": f"Resource {resource} capacity constraint",
                "realistic_capacity": capacity
            })
        
        assumptions.append(f"{num_projects} projects, {num_resources} resource types")
        assumptions.append("Project priorities: High, Medium, Low")
        assumptions.append("Resource costs: $50-$200 per unit")
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "maximize",
                "expression": "sum(project_value * allocation)",
                "description": "Maximize total project value"
            },
            "assumptions": assumptions,
            "benchmarks": {
                "typical_utilization": "80-95%",
                "project_success_rate": "70-85%",
                "resource_efficiency": "75-90%"
            }
        }
    
    def _generate_generic_data(self, problem_description: str) -> Dict[str, Any]:
        """Generate generic optimization data"""
        num_variables = random.randint(5, 15)
        
        variables = []
        constraints = []
        
        for i in range(num_variables):
            variables.append({
                "name": f"x_{i+1}",
                "type": "continuous",
                "bounds": "0 to 100",
                "description": f"Decision variable {i+1}",
                "realistic_value": random.uniform(10, 80)
            })
        
        # Generic constraints
        constraints.append({
            "type": "budget",
            "expression": "sum(x_*) <= 1000",
            "description": "Budget constraint"
        })
        
        return {
            "variables": variables,
            "constraints": constraints,
            "objective": {
                "type": "maximize",
                "expression": "sum(coefficient * x_*)",
                "description": "Maximize objective function"
            },
            "assumptions": ["Generic optimization problem"],
            "benchmarks": {"typical_solution_time": "1-10 seconds"}
        }
    
    def _calculate_readiness_score(self, mock_data: Dict[str, Any]) -> float:
        """Calculate data readiness score"""
        score = 0.9  # Mock data is always high quality
        
        # Adjust based on data completeness
        if mock_data.get('variables'):
            score += 0.05
        if mock_data.get('constraints'):
            score += 0.03
        if mock_data.get('assumptions'):
            score += 0.02
        
        return min(score, 1.0)


async def analyze_data_tool(problem_description: str, intent_data: Optional[Dict] = None) -> Dict[str, Any]:
    """Tool wrapper for data analysis"""
    # This would be called from the main tools orchestrator
    # For now, return a placeholder
    return {"status": "error", "error": "Tool not initialized"}
