src/agents/dandara.py

"""
Module: agents.dandara_agent
Description: Dandara - Social Justice Agent specialized in monitoring inclusion policies and social equity
Author: Anderson H. Silva
Date: 2025-01-24
License: Proprietary - All rights reserved
"""

import asyncio
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple
from dataclasses import dataclass

import numpy as np
import pandas as pd
from pydantic import BaseModel, Field as PydanticField

from src.agents.deodoro import BaseAgent, AgentContext, AgentMessage, AgentResponse
from src.core import get_logger
from src.core.exceptions import AgentExecutionError, DataAnalysisError


@dataclass
class EquityAnalysisResult:
    """Result of social equity analysis."""
    
    analysis_type: str
    gini_coefficient: float  # 0.0 to 1.0
    equity_score: int  # 0-100
    population_affected: int
    violations_detected: List[Dict[str, Any]]
    gaps_identified: List[Dict[str, Any]]
    recommendations: List[str]
    evidence_sources: List[str]
    analysis_timestamp: datetime
    confidence_level: float


class SocialJusticeRequest(BaseModel):
    """Request for social justice analysis."""
    
    query: str = PydanticField(description="Social equity analysis query")
    target_groups: Optional[List[str]] = PydanticField(default=None, description="Specific demographic groups to analyze")
    policy_areas: Optional[List[str]] = PydanticField(default=None, description="Policy areas (education, health, housing, etc)")
    geographical_scope: Optional[str] = PydanticField(default=None, description="Geographic scope (municipality, state, federal)")
    time_period: Optional[Tuple[str, str]] = PydanticField(default=None, description="Analysis period (start, end)")
    metrics_focus: Optional[List[str]] = PydanticField(default=None, description="Specific metrics to focus on")


class DandaraAgent(BaseAgent):
    """
    Dandara - Social Justice Agent
    
    Specialized in monitoring inclusion policies, social equity, and distributive justice indicators.
    Inspired by Dandara dos Palmares, warrior for social justice and equality.
    """
    
    def __init__(self):
        super().__init__(
            name="dandara",
            description="Social Justice Agent specialized in monitoring inclusion policies and social equity",
            capabilities=[
                "social_equity_analysis",
                "inclusion_policy_monitoring", 
                "gini_coefficient_calculation",
                "demographic_disparity_detection",
                "social_justice_violation_identification",
                "distributive_justice_assessment",
                "policy_effectiveness_evaluation",
                "intersectional_analysis",
                "vulnerability_mapping",
                "equity_gap_identification"
            ]
        )
        self.logger = get_logger("agent.dandara")
        
        # Social justice analysis tools
        self._equity_metrics = {
            "gini_coefficient": self._calculate_gini,
            "atkinson_index": self._calculate_atkinson,
            "theil_index": self._calculate_theil,
            "palma_ratio": self._calculate_palma,
            "quintile_ratio": self._calculate_quintile_ratio
        }
        
        # Data sources for social analysis
        self._data_sources = [
            "IBGE", "DataSUS", "INEP", "MDS", "SNIS", 
            "Portal da Transparência", "RAIS", "PNAD"
        ]
    
    async def process(
        self,
        message: AgentMessage,
        context: AgentContext,
    ) -> AgentResponse:
        """
        Process social justice analysis request.
        
        Args:
            message: Analysis request message
            context: Agent execution context
            
        Returns:
            Social equity analysis results
        """
        try:
            self.logger.info(
                "Processing social justice analysis request",
                investigation_id=context.investigation_id,
                message_type=message.type,
            )
            
            # Parse request
            if isinstance(message.data, dict):
                request = SocialJusticeRequest(**message.data)
            else:
                request = SocialJusticeRequest(query=str(message.data))
            
            # Perform comprehensive social justice analysis
            analysis_result = await self._analyze_social_equity(request, context)
            
            # Generate actionable recommendations
            recommendations = await self._generate_justice_recommendations(
                analysis_result, request, context
            )
            
            # Create audit trail
            audit_hash = self._generate_audit_hash(analysis_result, request)
            
            response_data = {
                "analysis_id": context.investigation_id,
                "timestamp": datetime.utcnow().isoformat(),
                "agent": "dandara",
                "analysis_type": "social_justice",
                "results": analysis_result,
                "recommendations": recommendations,
                "audit_hash": audit_hash,
                "data_sources": self._data_sources,
                "methodology": "gini_theil_palma_analysis",
                "confidence": analysis_result.confidence_level
            }
            
            self.logger.info(
                "Social justice analysis completed",
                investigation_id=context.investigation_id,
                equity_score=analysis_result.equity_score,
                violations_count=len(analysis_result.violations_detected),
            )
            
            return AgentResponse(
                agent_name=self.name,
                response_type="social_justice_analysis",
                data=response_data,
                success=True,
                context=context,
            )
            
        except Exception as e:
            self.logger.error(
                "Social justice analysis failed",
                investigation_id=context.investigation_id,
                error=str(e),
                exc_info=True,
            )
            
            return AgentResponse(
                agent_name=self.name,
                response_type="error",
                data={"error": str(e), "analysis_type": "social_justice"},
                success=False,
                context=context,
            )
    
    async def _analyze_social_equity(
        self, 
        request: SocialJusticeRequest, 
        context: AgentContext
    ) -> EquityAnalysisResult:
        """Perform comprehensive social equity analysis."""
        
        self.logger.info(
            "Starting social equity analysis",
            query=request.query,
            target_groups=request.target_groups,
        )
        
        # Simulate comprehensive analysis (replace with real implementation)
        await asyncio.sleep(2)  # Simulate processing time
        
        # Calculate equity metrics
        gini_coeff = await self._calculate_regional_gini(request)
        equity_score = max(0, min(100, int((1 - gini_coeff) * 100)))
        
        # Identify violations and gaps
        violations = await self._detect_equity_violations(request, context)
        gaps = await self._identify_inclusion_gaps(request, context)
        
        return EquityAnalysisResult(
            analysis_type="comprehensive_social_equity",
            gini_coefficient=gini_coeff,
            equity_score=equity_score,
            population_affected=self._estimate_affected_population(request),
            violations_detected=violations,
            gaps_identified=gaps,
            recommendations=await self._generate_evidence_based_recommendations(violations, gaps),
            evidence_sources=self._data_sources,
            analysis_timestamp=datetime.utcnow(),
            confidence_level=0.85
        )
    
    async def _calculate_regional_gini(self, request: SocialJusticeRequest) -> float:
        """Calculate Gini coefficient for specified region/groups."""
        # Placeholder - implement real Gini calculation
        return np.random.uniform(0.3, 0.7)  # Brazil typically 0.5-0.6
    
    async def _detect_equity_violations(
        self, 
        request: SocialJusticeRequest, 
        context: AgentContext
    ) -> List[Dict[str, Any]]:
        """Detect potential equity violations."""
        violations = []
        
        # Simulate violation detection
        violation_types = [
            "discriminatory_resource_allocation",
            "unequal_service_access", 
            "policy_exclusion_bias",
            "demographic_underrepresentation"
        ]
        
        for violation_type in violation_types[:2]:  # Sample violations
            violations.append({
                "type": violation_type,
                "severity": np.random.uniform(0.6, 0.9),
                "legal_reference": "CF/88 Art. 5º",
                "evidence": f"Statistical disparity detected in {violation_type}",
                "affected_groups": request.target_groups or ["vulnerable_populations"],
                "remediation_urgency": "high"
            })
        
        return violations
    
    async def _identify_inclusion_gaps(
        self, 
        request: SocialJusticeRequest, 
        context: AgentContext
    ) -> List[Dict[str, Any]]:
        """Identify inclusion gaps in policies."""
        gaps = []
        
        gap_areas = ["digital_inclusion", "healthcare_access", "education_equity", "employment_opportunities"]
        
        for area in gap_areas[:3]:  # Sample gaps
            gaps.append({
                "area": area,
                "gap_size": np.random.uniform(0.3, 0.8),
                "target_population": request.target_groups or ["general_population"],
                "current_coverage": np.random.uniform(0.2, 0.7),
                "recommended_coverage": 0.95,
                "implementation_complexity": np.random.choice(["low", "medium", "high"])
            })
        
        return gaps
    
    def _estimate_affected_population(self, request: SocialJusticeRequest) -> int:
        """Estimate affected population size."""
        # Placeholder - implement real population estimation
        return np.random.randint(50000, 2000000)
    
    async def _generate_evidence_based_recommendations(
        self, 
        violations: List[Dict[str, Any]], 
        gaps: List[Dict[str, Any]]
    ) -> List[str]:
        """Generate evidence-based recommendations."""
        recommendations = [
            "Implement targeted resource redistribution policies",
            "Establish monitoring systems for equity metrics",
            "Create inclusive policy design frameworks",
            "Develop intersectional analysis capabilities",
            "Enhance data collection on vulnerable groups"
        ]
        
        # Customize based on findings
        if violations:
            recommendations.insert(0, "Address identified legal compliance violations immediately")
        
        if gaps:
            recommendations.append("Close identified inclusion gaps through targeted interventions")
        
        return recommendations
    
    async def _generate_justice_recommendations(
        self,
        analysis: EquityAnalysisResult,
        request: SocialJusticeRequest,
        context: AgentContext
    ) -> List[Dict[str, Any]]:
        """Generate detailed justice recommendations."""
        
        recommendations = []
        
        for rec_text in analysis.recommendations:
            recommendations.append({
                "recommendation": rec_text,
                "priority": "high" if analysis.equity_score < 60 else "medium",
                "implementation_timeframe": "immediate" if analysis.equity_score < 40 else "short_term",
                "expected_impact": np.random.uniform(0.6, 0.9),
                "required_resources": np.random.choice(["low", "medium", "high"]),
                "stakeholders": ["government", "civil_society", "affected_communities"],
                "success_metrics": [f"Improve equity score by {np.random.randint(10, 25)} points"]
            })
        
        return recommendations
    
    def _generate_audit_hash(
        self, 
        analysis: EquityAnalysisResult, 
        request: SocialJusticeRequest
    ) -> str:
        """Generate SHA-256 hash for audit trail."""
        import hashlib
        
        audit_data = f"{analysis.analysis_timestamp}{analysis.gini_coefficient}{len(analysis.violations_detected)}{request.query}"
        return hashlib.sha256(audit_data.encode()).hexdigest()
    
    # Equity calculation methods
    async def _calculate_gini(self, data: List[float]) -> float:
        """Calculate Gini coefficient."""
        if not data:
            return 0.0
        
        sorted_data = np.sort(data)
        n = len(sorted_data)
        cumsum = np.cumsum(sorted_data)
        
        return (n + 1 - 2 * np.sum(cumsum) / cumsum[-1]) / n
    
    async def _calculate_atkinson(self, data: List[float], epsilon: float = 0.5) -> float:
        """Calculate Atkinson inequality index."""
        if not data:
            return 0.0
        
        mean_income = np.mean(data)
        if epsilon == 1:
            geometric_mean = np.exp(np.mean(np.log(data)))
            return 1 - geometric_mean / mean_income
        else:
            weighted_sum = np.mean(np.power(data, 1 - epsilon))
            return 1 - np.power(weighted_sum, 1/(1 - epsilon)) / mean_income
    
    async def _calculate_theil(self, data: List[float]) -> float:
        """Calculate Theil inequality index."""
        if not data:
            return 0.0
        
        mean_income = np.mean(data)
        return np.mean((data / mean_income) * np.log(data / mean_income))
    
    async def _calculate_palma(self, data: List[float]) -> float:
        """Calculate Palma ratio (top 10% / bottom 40%)."""
        if len(data) < 10:
            return 0.0
        
        sorted_data = np.sort(data)
        n = len(sorted_data)
        
        bottom_40_pct = np.sum(sorted_data[:int(0.4 * n)])
        top_10_pct = np.sum(sorted_data[int(0.9 * n):])
        
        return top_10_pct / bottom_40_pct if bottom_40_pct > 0 else float('inf')
    
    async def _calculate_quintile_ratio(self, data: List[float]) -> float:
        """Calculate ratio of top to bottom quintile."""
        if len(data) < 5:
            return 0.0
        
        sorted_data = np.sort(data)
        n = len(sorted_data)
        
        bottom_quintile = np.mean(sorted_data[:int(0.2 * n)])
        top_quintile = np.mean(sorted_data[int(0.8 * n):])
        
        return top_quintile / bottom_quintile if bottom_quintile > 0 else float('inf')