"""
Quality Reporting Module

This module provides comprehensive quality reporting capabilities for the
Semantica framework, enabling generation of quality reports, issue tracking,
and improvement suggestions.

Key Features:
    - Quality report generation
    - Issue identification and tracking
    - Improvement suggestions generation
    - Report export (JSON, YAML, HTML)
    - Issue management (add, get, list, resolve)

Main Classes:
    - QualityReporter: Quality report generation engine
    - IssueTracker: Issue tracking and management
    - ImprovementSuggestions: Improvement suggestions generator

Example Usage:
    >>> from semantica.kg_qa import QualityReporter
    >>> reporter = QualityReporter()
    >>> report = reporter.generate_report(knowledge_graph, quality_metrics)
    >>> json_report = reporter.export_report(report, format="json")

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional
from dataclasses import dataclass, field
from datetime import datetime

from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker


@dataclass
class QualityIssue:
    """
    Quality issue dataclass.
    
    This dataclass represents a quality issue found in a knowledge graph,
    containing issue identification, type, severity, and related entity/relationship
    information.
    
    Attributes:
        id: Unique issue identifier
        type: Issue type (e.g., "completeness", "consistency", "quality")
        severity: Issue severity ("low", "medium", "high")
        description: Human-readable issue description
        entity_id: Related entity ID (optional)
        relationship_id: Related relationship ID (optional)
        metadata: Additional issue metadata dictionary
    """
    
    id: str
    type: str
    severity: str
    description: str
    entity_id: Optional[str] = None
    relationship_id: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class QualityReport:
    """
    Quality report dataclass.
    
    This dataclass represents a comprehensive quality report for a knowledge graph,
    containing quality scores, identified issues, recommendations, and metadata.
    
    Attributes:
        timestamp: Report generation timestamp
        overall_score: Overall quality score (0.0 to 1.0)
        completeness_score: Completeness score (0.0 to 1.0)
        consistency_score: Consistency score (0.0 to 1.0)
        issues: List of identified quality issues
        recommendations: List of improvement recommendations
        metadata: Additional report metadata dictionary
    """
    
    timestamp: datetime
    overall_score: float
    completeness_score: float
    consistency_score: float
    issues: List[QualityIssue] = field(default_factory=list)
    recommendations: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)


class QualityReporter:
    """
    Quality report generation engine.
    
    This class provides quality report generation capabilities, including issue
    identification, recommendation generation, and report export in various formats.
    
    Features:
        - Quality report generation
        - Issue identification
        - Recommendation generation
        - Report export (JSON, YAML, HTML)
    
    Example Usage:
        >>> reporter = QualityReporter()
        >>> report = reporter.generate_report(knowledge_graph, quality_metrics)
        >>> json_report = reporter.export_report(report, format="json")
    """
    
    def __init__(self, **kwargs):
        """
        Initialize quality reporter.
        
        Sets up the reporter with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("quality_reporter")
        self.config = kwargs
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Quality reporter initialized")
    
    def generate_report(
        self,
        knowledge_graph: Any,
        quality_metrics: Dict[str, float]
    ) -> QualityReport:
        """
        Generate quality report.
        
        This method generates a comprehensive quality report by identifying
        issues based on quality metrics and generating recommendations.
        
        Args:
            knowledge_graph: Knowledge graph instance
            quality_metrics: Quality metrics dictionary containing:
                - overall: Overall quality score
                - completeness: Completeness score
                - consistency: Consistency score
            
        Returns:
            QualityReport: Comprehensive quality report with scores, issues,
                          and recommendations
        """
        # Track report generation
        tracking_id = self.progress_tracker.start_tracking(
            file=None,
            module="kg_qa",
            submodule="QualityReporter",
            message="Generating quality report"
        )
        
        try:
            self.progress_tracker.update_tracking(tracking_id, message="Identifying issues...")
            issues = self._identify_issues(knowledge_graph, quality_metrics)
            self.progress_tracker.update_tracking(tracking_id, message="Generating recommendations...")
            recommendations = self._generate_recommendations(issues)
            
            report = QualityReport(
                timestamp=datetime.now(),
                overall_score=quality_metrics.get("overall", 0.0),
                completeness_score=quality_metrics.get("completeness", 0.0),
                consistency_score=quality_metrics.get("consistency", 0.0),
                issues=issues,
                recommendations=recommendations
            )
            
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Generated quality report with {len(issues)} issues")
            return report
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def export_report(
        self,
        report: QualityReport,
        format: str = "json"
    ) -> str:
        """
        Export report to specified format.
        
        This method exports a quality report to the specified format (JSON, YAML,
        or HTML). For unsupported formats, returns string representation.
        
        Args:
            report: Quality report to export
            format: Export format ("json", "yaml", or "html", default: "json")
            
        Returns:
            str: Exported report as string in the specified format
            
        Note:
            YAML export requires the `pyyaml` library. If not available, falls
            back to string representation.
        """
        if format == "json":
            import json
            return json.dumps({
                "timestamp": report.timestamp.isoformat(),
                "overall_score": report.overall_score,
                "completeness_score": report.completeness_score,
                "consistency_score": report.consistency_score,
                "issues": [
                    {
                        "id": issue.id,
                        "type": issue.type,
                        "severity": issue.severity,
                        "description": issue.description
                    }
                    for issue in report.issues
                ],
                "recommendations": report.recommendations
            }, indent=2)
        
        elif format == "yaml":
            try:
                import yaml
                return yaml.dump({
                    "timestamp": report.timestamp.isoformat(),
                    "overall_score": report.overall_score,
                    "issues": [
                        {
                            "id": issue.id,
                            "type": issue.type,
                            "description": issue.description
                        }
                        for issue in report.issues
                    ]
                })
            except ImportError:
                self.logger.warning("PyYAML not available, falling back to string representation")
                return str(report)
        
        else:
            return str(report)
    
    def _identify_issues(
        self,
        knowledge_graph: Any,
        metrics: Dict[str, float]
    ) -> List[QualityIssue]:
        """
        Identify quality issues.
        
        This method identifies quality issues based on quality metrics,
        checking for low scores and generating appropriate issue objects.
        
        Args:
            knowledge_graph: Knowledge graph instance
            metrics: Quality metrics dictionary
            
        Returns:
            list: List of identified quality issues
        """
        issues = []
        
        # Check for low scores
        if metrics.get("overall", 1.0) < 0.7:
            issues.append(QualityIssue(
                id="low_overall_score",
                type="quality",
                severity="high",
                description="Overall quality score is below threshold"
            ))
        
        if metrics.get("completeness", 1.0) < 0.8:
            issues.append(QualityIssue(
                id="low_completeness",
                type="completeness",
                severity="medium",
                description="Completeness score is below threshold"
            ))
        
        return issues
    
    def _generate_recommendations(
        self,
        issues: List[QualityIssue]
    ) -> List[str]:
        """
        Generate improvement recommendations.
        
        This method generates improvement recommendations based on identified
        quality issues, providing actionable suggestions for improving
        knowledge graph quality.
        
        Args:
            issues: List of quality issues
            
        Returns:
            list: List of improvement recommendation strings
        """
        recommendations = []
        
        for issue in issues:
            if issue.type == "completeness":
                recommendations.append(
                    "Add missing required properties to entities"
                )
            elif issue.type == "consistency":
                recommendations.append(
                    "Resolve consistency violations in the knowledge graph"
                )
        
        return recommendations


class IssueTracker:
    """
    Issue tracking and management engine.
    
    This class provides issue tracking capabilities, enabling storage, retrieval,
    filtering, and resolution of quality issues.
    
    Features:
        - Issue storage and retrieval
        - Issue filtering by severity
        - Issue resolution tracking
    
    Example Usage:
        >>> tracker = IssueTracker()
        >>> tracker.add_issue(issue)
        >>> issues = tracker.list_issues(severity="high")
        >>> tracker.resolve_issue(issue_id)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize issue tracker.
        
        Sets up the tracker with configuration and initializes issue storage.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("issue_tracker")
        self.config = kwargs
        self.issues: Dict[str, QualityIssue] = {}
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Issue tracker initialized")
    
    def add_issue(self, issue: QualityIssue) -> None:
        """
        Add an issue to the tracker.
        
        This method adds a quality issue to the tracker's issue dictionary,
        using the issue ID as the key.
        
        Args:
            issue: Quality issue to add
        """
        self.issues[issue.id] = issue
    
    def get_issue(self, issue_id: str) -> Optional[QualityIssue]:
        """
        Get issue by ID.
        
        This method retrieves a quality issue from the tracker by its ID.
        
        Args:
            issue_id: Issue identifier
            
        Returns:
            QualityIssue: The issue if found, None otherwise
        """
        return self.issues.get(issue_id)
    
    def list_issues(
        self,
        severity: Optional[str] = None
    ) -> List[QualityIssue]:
        """
        List issues, optionally filtered by severity.
        
        This method returns all tracked issues, optionally filtered by severity
        level ("low", "medium", "high").
        
        Args:
            severity: Optional severity filter ("low", "medium", "high")
            
        Returns:
            list: List of quality issues (filtered by severity if provided)
        """
        issues = list(self.issues.values())
        
        if severity:
            issues = [i for i in issues if i.severity == severity]
        
        return issues
    
    def resolve_issue(self, issue_id: str) -> bool:
        """
        Mark issue as resolved.
        
        This method removes an issue from the tracker, effectively marking
        it as resolved.
        
        Args:
            issue_id: Issue identifier to resolve
            
        Returns:
            bool: True if issue was found and resolved, False otherwise
        """
        if issue_id in self.issues:
            del self.issues[issue_id]
            return True
        return False


class ImprovementSuggestions:
    """
    Improvement suggestions generator.
    
    This class provides improvement suggestions generation capabilities,
    analyzing quality reports and generating actionable recommendations
    for improving knowledge graph quality.
    
    Features:
        - Issue-based suggestions
        - Score-based suggestions
        - Actionable recommendations
    
    Example Usage:
        >>> generator = ImprovementSuggestions()
        >>> suggestions = generator.generate_suggestions(quality_report)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize improvement suggestions generator.
        
        Sets up the generator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("improvement_suggestions")
        self.config = kwargs
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Improvement suggestions generator initialized")
    
    def generate_suggestions(
        self,
        quality_report: QualityReport
    ) -> List[str]:
        """
        Generate improvement suggestions.
        
        This method generates improvement suggestions based on the quality report,
        analyzing issues and scores to provide actionable recommendations.
        
        Args:
            quality_report: Quality report containing scores and issues
            
        Returns:
            list: List of improvement suggestion strings
        """
        suggestions = []
        
        # Based on issues
        for issue in quality_report.issues:
            if issue.type == "completeness":
                suggestions.append(
                    f"Improve completeness for {issue.description}"
                )
            elif issue.type == "consistency":
                suggestions.append(
                    f"Resolve consistency issue: {issue.description}"
                )
        
        # Based on scores
        if quality_report.overall_score < 0.7:
            suggestions.append("Overall quality needs improvement")
        
        if quality_report.completeness_score < 0.8:
            suggestions.append("Add missing required properties")
        
        return suggestions

