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

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
