#!/usr/bin/env python3
"""
Synthetic Data Platform MCP Server

Main MCP server implementing synthetic data generation with domain-specific
compliance and privacy protection.
"""

import asyncio
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

from fastmcp import FastMCP
from loguru import logger
from pydantic import BaseModel, Field

# Import core components
from .core.generator import SyntheticDataGenerator
from .compliance.validator import ComplianceValidator, ComplianceFramework
from .privacy.engine import PrivacyEngine, PrivacyLevel
from .schemas.base import DataDomain, OutputFormat
from .validation.statistical import StatisticalValidator
from .utils.audit import AuditTrail

# Import ingestion components
from .ingestion.data_ingestion import DataIngestionPipeline
from .ingestion.pattern_analyzer import PatternAnalyzer
from .ingestion.knowledge_loader import DynamicKnowledgeLoader

# Import database components
from .database.manager import DatabaseManager, DatabaseType, DatabaseRole, database_manager
from .database.migrations import MigrationManager, MigrationStatus
from .database.schema_inspector import SchemaInspector


class GenerateSyntheticDatasetRequest(BaseModel):
    """Request model for generating synthetic datasets."""
    
    domain: DataDomain = Field(description="Target domain (healthcare, finance, custom)")
    dataset_type: str = Field(description="Specific dataset type (patient_records, transactions, etc.)")
    record_count: int = Field(description="Number of synthetic records to generate", gt=0, le=1000000)
    privacy_level: PrivacyLevel = Field(description="Privacy protection level", default=PrivacyLevel.HIGH)
    compliance_frameworks: List[ComplianceFramework] = Field(description="Required compliance validations", default=[])
    output_format: OutputFormat = Field(description="Output format", default=OutputFormat.JSON)
    validation_level: str = Field(description="Statistical validation depth", default="standard")
    custom_schema: Optional[Dict[str, Any]] = Field(description="Custom Pydantic schema", default=None)
    seed: Optional[int] = Field(description="Random seed for reproducibility", default=None)


class ValidateDatasetComplianceRequest(BaseModel):
    """Request model for dataset compliance validation."""
    
    dataset: Union[List[Dict[str, Any]], Dict[str, Any]] = Field(description="Dataset to validate")
    compliance_frameworks: List[ComplianceFramework] = Field(description="Frameworks to validate against")
    domain: DataDomain = Field(description="Domain-specific validation rules")
    risk_threshold: float = Field(description="Acceptable risk level", default=0.01, ge=0.0, le=1.0)


class AnalyzePrivacyRiskRequest(BaseModel):
    """Request model for privacy risk analysis."""
    
    dataset: Union[List[Dict[str, Any]], Dict[str, Any]] = Field(description="Dataset to analyze")
    auxiliary_data: Optional[List[Dict[str, Any]]] = Field(description="External data for re-identification testing", default=None)
    attack_scenarios: List[str] = Field(description="Privacy attack types to test", default=["linkage", "inference", "membership"])


class GenerateDomainSchemaRequest(BaseModel):
    """Request model for generating domain schemas."""
    
    domain: DataDomain = Field(description="Target domain")
    data_type: str = Field(description="Specific data structure type")
    compliance_requirements: List[ComplianceFramework] = Field(description="Required validation rules", default=[])
    custom_fields: Optional[List[Dict[str, Any]]] = Field(description="Additional fields to include", default=None)


class BenchmarkSyntheticDataRequest(BaseModel):
    """Request model for benchmarking synthetic data."""
    
    synthetic_data: List[Dict[str, Any]] = Field(description="Generated synthetic dataset")
    real_data_sample: List[Dict[str, Any]] = Field(description="Representative real data sample")
    ml_tasks: List[str] = Field(description="ML tasks to benchmark", default=["classification", "regression"])
    metrics: Optional[List[str]] = Field(description="Custom evaluation metrics", default=None)


# Database management request models
class AddDatabaseConnectionRequest(BaseModel):
    """Request model for adding database connection."""
    
    name: str = Field(description="Unique database connection name")
    db_type: str = Field(description="Database type (postgresql, mysql, mongodb, redis, bigquery, snowflake, redshift)")
    config: Dict[str, Any] = Field(description="Database connection configuration")
    role: str = Field(description="Database role (primary, replica, cache, analytics, archive)", default="primary")
    auto_connect: bool = Field(description="Connect immediately after adding", default=True)


class ExecuteDatabaseQueryRequest(BaseModel):
    """Request model for executing database queries."""
    
    query: str = Field(description="SQL or database-specific query")
    parameters: Optional[Dict[str, Any]] = Field(description="Query parameters", default=None)
    database: Optional[str] = Field(description="Specific database connection name", default=None)
    role: Optional[str] = Field(description="Database role to use", default=None)


class CreateTableRequest(BaseModel):
    """Request model for creating tables."""
    
    table_name: str = Field(description="Name of table to create")
    schema: Dict[str, Any] = Field(description="Table schema definition")
    database: Optional[str] = Field(description="Target database connection", default=None)
    role: Optional[str] = Field(description="Database role to use", default=None)


class InsertDataRequest(BaseModel):
    """Request model for bulk data insertion."""
    
    table_name: str = Field(description="Target table name")
    data: List[Dict[str, Any]] = Field(description="Data records to insert")
    database: Optional[str] = Field(description="Target database connection", default=None)
    role: Optional[str] = Field(description="Database role to use", default=None)


class CreateMigrationRequest(BaseModel):
    """Request model for creating database migrations."""
    
    name: str = Field(description="Migration name")
    description: str = Field(description="Migration description")
    source_db_type: str = Field(description="Source database type")
    target_db_type: str = Field(description="Target database type")
    up_sql: str = Field(description="SQL to apply migration")
    down_sql: str = Field(description="SQL to rollback migration", default="")
    data_transformations: Optional[List[Dict[str, Any]]] = Field(description="Data transformation rules", default=None)
    dependencies: Optional[List[str]] = Field(description="Migration dependencies", default=None)


class ExecuteMigrationRequest(BaseModel):
    """Request model for executing migrations."""
    
    migration_id: str = Field(description="Migration ID to execute")
    source_db: str = Field(description="Source database connection name")
    target_db: str = Field(description="Target database connection name")


class SchemaAnalysisRequest(BaseModel):
    """Request model for schema analysis."""
    
    database: str = Field(description="Database connection name to analyze")
    deep_analysis: bool = Field(description="Perform deep analysis", default=True)
    sample_size: int = Field(description="Sample size for analysis", default=1000)


class CompareSchemaRequest(BaseModel):
    """Request model for schema comparison."""
    
    database1: str = Field(description="First database to compare")
    database2: str = Field(description="Second database to compare")
    table_name: Optional[str] = Field(description="Specific table to compare", default=None)


class IngestDataRequest(BaseModel):
    """Request model for ingesting real data to learn patterns."""
    
    data: Union[List[Dict[str, Any]], str] = Field(description="Data to ingest (list of records or file path)")
    format: str = Field(description="Data format (csv, json, excel, auto)", default="auto")
    domain: str = Field(description="Domain category (healthcare, finance, custom)", default="custom")
    anonymize: bool = Field(description="Whether to anonymize PII before analysis", default=True)
    learn_patterns: bool = Field(description="Extract statistical patterns", default=True)
    sample_size: Optional[int] = Field(description="Sample size for large datasets", default=None)


class GenerateFromPatternRequest(BaseModel):
    """Request model for generating data from learned patterns."""
    
    pattern_id: str = Field(description="ID from previous ingestion")
    record_count: int = Field(description="Number of records to generate", gt=0, le=1000000)
    variation: float = Field(description="Amount of variation (0.0-1.0)", default=0.3, ge=0.0, le=1.0)
    privacy_level: PrivacyLevel = Field(description="Privacy protection level", default=PrivacyLevel.MEDIUM)
    preserve_distributions: bool = Field(description="Maintain statistical properties", default=True)


class AnonymizeDataRequest(BaseModel):
    """Request model for anonymizing existing data."""
    
    data: Union[List[Dict[str, Any]], str] = Field(description="Data to anonymize (list of records or file path)")
    privacy_level: PrivacyLevel = Field(description="Level of anonymization", default=PrivacyLevel.HIGH)
    preserve_relationships: bool = Field(description="Maintain data relationships", default=True)
    format: str = Field(description="Data format (csv, json, excel, auto)", default="auto")


# Initialize FastMCP server
app = FastMCP("synthetic-data-mcp", version="0.1.0")

# Initialize core components
generator = SyntheticDataGenerator()
compliance_validator = ComplianceValidator()
privacy_engine = PrivacyEngine()
statistical_validator = StatisticalValidator()
audit_trail = AuditTrail()

# Initialize ingestion components
ingestion_pipeline = DataIngestionPipeline(privacy_engine)
pattern_analyzer = PatternAnalyzer()
knowledge_loader = DynamicKnowledgeLoader()

# Initialize database components
db_manager = database_manager  # Use singleton instance
migration_manager = MigrationManager(db_manager)
schema_inspector = SchemaInspector(db_manager)

# Configure logging
logger.remove()  # Remove default handler
logger.add(
    sys.stdout,
    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
    level="INFO"
)
logger.add(
    "logs/synthetic-data-mcp.log",
    rotation="10 MB",
    retention="30 days",
    format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
    level="DEBUG"
)


@app.tool()
async def generate_synthetic_dataset(
    request: GenerateSyntheticDatasetRequest
) -> Dict[str, Any]:
    """
    Generate domain-specific synthetic datasets with compliance validation.
    
    This is the core function for creating synthetic data that maintains statistical
    fidelity while ensuring privacy protection and regulatory compliance.
    
    Args:
        request: Configuration for synthetic data generation
        
    Returns:
        Dictionary containing:
        - dataset: Generated synthetic data
        - compliance_report: Validation results for each framework
        - statistical_analysis: Fidelity metrics and validation results
        - privacy_analysis: Privacy preservation metrics and risk assessment
        - audit_trail: Complete generation process documentation
    """
    start_time = datetime.now()
    
    try:
        # Log the request
        logger.info(f"Starting synthetic data generation: domain={request.domain}, type={request.dataset_type}, records={request.record_count}")
        
        # Initialize audit trail
        audit_id = audit_trail.start_operation(
            operation="generate_synthetic_dataset",
            parameters=request.dict(),
            user_id="system",  # TODO: Implement user authentication
            timestamp=start_time
        )
        
        # Step 1: Generate synthetic data
        logger.info("Generating synthetic dataset...")
        dataset = await generator.generate_dataset(
            domain=request.domain,
            dataset_type=request.dataset_type,
            record_count=request.record_count,
            privacy_level=request.privacy_level,
            custom_schema=request.custom_schema,
            seed=request.seed
        )
        
        # Step 2: Apply privacy protection
        logger.info("Applying privacy protection...")
        protected_dataset, privacy_metrics = await privacy_engine.protect_dataset(
            dataset=dataset,
            privacy_level=request.privacy_level,
            domain=request.domain
        )
        
        # Step 3: Validate compliance
        compliance_results = {}
        if request.compliance_frameworks:
            logger.info(f"Validating compliance for frameworks: {request.compliance_frameworks}")
            compliance_results = await compliance_validator.validate_dataset(
                dataset=protected_dataset,
                frameworks=request.compliance_frameworks,
                domain=request.domain
            )
        
        # Step 4: Statistical validation
        logger.info("Performing statistical validation...")
        statistical_results = await statistical_validator.validate_fidelity(
            synthetic_data=protected_dataset,
            validation_level=request.validation_level,
            domain=request.domain
        )
        
        # Step 5: Format output
        if request.output_format == OutputFormat.JSON:
            formatted_dataset = protected_dataset
        elif request.output_format == OutputFormat.CSV:
            # TODO: Implement CSV formatting
            formatted_dataset = protected_dataset  # Placeholder
        else:
            formatted_dataset = protected_dataset
        
        # Prepare response
        end_time = datetime.now()
        generation_time = (end_time - start_time).total_seconds()
        
        result = {
            "success": True,
            "dataset": formatted_dataset,
            "metadata": {
                "record_count": len(formatted_dataset),
                "generation_time_seconds": generation_time,
                "domain": request.domain,
                "dataset_type": request.dataset_type,
                "privacy_level": request.privacy_level,
                "output_format": request.output_format
            },
            "compliance_report": compliance_results,
            "statistical_analysis": statistical_results,
            "privacy_analysis": privacy_metrics,
            "audit_trail_id": audit_id
        }
        
        # Complete audit trail
        audit_trail.complete_operation(
            audit_id=audit_id,
            result="success",
            end_time=end_time,
            metadata={
                "records_generated": len(formatted_dataset),
                "generation_time": generation_time,
                "compliance_passed": all(r.get("passed", False) for r in compliance_results.values()) if compliance_results else True,
                "privacy_risk": privacy_metrics.get("risk_score", 0.0)
            }
        )
        
        logger.success(f"Successfully generated {len(formatted_dataset)} synthetic records in {generation_time:.2f}s")
        return result
        
    except Exception as e:
        logger.error(f"Error generating synthetic dataset: {str(e)}")
        
        # Record failure in audit trail
        if 'audit_id' in locals():
            audit_trail.complete_operation(
                audit_id=audit_id,
                result="failure",
                end_time=datetime.now(),
                error=str(e)
            )
        
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def validate_dataset_compliance(
    request: ValidateDatasetComplianceRequest
) -> Dict[str, Any]:
    """
    Validate existing datasets against regulatory requirements.
    
    Args:
        request: Dataset and validation configuration
        
    Returns:
        Dictionary containing:
        - compliance_status: Pass/fail for each framework
        - risk_assessment: Detailed risk analysis
        - recommendations: Specific remediation actions
        - certification_package: Documentation for regulatory submission
    """
    try:
        logger.info(f"Validating dataset compliance for frameworks: {request.compliance_frameworks}")
        
        # Normalize dataset format
        if isinstance(request.dataset, dict):
            dataset = [request.dataset]
        else:
            dataset = request.dataset
        
        # Perform compliance validation
        results = await compliance_validator.validate_dataset(
            dataset=dataset,
            frameworks=request.compliance_frameworks,
            domain=request.domain,
            risk_threshold=request.risk_threshold
        )
        
        # Generate recommendations
        recommendations = []
        for framework, result in results.items():
            if not result.get("passed", False):
                recommendations.extend(result.get("recommendations", []))
        
        return {
            "success": True,
            "compliance_status": {
                framework: result.get("passed", False) 
                for framework, result in results.items()
            },
            "detailed_results": results,
            "overall_compliance": all(r.get("passed", False) for r in results.values()),
            "risk_assessment": {
                "overall_risk": max(r.get("risk_score", 0.0) for r in results.values()) if results else 0.0,
                "risk_factors": [r.get("risk_factors", []) for r in results.values()]
            },
            "recommendations": recommendations,
            "certification_ready": all(r.get("passed", False) for r in results.values())
        }
        
    except Exception as e:
        logger.error(f"Error validating dataset compliance: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def analyze_privacy_risk(
    request: AnalyzePrivacyRiskRequest
) -> Dict[str, Any]:
    """
    Comprehensive privacy risk assessment for datasets.
    
    Args:
        request: Dataset and privacy analysis configuration
        
    Returns:
        Dictionary containing:
        - risk_score: Overall privacy risk (0-100)
        - vulnerability_analysis: Specific privacy vulnerabilities
        - mitigation_strategies: Recommended privacy enhancements
        - differential_privacy_recommendations: Optimal privacy parameters
    """
    try:
        logger.info("Performing privacy risk analysis...")
        
        # Normalize dataset format
        if isinstance(request.dataset, dict):
            dataset = [request.dataset]
        else:
            dataset = request.dataset
        
        # Perform privacy analysis
        risk_analysis = await privacy_engine.analyze_privacy_risk(
            dataset=dataset,
            auxiliary_data=request.auxiliary_data,
            attack_scenarios=request.attack_scenarios
        )
        
        return {
            "success": True,
            "risk_score": risk_analysis.get("overall_risk", 0.0),
            "vulnerability_analysis": risk_analysis.get("vulnerabilities", []),
            "attack_scenario_results": risk_analysis.get("attack_results", {}),
            "mitigation_strategies": risk_analysis.get("recommendations", []),
            "differential_privacy_recommendations": risk_analysis.get("dp_recommendations", {}),
            "privacy_budget_usage": risk_analysis.get("privacy_budget", {}),
            "anonymization_metrics": risk_analysis.get("anonymization", {})
        }
        
    except Exception as e:
        logger.error(f"Error analyzing privacy risk: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def generate_domain_schema(
    request: GenerateDomainSchemaRequest
) -> Dict[str, Any]:
    """
    Create Pydantic schemas for domain-specific data structures.
    
    Args:
        request: Domain and schema configuration
        
    Returns:
        Dictionary containing:
        - schema: Generated Pydantic schema
        - validation_rules: Compliance validation rules
        - documentation: Schema documentation and usage examples
    """
    try:
        logger.info(f"Generating domain schema: domain={request.domain}, type={request.data_type}")
        
        # Generate schema based on domain and data type
        schema_result = await generator.generate_schema(
            domain=request.domain,
            data_type=request.data_type,
            compliance_requirements=request.compliance_requirements,
            custom_fields=request.custom_fields
        )
        
        return {
            "success": True,
            "schema": schema_result.get("schema", {}),
            "schema_class": schema_result.get("schema_class", ""),
            "validation_rules": schema_result.get("validation_rules", []),
            "field_descriptions": schema_result.get("field_descriptions", {}),
            "compliance_mappings": schema_result.get("compliance_mappings", {}),
            "usage_examples": schema_result.get("examples", []),
            "documentation": schema_result.get("documentation", "")
        }
        
    except Exception as e:
        logger.error(f"Error generating domain schema: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def benchmark_synthetic_data(
    request: BenchmarkSyntheticDataRequest
) -> Dict[str, Any]:
    """
    Performance and utility benchmarking against real data.
    
    Args:
        request: Synthetic and real data for benchmarking
        
    Returns:
        Dictionary containing:
        - performance_comparison: ML model performance on synthetic vs real data
        - statistical_similarity: Comprehensive statistical comparison
        - utility_preservation: Task-specific utility metrics
        - recommendations: Optimization suggestions
    """
    try:
        logger.info("Benchmarking synthetic data against real data...")
        
        # Perform statistical comparison
        statistical_comparison = await statistical_validator.compare_datasets(
            synthetic_data=request.synthetic_data,
            real_data=request.real_data_sample
        )
        
        # Perform ML utility benchmarking
        utility_results = await statistical_validator.benchmark_utility(
            synthetic_data=request.synthetic_data,
            real_data=request.real_data_sample,
            tasks=request.ml_tasks,
            metrics=request.metrics
        )
        
        return {
            "success": True,
            "statistical_similarity": statistical_comparison,
            "utility_benchmarks": utility_results,
            "overall_score": {
                "statistical_fidelity": statistical_comparison.get("similarity_score", 0.0),
                "utility_preservation": utility_results.get("average_performance_ratio", 0.0),
                "overall_quality": (
                    statistical_comparison.get("similarity_score", 0.0) + 
                    utility_results.get("average_performance_ratio", 0.0)
                ) / 2
            },
            "recommendations": [
                *statistical_comparison.get("recommendations", []),
                *utility_results.get("recommendations", [])
            ],
            "detailed_metrics": {
                "statistical_tests": statistical_comparison.get("test_results", {}),
                "ml_performance": utility_results.get("task_results", {}),
                "distribution_analysis": statistical_comparison.get("distribution_analysis", {})
            }
        }
        
    except Exception as e:
        logger.error(f"Error benchmarking synthetic data: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


# Data Ingestion and Pattern Learning Tools

@app.tool()
async def ingest_data_samples(request: IngestDataRequest) -> Dict[str, Any]:
    """
    Ingest real data samples to learn patterns and structure.
    
    This tool allows you to provide existing real data as a model for synthetic generation.
    The system will analyze the data structure, learn patterns, and optionally anonymize PII.
    
    Args:
        request: IngestDataRequest with data samples and configuration
        
    Returns:
        Dictionary containing:
        - pattern_id: ID for learned pattern (use for generation)
        - structure: Detected data structure
        - statistics: Statistical analysis
        - privacy_report: PII detection results if anonymization was applied
    """
    try:
        logger.info(f"Ingesting data samples - Format: {request.format}, Domain: {request.domain}")
        
        # Load data based on input type
        if isinstance(request.data, str):
            # File path provided
            data_source = request.data
        else:
            # Direct data provided
            data_source = request.data
            
        # Ingest and analyze data
        result = await ingestion_pipeline.ingest(
            source=data_source,
            format=request.format,
            anonymize=request.anonymize,
            learn_patterns=request.learn_patterns,
            sample_size=request.sample_size
        )
        
        # If patterns were learned, store them in the generator
        if result.get("pattern_id") and request.learn_patterns:
            # Make patterns available to the generator
            await generator.learn_from_data(
                data_samples=data_source if isinstance(data_source, list) else result.get("data", []),
                domain=request.domain
            )
            
        # Log audit trail
        await audit_trail.log_event(
            event_type="data_ingestion",
            metadata={
                "pattern_id": result.get("pattern_id"),
                "rows_ingested": result.get("rows_ingested", 0),
                "domain": request.domain,
                "anonymized": request.anonymize
            }
        )
        
        return {
            "success": True,
            **result,
            "message": f"Successfully ingested {result.get('rows_ingested', 0)} records"
        }
        
    except Exception as e:
        logger.error(f"Data ingestion failed: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def generate_from_pattern(request: GenerateFromPatternRequest) -> Dict[str, Any]:
    """
    Generate synthetic data based on previously learned patterns.
    
    Use this after ingesting real data with ingest_data_samples. The system will
    generate new synthetic records that match the statistical patterns of your data.
    
    Args:
        request: GenerateFromPatternRequest with pattern ID and parameters
        
    Returns:
        Dictionary containing:
        - synthetic_data: Generated records matching the pattern
        - validation_report: Comparison with original patterns
        - metadata: Generation details
    """
    try:
        logger.info(f"Generating from pattern: {request.pattern_id}, Count: {request.record_count}")
        
        # Generate using learned patterns
        result = await generator.generate_from_pattern(
            pattern_id=request.pattern_id,
            record_count=request.record_count,
            variation=request.variation,
            privacy_level=request.privacy_level
        )
        
        # Apply privacy protection if needed
        if request.privacy_level != PrivacyLevel.LOW:
            protected_data = []
            for record in result.get("data", []):
                protected_record = await privacy_engine.apply_privacy_protection(
                    record,
                    request.privacy_level
                )
                protected_data.append(protected_record)
            result["data"] = protected_data
            
        # Validate if distributions are preserved
        if request.preserve_distributions and result.get("data"):
            validation_report = await statistical_validator.validate_distribution_preservation(
                original_pattern_id=request.pattern_id,
                synthetic_data=result["data"]
            )
            result["validation_report"] = validation_report
            
        # Log audit trail
        await audit_trail.log_event(
            event_type="pattern_generation",
            metadata={
                "pattern_id": request.pattern_id,
                "records_generated": request.record_count,
                "variation": request.variation,
                "privacy_level": request.privacy_level.value
            }
        )
        
        return {
            "success": True,
            **result,
            "message": f"Successfully generated {request.record_count} synthetic records from pattern"
        }
        
    except Exception as e:
        logger.error(f"Pattern-based generation failed: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def anonymize_existing_data(request: AnonymizeDataRequest) -> Dict[str, Any]:
    """
    Anonymize existing real data while preserving utility.
    
    This tool cleans PII from your data while maintaining relationships and
    statistical properties, making it safe for development and testing.
    
    Args:
        request: AnonymizeDataRequest with data and privacy settings
        
    Returns:
        Dictionary containing:
        - anonymized_data: Cleaned version of input data
        - transformation_report: Details of what was changed
        - privacy_score: Privacy protection level achieved
    """
    try:
        logger.info(f"Anonymizing data - Privacy Level: {request.privacy_level.value}")
        
        # Load data based on input type
        if isinstance(request.data, str):
            # File path provided
            data_source = request.data
        else:
            # Direct data provided
            data_source = request.data
            
        # Ingest data without learning patterns
        ingestion_result = await ingestion_pipeline.ingest(
            source=data_source,
            format=request.format,
            anonymize=True,
            learn_patterns=False
        )
        
        # Get the anonymized data
        anonymized_data = ingestion_result.get("data", [])
        
        # Apply additional privacy protection based on level
        if request.privacy_level == PrivacyLevel.HIGH:
            # Apply stronger anonymization
            protected_data = []
            for record in anonymized_data:
                protected_record = await privacy_engine.apply_privacy_protection(
                    record,
                    request.privacy_level
                )
                protected_data.append(protected_record)
            anonymized_data = protected_data
            
        # Calculate privacy metrics
        privacy_score = await privacy_engine.calculate_privacy_score(
            anonymized_data,
            request.privacy_level
        )
        
        # Log audit trail
        await audit_trail.log_event(
            event_type="data_anonymization",
            metadata={
                "records_anonymized": len(anonymized_data),
                "privacy_level": request.privacy_level.value,
                "privacy_score": privacy_score
            }
        )
        
        return {
            "success": True,
            "anonymized_data": anonymized_data,
            "transformation_report": ingestion_result.get("pii_report", {}),
            "privacy_score": privacy_score,
            "records_processed": len(anonymized_data),
            "message": f"Successfully anonymized {len(anonymized_data)} records"
        }
        
    except Exception as e:
        logger.error(f"Data anonymization failed: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def list_learned_patterns() -> Dict[str, Any]:
    """
    List all previously learned patterns available for generation.
    
    Returns:
        Dictionary containing list of pattern IDs with metadata
    """
    try:
        patterns = []
        
        # Get patterns from generator
        for pattern_id, pattern_info in generator.learned_patterns.items():
            patterns.append({
                "pattern_id": pattern_id,
                "domain": pattern_info.get("domain"),
                "sample_count": pattern_info.get("sample_count"),
                "learned_at": pattern_info.get("learned_at", "unknown")
            })
            
        # Get patterns from ingestion pipeline
        for pattern_id in ingestion_pipeline.list_stored_patterns():
            if not any(p["pattern_id"] == pattern_id for p in patterns):
                pattern_data = ingestion_pipeline.get_stored_pattern(pattern_id)
                if pattern_data:
                    patterns.append({
                        "pattern_id": pattern_id,
                        "domain": pattern_data.get("metadata", {}).get("domain", "unknown"),
                        "sample_count": pattern_data.get("rows_ingested", 0),
                        "learned_at": pattern_data.get("metadata", {}).get("ingested_at", "unknown")
                    })
                    
        return {
            "success": True,
            "patterns": patterns,
            "count": len(patterns),
            "message": f"Found {len(patterns)} learned patterns"
        }
        
    except Exception as e:
        logger.error(f"Failed to list patterns: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


# Database Management Tools

@app.tool()
async def add_database_connection(request: AddDatabaseConnectionRequest) -> Dict[str, Any]:
    """
    Add a new database connection to the system.
    
    Supports PostgreSQL, MySQL, MongoDB, Redis, BigQuery, Snowflake, and Redshift.
    
    Args:
        request: Database connection configuration
        
    Returns:
        Connection status and details
    """
    try:
        logger.info(f"Adding database connection: {request.name} ({request.db_type})")
        
        # Map string to enum
        try:
            db_type_enum = DatabaseType(request.db_type.lower())
            role_enum = DatabaseRole(request.role.lower())
        except ValueError as e:
            return {
                "success": False,
                "error": f"Invalid database type or role: {str(e)}",
                "supported_types": [t.value for t in DatabaseType],
                "supported_roles": [r.value for r in DatabaseRole]
            }
        
        # Add database connection
        success = await db_manager.add_database(
            name=request.name,
            db_type=db_type_enum,
            config=request.config,
            role=role_enum,
            auto_connect=request.auto_connect
        )
        
        if success:
            # Get connection info
            db_info = db_manager.get_database_info()
            
            return {
                "success": True,
                "database_name": request.name,
                "database_type": request.db_type,
                "role": request.role,
                "connected": request.auto_connect,
                "total_databases": db_info["total_databases"],
                "timestamp": datetime.now().isoformat()
            }
        else:
            return {
                "success": False,
                "error": f"Failed to add database connection: {request.name}",
                "timestamp": datetime.now().isoformat()
            }
            
    except Exception as e:
        logger.error(f"Error adding database connection: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def execute_database_query(request: ExecuteDatabaseQueryRequest) -> Dict[str, Any]:
    """
    Execute a database query with intelligent database selection.
    
    Args:
        request: Query execution configuration
        
    Returns:
        Query results with performance metrics
    """
    try:
        logger.info(f"Executing database query on {request.database or 'auto-selected'} database")
        
        # Map role string to enum if provided
        role_enum = None
        if request.role:
            try:
                role_enum = DatabaseRole(request.role.lower())
            except ValueError:
                return {
                    "success": False,
                    "error": f"Invalid database role: {request.role}",
                    "supported_roles": [r.value for r in DatabaseRole]
                }
        
        # Execute query
        result = await db_manager.execute_query(
            query=request.query,
            parameters=request.parameters,
            database=request.database,
            role=role_enum
        )
        
        return result
        
    except Exception as e:
        logger.error(f"Error executing database query: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def create_database_table(request: CreateTableRequest) -> Dict[str, Any]:
    """
    Create a table in the specified database(s).
    
    Args:
        request: Table creation configuration
        
    Returns:
        Table creation results
    """
    try:
        logger.info(f"Creating table: {request.table_name}")
        
        # Map role string to enum if provided
        role_enum = None
        if request.role:
            try:
                role_enum = DatabaseRole(request.role.lower())
            except ValueError:
                return {
                    "success": False,
                    "error": f"Invalid database role: {request.role}",
                    "supported_roles": [r.value for r in DatabaseRole]
                }
        
        # Create table
        result = await db_manager.create_table(
            table_name=request.table_name,
            schema=request.schema,
            database=request.database,
            role=role_enum
        )
        
        return result
        
    except Exception as e:
        logger.error(f"Error creating database table: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def insert_database_data(request: InsertDataRequest) -> Dict[str, Any]:
    """
    Insert data into a database table with bulk optimization.
    
    Args:
        request: Data insertion configuration
        
    Returns:
        Insertion results with performance metrics
    """
    try:
        logger.info(f"Inserting {len(request.data)} records into {request.table_name}")
        
        # Map role string to enum if provided
        role_enum = None
        if request.role:
            try:
                role_enum = DatabaseRole(request.role.lower())
            except ValueError:
                return {
                    "success": False,
                    "error": f"Invalid database role: {request.role}",
                    "supported_roles": [r.value for r in DatabaseRole]
                }
        
        # Insert data
        result = await db_manager.insert_bulk(
            table_name=request.table_name,
            data=request.data,
            database=request.database,
            role=role_enum
        )
        
        return result
        
    except Exception as e:
        logger.error(f"Error inserting database data: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def get_database_health() -> Dict[str, Any]:
    """
    Get comprehensive health status of all database connections.
    
    Returns:
        Database health report with performance metrics
    """
    try:
        logger.info("Performing database health check")
        
        health_results = await db_manager.health_check_all()
        performance_metrics = await db_manager.get_performance_metrics()
        
        return {
            "success": True,
            "health_status": health_results,
            "performance_metrics": performance_metrics,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error checking database health: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def get_database_info() -> Dict[str, Any]:
    """
    Get information about all managed database connections.
    
    Returns:
        Database configuration and status information
    """
    try:
        db_info = db_manager.get_database_info()
        return {
            "success": True,
            "database_info": db_info,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error getting database info: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


# Database Migration Tools

@app.tool()
async def create_database_migration(request: CreateMigrationRequest) -> Dict[str, Any]:
    """
    Create a new database migration.
    
    Args:
        request: Migration configuration
        
    Returns:
        Migration creation results
    """
    try:
        logger.info(f"Creating migration: {request.name}")
        
        # Map database type strings to enums
        try:
            source_type = DatabaseType(request.source_db_type.lower())
            target_type = DatabaseType(request.target_db_type.lower())
        except ValueError as e:
            return {
                "success": False,
                "error": f"Invalid database type: {str(e)}",
                "supported_types": [t.value for t in DatabaseType]
            }
        
        # Create migration
        migration_id = await migration_manager.create_migration(
            name=request.name,
            description=request.description,
            source_db_type=source_type,
            target_db_type=target_type,
            up_sql=request.up_sql,
            down_sql=request.down_sql,
            data_transformations=request.data_transformations,
            dependencies=request.dependencies
        )
        
        return {
            "success": True,
            "migration_id": migration_id,
            "name": request.name,
            "source_type": request.source_db_type,
            "target_type": request.target_db_type,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error creating migration: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def execute_database_migration(request: ExecuteMigrationRequest) -> Dict[str, Any]:
    """
    Execute a database migration.
    
    Args:
        request: Migration execution configuration
        
    Returns:
        Migration execution results
    """
    try:
        logger.info(f"Executing migration: {request.migration_id}")
        
        result = await migration_manager.execute_migration(
            migration_id=request.migration_id,
            source_db=request.source_db,
            target_db=request.target_db
        )
        
        return result
        
    except Exception as e:
        logger.error(f"Error executing migration: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def get_migration_status(migration_id: Optional[str] = None) -> Dict[str, Any]:
    """
    Get migration status and history.
    
    Args:
        migration_id: Specific migration ID (optional)
        
    Returns:
        Migration status information
    """
    try:
        result = await migration_manager.get_migration_status(migration_id)
        return {
            "success": True,
            "migration_status": result,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error getting migration status: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def create_sqlite_to_postgresql_migration(
    sqlite_db: str,
    postgresql_db: str,
    migration_name: str = "sqlite_to_postgresql"
) -> Dict[str, Any]:
    """
    Create a migration from SQLite to PostgreSQL.
    
    Args:
        sqlite_db: Source SQLite database connection name
        postgresql_db: Target PostgreSQL database connection name
        migration_name: Name for the migration
        
    Returns:
        Migration creation results
    """
    try:
        logger.info(f"Creating SQLite to PostgreSQL migration: {migration_name}")
        
        migration_id = await migration_manager.create_sqlite_to_postgresql_migration(
            sqlite_db=sqlite_db,
            postgresql_db=postgresql_db,
            migration_name=migration_name
        )
        
        return {
            "success": True,
            "migration_id": migration_id,
            "migration_name": migration_name,
            "source_db": sqlite_db,
            "target_db": postgresql_db,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error creating SQLite to PostgreSQL migration: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


# Database Schema Analysis Tools

@app.tool()
async def analyze_database_schema(request: SchemaAnalysisRequest) -> Dict[str, Any]:
    """
    Perform comprehensive database schema analysis.
    
    Args:
        request: Schema analysis configuration
        
    Returns:
        Detailed schema analysis results
    """
    try:
        logger.info(f"Analyzing schema for database: {request.database}")
        
        analysis = await schema_inspector.analyze_database_schema(
            database=request.database,
            deep_analysis=request.deep_analysis,
            sample_size=request.sample_size
        )
        
        return {
            "success": True,
            "schema_analysis": {
                "database_name": analysis.database_name,
                "database_type": analysis.database_type.value,
                "total_tables": len(analysis.tables),
                "total_relationships": len(analysis.relationships),
                "orphaned_tables": analysis.orphaned_tables,
                "performance_issues": len(analysis.performance_issues),
                "data_quality_issues": len(analysis.data_quality_issues),
                "duplicate_indexes": len(analysis.duplicate_indexes),
                "analysis_timestamp": analysis.analysis_timestamp,
                "detailed_results": {
                    "tables": [
                        {
                            "name": table.name,
                            "row_count": table.row_count,
                            "column_count": len(table.columns),
                            "size_bytes": table.size_bytes,
                            "indexes": len(table.indexes),
                            "foreign_keys": len(table.foreign_keys)
                        }
                        for table in analysis.tables
                    ],
                    "relationships": [
                        {
                            "parent_table": rel.parent_table,
                            "child_table": rel.child_table,
                            "relationship_type": rel.relationship_type
                        }
                        for rel in analysis.relationships
                    ],
                    "performance_issues": analysis.performance_issues,
                    "data_quality_issues": analysis.data_quality_issues,
                    "duplicate_indexes": analysis.duplicate_indexes
                }
            },
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error analyzing database schema: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def compare_database_schemas(request: CompareSchemaRequest) -> Dict[str, Any]:
    """
    Compare schemas between two databases.
    
    Args:
        request: Schema comparison configuration
        
    Returns:
        Schema comparison results
    """
    try:
        logger.info(f"Comparing schemas: {request.database1} vs {request.database2}")
        
        comparison = await schema_inspector.compare_schemas(
            database1=request.database1,
            database2=request.database2,
            table_name=request.table_name
        )
        
        return {
            "success": True,
            "schema_comparison": comparison,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error comparing database schemas: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def get_schema_health_report(database: str) -> Dict[str, Any]:
    """
    Generate comprehensive schema health report.
    
    Args:
        database: Database connection name
        
    Returns:
        Schema health report with recommendations
    """
    try:
        logger.info(f"Generating schema health report for: {database}")
        
        health_report = await schema_inspector.get_schema_health_report(database)
        
        return {
            "success": True,
            "health_report": health_report,
            "timestamp": datetime.now().isoformat()
        }
        
    except Exception as e:
        logger.error(f"Error generating schema health report: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "timestamp": datetime.now().isoformat()
        }


@app.tool()
async def get_supported_domains() -> Dict[str, Any]:
    """
    Get list of supported domains and data types.
    
    Returns:
        Dictionary containing supported domains, data types, and compliance frameworks.
    """
    return {
        "domains": {
            "healthcare": {
                "description": "Healthcare and medical data with HIPAA compliance",
                "data_types": [
                    "patient_records",
                    "clinical_trials",
                    "medical_claims",
                    "laboratory_results",
                    "medical_imaging_metadata",
                    "electronic_health_records",
                    "pharmacovigilance_data"
                ],
                "compliance_frameworks": ["hipaa", "fda", "gdpr", "hitech"]
            },
            "finance": {
                "description": "Financial services data with regulatory compliance",
                "data_types": [
                    "transaction_records",
                    "credit_assessments",
                    "trading_data",
                    "loan_applications",
                    "fraud_detection_datasets",
                    "regulatory_reports",
                    "market_data"
                ],
                "compliance_frameworks": ["sox", "pci_dss", "basel_iii", "mifid_ii", "dodd_frank"]
            },
            "custom": {
                "description": "Custom domain with configurable schemas and compliance",
                "data_types": ["custom"],
                "compliance_frameworks": ["gdpr", "ccpa", "custom"]
            }
        },
        "privacy_levels": {
            "low": {"epsilon": 10.0, "description": "Minimal privacy protection for internal use"},
            "medium": {"epsilon": 1.0, "description": "Standard privacy protection for most use cases"},
            "high": {"epsilon": 0.1, "description": "Strong privacy protection for sensitive data"},
            "maximum": {"epsilon": 0.01, "description": "Maximum privacy protection for highly sensitive data"}
        },
        "output_formats": ["json", "csv", "parquet", "database"],
        "validation_levels": ["basic", "standard", "comprehensive", "exhaustive"]
    }


if __name__ == "__main__":
    # Create logs directory
    os.makedirs("logs", exist_ok=True)
    
    # Run the server
    app.run()