"""
License Compliance Agent for SPARKNET

AI-powered license agreement monitoring and compliance verification.
Part of Scenario 3: License Compliance Monitoring.

PRIVACY & SECURITY NOTES:
-------------------------
This agent handles sensitive financial and contractual data. For production deployments:

1. DATA ISOLATION:
   - License data should be stored in isolated database schemas
   - Implement row-level security for multi-tenant deployments
   - Consider geographic data residency requirements

2. GDPR COMPLIANCE:
   - Implement right-to-erasure for terminated agreements
   - Maintain data processing records
   - Enable data portability exports

3. AUDIT REQUIREMENTS:
   - All compliance checks should be logged
   - Maintain immutable audit trail
   - Enable compliance report generation

4. PRIVATE DEPLOYMENT:
   - Use Ollama for local LLM inference (no data leaves network)
   - Configure local vector store for document embeddings
   - Implement on-premise authentication

Author: SPARKNET Team
Project: VISTA/Horizon EU
Status: Placeholder - In Development
"""

from typing import Optional, Dict, Any, List
from dataclasses import dataclass
from datetime import datetime, date
from enum import Enum
from loguru import logger

# Note: These imports would be implemented when the module is fully developed
# from ..base_agent import BaseAgent, Task
# from ...llm.langchain_ollama_client import LangChainOllamaClient


class ComplianceStatus(str, Enum):
    """License compliance status."""
    COMPLIANT = "compliant"
    NON_COMPLIANT = "non_compliant"
    AT_RISK = "at_risk"
    PENDING_REVIEW = "pending_review"
    EXPIRED = "expired"


class PaymentStatus(str, Enum):
    """Payment tracking status."""
    PAID = "paid"
    PENDING = "pending"
    OVERDUE = "overdue"
    DISPUTED = "disputed"
    WAIVED = "waived"


@dataclass
class LicenseAgreement:
    """
    License agreement data model.

    GDPR Note: Contains potentially sensitive business information.
    Implement appropriate access controls and retention policies.
    """
    license_id: str
    agreement_name: str
    licensee_name: str
    licensor_name: str
    technology_name: str
    effective_date: date
    expiration_date: Optional[date]
    status: ComplianceStatus
    total_value: Optional[float]
    currency: str = "EUR"
    payment_schedule: Optional[List[Dict[str, Any]]] = None
    milestones: Optional[List[Dict[str, Any]]] = None
    metadata: Optional[Dict[str, Any]] = None


@dataclass
class PaymentRecord:
    """
    Payment tracking record.

    GDPR Note: Financial data - ensure encryption and access logging.
    """
    payment_id: str
    license_id: str
    amount: float
    currency: str
    due_date: date
    paid_date: Optional[date]
    status: PaymentStatus
    payment_type: str  # royalty, upfront, milestone, etc.
    notes: Optional[str] = None


@dataclass
class ComplianceAlert:
    """
    Compliance monitoring alert.

    Used for notifying TTO staff of compliance issues.
    """
    alert_id: str
    license_id: str
    alert_type: str  # payment_overdue, milestone_missed, expiring_soon, etc.
    severity: str  # low, medium, high, critical
    message: str
    created_at: datetime
    resolved: bool = False
    resolved_at: Optional[datetime] = None


class LicenseComplianceAgent:
    """
    Agent for monitoring license agreement compliance.

    This agent tracks:
    - Payment schedules and overdue payments
    - Milestone completion and deadlines
    - Agreement expiration dates
    - Compliance violations and alerts

    DEPLOYMENT CONSIDERATIONS:
    --------------------------
    For private/on-premise deployment:
    1. Configure local Ollama instance for LLM inference
    2. Use PostgreSQL with encryption for data storage
    3. Implement SSO integration for authentication
    4. Enable audit logging for all operations

    For cloud deployment (Streamlit Cloud):
    1. Use secrets management for API keys
    2. Configure secure database connection
    3. Enable HTTPS for all communications
    4. Implement rate limiting for API calls
    """

    def __init__(
        self,
        llm_client: Optional[Any] = None,  # LangChainOllamaClient when implemented
        database_url: Optional[str] = None,
    ):
        """
        Initialize License Compliance Agent.

        Args:
            llm_client: LangChain LLM client for AI analysis
            database_url: Database connection URL (use secrets management)
        """
        self.llm_client = llm_client
        self.database_url = database_url
        self.name = "LicenseComplianceAgent"
        self.description = "License agreement monitoring and compliance tracking"

        logger.info(f"Initialized {self.name} (placeholder)")

    async def check_payment_compliance(
        self,
        license_id: str,
    ) -> Dict[str, Any]:
        """
        Check payment compliance for a license agreement.

        Args:
            license_id: License agreement identifier

        Returns:
            Compliance status with payment details

        TODO: Implement actual payment tracking logic
        """
        logger.info(f"Checking payment compliance for license: {license_id}")

        # Placeholder response
        return {
            "license_id": license_id,
            "status": ComplianceStatus.PENDING_REVIEW.value,
            "message": "Payment compliance check not yet implemented",
            "payments_due": [],
            "payments_overdue": [],
            "next_payment_date": None,
            "total_outstanding": 0.0,
        }

    async def verify_milestone(
        self,
        license_id: str,
        milestone_id: str,
    ) -> Dict[str, Any]:
        """
        Verify milestone completion for a license agreement.

        Args:
            license_id: License agreement identifier
            milestone_id: Milestone identifier

        Returns:
            Milestone verification result

        TODO: Implement actual milestone verification logic
        """
        logger.info(f"Verifying milestone {milestone_id} for license: {license_id}")

        # Placeholder response
        return {
            "license_id": license_id,
            "milestone_id": milestone_id,
            "status": "pending_verification",
            "message": "Milestone verification not yet implemented",
            "evidence_required": True,
            "verification_deadline": None,
        }

    async def generate_compliance_report(
        self,
        license_ids: Optional[List[str]] = None,
        date_range: Optional[tuple] = None,
    ) -> Dict[str, Any]:
        """
        Generate compliance report for license agreements.

        Args:
            license_ids: Optional list of specific licenses to report on
            date_range: Optional (start_date, end_date) tuple

        Returns:
            Compliance report with summary and details

        TODO: Implement actual report generation logic
        """
        logger.info("Generating compliance report")

        # Placeholder response
        return {
            "report_id": f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "generated_at": datetime.now().isoformat(),
            "status": "placeholder",
            "message": "Compliance report generation not yet implemented",
            "summary": {
                "total_licenses": 0,
                "compliant": 0,
                "non_compliant": 0,
                "at_risk": 0,
            },
            "details": [],
        }

    async def create_alert(
        self,
        license_id: str,
        alert_type: str,
        severity: str,
        message: str,
    ) -> ComplianceAlert:
        """
        Create a compliance alert for TTO staff notification.

        Args:
            license_id: License agreement identifier
            alert_type: Type of alert (payment_overdue, milestone_missed, etc.)
            severity: Alert severity (low, medium, high, critical)
            message: Alert message

        Returns:
            Created compliance alert

        TODO: Implement actual alert creation and notification logic
        """
        logger.info(f"Creating {severity} alert for license: {license_id}")

        alert = ComplianceAlert(
            alert_id=f"alert_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            license_id=license_id,
            alert_type=alert_type,
            severity=severity,
            message=message,
            created_at=datetime.now(),
        )

        # TODO: Send notification (email, Slack, etc.)

        return alert

    def get_vista_quality_criteria(self) -> Dict[str, Any]:
        """
        Get VISTA quality criteria for compliance monitoring.

        Returns quality thresholds aligned with VISTA project objectives.
        """
        return {
            "payment_tracking": {
                "weight": 0.30,
                "threshold": 0.95,
                "description": "Payment records must be accurate and complete",
            },
            "milestone_verification": {
                "weight": 0.25,
                "threshold": 0.90,
                "description": "Milestone verification must include evidence",
            },
            "alert_timeliness": {
                "weight": 0.25,
                "threshold": 0.95,
                "description": "Alerts must be generated within 24 hours of trigger",
            },
            "report_accuracy": {
                "weight": 0.20,
                "threshold": 0.98,
                "description": "Reports must accurately reflect current state",
            },
        }