# sbar_communication.py
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
from enum import Enum

class Severity(Enum):
    SEV1 = "Critical - Complete outage"
    SEV2 = "High - Major functionality impaired"
    SEV3 = "Medium - Partial functionality impaired"
    SEV4 = "Low - Minor issue"

@dataclass
class SBARIncidentUpdate:
    """
    SBAR (Situation, Background, Assessment, Recommendation) framework
    for incident communication
    """
    # Situation: What is happening right now?
    situation: str
    severity: Severity
    impacted_services: List[str]
    user_impact: str

    # Background: What led to this situation?
    background: str
    incident_start: datetime
    detection_method: str

    # Assessment: What do we think is the problem?
    assessment: str
    root_cause_hypothesis: Optional[str]
    current_metrics: dict

    # Recommendation: What should be done?
    recommendation: str
    next_steps: List[str]
    eta_resolution: Optional[datetime]

    # Metadata
    incident_id: str
    update_number: int
    timestamp: datetime
    incident_commander: str

    def to_message(self, audience: str = "technical") -> str:
        """Generate formatted message for specific audience"""

        if audience == "executive":
            return self._executive_summary()
        elif audience == "customer":
            return self._customer_facing()
        else:
            return self._technical_summary()

    def _executive_summary(self) -> str:
        """Executive-friendly summary"""
        return f"""
**Incident Update #{self.update_number}** - {self.severity.value}

**SITUATION**
{self.situation}

**USER IMPACT**
{self.user_impact}

**CURRENT STATUS**
{self.assessment}

**ACTIONS BEING TAKEN**
{self.recommendation}

**ESTIMATED RESOLUTION**
{self.eta_resolution.strftime('%I:%M %p') if self.eta_resolution else 'Under investigation'}

_Last Updated: {self.timestamp.strftime('%I:%M %p')}_
_Incident Commander: {self.incident_commander}_
"""

    def _customer_facing(self) -> str:
        """Customer-facing status update"""
        return f"""
We are currently experiencing {self.severity.value.lower()} affecting {', '.join(self.impacted_services)}.

{self.user_impact}

Our team is actively working to restore full service. We will provide updates every 30 minutes or as the situation changes.

{f"Expected resolution: {self.eta_resolution.strftime('%I:%M %p')}" if self.eta_resolution else "We are working to resolve this as quickly as possible."}

We apologize for any inconvenience.

_Last Updated: {self.timestamp.strftime('%I:%M %p')}_
"""

    def _technical_summary(self) -> str:
        """Detailed technical summary"""
        metrics_str = '\n'.join([f"- {k}: {v}" for k, v in self.current_metrics.items()])
        steps_str = '\n'.join([f"- {step}" for step in self.next_steps])

        return f"""
## Incident Update #{self.update_number} - {self.incident_id}

**Severity:** {self.severity.value}
**Started:** {self.incident_start.strftime('%Y-%m-%d %H:%M:%S')}
**Commander:** {self.incident_commander}

### Situation
{self.situation}

**Impacted Services:** {', '.join(self.impacted_services)}
**User Impact:** {self.user_impact}

### Background
{self.background}
**Detection:** {self.detection_method}

### Assessment
{self.assessment}

{f"**Root Cause Hypothesis:** {self.root_cause_hypothesis}" if self.root_cause_hypothesis else ""}

**Current Metrics:**
{metrics_str}

### Recommendation
{self.recommendation}

**Next Steps:**
{steps_str}

{f"**ETA:** {self.eta_resolution.strftime('%Y-%m-%d %H:%M')}" if self.eta_resolution else "**ETA:** Under investigation"}

---
_Update Time: {self.timestamp.strftime('%Y-%m-%d %H:%M:%S')}_
"""

# Example usage
update = SBARIncidentUpdate(
    situation="Payment processing API is returning 500 errors for 30% of requests",
    severity=Severity.SEV2,
    impacted_services=["Payment API", "Checkout Flow"],
    user_impact="Users unable to complete purchases, estimated 30% transaction failure rate",
    background="Issue started at 14:35 following deployment of payment-service v2.1.3. Deployment included database schema changes and new caching layer.",
    incident_start=datetime(2026, 2, 7, 14, 35),
    detection_method="Automated error rate alert triggered at 14:37",
    assessment="Database connection pool exhaustion due to inefficient query in new code path. Connection pool size (20) insufficient for current load (avg 45 concurrent requests).",
    root_cause_hypothesis="New caching layer not properly releasing database connections",
    current_metrics={
        "Error Rate": "28%",
        "DB Connection Pool Usage": "100%",
        "API Latency P95": "8.5s",
        "Active DB Connections": "20/20"
    },
    recommendation="Rollback payment-service to v2.1.2 and increase connection pool size",
    next_steps=[
        "Initiate rollback to v2.1.2 (ETA: 5 minutes)",
        "Scale connection pool from 20 to 50",
        "Monitor error rate and latency",
        "Review caching layer connection management"
    ],
    eta_resolution=datetime(2026, 2, 7, 15, 10),
    incident_id="INC-2026-0207-001",
    update_number=2,
    timestamp=datetime.now(),
    incident_commander="Alice Johnson"
)

# Generate messages for different audiences
print("=== TECHNICAL ===")
print(update.to_message("technical"))
print("\n=== EXECUTIVE ===")
print(update.to_message("executive"))
print("\n=== CUSTOMER ===")
print(update.to_message("customer"))