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from enum import Enum
from dataclasses import dataclass
from typing import List

class CrisisLevel(Enum):
    LEVEL_1 = "routine"      # Single reporter can handle
    LEVEL_2 = "elevated"     # Editor involvement needed
    LEVEL_3 = "major"        # Multiple reporters, editor oversight
    LEVEL_4 = "critical"     # All hands, executive involvement

@dataclass
class BreakingEvent:
    description: str
    level: CrisisLevel
    confirmed_facts: List[str]
    unconfirmed_claims: List[str]
    sources_contacted: List[str]
    assigned_reporters: List[str]

    def escalation_needed(self) -> bool:
        """Determine if event needs escalation."""
        triggers = [
            len(self.unconfirmed_claims) > 5,  # Too many unknowns
            "fatalities" in self.description.lower(),
            "official" in self.description.lower(),
            "government" in self.description.lower(),
        ]
        return any(triggers)

ESCALATION_TRIGGERS = {
    CrisisLevel.LEVEL_2: [
        "Multiple fatalities confirmed",
        "Major public figure involved",
        "Legal/liability concerns",
        "Significant local impact",
    ],
    CrisisLevel.LEVEL_3: [
        "National news potential",
        "Active danger to public",
        "Major institution affected",
        "Coordinated misinformation detected",
    ],
    CrisisLevel.LEVEL_4: [
        "Mass casualty event",
        "Government/democracy implications",
        "Our organization directly involved",
        "Imminent physical threat",
    ],
}

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from dataclasses import dataclass
from datetime import datetime
from typing import Optional
from enum import Enum

class VerificationStatus(Enum):
    UNVERIFIED = "unverified"
    PARTIALLY_VERIFIED = "partially_verified"
    VERIFIED = "verified"
    DEBUNKED = "debunked"
    UNCERTAIN = "uncertain"

@dataclass
class Claim:
    text: str
    source: str
    first_seen: datetime
    urgency: int  # 1-5, higher = more urgent to verify
    status: VerificationStatus = VerificationStatus.UNVERIFIED
    verification_notes: str = ""
    verifier: Optional[str] = None

class VerificationQueue:
    """Manage claims awaiting verification."""

    def __init__(self):
        self.claims = []

    def add_claim(self, claim: Claim):
        self.claims.append(claim)
        self._sort_by_priority()

    def _sort_by_priority(self):
        """Urgent claims first, then by time seen."""
        self.claims.sort(
            key=lambda c: (-c.urgency, c.first_seen)
        )

    def next_claim(self) -> Optional[Claim]:
        """Get highest priority unverified claim."""
        for claim in self.claims:
            if claim.status == VerificationStatus.UNVERIFIED:
                return claim
        return None

    def update_status(self, claim: Claim,
                      status: VerificationStatus,
                      notes: str,
                      verifier: str):
        claim.status = status
        claim.verification_notes = notes
        claim.verifier = verifier

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from dataclasses import dataclass
from datetime import datetime
from typing import List, Dict
from collections import Counter

@dataclass
class CrisisMention:
    platform: str
    content: str
    author: str
    timestamp: datetime
    sentiment: str  # positive, negative, neutral, misinformation
    reach: int  # followers/potential impressions
    requires_response: bool = False

class CrisisMonitor:
    """Track crisis-related social mentions."""

    def __init__(self, crisis_keywords: List[str]):
        self.keywords = crisis_keywords
        self.mentions: List[CrisisMention] = []

    def add_mention(self, mention: CrisisMention):
        self.mentions.append(mention)

    def get_dashboard(self) -> dict:
        """Real-time crisis overview."""
        recent = [m for m in self.mentions
                  if (datetime.now() - m.timestamp).seconds < 3600]

        return {
            'total_mentions_1h': len(recent),
            'sentiment_breakdown': self._sentiment_counts(recent),
            'top_platforms': self._platform_counts(recent),
            'misinformation_count': len([
                m for m in recent
                if m.sentiment == 'misinformation'
            ]),
            'high_reach_negative': [
                m for m in recent
                if m.sentiment == 'negative' and m.reach > 10000
            ],
            'pending_responses': len([
                m for m in self.mentions
                if m.requires_response
            ])
        }

    def _sentiment_counts(self, mentions: List[CrisisMention]) -> Dict:
        return dict(Counter(m.sentiment for m in mentions))

    def _platform_counts(self, mentions: List[CrisisMention]) -> Dict:
        return dict(Counter(m.platform for m in mentions))

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from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime

@dataclass
class CrisisTeamMember:
    name: str
    role: str
    phone: str
    email: str
    backup: Optional['CrisisTeamMember'] = None

@dataclass
class CrisisLog:
    """Maintain record of all crisis decisions."""
    entries: List[dict] = None

    def __post_init__(self):
        self.entries = self.entries or []

    def log(self, action: str, decision_maker: str,
            rationale: str, outcome: str = "pending"):
        self.entries.append({
            'timestamp': datetime.now().isoformat(),
            'action': action,
            'decision_maker': decision_maker,
            'rationale': rationale,
            'outcome': outcome
        })

    def export_for_postmortem(self) -> str:
        """Generate timeline for after-action review."""
        lines = ["# Crisis Response Timeline\n"]
        for entry in self.entries:
            lines.append(
                f"**{entry['timestamp']}** - {entry['action']}\n"
                f"- Decision by: {entry['decision_maker']}\n"
                f"- Rationale: {entry['rationale']}\n"
                f"- Outcome: {entry['outcome']}\n"
            )
        return '\n'.join(lines)

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