"""Pydantic schemas for FlowPilot analytics dashboard."""
from __future__ import annotations

from typing import Optional, Any
from datetime import datetime

from pydantic import BaseModel


class MTTRDataPoint(BaseModel):
    date: str
    mttr_minutes: float
    session_count: int


class DomainBreakdown(BaseModel):
    domain: str
    total: int
    resolved: int
    escalated: int
    resolution_rate: float


class ConfidenceBreakdown(BaseModel):
    guided_sessions: int
    guided_resolution_rate: float
    exploring_sessions: int
    exploring_resolution_rate: float
    discovery_sessions: int
    discovery_resolution_rate: float


class DomainCoverage(BaseModel):
    domain: str
    flow_count: int
    session_count: int
    guided_rate: float


class KnowledgeCoverage(BaseModel):
    total_flows: int
    ai_generated_flows: int
    total_proposals_pending: int
    proposals_approved_this_period: int
    proposals_rejected_this_period: int
    coverage_by_domain: list[DomainCoverage] = []


class PsaMetrics(BaseModel):
    ticket_link_rate: float
    auto_push_success_rate: float
    auto_push_retry_success_rate: float
    total_time_entries_logged: int
    total_hours_logged: float


class CoverageDomainRow(BaseModel):
    domain: str
    flow_count: int
    session_count: int
    resolution_rate: float
    escalation_rate: float
    guided_rate: float
    avg_resolution_minutes: float | None = None


class CoverageResponse(BaseModel):
    domains: list[CoverageDomainRow]
    unmapped_session_count: int
    total_domains: int


class FlowQualityRow(BaseModel):
    flow_id: str
    name: str
    tree_type: str
    usage_count: int
    success_rate: float | None = None
    last_matched_at: datetime | None = None
    avg_confidence: float | None = None
    quality_score: float


class FlowQualityResponse(BaseModel):
    flows: list[FlowQualityRow]
    top_performers: list[FlowQualityRow]
    needs_attention: list[FlowQualityRow]


class PsaFunnel(BaseModel):
    total_sessions: int
    linked_to_ticket: int
    doc_pushed: int
    time_entry_logged: int


class PsaDailyTrend(BaseModel):
    date: str
    entries: int
    hours: float


class EnhancedPsaMetrics(BaseModel):
    total_time_entries: int
    total_hours_logged: float
    avg_hours_per_session: float
    push_funnel: PsaFunnel
    daily_trend: list[PsaDailyTrend]


class FlowPilotDashboard(BaseModel):
    period: str
    total_sessions: int
    resolved_sessions: int
    escalated_sessions: int
    abandoned_sessions: int
    resolution_rate: float
    avg_steps_to_resolution: float
    avg_session_duration_minutes: float
    avg_rating: float | None = None
    mttr_minutes: float | None = None
    mttr_trend: list[MTTRDataPoint] = []
    sessions_by_domain: list[DomainBreakdown] = []
    confidence_breakdown: ConfidenceBreakdown
    knowledge_coverage: KnowledgeCoverage
    psa_metrics: PsaMetrics | None = None


class EscalationMetrics(BaseModel):
    """In-product time-to-first-action metric for the Escalation Mode wedge.

    NOTE: this is the *in-product* metric (post-claim time-to-first-action). The
    "minutes recovered" sales claim requires a manual baseline measurement of the
    pre-Escalation-Mode verbal-handoff time. See
    docs/plans/2026-04-27-escalation-mode-wedge-design.md for the two-metric
    framing — do not roll this number alone into "minutes recovered."
    """

    period: str
    n_handoffs_claimed: int
    n_handoffs_with_action: int
    avg_seconds_to_first_action: float | None = None
    median_seconds_to_first_action: float | None = None
    p95_seconds_to_first_action: float | None = None
    metric_definition: str = (
        "elapsed_seconds(first ai_session_step in session where "
        "created_at > SessionHandoff.claimed_at) — measures post-claim activity "
        "lag, NOT verbal-handoff savings. Pair with manual baseline."
    )