"""Proactive refresh schemas.

Defines the topic model (v2), the chain-shared context state, and the
``daily/_proactive.yaml`` truth-source file model. The LLM reply contract is
validated structurally by ``parse_extract_reply`` instead of a model here.
See ``PROACTIVE_SPEC.md`` sections F1/A2 for the full contracts.
"""

from pydantic import BaseModel, ConfigDict, Field, field_validator

TOPIC_KINDS = ("follow_up", "interest_extend")


def clamp_confidence(value) -> float:
    """Coerce confidence into [0, 1]; any conversion failure falls back to 0.5."""
    try:
        return min(1.0, max(0.0, float(value)))
    except (TypeError, ValueError):
        return 0.5


class ProactiveTopic(BaseModel):
    """One proactive topic; every field has a default so v1 files parse seamlessly.

    Fallback rules (A2): invalid ``kind`` -> ``interest_extend``; unparseable
    ``confidence`` -> 0.5. Missing ``id``, ``first_seen`` and
    ``last_evidence_at`` are context-dependent and therefore resolved by the
    loaders, not here.
    """

    id: str = ""
    title: str = ""
    reason: str = ""
    kind: str = "interest_extend"
    confidence: float = 0.5
    first_seen: str = ""
    last_evidence_at: str = ""
    evidence: str = ""
    paths: list[str] = Field(default_factory=list)

    @field_validator("kind", mode="before")
    @classmethod
    def _fallback_kind(cls, value):
        text = str(value or "").strip()
        return text if text in TOPIC_KINDS else "interest_extend"

    @field_validator("confidence", mode="before")
    @classmethod
    def _fallback_confidence(cls, value):
        return clamp_confidence(value)

    @field_validator("paths", mode="before")
    @classmethod
    def _clean_str_list(cls, value):
        if not isinstance(value, list):
            return []
        return [str(item).strip() for item in value if str(item).strip()]


class ProactiveState(BaseModel):
    """Chain-shared proactive context state (``context['proactive']``).

    Extract fills change-detection/carry-forward/LLM output fields; topics fills the
    filtering fields plus ``push_candidates`` (today's pushable topics); plan
    expands candidates into ``scenario_cards``; agenda selects the ordered
    ``agenda`` and records ``suppressed`` candidates with reasons; finish
    records the catalog checkpoint. ``plan_llm_calls`` counts plan+agenda LLM
    calls separately from extract's ``llm_calls``.
    ``file_skip_reason`` is metadata/log only and never persisted to
    interests.yaml (v5 simplification R7).
    """

    date: str = ""
    daily_dir: str = "daily"
    workspace: str = ""
    scan_days: int = 2
    carry_forward_days: int = 14
    changed_paths: list[str] = Field(default_factory=list)
    changed_mtimes: dict[str, float] = Field(default_factory=dict)
    carry_forward_count: int = 0
    carry_forward_prompt: list[ProactiveTopic] = Field(default_factory=list)
    llm_calls: int = 0
    follow_ups: list[dict] = Field(default_factory=list)
    extends: list[dict] = Field(default_factory=list)
    updates: list[dict] = Field(default_factory=list)
    early_exit: str = ""
    updates_applied: int = 0
    updates_resolved: int = 0
    candidates_in: int = 0
    candidates: list[dict] = Field(default_factory=list)
    dropped_missing: int = 0
    dropped_duplicate: int = 0
    dropped_known: int = 0
    topics_out: list[dict] = Field(default_factory=list)
    push_candidates: list[dict] = Field(default_factory=list)
    scenario_cards: list[dict] = Field(default_factory=list)
    agenda: list[dict] = Field(default_factory=list)
    suppressed: list[dict] = Field(default_factory=list)
    plan_llm_calls: int = 0
    push: bool = False
    file_skip_reason: str = ""
    interests_path: str = ""
    interests_written: bool = False
    checkpoint_paths: list[str] = Field(default_factory=list)
    duration_ms: int = 0


class ProactiveStateFile(BaseModel):
    """On-disk truth-source ``daily/_proactive.yaml`` (F1.3, v5: 3 sections).

    ``resolved`` tombstones carry ``first_seen`` so a resurrected topic can
    keep its original age anchor (F2.4 reopen channel).
    """

    model_config = ConfigDict(extra="ignore")

    version: int = 1
    open_topics: list[ProactiveTopic] = Field(default_factory=list)
    resolved: list[dict] = Field(default_factory=list)


class ProactiveResult(BaseModel):
    """Result of reading daily interest topics (F5)."""

    date: str = ""
    path: str = ""
    topics: list[dict] = Field(default_factory=list)
    content: str = ""
    skipped: bool = False
    error: str = ""
    summary: str = ""
    push: bool | None = None
    generated_at: str = ""
    agenda: list[dict] = Field(default_factory=list)