from __future__ import annotations

from collections import Counter
from datetime import datetime, timezone
from typing import ClassVar

from .base import BaseAgent, AgentInput, AgentOutput
from .inference.history import UserHistory
from .manifest import AgentManifest, InferredParam


def _infer_window_days(history: UserHistory) -> int:
    """Infer the optimal lookback window from feedback event density.

    More events per day → a shorter window captures the user's current state
    accurately. Sparse feedback → widen the window to gather signal.
    """
    n = len(history.events)
    if n >= 14:
        return 7
    if n >= 7:
        return 14
    return 30


MANIFEST = AgentManifest(
    id="recent-patterns",
    version="1.1.0",  # bumped: window_days InferredParam added (#116)
    description="Surfaces the user's reaction pattern from recent feedback.",
    pref_schema={
        "type": "object",
        "additionalProperties": False,
        "properties": {
            "window_days": {
                "type": "integer",
                "minimum": 1,
                "maximum": 30,
                "default": 7,
                "description": "Lookback window for pattern analysis.",
            },
        },
    },
    context_schema=["tip_feedback", "profile.features"],
    required_consents=["data:core", "agent:recent-patterns"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=86_400,
    inferred_params=[
        InferredParam(
            key="window_days",
            ttl_sec=86_400,        # recompute daily alongside snippet
            cold_start_default=7,
            min_history=5,
            infer=_infer_window_days,
        ),
    ],
)


class RecentPatternsAgent(BaseAgent):
    """Surfaces the user's reaction pattern from recent feedback."""
    agent_id: ClassVar[str] = MANIFEST.id
    ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
    version: ClassVar[str] = MANIFEST.version

    def compute(self, inp: AgentInput) -> AgentOutput:
        window_days = max(1, int(inp.agent_prefs.get("window_days", 7)))
        window_s = window_days * 86_400
        now_ts = inp.now.timestamp()

        recent = [
            f for f in inp.feedback_history
            if self._age_s(f.get("created_at", ""), now_ts) <= window_s
        ]

        counts: Counter[str] = Counter(f.get("action") for f in recent)
        total = len(recent)
        dwell_ms = inp.profile.get("mean_dwell_ms_30d")

        if total == 0:
            prompt = f"No tip reactions recorded in the last {window_days} days."
        else:
            done = counts.get("done", 0)
            dismissed = counts.get("dismiss", 0)
            snoozed = counts.get("snooze", 0)
            parts = [
                f"Last {window_days} days: {total} tip reaction{'s' if total != 1 else ''} — "
                f"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
            ]
            if dwell_ms is not None:
                dwell_s = round(dwell_ms / 1000)
                if dwell_s < 15:
                    parts.append(
                        "Average dwell is very short — user may be acting on auto-pilot; vary tip content."
                    )
                elif dwell_s < 60:
                    parts.append(f"Average dwell {dwell_s}s — tips are being read.")
                else:
                    parts.append(
                        f"Average dwell {dwell_s}s — user deliberates; prefer tips that reward reflection."
                    )
            prompt = " ".join(parts)

        snapshot = {
            "window_days": window_days,
            "recent_total": total,
            "action_counts": dict(counts),
            "mean_dwell_ms_30d": dwell_ms,
        }
        return self._make_output(inp, prompt, snapshot)

    @staticmethod
    def _age_s(iso: str, now_ts: float) -> float:
        if not iso:
            return float("inf")
        try:
            dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
            if dt.tzinfo is None:
                dt = dt.replace(tzinfo=timezone.utc)
            return now_ts - dt.timestamp()
        except Exception:
            return float("inf")