from __future__ import annotations
from collections import Counter
from datetime import datetime, timezone
from typing import ClassVar
from .base import BaseAgent, AgentInput, AgentOutput

_SEVEN_DAYS_S = 7 * 86_400


class RecentPatternsAgent(BaseAgent):
    """Surfaces the user's reaction pattern from the last 7 days of feedback."""
    agent_id: ClassVar[str] = "recent-patterns"
    ttl_seconds: ClassVar[int] = 86_400  # 24h
    version: ClassVar[str] = "1.0.0"

    def compute(self, inp: AgentInput) -> AgentOutput:
        now_ts = inp.now.timestamp()
        recent = [
            f for f in inp.feedback_history
            if self._age_s(f.get("created_at", ""), now_ts) <= _SEVEN_DAYS_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 = "No tip reactions recorded in the last 7 days."
        else:
            done = counts.get("done", 0)
            dismissed = counts.get("dismiss", 0)
            snoozed = counts.get("snooze", 0)
            parts = [
                f"Last 7 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 = {
            "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")