from __future__ import annotations

from datetime import datetime, timedelta, timezone
from typing import ClassVar

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


def _infer_engagement_trend(history: UserHistory) -> str:
    """Compare done-rate in the most recent 7 days vs the 7 days before that."""
    events = sorted(history.events, key=lambda e: e.created_at)
    if not events:
        return "stable"

    try:
        latest = datetime.fromisoformat(events[-1].created_at.replace("Z", "+00:00"))
    except ValueError:
        return "stable"

    cutoff_recent = latest - timedelta(days=7)
    cutoff_older = latest - timedelta(days=14)

    recent = [e for e in events if _parse_dt(e.created_at) >= cutoff_recent]
    older = [e for e in events if cutoff_older <= _parse_dt(e.created_at) < cutoff_recent]

    if len(older) < 3:
        return "stable"  # not enough baseline to compare

    recent_rate = sum(1 for e in recent if e.action == "done") / max(len(recent), 1)
    older_rate = sum(1 for e in older if e.action == "done") / max(len(older), 1)

    delta = recent_rate - older_rate
    if delta > 0.10:
        return "up"
    if delta < -0.10:
        return "down"
    return "stable"


def _parse_dt(iso: str) -> datetime:
    try:
        dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=timezone.utc)
        return dt
    except ValueError:
        return datetime.min.replace(tzinfo=timezone.utc)


MANIFEST = AgentManifest(
    id="momentum",
    version="1.1.0",  # bumped: engagement_trend InferredParam added (#114)
    description="Characterises the user's recent engagement trend from profile features.",
    pref_schema={
        "type": "object",
        "additionalProperties": False,
        "properties": {
            "low_engagement_threshold_pct": {
                "type": "integer",
                "minimum": 0,
                "maximum": 100,
                "default": 25,
                "description": "Completion rate below which momentum hints at low engagement.",
            },
        },
    },
    context_schema=["profile.features"],
    required_consents=["data:core", "agent:momentum"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=21_600,
    inferred_params=[
        InferredParam(
            key="engagement_trend",
            ttl_sec=21_600,          # recompute every 6 hours alongside snippet
            cold_start_default="stable",
            min_history=10,
            infer=_infer_engagement_trend,
        ),
    ],
)


class MomentumAgent(BaseAgent):
    """Characterises the user's recent engagement trend from profile features."""
    agent_id: ClassVar[str] = MANIFEST.id
    ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
    version: ClassVar[str] = MANIFEST.version

    def compute(self, inp: AgentInput) -> AgentOutput:
        completion = inp.profile.get("completion_rate_30d")
        dismiss = inp.profile.get("dismiss_rate_30d")
        volume = inp.profile.get("tip_volume_30d")
        trend: str = inp.agent_prefs.get("engagement_trend", "stable")

        parts: list[str] = []

        if completion is not None:
            pct = round(completion * 100)
            if pct >= 50:
                parts.append(f"The user completes {pct}% of tips (strong engagement).")
            elif pct >= 25:
                parts.append(f"The user completes {pct}% of tips (moderate engagement).")
            else:
                parts.append(
                    f"The user completes {pct}% of tips "
                    f"(low engagement — prefer simple, immediately actionable tips)."
                )
        else:
            parts.append("No completion-rate data yet (new user).")

        if dismiss is not None:
            dpct = round(dismiss * 100)
            if dpct >= 40:
                parts.append(f"Dismiss rate is high ({dpct}%) — avoid repetitive or irrelevant tips.")
            elif dpct <= 10:
                parts.append(f"Dismiss rate is low ({dpct}%).")

        if volume is not None and int(volume) < 5:
            parts.append("Very few tips served so far — this is an early-stage user.")

        if trend == "up":
            parts.append("Engagement is trending up compared to last week — build on the momentum.")
        elif trend == "down":
            parts.append("Engagement is trending down — a motivational or easy-win tip may help.")

        prompt = " ".join(parts) if parts else "No engagement data available yet."
        snapshot = {
            "completion_rate_30d": completion,
            "dismiss_rate_30d": dismiss,
            "tip_volume_30d": volume,
            "engagement_trend": trend,
        }
        return self._make_output(inp, prompt, snapshot)