# agent_v0/server_v1/loop_detector.py
"""LoopDetector composite — détection de stagnation de Léa pendant un replay (QW2).

Trois signaux indépendants :
- screen_static : N captures consécutives avec CLIP similarity > seuil
- action_repeat : N actions consécutives identiques (type + coords)
- retry_threshold : nombre de retries cumulés >= seuil

Un seul signal positif → verdict.detected=True. Le serveur bascule alors le
replay en paused_need_help avec pause_reason explicite.

Désactivable via env var RPA_LOOP_DETECTOR_ENABLED=0.
"""

import logging
import os
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional

logger = logging.getLogger(__name__)


@dataclass
class LoopVerdict:
    detected: bool = False
    reason: str = ""
    signal: str = ""  # "screen_static" | "action_repeat" | "retry_threshold" | ""
    evidence: Dict[str, Any] = field(default_factory=dict)


def _env_int(name: str, default: int) -> int:
    try:
        return int(os.environ.get(name, default))
    except (TypeError, ValueError):
        return default


def _env_float(name: str, default: float) -> float:
    try:
        return float(os.environ.get(name, default))
    except (TypeError, ValueError):
        return default


def _env_bool_enabled(name: str) -> bool:
    val = os.environ.get(name, "1").strip().lower()
    return val not in ("0", "false", "no", "off", "")


def _cosine_similarity(a, b) -> float:
    """Similarité cosine entre deux vecteurs (listes ou np.array). Robuste vecteur nul."""
    import numpy as np
    av = np.asarray(a, dtype=np.float32).flatten()
    bv = np.asarray(b, dtype=np.float32).flatten()
    na, nb = float(np.linalg.norm(av)), float(np.linalg.norm(bv))
    if na < 1e-8 or nb < 1e-8:
        return 0.0
    return float(np.dot(av, bv) / (na * nb))


class LoopDetector:
    def __init__(self, clip_embedder=None):
        self.clip_embedder = clip_embedder

    def evaluate(
        self,
        state: Dict[str, Any],
        screenshots: List[Any],
        actions: List[Dict[str, Any]],
    ) -> LoopVerdict:
        """Évalue les 3 signaux. Retourne le premier déclenché.

        Args:
            state: replay_state (utilisé pour retried_actions)
            screenshots: anneau d'embeddings CLIP (les N derniers)
            actions: anneau des N dernières actions exécutées
        """
        if not _env_bool_enabled("RPA_LOOP_DETECTOR_ENABLED"):
            return LoopVerdict(detected=False)

        # Signal A : screen_static
        verdict = self._check_screen_static(screenshots)
        if verdict.detected:
            return verdict

        # Signal B : action_repeat
        verdict = self._check_action_repeat(actions)
        if verdict.detected:
            return verdict

        # Signal C : retry_threshold
        verdict = self._check_retry_threshold(state)
        if verdict.detected:
            return verdict

        return LoopVerdict(detected=False)

    def _check_screen_static(self, screenshots: List[Any]) -> LoopVerdict:
        n_required = _env_int("RPA_LOOP_SCREEN_STATIC_N", 4)
        threshold = _env_float("RPA_LOOP_SCREEN_STATIC_THRESHOLD", 0.99)

        if self.clip_embedder is None or len(screenshots) < n_required:
            return LoopVerdict()

        try:
            recent = screenshots[-n_required:]
            # Embed chaque capture via le CLIP embedder (peut lever)
            embeddings = [self.clip_embedder.embed_image(img) for img in recent]
            sims = [_cosine_similarity(embeddings[i], embeddings[i + 1])
                    for i in range(len(embeddings) - 1)]
            min_sim = min(sims)
            if min_sim > threshold:
                return LoopVerdict(
                    detected=True,
                    reason="loop_detected",
                    signal="screen_static",
                    evidence={"min_similarity": round(min_sim, 4),
                              "n_captures": n_required,
                              "threshold": threshold},
                )
        except Exception as e:
            logger.warning("LoopDetector signal_A erreur (%s) — signal inerte ce tick", e)
        return LoopVerdict()

    def _check_action_repeat(self, actions: List[Dict[str, Any]]) -> LoopVerdict:
        n_required = _env_int("RPA_LOOP_ACTION_REPEAT_N", 3)
        if len(actions) < n_required:
            return LoopVerdict()
        recent = actions[-n_required:]

        def _signature(a: Dict[str, Any]) -> tuple:
            return (a.get("type"), a.get("x_pct"), a.get("y_pct"))

        sigs = [_signature(a) for a in recent]
        if all(s == sigs[0] for s in sigs):
            return LoopVerdict(
                detected=True,
                reason="loop_detected",
                signal="action_repeat",
                evidence={"signature": sigs[0], "count": n_required},
            )
        return LoopVerdict()

    def _check_retry_threshold(self, state: Dict[str, Any]) -> LoopVerdict:
        threshold = _env_int("RPA_LOOP_RETRY_THRESHOLD", 3)
        retried = int(state.get("retried_actions", 0))
        if retried >= threshold:
            return LoopVerdict(
                detected=True,
                reason="loop_detected",
                signal="retry_threshold",
                evidence={"retried_actions": retried, "threshold": threshold},
            )
        return LoopVerdict()