"""
IRIS - Priority / Context Engine

Takes YOLO detections + optional VLM reasoning output and selects
EXACTLY ONE instruction to speak. Never a list — always the single
most relevant thing for the visually impaired user right now.

Priority order:
  1. VLM reasoning output (context-aware, if available)
  2. Urgent object blocking center path (person, car, etc.)
  3. Caution object in center (chair, bench, stairs, etc.)
  4. Closest high-conf object on left or right
  5. General scene clear message
"""


class PriorityEngine:
    """
    Selects exactly one navigation instruction from structured detections
    and optional VLM context reasoning.
    """

    # Objects that trigger immediate caution warnings
    URGENT = {
        "person", "car", "truck", "bus", "motorcycle", "bicycle",
        "dog", "cat", "horse", "traffic light", "stop sign",
    }

    # Objects that need caution but are less mobile
    CAUTION = {
        "chair", "bench", "dining table", "potted plant", "suitcase",
        "backpack", "umbrella", "fire hydrant", "parking meter",
        "stairs", "step", "pole", "bollard",
    }

    # Position → spoken phrase
    POS_PHRASE = {
        "left":   "on your left",
        "center": "directly ahead",
        "right":  "on your right",
    }

    def pick(self, detections: list, vlm_text: str = "") -> str:
        """
        Return exactly ONE instruction string.

        Args:
            detections: sorted YOLO detections (highest confidence first)
            vlm_text:   reasoning from VLM engine (empty string if unavailable)

        Returns:
            A single short instruction for TTS.
        """

        # ── 1. VLM reasoning takes highest priority (context-aware) ──────────
        if vlm_text and len(vlm_text.strip()) > 5:
            return self._clean(vlm_text)

        if not detections:
            return "Path ahead looks clear."

        # ── 2. Urgent object directly ahead ──────────────────────────────────
        center_urgent = [
            d for d in detections
            if d["position"] == "center" and d["object"] in self.URGENT
        ]
        if center_urgent:
            obj = center_urgent[0]["object"]
            return f"Caution! {obj.capitalize()} directly ahead."

        # ── 3. Any object blocking center ────────────────────────────────────
        center_any = [d for d in detections if d["position"] == "center"]
        if center_any:
            obj = center_any[0]["object"]
            if obj in self.CAUTION:
                return f"Watch out — {obj} ahead. Step around it."
            return f"{obj.capitalize()} ahead. Proceed carefully."

        # ── 4. Urgent object on sides ────────────────────────────────────────
        side_urgent = [
            d for d in detections
            if d["position"] in ("left", "right") and d["object"] in self.URGENT
        ]
        if side_urgent:
            d   = side_urgent[0]
            pos = self.POS_PHRASE.get(d["position"], d["position"])
            return f"{d['object'].capitalize()} {pos}. Stay aware."

        # ── 5. Highest confidence detection anywhere ──────────────────────────
        top = detections[0]
        pos = self.POS_PHRASE.get(top["position"], top["position"])
        return f"{top['object'].capitalize()} {pos}."

    @staticmethod
    def _clean(text: str) -> str:
        """Ensure sentence ends with a period and is clean."""
        text = text.strip()
        if text and not text.endswith((".", "!", "?")):
            text += "."
        return text