"""
Thin wrapper around Ultralytics YOLO11.

Why YOLO here at all, when the VLM can already "see" the image?
-> VLMs are known to hallucinate counts ("about 3 people") because they're
   generating text, not measuring pixels. YOLO gives us *pixel-grounded*,
   confidence-scored counts and boxes. We feed these counts into the VLM
   prompt as ground truth context, and use the boxes for drawing on the
   annotated image. This is a "detector-grounded VLM" pattern — cheap to
   build, and meaningfully reduces hallucination vs a VLM-only pipeline.
"""

from ultralytics import YOLO
from .config import YOLO_MODEL_ID, RELEVANT_CLASSES, YOLO_CONF_THRESHOLD


class DisasterObjectDetector:
    def __init__(self, model_id: str = YOLO_MODEL_ID):
        self.model = YOLO(model_id)

    def detect(self, image_path: str):
        """Run detection, return list of dicts: {class, conf, box=[x1,y1,x2,y2]}"""
        results = self.model.predict(
            source=image_path,
            conf=YOLO_CONF_THRESHOLD,
            verbose=False,
        )[0]

        detections = []
        names = results.names
        for box in results.boxes:
            cls_id = int(box.cls.item())
            cls_name = names[cls_id]
            if cls_name not in RELEVANT_CLASSES:
                continue
            conf = float(box.conf.item())
            x1, y1, x2, y2 = [float(v) for v in box.xyxy[0].tolist()]
            detections.append({
                "class": cls_name,
                "conf": conf,
                "box": [x1, y1, x2, y2],
            })
        return detections

    @staticmethod
    def summarize(detections):
        """Turn a list of detections into {'people': 2, 'cars': 1, ...} counts."""
        counts = {}
        for d in detections:
            label = RELEVANT_CLASSES.get(d["class"], d["class"])
            counts[label] = counts.get(label, 0) + 1
        return counts

    @staticmethod
    def to_context_string(counts: dict) -> str:
        """Human-readable string injected into the VLM prompt as grounding context."""
        if not counts:
            return "No standard objects (people/vehicles) were confidently detected by the object detector."
        parts = [f"{v} {k}" for k, v in counts.items()]
        return "Object detector confidently found: " + ", ".join(parts) + \
               ". Treat these counts as reliable ground truth; do not contradict them."