"""Base model versus new model, on the same val set (REQ-063).

Both models are validated against one `data.yaml`, so the numbers differ only
because the weights differ. Combined with the stable val split in `dataset.py`,
that is what makes "the model improved" a claim rather than a hope.
"""

from typing import Optional


class EvaluateError(Exception):
    pass


def class_names(weights: str) -> list:
    from ultralytics import YOLO

    names = YOLO(weights).names
    return [names[key] for key in sorted(names)] if isinstance(names, dict) else list(names)


def validate(weights: str, data_yaml: str, imgsz: int = 640, device=0,
             batch: int = 8) -> dict:
    from ultralytics import YOLO

    metrics = YOLO(weights).val(
        data=data_yaml, imgsz=imgsz, device=device, batch=batch,
        split="val", plots=False, verbose=False,
    )
    box = metrics.box
    return {
        "map50": round(float(box.map50), 4),
        "map50_95": round(float(box.map), 4),
        "precision": round(float(box.mp), 4),
        "recall": round(float(box.mr), 4),
    }


def compare(base_weights: Optional[str], new_weights: str, data_yaml: str,
            expected_classes: list, imgsz: int = 640, device=0,
            batch: int = 8) -> dict:
    """Validate both models where that is meaningful, and say so when it is not.

    A base model whose class list differs from the project's cannot be scored on
    this dataset — its class ids mean something else. Reporting nothing beats
    reporting a number that looks like a regression but is a mismatch.
    """
    new_metrics = validate(new_weights, data_yaml, imgsz, device, batch)

    base_metrics = None
    skipped = None
    if not base_weights:
        skipped = "This project has no base model yet — nothing to compare against."
    else:
        try:
            base_metrics = validate(base_weights, data_yaml, imgsz, device, batch)
        except Exception as exc:
            base_metrics, skipped = None, f"Could not evaluate base model: {exc}"

    delta = None
    if base_metrics:
        delta = {key: round(new_metrics[key] - base_metrics[key], 4) for key in new_metrics}

    return {"base": base_metrics, "new": new_metrics, "delta": delta, "skipped": skipped}