"""Generate example grids of false positives and false negatives."""

import pickle
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from PIL import Image
from sklearn.model_selection import train_test_split
from pathlib import Path

OUT_DIR = Path(__file__).parent
CLF_PATH = OUT_DIR / "laion_natural_img_clf_vitl14.pkl"
DATA_PATH = Path("/home/jroth/photograph_detector/scripts/outputs/extract_openai_vitl14_features/clip_vitl14_features_labeled.pkl")
PROJECT_ROOT = Path("/home/jroth/photograph_detector")

THRESHOLD = 0.7
N_EXAMPLES = 12  # per category
COLS = 4
ROWS = 3


def load_image(path, max_size=256):
    try:
        img = Image.open(path).convert("RGB")
        img.thumbnail((max_size, max_size))
        return img
    except Exception:
        return Image.new("RGB", (max_size, max_size), color="gray")


def make_grid(image_paths, scores, true_labels, title, out_path):
    fig, axes = plt.subplots(ROWS, COLS, figsize=(COLS * 3.2, ROWS * 3.2 + 0.8))
    fig.suptitle(title, fontsize=14, fontweight="bold", y=1.01)

    for idx, ax in enumerate(axes.flat):
        if idx < len(image_paths):
            img = load_image(image_paths[idx])
            ax.imshow(img)
            label_str = "natural" if true_labels[idx] == 1 else "non-natural"
            ax.set_title(f"score: {scores[idx]:.2f}\ntrue: {label_str}", fontsize=9)
        ax.axis("off")

    fig.tight_layout()
    fig.savefig(out_path, dpi=200, bbox_inches="tight")
    plt.close()
    print(f"Saved {out_path.name} ({len(image_paths)} examples)")


def main():
    with open(CLF_PATH, "rb") as f:
        clf = pickle.load(f)
    with open(DATA_PATH, "rb") as f:
        data = pickle.load(f)

    features = data["features"]
    labels = data["labels"]
    image_paths = data["image_paths"]

    # Resolve relative paths
    image_paths = [
        str(PROJECT_ROOT / p) if not Path(p).is_absolute() else p
        for p in image_paths
    ]

    # Same split as training
    (train_feat, test_feat,
     train_labels, test_labels,
     train_paths, test_paths) = train_test_split(
        features, labels, image_paths,
        test_size=0.2, random_state=42
    )

    test_scores = clf.predict_proba(test_feat)[:, 1]
    test_preds = (test_scores >= THRESHOLD).astype(int)

    # False positives: predicted natural (score >= 0.7) but truly non-natural
    fp_mask = (test_preds == 1) & (test_labels == 0)
    fp_indices = np.where(fp_mask)[0]
    # Sort by score descending (most confident false positives first)
    fp_indices = fp_indices[np.argsort(-test_scores[fp_indices])][:N_EXAMPLES]

    # False negatives: predicted non-natural (score < 0.7) but truly natural
    fn_mask = (test_preds == 0) & (test_labels == 1)
    fn_indices = np.where(fn_mask)[0]
    # Sort by score ascending (most confident false negatives first)
    fn_indices = fn_indices[np.argsort(test_scores[fn_indices])][:N_EXAMPLES]

    print(f"Total false positives at t={THRESHOLD}: {fp_mask.sum()}")
    print(f"Total false negatives at t={THRESHOLD}: {fn_mask.sum()}")

    fp_paths = [test_paths[i] for i in fp_indices]
    fp_scores = test_scores[fp_indices]
    fp_labels = test_labels[fp_indices]

    fn_paths = [test_paths[i] for i in fn_indices]
    fn_scores = test_scores[fn_indices]
    fn_labels = test_labels[fn_indices]

    make_grid(
        fp_paths, fp_scores, fp_labels,
        f"False Positives (threshold = {THRESHOLD})\nPredicted natural, actually non-natural",
        OUT_DIR / "false_positives.png",
    )

    make_grid(
        fn_paths, fn_scores, fn_labels,
        f"False Negatives (threshold = {THRESHOLD})\nPredicted non-natural, actually natural",
        OUT_DIR / "false_negatives.png",
    )

    print("Done!")


if __name__ == "__main__":
    main()