# fluo_sc.py
import os
import csv
import glob
import datasets

_CITATION = r"""
@dataset{rocha_fluo_sc_2024,
  title        = {FLUO-SC: a fluorescence image dataset of skin lesions collected from smartphones},
  author       = {Rocha, Matheus Becali and Krohling, Renato and Pratavieira, Sebasti{\~a}o and others},
  year         = {2024},
  publisher    = {Mendeley Data},
  version      = {1},
  doi          = {10.17632/s8n68jj678.1},
  url          = {https://doi.org/10.17632/s8n68jj678.1}
}
"""

_DESCRIPTION = """
FLUO-SC: Fluorescence skin lesion image dataset (clinical/white-light and fluorescence images).
This Hugging Face repository mirrors the original dataset published on Mendeley Data (CC BY 4.0).
"""

_HOMEPAGE = "https://data.mendeley.com/datasets/s8n68jj678/1"
_LICENSE = "CC BY 4.0"

LABELS = ["BCC", "SCC", "MEL", "ACK", "SEK", "NEV"]
MODALITIES = ["CLI", "FLUO"]


def _repo_root():
    # On HF Hub, working dir is repo root.
    return os.getcwd()


def _find_data_dir():
    data_dir = os.path.join(_repo_root(), "data")
    if not os.path.isdir(data_dir):
        raise FileNotFoundError("Could not find 'data/' directory at repo root.")
    return data_dir

def iter_images(root):
    for ext in exts:
        yield from glob.glob(os.path.join(root, "**", ext), recursive=True)

class FluoSc(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.3")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=LABELS),
                    "modality": datasets.ClassLabel(names=MODALITIES),
                    "path": datasets.Value("string"),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        data_dir = _find_data_dir()

        # If zipped shards exist, extract them (reduces Hub request count massively).
        cli_zip = os.path.join(data_dir, "CLI.zip")
        fluo_zip = os.path.join(data_dir, "FLUO.zip")

        extracted_base = None
        extracted_any = False

        # dl_manager.extract returns a folder path where the archive is extracted
        # For ZIP containing folder "CLI/...", extracted path typically ends with ".../CLI"
        # We'll use its parent as base to have ".../<CLI|FLUO>/..."
        if os.path.isfile(cli_zip):
            cli_extracted = dl_manager.extract(cli_zip)
            extracted_base = os.path.dirname(cli_extracted)
            extracted_any = True

        if os.path.isfile(fluo_zip):
            fluo_extracted = dl_manager.extract(fluo_zip)
            # If only FLUO.zip exists, base should be parent of FLUO extracted dir
            if extracted_base is None:
                extracted_base = os.path.dirname(fluo_extracted)
            extracted_any = True

        # Prefer extracted content if present; otherwise use plain folders under data/
        scan_base = extracted_base if extracted_any else data_dir

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"scan_base": scan_base, "repo_root": _repo_root(), "data_dir": data_dir},
            )
        ]

    def _generate_examples(self, scan_base, repo_root, data_dir):
    import os
    import glob

    idx = 0
    exts = ("*.jpg", "*.jpeg", "*.png", "*.JPG", "*.JPEG", "*.PNG")

    def iter_images(root):
        for ext in exts:
            yield from glob.glob(os.path.join(root, "**", ext), recursive=True)

    for img_path in sorted(iter_images(scan_base)):
        parts = os.path.normpath(img_path).split(os.sep)

        # 1) find the modality at any depth
        mod_i = None
        modality = None
        for i, p in enumerate(parts):
            if p in MODALITIES:   # ["CLI", "FLUO"]
                modality = p
                mod_i = i
                break
        if modality is None:
            continue

        # 2) find the FIRST valid class after the modality
        label = None
        for j in range(mod_i + 1, len(parts)):
            if parts[j] in LABELS:  # ["BCC","SCC","MEL","ACK","SEK","NEV"]
                label = parts[j]
                break
        if label is None:
            continue

        yield idx, {
            "image": img_path,
            "label": label,
            "modality": modality,
            "path": f"{modality}/{label}/{os.path.basename(img_path)}",
        }
        idx += 1

    if idx == 0:
        raise FileNotFoundError(
            "No valid images found. Expected paths containing CLI/ or FLUO/ and a label folder (BCC/SCC/MEL/ACK/SEK/NEV)."
        )