import os
import pandas as pd
import datasets


_DESCRIPTION = """\
Demo.
"""
_CITATION = """\

"""
_HOMEPAGE = "xyz"

_LICENSE = "BSD 3-Clause License"

_URL="https://huggingface.co/datasets/mukesh3444/manual-window-detect/resolve/main/dataset.zip?download=true"

_SCENE_CATEGORIES = ["bow_window_indoor"]

class ManualWindowDetect (datasets.GeneratorBasedBuilder):

    """MIT Scene Parsing Benchmark dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [datasets.BuilderConfig(name="manual-window-detect", version=VERSION, description="Detect Windows inside an image")]

    def _info(self):
        features = datasets.Features(
              {
                "image": datasets.Image(),
                "annotation": datasets.Image(),
                "scene_category": datasets.ClassLabel(names=_SCENE_CATEGORIES),
              }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        url = _URL
        data_dirs = dl_manager.download_and_extract(url)
        print(data_dirs)
        train_data = val_data= os.path.join(data_dirs)
        test_data = os.path.join(data_dirs)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data": train_data,
                    "split": "training",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"data": test_data, "split": "testing"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data": val_data,
                    "split": "validation",
                },
            ),
        ]

    def _generate_examples(self, data, split):
        if split == "testing":
            image_dir = os.path.join(data,"images",split)
            for idx, image_file in enumerate(os.listdir(image_dir)):
                yield idx, {
                    "image": os.path.join(image_dir, image_file),
                    "annotation": None,
                    "scene_category": None,
                }
        else:
            image_id2cat = pd.read_csv(
                os.path.join(data, "sceneCategories.txt"), sep=" ", names=["image_id", "scene_category"]
            )
            image_id2cat = image_id2cat.set_index("image_id")
            images_dir = os.path.join(data, "images", split)
            annotations_dir = os.path.join(data, "annotations", split)
            for idx, image_file in enumerate(os.listdir(images_dir)):
                image_id = image_file.split(".")[0]
                yield idx, {
                    "image": os.path.join(images_dir, image_file),
                    "annotation": os.path.join(annotations_dir, image_id + ".png"),
                    "scene_category": 8 #image_id2cat.loc[image_id, "scene_category"]
                }