from datasets import DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np

# Rest of your code...

# Google Drive ID for your ZIP file
_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH"
_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}"

class PlantsDataset(GeneratorBasedBuilder):
    class MyConfig(BuilderConfig):
        def __init__(self, **kwargs):
            super().__init__(**kwargs)

    BUILDER_CONFIGS = [
        MyConfig(name="plants_default", description="Default configuration for PlantsDataset"),
    ]
    
    BUILDER_CONFIGS = [
        MyConfig(name="default", description="Default configuration"),
    ]

    def _info(self):
        features = Features({
            "image": Value("string"),
            "label": ClassLabel(names=["aleo vera", "calotropis gigantea"]),
        })
        return DatasetInfo(
            description="Your dataset description",
            features=features,
            supervised_keys=("image", "label"),
            homepage="Your dataset homepage",
            citation="Citation for your dataset",
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URL)

        return [
            SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={
                    "data_folder": os.path.join(downloaded_file, "train"),
                },
            ),
            SplitGenerator(
                name=Split.TEST,
                gen_kwargs={
                    "data_folder": os.path.join(downloaded_file, "test"),
                },
            ),
        ]

    def _generate_examples(self, data_folder):
        label_names = self.info.features['label'].names
        for label, subfolder in enumerate(label_names):
            subfolder_path = os.path.join(data_folder, subfolder)
            for root, _, files in os.walk(subfolder_path):
                for file_name in files:
                    file_path = os.path.join(root, file_name)
                    if os.path.isfile(file_path):
                        # Open the image using Pillow and convert it to an array
                        with Image.open(file_path) as image:
                            image_array = np.array(image)
    
                        # Yield the example with the image data and label
                        yield file_path, {
                            "image": image_array.tolist(),  # Convert array to list
                            "label": label,
                        }

    def _display_image(self, image_path, label):
        with Image.open(image_path) as img:
            plt.imshow(img)
            plt.title(f"Label: {self.info.features['label'].int2str(label)}")
            plt.axis('off')  # Hide the axis
            plt.show()

# Create an instance of the PlantsDataset class
plants_dataset = PlantsDataset()

# Build and upload the dataset
plants_dataset.download_and_prepare()