from pathlib import Path
import os
import gradio as gr
from gradio.components.gallery import GalleryImageType
import datasets
from datasets import load_dataset
from huggingface_hub import HfApi, HfFileSystem, login
from dotenv import load_dotenv

load_dotenv()
HF_TOKEN = os.getenv('HF_TOKEN')

login(token=HF_TOKEN, add_to_git_credential=True)


def stream_dataset_from_hub(split):
    dataset = load_dataset('mcarthuradal/arm-unicef',
                           data_dir='images',
                           split=split,
                           streaming=True)
    return dataset


dataset = stream_dataset_from_hub('train')


def get_images(path: str | Path) -> list[GalleryImageType]:
    # filenames = os.listdir(path)

    # image_names = [filename for filename in filenames if filename.endswith('.tif')]
    n = 50
    image_batch = dataset.iter(n)

    yield image_batch['image']


iface = gr.Interface(fn=get_images,
                     inputs='text',
                     outputs='gallery',
                     title='Aerial Images Gallery',
                     description='A gallery of the train and test data to be used without annotations',
                     analytics_enabled=False,
                     allow_flagging='never', )
gr.Gallery(columns=5,
           rows=10,
           min_width=500,
           allow_preview=True,
           show_download_button=False,
           show_share_button=False)

iface.launch(inbrowser=True)