import gradio as gr from functools import lru_cache from hffs.fs import HfFileSystem from typing import List, Tuple, Callable import pandas as pd import numpy as np import pyarrow as pa import pyarrow.parquet as pq from functools import partial from io import StringIO from tqdm.contrib.concurrent import thread_map from datasets import Features class AppError(RuntimeError): pass PAGE_SIZE = 20 @lru_cache(maxsize=128) def get_parquet_fs(dataset: str) -> HfFileSystem: try: fs = HfFileSystem(dataset, repo_type="dataset", revision="refs/convert/parquet") if any(fs.isfile(path) for path in fs.ls("") if not path.startswith(".")): raise AppError(f"Parquet export doesn't exist for '{dataset}'.") return fs except: raise AppError(f"Parquet export doesn't exist for '{dataset}'.") @lru_cache(maxsize=128) def get_parquet_configs(dataset: str) -> List[str]: fs = get_parquet_fs(dataset) return [path for path in fs.ls("") if fs.isdir(path)] def _sorted_split_key(split: str) -> str: return split if not split.startswith("train") else chr(0) + split # always "train" first @lru_cache(maxsize=128) def get_parquet_splits(dataset: str, config: str) -> List[str]: fs = get_parquet_fs(dataset) all_parts = [path.rsplit(".", 1)[0].split("-") for path in fs.glob(f"{config}/*.parquet")] return sorted(set(parts[-4] if len(parts) > 3 and parts[-2] == "of" else parts[-1] for parts in all_parts), key=_sorted_split_key) def sanitize_inputs(dataset: str, config: str, split: str, page: str) -> Tuple[str, str, str, int]: try: page = int(page) assert page > 0 except: raise AppError(f"Bad page: {page}") if not dataset: raise AppError("Empty dataset name") if not config: raise AppError(f"Empty config. Available configs are: {', '.join(get_parquet_configs(dataset))}.") if not split: raise AppError(f"Empty split. Available splits are: {', '.join(get_parquet_splits(dataset, config))}.") return dataset, config, split, int(page) RowGroupReaders = List[Callable[[], pa.Table]] @lru_cache(maxsize=128) def index(dataset: str, config: str, split: str) -> Tuple[np.ndarray, RowGroupReaders, int, str]: fs = get_parquet_fs(dataset) sources = fs.glob(f"{config}/*-{split}.parquet") + fs.glob(f"{config}/*-{split}-*-of-*.parquet") if not sources: if config not in get_parquet_configs(dataset): raise AppError(f"Invalid config {config}. Available configs are: {', '.join(get_parquet_configs(dataset))}.") else: raise AppError(f"Invalid split {split}. Available splits are: {', '.join(get_parquet_splits(dataset, config))}.") all_pf: List[pq.ParquetFile] = thread_map(partial(pq.ParquetFile, filesystem=fs), sources) features = Features.from_arrow_schema(all_pf[0].schema.to_arrow_schema()) columns = [col for col in features if all(bad_type not in str(features[col]) for bad_type in ["Image(", "Audio(", "'binary'"])] info = "" if len(columns) == len(features) else f"Some columns are not supported yet: {sorted(set(features) - set(columns))}" rg_offsets = np.cumsum([pf.metadata.row_group(i).num_rows for pf in all_pf for i in range(pf.metadata.num_row_groups)]) rg_readers = [partial(pf.read_row_group, i, columns=columns) for pf in all_pf for i in range(pf.metadata.num_row_groups)] max_page = rg_offsets[-1] // PAGE_SIZE return rg_offsets, rg_readers, max_page, info def query(page: int, page_size: int, rg_offsets: np.ndarray, rg_readers: RowGroupReaders) -> pd.DataFrame: start_row, end_row = (page - 1) * page_size, page * page_size start_rg, end_rg = np.searchsorted(rg_offsets, [start_row, end_row], side="right") if page < 1 or end_rg >= len(rg_readers): raise AppError(f"Page {page} does not exist") pa_table = pa.concat_tables([rg_readers[i]() for i in range(start_rg, end_rg + 1)]) offset = start_row - rg_offsets[start_rg - 1] if start_rg else start_row pa_table = pa_table.slice(offset, end_row - start_row) return pa_table.to_pandas() @lru_cache(maxsize=128) def get_page(dataset: str, config: str, split: str, page: str) -> Tuple[str, int, str]: dataset, config, split, page = sanitize_inputs(dataset, config, split, page) rg_offsets, rg_readers, max_page, info = index(dataset, config, split) df = query(page, PAGE_SIZE, rg_offsets=rg_offsets, rg_readers=rg_readers) buf = StringIO() df.to_json(buf, lines=True, orient="records") return buf.getvalue(), max_page, info with gr.Blocks() as demo: gr.Markdown("# 📖 Dataset Explorer\n\nAccess any slice of data of any dataset on the [Hugging Face Dataset Hub](https://huggingface.co/datasets)") cp_dataset = gr.Textbox("squad", label="Pick a dataset", placeholder="squad") cp_go = gr.Button("Explore") cp_config = gr.Dropdown(["plain_text"], value="plain_text", label="Config", visible=False) cp_split = gr.Dropdown(["train", "validation"], value="train", label="Split", visible=False) with gr.Row(): cp_page = gr.Textbox("1", label="Page", placeholder="1", visible=False) cp_goto_page = gr.Button("Go to page", visible=False) cp_error = gr.Markdown("", visible=False) cp_info = gr.Markdown("", visible=False) cp_result = gr.Markdown("", visible=False) def show_error(message: str) -> dict(): return { cp_error: gr.update(visible=True, value=f"## ❌ Error:\n\n{message}"), cp_info: gr.update(visible=False, value=""), cp_result: gr.update(visible=False, value=""), } def show_dataset_at_config_and_split_and_page(dataset: str, config: str, split: str, page: str) -> dict: try: jsonl_result, max_page, info = get_page(dataset, config, split, page) info = f"({info})" if info else "" return { cp_result: gr.update(visible=True, value=f"```json\n{jsonl_result}\n```"), cp_info: gr.update(visible=True, value=f"Page {page}/{max_page}) {info}"), cp_error: gr.update(visible=False, value="") } except AppError as err: return show_error(str(err)) def show_dataset_at_config_and_split(dataset: str, config: str, split: str) -> dict: try: return { **show_dataset_at_config_and_split_and_page(dataset, config, split, "1"), cp_page: gr.update(value="1", visible=True), cp_goto_page: gr.update(visible=True), } except AppError as err: return show_error(str(err)) def show_dataset_at_config(dataset: str, config: str) -> dict: try: splits = get_parquet_splits(dataset, config) if not splits: raise AppError(f"Dataset {dataset} with config {config} has no splits.") else: split = splits[0] return { **show_dataset_at_config_and_split(dataset, config, split), cp_split: gr.update(value=split, choices=splits, visible=len(splits) > 1), } except AppError as err: return show_error(str(err)) def show_dataset(dataset: str) -> dict: try: configs = get_parquet_configs(dataset) if not configs: raise AppError(f"Dataset {dataset} has no configs.") else: config = configs[0] return { **show_dataset_at_config(dataset, config), cp_config: gr.update(value=config, choices=configs, visible=len(configs) > 1), } except AppError as err: return show_error(str(err)) all_outputs = [cp_config, cp_split, cp_page, cp_goto_page, cp_result, cp_info, cp_error] cp_go.click(show_dataset, inputs=[cp_dataset], outputs=all_outputs) cp_config.change(show_dataset_at_config, inputs=[cp_dataset, cp_config], outputs=all_outputs) cp_split.change(show_dataset_at_config_and_split, inputs=[cp_dataset, cp_config, cp_split], outputs=all_outputs) cp_goto_page.click(show_dataset_at_config_and_split_and_page, inputs=[cp_dataset, cp_config, cp_split, cp_page], outputs=all_outputs) if __name__ == "__main__": demo.launch()