app.py

from functools import partial, lru_cache

import duckdb
import gradio as gr
import pandas as pd
import requests
from huggingface_hub import HfApi

READ_PARQUET_FUNCTIONS = ("dd.read_parquet", "pd.read_parquet")
EMPTY_DF = pd.DataFrame([{str(i): "" for i in range(4)}] * 10)
MAX_NUM_COLUMNS = 20
css = """
@media (prefers-color-scheme: dark) {
    .transparent-dropdown, .transparent-dropdown .container .wrap  {
        background: var(--bg-dark);
    }
}
@media (prefers-color-scheme: light) {
    .transparent-dropdown, .transparent-dropdown .container .wrap  {
        background: var(--bg);
    }
}
input {
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;          
}
.cell-menu-button {
    z-index: -1;
}
thead {
    display: none;
}
"""
js = """
function setDataFrameReadonly() {
    MutationObserver = window.MutationObserver || window.WebKitMutationObserver;
    var observer = new MutationObserver(function(mutations, observer) {
        // fired when a mutation occurs
        document.querySelectorAll('.readonly-dataframe div .table-wrap button svelte-virtual-table-viewport table tbody tr td .cell-wrap input').forEach(i => i.setAttribute("readonly", "true"));
    });
    // define what element should be observed by the observer
    // and what types of mutations trigger the callback
    observer.observe(document, {
        subtree: true,
        childList: true
    });
    
}
"""
text_functions_df = pd.read_csv("text_functions.tsv", delimiter="\t")

@lru_cache(maxsize=3)
def duckdb_sql(query: str) -> duckdb.DuckDBPyRelation:
    return duckdb.sql(query)

def prepare_function(func: str, placeholder: str, column_name: str) -> str:
    if "(" in func:
        prepared_func = func.split("(")
        prepared_func[1] = prepared_func[1].replace(placeholder, column_name, 1)
        prepared_func = "(".join(prepared_func)
    else:
        prepared_func = func.replace(placeholder, column_name, 1)
    return prepared_func

with gr.Blocks(css=css, js=js) as demo:
    loading_codes_json = gr.JSON(visible=False)
    dataset_subset_split_textbox = gr.Textbox(visible=False)
    input_dataframe = gr.DataFrame(visible=False)
    with gr.Group():
        with gr.Row():
            dataset_dropdown = gr.Dropdown(label="Open Dataset", allow_custom_value=True, scale=10)
            subset_dropdown = gr.Dropdown(info="Subset", allow_custom_value=True, show_label=False, visible=False, elem_classes="transparent-dropdown")
            split_dropdown = gr.Dropdown(info="Split", allow_custom_value=True, show_label=False, visible=False, elem_classes="transparent-dropdown")
            gr.LoginButton()
        with gr.Row():
            transform_dropdowns = [gr.Dropdown(choices=[column_name] + [prepare_function(text_func, "string", column_name) for text_func in text_functions_df.Name if "string" in text_func], value=column_name, container=False, interactive=True, allow_custom_value=True, visible=True) for column_name in EMPTY_DF.columns]
            transform_dropdowns += [gr.Dropdown(choices=[None], value=None, container=False, interactive=True, allow_custom_value=True, visible=False) for _ in range(MAX_NUM_COLUMNS - len(transform_dropdowns))]
        dataframe = gr.DataFrame(EMPTY_DF, column_widths=[f"{1/len(EMPTY_DF.columns):.0%}"] * len(EMPTY_DF.columns), interactive=True, elem_classes="readonly-dataframe")

    def show_subset_dropdown(dataset: str):
        if dataset and "/" not in dataset.strip().strip("/"):
            return []
        resp = requests.get(f"https://datasets-server.huggingface.co/compatible-libraries?dataset={dataset}", timeout=3).json()
        loading_codes = ([lib["loading_codes"] for lib in resp.get("libraries", []) if lib["function"] in READ_PARQUET_FUNCTIONS] or [[]])[0] or []
        subsets = [loading_code["config_name"] for loading_code in loading_codes]
        subset = (subsets or [""])[0]
        return dict(choices=subsets, value=subset, visible=len(subsets) > 1, key=hash(str(loading_codes))), loading_codes

    def show_split_dropdown(subset: str, loading_codes: list[dict]):
        splits = ([list(loading_code["arguments"]["splits"]) for loading_code in loading_codes if loading_code["config_name"] == subset] or [[]])[0]
        split = (splits or [""])[0]
        return dict(choices=splits, value=split, visible=len(splits) > 1, key=hash(str(loading_codes) + subset))

    def show_input_dataframe(dataset: str, subset: str, split: str, loading_codes: list[dict]) -> pd.DataFrame:
        pattern = ([loading_code["arguments"]["splits"][split] for loading_code in loading_codes if loading_code["config_name"] == subset] or [None])[0]
        if dataset and subset and split and pattern:
            df = duckdb_sql(f"SELECT * FROM 'hf://datasets/{dataset}/{pattern}' LIMIT 10").df()
            input_df = df
        else:
            input_df = EMPTY_DF
        new_transform_dropdowns = [dict(choices=[column_name] + [prepare_function(text_func, "string", column_name) for text_func in text_functions_df.Name if "string" in text_func], value=column_name, container=False, interactive=True, allow_custom_value=True, visible=True) for column_name in input_df.columns]
        new_transform_dropdowns += [dict(choices=[None], value=None, container=False, interactive=True, allow_custom_value=True, visible=False) for _ in range(MAX_NUM_COLUMNS - len(new_transform_dropdowns))]
        return [dict(value=df, column_widths=[f"{1/len(df.columns):.0%}"] * len(df.columns))] + new_transform_dropdowns
    
    def set_dataframe(input_df: pd.DataFrame, *transforms: tuple[str], column_index: int):
        try:
            return duckdb.sql(f"SELECT {', '.join(transform for transform in transforms if transform)} FROM input_df;").df()
        except Exception as e:
            gr.Error(f"{type(e).__name__}: {e}")
            return input_df
    
    for column_index, transform_dropdown in enumerate(transform_dropdowns):
        transform_dropdown.select(partial(set_dataframe, column_index=column_index), inputs=[input_dataframe] + transform_dropdowns, outputs=dataframe)

    @demo.load(outputs=[dataset_dropdown, loading_codes_json, subset_dropdown, split_dropdown, input_dataframe, dataframe] + transform_dropdowns)
    def _fetch_datasets(request: gr.Request, oauth_token: gr.OAuthToken | None):
        api = HfApi(token=oauth_token.token if oauth_token else None)
        datasets = list(api.list_datasets(limit=3, sort="trendingScore", direction=-1, filter=["format:parquet"]))
        if oauth_token and (user := api.whoami().get("name")):
            datasets += list(api.list_datasets(limit=3, sort="trendingScore", direction=-1, filter=["format:parquet"], author=user))
        dataset = request.query_params.get("dataset") or datasets[0].id
        subsets, loading_codes = show_subset_dropdown(dataset)
        splits = show_split_dropdown(subsets["value"], loading_codes)
        input_df, *new_transform_dropdowns = show_input_dataframe(dataset, subsets["value"], splits["value"], loading_codes)
        return {
            dataset_dropdown: gr.Dropdown(choices=[dataset.id for dataset in datasets], value=dataset),
            loading_codes_json: loading_codes,
            subset_dropdown: gr.Dropdown(**subsets),
            split_dropdown: gr.Dropdown(**splits),
            input_dataframe: gr.DataFrame(**input_df),
            dataframe: gr.DataFrame(**input_df),
            **dict(zip(transform_dropdowns, [gr.Dropdown(**new_transform_dropdown) for new_transform_dropdown in new_transform_dropdowns]))
        }
    
    @dataset_dropdown.select(inputs=dataset_dropdown, outputs=[loading_codes_json, subset_dropdown, split_dropdown, input_dataframe, dataframe] + transform_dropdowns)
    def _show_subset_dropdown(dataset: str):
        subsets, loading_codes = show_subset_dropdown(dataset)
        splits = show_split_dropdown(subsets["value"], loading_codes)
        input_df, *new_transform_dropdowns = show_input_dataframe(dataset, subsets["value"], splits["value"], loading_codes)
        return {
            loading_codes_json: loading_codes,
            subset_dropdown: gr.Dropdown(**subsets),
            split_dropdown: gr.Dropdown(**splits),
            input_dataframe: gr.DataFrame(**input_df),
            dataframe: gr.DataFrame(**input_df),
            **dict(zip(transform_dropdowns, [gr.Dropdown(**new_transform_dropdown) for new_transform_dropdown in new_transform_dropdowns]))
        }
    
    @subset_dropdown.select(inputs=[dataset_dropdown, subset_dropdown, loading_codes_json], outputs=[split_dropdown, input_dataframe, dataframe] + transform_dropdowns)
    def _show_split_dropdown(dataset: str, subset: str, loading_codes: list[dict]):
        splits = show_split_dropdown(subset, loading_codes)
        input_df, *new_transform_dropdowns = show_input_dataframe(dataset, subset, splits["value"], loading_codes)
        return {
            split_dropdown: gr.Dropdown(**splits),
            input_dataframe: gr.DataFrame(**input_df),
            dataframe: gr.DataFrame(**input_df),
            **dict(zip(transform_dropdowns, [gr.Dropdown(**new_transform_dropdown) for new_transform_dropdown in new_transform_dropdowns]))
        }
    
    @split_dropdown.select(inputs=[dataset_dropdown, subset_dropdown, split_dropdown, loading_codes_json], outputs=[input_dataframe, dataframe] + transform_dropdowns)
    def _show_input_dataframe(dataset: str, subset: str, split: str, loading_codes: list[dict]) -> pd.DataFrame:
        input_df, *new_transform_dropdowns = show_input_dataframe(dataset, subset, split, loading_codes)
        return {
            input_dataframe: gr.DataFrame(**input_df),
            dataframe: gr.DataFrame(**input_df),
            **dict(zip(transform_dropdowns, [gr.Dropdown(**new_transform_dropdown) for new_transform_dropdown in new_transform_dropdowns]))
        }


if __name__ == "__main__":
    demo.launch()