from typing import List, Dict import httpx import gradio as gr import pandas as pd async def get_splits(dataset_name: str) -> Dict[str, List[Dict]]: URL = f"https://datasets-server.huggingface.co/splits?dataset={dataset_name}" async with httpx.AsyncClient() as session: response = await session.get(URL) return response.json() async def get_valid_datasets() -> Dict[str, List[str]]: URL = f"https://datasets-server.huggingface.co/valid" async with httpx.AsyncClient() as session: response = await session.get(URL) datasets = response.json()["valid"] return gr.Dropdown.update(choices=datasets, value="glue") async def get_first_rows(dataset: str, config: str, split: str) -> Dict[str, Dict[str, List[Dict]]]: URL = f"https://datasets-server.huggingface.co/first-rows?dataset={dataset}&config={config}&split={split}" async with httpx.AsyncClient() as session: response = await session.get(URL) return response.json() def get_df_from_rows(api_output): return pd.DataFrame([row["row"] for row in api_output["rows"]]) async def update_configs(dataset_name: str): splits = await get_splits(dataset_name) all_configs = sorted(set([s["config"] for s in splits["splits"]])) return (gr.Dropdown.update(choices=all_configs, value=all_configs[0]), splits) async def update_splits(config_name: str, state: gr.State): splits_for_config = sorted(set([s["split"] for s in state["splits"] if s["config"] == config_name])) dataset_name = state["splits"][0]["dataset"] dataset = await update_dataset(splits_for_config[0], config_name, dataset_name) return (gr.Dropdown.update(choices=splits_for_config, value=splits_for_config[0]), dataset) async def update_dataset(split_name: str, config_name: str, dataset_name: str): rows = await get_first_rows(dataset_name, config_name, split_name) df = get_df_from_rows(rows) return df # Guido von Roissum: https://www.youtube.com/watch?v=-DVyjdw4t9I # Guido says indentation style in python helps readability. Emoji's do too: https://www.kaggle.com/datasets/subinium/emojiimage-dataset # four spaces was a compromise. Google style guide (2 spaces) Harder at a glance to understand code. 8 spaces is a whole tab stop. after inddent levels you have little room left. with gr.Blocks() as demo: gr.Markdown("

🥫Datasets🎨

") gr.Markdown("""
Curated Datasets: Kaggle. NLM UMLS. LOINC. ICD10 Diagnosis. ICD11. Papers,Code,Datasets for SOTA in Medicine. CMS Downloads. CMS CPT and HCPCS Procedures and Services """) splits_data = gr.State() with gr.Row(): dataset_name = gr.Dropdown(label="Dataset") config = gr.Dropdown(label="Subset") split = gr.Dropdown(label="Split") with gr.Row(): dataset = gr.DataFrame(wrap=True) demo.load(get_valid_datasets, inputs=None, outputs=[dataset_name]) dataset_name.change(update_configs, inputs=[dataset_name], outputs=[config, splits_data]) config.change(update_splits, inputs=[config, splits_data], outputs=[split, dataset]) split.change(update_dataset, inputs=[split, config, dataset_name], outputs=[dataset]) demo.launch(debug=True) # original: https://huggingface.co/spaces/freddyaboulton/dataset-viewer -- Freddy thanks! Your examples are the best. # playlist on Gradio and Mermaid: https://www.youtube.com/watch?v=o7kCD4aWMR4&list=PLHgX2IExbFosW7hWNryq8hs2bt2aj91R- # Link to Mermaid model and code: [![](https://mermaid.ink/img/pako:eNp1U8mO2zAM_RXCZ-eQpZccCmSZTIpOMQESIAdnDrRMx0JkydXSNDOYfy_lpUgD1AfBfnx8fCTlj0SYgpJ5UipzFRVaD4flSQM_YjwafcVJ9-FCfrbYVGA0ZQeLUkt9futiOM72pEh4QFijR9iTf2tzsx3Z0ti6hxslvb_Lm0TSNPvBDhQsg1TFXXAag7NBef_9hdDqFA6knbEbdgvGwu7mjRXVkDOLOV-yNXmytdQEsoROvTfi4EhK9XTSxUNz_mo4uVHm1lPyce-uR1k_n2RHymHRNPAvNXaTT7NVZYwjeDECVbS4UiYUAyc2lc-yFoPXxkujHaAl2G54PCjIpfBssZAGtsZ5KlLYkjWXkMLiuOfjPVhiymr3_x4qS7wicneTFuMW6Gdxlb6Cb7oJvt1LbEpMso08sza8MnqskA9jL27Ij72Jafb0G-tGkQNTdgKOy_XcFP5GDxFbWsJLV3FQid2LWfZsfpHVqAXBCBYa1e2dAHUBu5Ar6dgby0ghPWxQWk2Oh_L0M0h_S2Ep0YHUrXFHXD_msefo5XEkfFWBK8atdkA7mgfoalpATJI0qfnWoCz4b_iI0VPiK6rplMz5taASg_Kn5KQ_mYrBm_1Ni2TubaA0CU2BntYSeQl1Mi9ROfr8A8FBGds?type=png)](https://mermaid.live/edit#pako:eNp1U8mO2zAM_RXCZ-eQpZccCmSZTIpOMQESIAdnDrRMx0JkydXSNDOYfy_lpUgD1AfBfnx8fCTlj0SYgpJ5UipzFRVaD4flSQM_YjwafcVJ9-FCfrbYVGA0ZQeLUkt9futiOM72pEh4QFijR9iTf2tzsx3Z0ti6hxslvb_Lm0TSNPvBDhQsg1TFXXAag7NBef_9hdDqFA6knbEbdgvGwu7mjRXVkDOLOV-yNXmytdQEsoROvTfi4EhK9XTSxUNz_mo4uVHm1lPyce-uR1k_n2RHymHRNPAvNXaTT7NVZYwjeDECVbS4UiYUAyc2lc-yFoPXxkujHaAl2G54PCjIpfBssZAGtsZ5KlLYkjWXkMLiuOfjPVhiymr3_x4qS7wicneTFuMW6Gdxlb6Cb7oJvt1LbEpMso08sza8MnqskA9jL27Ij72Jafb0G-tGkQNTdgKOy_XcFP5GDxFbWsJLV3FQid2LWfZsfpHVqAXBCBYa1e2dAHUBu5Ar6dgby0ghPWxQWk2Oh_L0M0h_S2Ep0YHUrXFHXD_msefo5XEkfFWBK8atdkA7mgfoalpATJI0qfnWoCz4b_iI0VPiK6rplMz5taASg_Kn5KQ_mYrBm_1Ni2TubaA0CU2BntYSeQl1Mi9ROfr8A8FBGds)