import base64 import copy from datetime import datetime, timedelta from io import BytesIO import random import gradio as gr from functools import lru_cache from hffs.fs import HfFileSystem from typing import List, Tuple, Callable from matplotlib import pyplot as plt import pandas as pd import numpy as np import pyarrow as pa import pyarrow.parquet as pq from functools import partial from tqdm.contrib.concurrent import thread_map from datasets import Features, Image, Audio, Sequence from fastapi import FastAPI, Response import uvicorn import os from gradio_datetimerange import DateTimeRange class AppError(RuntimeError): pass APP_URL = "http://127.0.0.1:7860" if os.getenv("DEV") else "https://lhoestq-datasets-explorer.hf.space" PAGE_SIZE = 5 MAX_CACHED_BLOBS = PAGE_SIZE * 10 TIME_PLOTS_NUM = 5 _blobs_cache = {} ##################################################### # Utils ##################################################### def ndarray_to_base64(ndarray): """ 将一维np.ndarray绘图并转换为Base64编码。 """ # 创建绘图 plt.figure(figsize=(8, 4)) plt.plot(ndarray) plt.title("Vector Plot") plt.xlabel("Index") plt.ylabel("Value") plt.tight_layout() # 保存图像到内存字节流 buffer = BytesIO() plt.savefig(buffer, format="png") plt.close() buffer.seek(0) # 转换为Base64字符串 base64_str = base64.b64encode(buffer.getvalue()).decode('utf-8') return f"data:image/png;base64,{base64_str}" def flatten_ndarray_column(df, column_name): def flatten_ndarray(ndarray): if isinstance(ndarray, np.ndarray) and ndarray.dtype == 'O': return np.concatenate([flatten_ndarray(subarray) for subarray in ndarray]) elif isinstance(ndarray, np.ndarray) and ndarray.ndim == 1: return np.expand_dims(ndarray, axis=0) return ndarray flattened_data = df[column_name].apply(flatten_ndarray) max_length = max(flattened_data.apply(len)) for i in range(max_length): df[f'{column_name}_{i}'] = flattened_data.apply(lambda x: x[i] if i < len(x) else np.nan) return df ##################################################### # Define routes for image and audio files ##################################################### app = FastAPI() @app.get( "/image", responses={200: {"content": {"image/png": {}}}}, response_class=Response, ) def image(id: str): blob = get_blob(id) return Response(content=blob, media_type="image/png") @app.get( "/audio", responses={200: {"content": {"audio/wav": {}}}}, response_class=Response, ) def audio(id: str): blob = get_blob(id) return Response(content=blob, media_type="audio/wav") def push_blob(blob: bytes, blob_id: str) -> str: global _blobs_cache if blob_id in _blobs_cache: del _blobs_cache[blob_id] _blobs_cache[blob_id] = blob if len(_blobs_cache) > MAX_CACHED_BLOBS: del _blobs_cache[next(iter(_blobs_cache))] return blob_id def get_blob(blob_id: str) -> bytes: global _blobs_cache return _blobs_cache[blob_id] def blobs_to_urls(blobs: List[bytes], type: str, prefix: str) -> List[str]: image_blob_ids = [push_blob(blob, f"{prefix}-{i}") for i, blob in enumerate(blobs)] return [APP_URL + f"/{type}?id={blob_id}" for blob_id in image_blob_ids] ##################################################### # List configs, splits and parquet files ##################################################### @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) return [path.split("/")[1] for path in fs.ls(config) if fs.isdir(path)] ##################################################### # Index and query Parquet data ##################################################### RowGroupReaders = List[Callable[[], pa.Table]] @lru_cache(maxsize=128) def index(dataset: str, config: str, split: str) -> Tuple[np.ndarray, RowGroupReaders, int, Features]: fs = get_parquet_fs(dataset) sources = fs.glob(f"{config}/{split}/*.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))}.") desc = f"{dataset}/{config}/{split}" all_pf: List[pq.ParquetFile] = thread_map(partial(pq.ParquetFile, filesystem=fs), sources, desc=desc, unit="pq") features = Features.from_arrow_schema(all_pf[0].schema.to_arrow_schema()) 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) for pf in all_pf for i in range(pf.metadata.num_row_groups)] max_page = 1 + (rg_offsets[-1] - 1) // PAGE_SIZE return rg_offsets, rg_readers, max_page, features def query(page: int, page_size: int, rg_offsets: np.ndarray, rg_readers: RowGroupReaders) -> pd.DataFrame: start_row, end_row = (page - 1) * page_size, min(page * page_size, rg_offsets[-1] - 1) # both included # rg_offsets[start_rg - 1] <= start_row < rg_offsets[start_rg] # rg_offsets[end_rg - 1] <= end_row < rg_offsets[end_rg] start_rg, end_rg = np.searchsorted(rg_offsets, [start_row, end_row], side="right") # both included 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 > 0 else 0) pa_table = pa_table.slice(offset, page_size) return pa_table.to_pandas() 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) @lru_cache(maxsize=128) def get_page_df(dataset: str, config: str, split: str, page: str) -> Tuple[pd.DataFrame, int, Features]: dataset, config, split, page = sanitize_inputs(dataset, config, split, page) rg_offsets, rg_readers, max_page, features = index(dataset, config, split) if page > max_page: raise AppError(f"Page {page} does not exist") df = query(page, PAGE_SIZE, rg_offsets=rg_offsets, rg_readers=rg_readers) return df, max_page, features ##################################################### # Format results ##################################################### def get_page(dataset: str, config: str, split: str, page: str) -> Tuple[str, int, str]: df_, max_page, features = get_page_df(dataset, config, split, page) df = copy.deepcopy(df_) unsupported_columns = [] if dataset != 'Salesforce/lotsa_data': for column, feature in features.items(): if isinstance(feature, Image): blob_type = "image" # TODO: support audio - right now it seems that the markdown renderer in gradio doesn't support audio and shows nothing blob_urls = blobs_to_urls([item.get("bytes") if isinstance(item, dict) else None for item in df[column]], blob_type, prefix=f"{dataset}-{config}-{split}-{page}-{column}") df = df.drop([column], axis=1) df[column] = [f"![]({url})" for url in blob_urls] elif any(bad_type in str(feature) for bad_type in ["Image(", "Audio(", "'binary'"]): unsupported_columns.append(column) df = df.drop([column], axis=1) elif isinstance(feature, Sequence): if feature.feature.dtype == 'float32': # 直接将内容绘图，并嵌入为Base64编码 base64_srcs = [ndarray_to_base64(vec) for vec in df[column]] df = df.drop([column], axis=1) df[column] = [f"![]({src})" for src in base64_srcs] info = "" if not unsupported_columns else f"Some columns are not supported yet: {unsupported_columns}" return df.reset_index().to_markdown(index=False), max_page, info else: # 其他的处理逻辑 info = "" if not unsupported_columns else f"Some columns are not supported yet: {unsupported_columns}" return df, max_page, info ##################################################### # Gradio app ##################################################### with gr.Blocks() as demo: gr.Markdown("# 📖 Datasets Explorer\n\nAccess any slice of data of any dataset on the [Hugging Face Dataset Hub](https://huggingface.co/datasets)") gr.Markdown("This is the dataset viewer from parquet export demo before the feature was added on the Hugging Face website.") cp_dataset = gr.Textbox("Salesforce/lotsa_data", label="Pick a dataset", placeholder="competitions/aiornot") 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) cp_goto_next_page = gr.Button("Next page", visible=False) cp_error = gr.Markdown("", visible=False) cp_info = gr.Markdown("", visible=False) cp_result = gr.Markdown("", visible=False) now = datetime.now() df = pd.DataFrame({ 'time': [now - timedelta(minutes=5*i) for i in range(25)] + [now], 'price': np.random.randint(100, 1000, 26), 'origin': [random.choice(["DFW", "DAL", "HOU"]) for _ in range(26)], 'destination': [random.choice(["JFK", "LGA", "EWR"]) for _ in range(26)], }) componets = [] for _ in range(TIME_PLOTS_NUM): with gr.Row(): textbox = gr.Textbox("名称或说明") with gr.Column(): daterange = DateTimeRange(["now - 24h", "now"]) plot1 = gr.LinePlot(df, x="time", y="price", color="origin") # plot2 = gr.LinePlot(df, x="time", y="price", color="origin") daterange.bind([plot1, # plot2, ]) comp = { "textbox" : textbox, "daterange" : daterange, "plot1" : plot1, # "plot2" : plot2, } componets.append(comp) with gr.Row(): cp_page = gr.Textbox("1", label="Page", placeholder="1", visible=False) cp_goto_page = gr.Button("Go to page", 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: ret = {} if dataset != 'Salesforce/lotsa_data': markdown_result, max_page, info = get_page(dataset, config, split, page) ret[cp_result] = gr.update(visible=True, value=markdown_result) else: df, max_page, info = get_page(dataset, config, split, page) print(df.columns) # TODO:target为一维数组时len(row['target'][0])会直接报错 df['timestamp'] = df.apply(lambda row: pd.date_range(start=row['start'], periods=len(row['target'][0]), freq=row['freq']).to_pydatetime().tolist(), axis=1) df = flatten_ndarray_column(df, 'target') # 删除原始的start和freq列 df.drop(columns=['start', 'freq', 'target'], inplace=True) if 'past_feat_dynamic_real' in df.columns: df.drop(columns=['past_feat_dynamic_real'], inplace=True) info = f"({info})" if info else "" for i, rows in df.iterrows(): index = rows['item_id'] df_without_index = rows.drop('item_id').to_frame().T df_expanded = df_without_index.apply(pd.Series.explode).reset_index(drop=True).fillna(0) ret.update({ componets[i]["textbox"]: gr.update(value=f"item_id: {index}"), componets[i]["daterange"]: gr.update(value=[df_without_index['timestamp'][i][0], df_without_index['timestamp'][i][-1]]), componets[i]["plot1"]: gr.update(value=df_expanded, x="timestamp", y="target_0"), }) return { **ret, 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_and_next_page(dataset: str, config: str, split: str, page: str) -> dict: try: next_page = str(int(page) + 1) return { **show_dataset_at_config_and_split_and_page(dataset, config, split, next_page), cp_page: gr.update(value=next_page, visible=True), } 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), cp_goto_next_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)) """ 动态生成组件时使用gr.LinePlot会有bug，直接卡死在show_dataset部分 """ # @gr.render(triggers=[cp_go.click]) # def create_test(): # now = datetime.now() # df = pd.DataFrame({ # 'time': [now - timedelta(minutes=5*i) for i in range(25)], # 'price': np.random.randint(100, 1000, 25), # 'origin': [random.choice(["DFW", "DAL", "HOU"]) for _ in range(25)], # 'destination': [random.choice(["JFK", "LGA", "EWR"]) for _ in range(25)], # }) # # componets = [] # # daterange = DateTimeRange(["now - 24h", "now"]) # plot1 = gr.LinePlot(df, x="time", y="price") # plot2 = gr.LinePlot(df, x="time", y="price", color="origin") # # # daterange.bind([plot1, plot2]) # # componets.append(plot1) # # componets.append(plot2) # # componets.append(daterange) # # test = gr.Textbox(label="input") # # componets.append(test) # # return componets all_outputs = [cp_config, cp_split, cp_page, cp_goto_page, cp_goto_next_page, cp_result, cp_info, cp_error] for comp in componets: all_outputs += list(comp.values()) 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) cp_goto_next_page.click(show_dataset_at_config_and_split_and_next_page, inputs=[cp_dataset, cp_config, cp_split, cp_page], outputs=all_outputs) if __name__ == "__main__": app = gr.mount_gradio_app(app, demo, path="/") uvicorn.run(app, host="127.0.0.1", port=7860) # 需求： # target多变量没办法同时打到一个图上。有几种选择，可以选择拉一个框选，一次一个；或者用强行用颜色区分，或者用两个框分别展示（动态生成多个框没办法指定位置） # 无法动态生成组件 # 没有聚合、统计值等功能 # 支持其他库的调用