from __future__ import annotations import urllib from pathlib import Path import gradio as gr import rerun as rr from datasets import load_dataset from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from gradio_huggingfacehub_search import HuggingfaceHubSearch from dataset_conversion import log_dataset_to_rerun CUSTOM_PATH = "/" app = FastAPI() origins = [ "https://app.rerun.io", ] app.add_middleware( CORSMiddleware, allow_origins=origins, ) def html_template(rrd: str, app_url: str = "https://app.rerun.io") -> str: encoded_url = urllib.parse.quote(rrd) return f"""
""" def show_dataset(dataset_id: str, episode_index: int) -> str: rr.init("dataset") # TODO(jleibs): manage cache better and put in proper storage filename = Path(f"tmp/{dataset_id}_{episode_index}.rrd") if not filename.exists(): filename.parent.mkdir(parents=True, exist_ok=True) rr.save(filename.as_posix()) dataset = load_dataset(dataset_id, split="train", streaming=True) # This is for LeRobot datasets (https://huggingface.co/lerobot): ds_subset = dataset.filter( lambda frame: "episode_index" not in frame or frame["episode_index"] == episode_index ) log_dataset_to_rerun(ds_subset) return filename.as_posix() with gr.Blocks() as demo: with gr.Row(): search_in = HuggingfaceHubSearch( "lerobot/pusht", label="Search Huggingface Hub", placeholder="Search for models on Huggingface", search_type="dataset", ) episode_index = gr.Number(1, label="Episode Index") button = gr.Button("Show Dataset") with gr.Row(): rrd = gr.File() with gr.Row(): viewer = gr.HTML() button.click(show_dataset, inputs=[search_in, episode_index], outputs=rrd) rrd.change( html_template, js="""(rrd) => { console.log(rrd.url); return rrd.url}""", inputs=[rrd], outputs=viewer, preprocess=False, ) app = gr.mount_gradio_app(app, demo, path=CUSTOM_PATH) # Run this from the terminal as you would normally start a FastAPI app: `uvicorn run:app` # and navigate to http://localhost:8000 in your browser.