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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"""<div style="width:100%; height:70vh;"><iframe style="width:100%; height:100%;" src="{app_url}?url={encoded_url}" frameborder="0" allowfullscreen=""></iframe></div>"""
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.
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