app.py

import spaces

@spaces.GPU
def dummy_gpu():
    pass

import gradio as gr
from huggingface_hub import HfApi, snapshot_download
from huggingface_hub.hf_api import RepoFile
from typing import Union
import tempfile
import shutil
from pathlib import Path
import time

def split_repo(repo_id: str, user_name: str, space_name: str, storage_name: str, storage_type: str, is_private: bool=True, threshold: int=10 * 1024 * 1024, hf_token: Union[str, None]="", progress=gr.Progress(track_tqdm=True)):
    TEMP_DIR = tempfile.mkdtemp()
    exist_ok = False
    use_dupe_api = False
    info_md = ""
    dl_code = ""
    space_id = f"{user_name}/{space_name}"
    storage_id = f"{user_name}/{storage_name}"
    try:
        kwargs = {}
        if hf_token: kwargs["token"] = hf_token
        else: raise Exception("Token not found.")
        api = HfApi()
        if not exist_ok:
            if api.repo_exists(repo_id=space_id, repo_type="space", **kwargs): raise Exception(f"{space_id} already exists.")
            if api.repo_exists(repo_id=storage_id, repo_type=storage_type, **kwargs): raise Exception(f"{space_id} already exists.")
        info = api.list_repo_tree(repo_id=repo_id, repo_type="space", recursive=True, **kwargs)
        lfiles = []
        sfiles = []
        for i in info:
            if not isinstance(i, RepoFile): continue
            if i.lfs is not None and i.lfs.size > threshold: lfiles.append(i.path)
            else: sfiles.append(i.path)
        #print("Large files: ", lfiles)
        #print("Small files: ", sfiles)
        if len(lfiles) == 0: raise Exception("Large file not found.")
        lfiles_str = "[" + ", ".join(['"' + s + '"' for s in lfiles]) + "]"
        sv = api.get_space_variables(repo_id=repo_id, **kwargs)
        sv = [{str(k): str(v)} for k, v in sv.items()] if sv and len(sv) > 0 else []
        if api.repo_exists(repo_id=space_id, repo_type="space", **kwargs) and exist_ok: api.delete_repo(repo_id=space_id, repo_type="space", **kwargs)
        if use_dupe_api:
            api.duplicate_space(from_id=repo_id, to_id=space_id, exist_ok=exist_ok, private=is_private, hardware="cpu-basic", variables=sv, **kwargs)
            time.sleep(10) # wait for finishing of space duplication
            api.delete_files(repo_id=space_id, repo_type="space", delete_patterns=lfiles, **kwargs)
        else:
            snapshot_download(repo_id=repo_id, repo_type="space", ignore_patterns=lfiles, local_dir=TEMP_DIR, **kwargs)
            api.create_repo(repo_id=space_id, repo_type="space", space_hardware="cpu-basic", space_variables=sv, space_sdk="gradio", exist_ok=exist_ok, private=is_private, **kwargs)
            api.upload_folder(repo_id=space_id, repo_type="space", ignore_patterns=lfiles, folder_path=TEMP_DIR, path_in_repo=".", **kwargs)
        snapshot_download(repo_id=repo_id, repo_type="space", allow_patterns=lfiles, local_dir=TEMP_DIR, **kwargs)
        api.create_repo(repo_id=storage_id, repo_type=storage_type, exist_ok=exist_ok, private=is_private, **kwargs)
        api.upload_folder(repo_id=storage_id, repo_type=storage_type, allow_patterns=lfiles, folder_path=TEMP_DIR, path_in_repo=".", **kwargs)
        lfiles_str = "[" + ", ".join(['"' + s + '"' for s in lfiles]) + "]"
        dl_code = f'from huggingface_hub import snapshot_download\nlarge_files = {lfiles_str}\nsnapshot_download(repo_id="{storage_id}", repo_type="{storage_type}", allow_patterns=large_files, local_dir=".")\n'
        info_md = f'## Your new space URL: [{space_id}](https://hf.co/spaces/{space_id})<br>\n## Your new storage URL: [{storage_id}](https://hf.co/{storage_id if storage_type == "model" else "datasets/" + storage_id})'
    except Exception as e:
        print(e)
        gr.Warning(f"Error: {e}")
    finally:
        if Path(TEMP_DIR).exists() and Path(TEMP_DIR).is_dir(): shutil.rmtree(TEMP_DIR)
        return info_md, dl_code

css = """

.title { font-size: 3em; align-items: center; text-align: center; }

.info { align-items: center; text-align: center; }

.block.result { margin: 1em 0; padding: 1em; box-shadow: 0 0 3px 3px #664422, 0 0 3px 2px #664422 inset; border-radius: 6px; background: #665544; }

.desc [src$='#float'] { float: right; margin: 20px; }

"""

with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
    repo_id = gr.Textbox(label="Source repo ID", placeholder="levihsu/OOTDiffusion", value="")
    with gr.Row(equal_height=True):
        user_name = gr.Textbox(label="Your user name", value="")
        space_name = gr.Textbox(label="Destination repo name", placeholder="OOTDiffusion", value="")
        storage_name = gr.Textbox(label="Storage repo name", placeholder="OOTDiffusion-storage", value="")
        storage_type = gr.Radio(label="Storage repo type", choices=["dataset", "model"], value="model")
        with gr.Column():
            hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="")
            gr.Markdown("Your token is available at [hf.co/settings/tokens](https://huggingface.co/settings/tokens).", elem_classes="info")
        threshold = gr.Number(label="Size threshold (bytes)", value=10 * 1024 * 1024, minimum=1, maximum=5 * 1024 * 1024 * 1024, step=1)
        is_private = gr.Checkbox(label="Private", value=True)
    run_button = gr.Button("Submit", variant="primary")
    dl_code = gr.Textbox(label="Code", value="", show_copy_button=True)
    info_md = gr.Markdown("<br><br><br>", elem_classes="result")

    run_button.click(split_repo, [repo_id, user_name, space_name, storage_name, storage_type, is_private, threshold, hf_token], [info_md, dl_code])

demo.queue().launch(ssr_mode=False)