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Upload the current repo to a fresh HuggingFace dataset via the Hub HTTP API.
Uploads only git-tracked files — raw SLTrans language folders are ignored.
Usage:
python upload_to_hub.py
python upload_to_hub.py --repo nlpscu/Multilingual-Code-Generator --private
"""
import argparse
import subprocess
import sys
from pathlib import Path
from huggingface_hub import HfApi
def git_tracked_files() -> list[str]:
result = subprocess.run(
["git", "ls-files"],
capture_output=True, text=True, cwd=Path(__file__).parent,
)
if result.returncode != 0:
print("ERROR: not inside a git repo or git not found", file=sys.stderr)
sys.exit(1)
return [f for f in result.stdout.strip().splitlines() if f]
def main():
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--repo", default="st-taro/csen346_temp",
help="HuggingFace dataset repo id (default: st-taro/csen346_temp)")
ap.add_argument("--private", action="store_true", default=False,
help="Create as private repo (note: private repos have a 10 GB LFS limit)")
ap.add_argument("--dry-run", action="store_true",
help="Print files that would be uploaded without uploading")
args = ap.parse_args()
root = Path(__file__).parent
files = git_tracked_files()
print(f"Repo : {args.repo}")
print(f"Private: {args.private}")
print(f"Files : {len(files)}")
for f in files:
size = (root / f).stat().st_size if (root / f).exists() else 0
print(f" {f} ({size / 1e6:.1f} MB)")
if args.dry_run:
print("\nDry run — nothing uploaded.")
return
api = HfApi()
# Create repo (no-op if already exists)
try:
api.create_repo(
repo_id=args.repo,
repo_type="dataset",
private=args.private,
exist_ok=True,
)
print(f"\nRepo ready: https://huggingface.co/datasets/{args.repo}")
except Exception as e:
print(f"ERROR creating repo: {e}", file=sys.stderr)
sys.exit(1)
# Upload files one at a time so progress is visible and failures are isolated
failed = []
for i, filepath in enumerate(files, 1):
local = root / filepath
if not local.exists():
print(f"[{i}/{len(files)}] SKIP (not on disk): {filepath}")
continue
size_mb = local.stat().st_size / 1e6
print(f"[{i}/{len(files)}] {filepath} ({size_mb:.1f} MB) ... ", end="", flush=True)
try:
api.upload_file(
path_or_fileobj=str(local),
path_in_repo=filepath,
repo_id=args.repo,
repo_type="dataset",
)
print("done")
except Exception as e:
print(f"FAILED: {e}")
failed.append((filepath, str(e)))
print()
if failed:
print(f"Upload finished with {len(failed)} failure(s):")
for f, err in failed:
print(f" {f}: {err}")
sys.exit(1)
else:
print(f"All {len(files)} files uploaded.")
print(f"Dataset: https://huggingface.co/datasets/{args.repo}")
if __name__ == "__main__":
main()
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