File size: 3,891 Bytes
07a7354 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import re
import unicodedata
from collections import defaultdict
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
INVALID_FILENAME_CHARS = r'[/\\:*?"<>|\n\r\t]'
def normalize_text(value: str) -> str:
return unicodedata.normalize("NFC", str(value).strip())
def safe_filename(name: str, max_len: int = 180) -> str:
name = normalize_text(name)
name = re.sub(INVALID_FILENAME_CHARS, "_", name)
name = re.sub(r"\s+", " ", name).strip()
if not name:
name = "untitled"
if len(name) > max_len:
name = name[:max_len].rstrip()
return name
def load_prompt_index(prompts_dir: Path) -> dict[str, dict[str, dict[str, object]]]:
prompt_index: dict[str, dict[str, dict[str, object]]] = defaultdict(dict)
duplicates: list[tuple[str, str]] = []
for prompt_file in sorted(prompts_dir.glob("*.json")):
category = prompt_file.stem
records = json.loads(prompt_file.read_text(encoding="utf-8"))
for prompt_id, record in enumerate(records):
content = normalize_text(record["content"])
key = safe_filename(content)
if key in prompt_index[category]:
duplicates.append((category, key))
prompt_index[category][key] = {
"content": content,
"prompt": normalize_text(record["prompt"]),
"prompt_id": prompt_id,
}
if duplicates:
duplicate_lines = "\n".join(f"- {category}: {key}" for category, key in duplicates[:20])
raise ValueError(f"Duplicate prompt filenames detected:\n{duplicate_lines}")
return prompt_index
def build_rows(repo_root: Path) -> list[dict[str, object]]:
prompt_index = load_prompt_index(repo_root / "prompts")
rows: list[dict[str, object]] = []
for video_path in sorted(repo_root.glob("*/*/*.mp4")):
model = video_path.parts[-3]
category = video_path.parts[-2]
filename = normalize_text(video_path.stem)
prompt_record = prompt_index.get(category, {}).get(filename)
rel_path = video_path.relative_to(repo_root).as_posix()
rows.append(
{
"file_name": rel_path,
"model": model,
"category": category,
"content": prompt_record["content"] if prompt_record else filename,
"prompt": prompt_record["prompt"] if prompt_record else None,
"prompt_id": prompt_record["prompt_id"] if prompt_record else None,
"matched_prompt": prompt_record is not None,
}
)
return rows
def write_parquet(rows: list[dict[str, object]], output_path: Path) -> None:
table = pa.Table.from_pylist(rows)
pq.write_table(table, output_path)
def main() -> None:
parser = argparse.ArgumentParser(
description="Build metadata.parquet for Hugging Face Dataset Viewer."
)
parser.add_argument(
"--repo-root",
type=Path,
default=Path(__file__).resolve().parents[1],
help="Path to the dataset repository root.",
)
parser.add_argument(
"--output",
type=Path,
default=None,
help="Output parquet path. Defaults to <repo-root>/metadata.parquet.",
)
args = parser.parse_args()
repo_root = args.repo_root.resolve()
output_path = args.output.resolve() if args.output else repo_root / "metadata.parquet"
rows = build_rows(repo_root)
if not rows:
raise ValueError(f"No MP4 files found under {repo_root}")
write_parquet(rows, output_path)
matched = sum(1 for row in rows if row["matched_prompt"])
print(f"Wrote {len(rows)} rows to {output_path}")
print(f"Matched prompts: {matched}/{len(rows)}")
if __name__ == "__main__":
main()
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