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Random-row viewer with bucket / shell / language filters.
Deploy to HuggingFace Spaces (CPU Free tier).
"""
import random
import gradio as gr
from datasets import load_dataset
_REVISION = "v1.0-rc1"
_DS = None # lazy-loaded on first query
SHELL_OPTS = ["(any)", "bash", "zsh", "fish", "powershell", "nu", "oils-osh"]
LANG_OPTS = ["(any)", "en", "zh", "de", "es", "fr", "ja", "it", "pt", "ru", "ar", "hi", "ko", "he"]
# Known buckets — avoids full dataset scan at startup
BUCKET_OPTS = ["(any)", "devops", "cloud", "database", "security", "pkg_mgmt",
"finance_web3", "bio_science", "data_ml", "network", "media",
"editor_term", "editor_writer", "lang_tool", "mobile_embed",
"modern_unix", "systems", "web_api", "misc"]
def _load():
global _DS
if _DS is None:
_DS = load_dataset(
"carosh/cli-1m", name="sample", revision=_REVISION, split="train"
)
return _DS
def sample_rows(shell_filter, lang_filter, bucket_filter, n_rows, seed):
try:
ds = _load()
except Exception as e:
return f"Error loading dataset: {e}", ""
filtered = ds
if shell_filter != "(any)":
filtered = filtered.filter(lambda r: r["shell"] == shell_filter)
if lang_filter != "(any)":
filtered = filtered.filter(lambda r: r["language"] == lang_filter)
if bucket_filter != "(any)":
filtered = filtered.filter(
lambda r: bucket_filter in (r["bucket"] if isinstance(r["bucket"], list) else [])
)
total = len(filtered)
if total == 0:
return "No rows match the selected filters.", "0"
rng = random.Random(int(seed) if str(seed).strip() else None)
n = min(int(n_rows), total)
indices = rng.sample(range(total), n)
rows = filtered.select(indices)
parts = [f"**{total:,} rows match** — showing {n}\n"]
for i, row in enumerate(rows):
msgs = row.get("messages") or []
user_msg = next((m["content"] for m in msgs if m.get("role") == "user"), "")
assistant_msg = next((m["content"] for m in msgs if m.get("role") == "assistant"), "")
bucket = ", ".join(row.get("bucket") or [])
parts.append(
f"---\n**Row {i+1}** · `shell={row.get('shell')}` · "
f"`lang={row.get('language')}` · `bucket={bucket}`\n\n"
f"**User:** {user_msg}\n\n"
f"```{row.get('shell', 'bash')}\n{assistant_msg}\n```\n"
)
return "\n".join(parts), f"{total:,}"
with gr.Blocks(title="CLI-1M Explorer", theme=gr.themes.Soft()) as demo:
gr.Markdown(
"# CLI-1M Dataset Explorer\n"
f"Browsing [`carosh/cli-1m`](https://huggingface.co/datasets/carosh/cli-1m) "
f"— `sample` config (50k stratified rows), revision `{_REVISION}`\n\n"
"Filter by shell, language, or industry bucket, then click **Sample rows**."
)
with gr.Row():
shell_dd = gr.Dropdown(SHELL_OPTS, value="(any)", label="Shell")
lang_dd = gr.Dropdown(LANG_OPTS, value="(any)", label="Language")
bucket_dd = gr.Dropdown(BUCKET_OPTS, value="(any)", label="Industry bucket")
with gr.Row():
n_rows = gr.Slider(1, 20, value=5, step=1, label="Rows to show")
seed = gr.Number(value=42, label="Random seed (blank = random)")
sample_btn = gr.Button("Sample rows", variant="primary")
match_count = gr.Textbox(label="Matching rows", interactive=False)
output = gr.Markdown()
sample_btn.click(
fn=sample_rows,
inputs=[shell_dd, lang_dd, bucket_dd, n_rows, seed],
outputs=[output, match_count],
)
gr.Markdown(
"---\n"
"**Links:** "
"[Dataset card](https://huggingface.co/datasets/carosh/cli-1m) · "
"[Eval split (gated)](https://huggingface.co/datasets/carosh/cli-1m-eval) · "
"[Source repo](https://github.com/wildcard/caro-eval) · "
"Apache-2.0"
)
if __name__ == "__main__":
demo.launch()
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