| """synthkit — Hugging Face Space (Gradio). |
| |
| A small live demo of the synthkit quality grader and offline generator. This |
| Space runs fully offline: template-based generation (no API key) and the lexical |
| grading axes. The CLI adds LLM-backed generation, fine-tuning output formats, and |
| an embedding-based semantic-dedup axis. Source: https://github.com/LaelaZorana/synthkit |
| """ |
| from __future__ import annotations |
|
|
| import html |
| import json |
|
|
| import gradio as gr |
|
|
| from synthkit import __version__ |
| from synthkit.grading import grade_dataset |
| from synthkit.report import to_html, to_json |
| from synthkit.text.generate import generate |
| from synthkit.text.seeds import BUILTIN_SEEDS |
|
|
| GH = "https://github.com/LaelaZorana/synthkit" |
|
|
|
|
| _MAX_INPUT_BYTES = 4_000_000 |
| _MAX_RECORDS = 5000 |
|
|
|
|
| def _records_from(text: str, file) -> list: |
| raw = "" |
| if file is not None: |
| path = file if isinstance(file, str) else getattr(file, "name", None) |
| with open(path, "r", encoding="utf-8") as fh: |
| raw = fh.read(_MAX_INPUT_BYTES + 1) |
| elif text and text.strip(): |
| raw = text |
| if len(raw) > _MAX_INPUT_BYTES: |
| raise ValueError(f"input too large (limit {_MAX_INPUT_BYTES // 1_000_000} MB)") |
| raw = raw.strip() |
| if not raw: |
| raise ValueError("Paste some JSONL/JSON or upload a file first.") |
| if raw[0] == "[": |
| recs = json.loads(raw) |
| else: |
| recs = [json.loads(line) for line in raw.splitlines() if line.strip()] |
| if not isinstance(recs, list) or not recs: |
| raise ValueError("No records found.") |
| if len(recs) > _MAX_RECORDS: |
| raise ValueError(f"too many records ({len(recs)}); this demo caps at {_MAX_RECORDS}") |
| return recs |
|
|
|
|
| def _iframe(report_html: str) -> str: |
| return (f'<iframe srcdoc="{html.escape(report_html, quote=True)}" ' |
| 'style="width:100%;height:760px;border:0;border-radius:14px;background:#0b1020">' |
| '</iframe>') |
|
|
|
|
| def _err(exc: Exception) -> str: |
| return f'<p style="color:#dc2626;font:14px sans-serif">⚠ {html.escape(str(exc))}</p>' |
|
|
|
|
| def grade_ui(dataset_text, dataset_file, eval_text): |
| try: |
| records = _records_from(dataset_text, dataset_file) |
| against = None |
| if eval_text and eval_text.strip(): |
| against = [json.loads(ln) for ln in eval_text.splitlines() if ln.strip()][:_MAX_RECORDS] |
| report = grade_dataset(records, against=against) |
| return _iframe(to_html(report, "uploaded dataset")), to_json(report, "uploaded dataset") |
| except Exception as exc: |
| return _err(exc), "" |
|
|
|
|
| def generate_ui(seed_choice, seed_text, n): |
| try: |
| spec = json.loads(seed_text) if seed_text and seed_text.strip() else BUILTIN_SEEDS[seed_choice] |
| data = generate(spec, max(1, min(int(n), 300)), seed=17) |
| report = grade_dataset(data) |
| jsonl = "\n".join(json.dumps(r, ensure_ascii=False) for r in data) |
| return _iframe(to_html(report, "generated dataset")), jsonl |
| except Exception as exc: |
| return _err(exc), "" |
|
|
|
|
| with gr.Blocks(title="synthkit", theme=gr.themes.Soft(), css="footer{visibility:hidden}") as demo: |
| gr.Markdown( |
| "# 🧪 synthkit\n" |
| "**Generate synthetic data — and grade it before you train on it.** " |
| "Scored on validity · uniqueness · diversity · contamination, with an A+→F headline.\n\n" |
| f"This Space runs offline (template generation + lexical grading). The full " |
| f"[CLI]({GH}) adds LLM-backed instruction data, fine-tuning formats " |
| f"(alpaca/sharegpt/openai), and an embedding semantic-dedup axis. · v{__version__}") |
|
|
| with gr.Tab("Grade a dataset"): |
| gr.Markdown("Upload or paste a `.jsonl` (one JSON record per line). Optionally " |
| "paste a held-out eval set to check **contamination** (train/eval leakage).") |
| with gr.Row(): |
| with gr.Column(): |
| g_file = gr.File(label="Dataset (.jsonl / .json)", |
| file_types=[".jsonl", ".json", ".txt"]) |
| g_text = gr.Textbox(label="…or paste records", lines=8, |
| placeholder='{"prompt": "Explain gradient descent"}\n' |
| '{"prompt": "What is a hash map?"}') |
| g_eval = gr.Textbox(label="Held-out eval set for contamination (optional)", |
| lines=3, placeholder='{"prompt": "Explain gradient descent"}') |
| g_btn = gr.Button("Grade", variant="primary") |
| with gr.Column(): |
| g_html = gr.HTML() |
| g_json = gr.Code(label="report.json", language="json") |
| g_btn.click(grade_ui, [g_text, g_file, g_eval], [g_html, g_json]) |
|
|
| with gr.Tab("Generate (offline)"): |
| gr.Markdown("Generate eval prompts from a built-in seed, or paste your own seed " |
| "spec (templates + slots). The result is graded automatically.") |
| with gr.Row(): |
| with gr.Column(): |
| s_choice = gr.Dropdown(list(BUILTIN_SEEDS), value="eval", label="Built-in seed") |
| s_n = gr.Slider(10, 300, value=120, step=10, label="How many records") |
| s_text = gr.Textbox( |
| label="…or paste a seed spec (JSON)", lines=8, |
| placeholder='{"kind":"eval","templates":["{a} about {b}"],' |
| '"slots":{"a":["Explain","Summarize"],"b":["TLS","DNS","OAuth"]},' |
| '"response":{"mode":"none"}}') |
| s_btn = gr.Button("Generate + grade", variant="primary") |
| with gr.Column(): |
| s_html = gr.HTML() |
| s_jsonl = gr.Code(label="dataset.jsonl") |
| s_btn.click(generate_ui, [s_choice, s_text, s_n], [s_html, s_jsonl]) |
|
|
| gr.Markdown(f"[⭐ Source on GitHub]({GH}) · MIT-licensed · zero-dependency core") |
|
|
| if __name__ == "__main__": |
| demo.queue(default_concurrency_limit=2, max_size=24).launch() |
|
|
