| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import gradio as gr |
|
|
| import legacy_app as legacy |
| from voynich_lab.full_pipeline import ( |
| pipeline_summary_markdown, |
| profile_names, |
| run_full_pipeline, |
| ) |
| from voynich_lab.transcript_catalog import ( |
| DEFAULT_PRESET, |
| IVTFF_EXAMPLE, |
| PLAIN_TEXT_EXAMPLE, |
| load_preset, |
| preset_description, |
| preset_names, |
| ) |
|
|
| APP_TITLE = legacy.APP_TITLE |
|
|
|
|
| def _fan_out_loaded(result, source_label: str): |
| raw, analysis, status, preview = result |
| return raw, analysis, status, preview, status, preview, source_label |
|
|
|
|
| def load_builtin_ui(name: str): |
| try: |
| raw, preset = load_preset(name) |
| except Exception as exc: |
| raise gr.Error(f"Could not load built-in transcript {name!r}: {exc}") from exc |
| result = legacy.load_uploaded(None, raw) |
| raw_text, analysis, status, preview = result |
| status = status.replace("pasted text", f"built-in preset '{preset.name}'") |
| if preset.is_remote: |
| status += " Source is pinned to the project's reproduction commit." |
| return _fan_out_loaded((raw_text, analysis, status, preview), preset.name) |
|
|
|
|
| def load_custom_ui(file_path: str | None, pasted: str): |
| if not (pasted and pasted.strip()) and not file_path: |
| raise gr.Error("Paste a transcription or choose a .txt, .eva, or .ivtff file first.") |
| label = "pasted text" if (pasted and pasted.strip()) else Path(file_path).name |
| return _fan_out_loaded(legacy.load_uploaded(file_path, pasted), label) |
|
|
|
|
| def preset_info_ui(name: str): |
| return preset_description(name) |
|
|
|
|
| def run_full_pipeline_ui(raw_text: str, analysis_text: str, profile: str, seed: int, source_label: str): |
| if not analysis_text.strip(): |
| raise gr.Error("Load a transcript before running the full pipeline.") |
| try: |
| report = run_full_pipeline( |
| raw_text, |
| analysis_text, |
| profile=str(profile), |
| seed=int(seed), |
| source_label=str(source_label or ""), |
| ) |
| except ValueError as exc: |
| raise gr.Error(str(exc)) from exc |
| rows = [ |
| [row["stage"], row["status"], row["seconds"], row["detail"]] |
| for row in report.get("stages", []) |
| ] |
| status = ( |
| f"Pipeline finished: {report.get('completed_stages', 0)} completed, " |
| f"{report.get('skipped_stages', 0)} skipped, {report.get('error_stages', 0)} errors." |
| ) |
| return status, rows, pipeline_summary_markdown(report), json.dumps(report, indent=2, sort_keys=True) |
|
|
|
|
| FORMAT_HELP = f""" |
| ### Accepted transcript formats |
| |
| **Plain EVA-style / plain text** |
| |
| Use one manuscript line per line. Separate conventional transcription tokens with whitespace. This is the simplest format when you do not need quire, Currier-language, hand, folio, or separator-certainty metadata. |
| |
| ```text |
| {PLAIN_TEXT_EXAMPLE.rstrip()} |
| ``` |
| |
| **IVTFF** |
| |
| IVTFF is preferred for analyses that need native manuscript metadata. Page headers can contain variables such as `$Q` (quire), `$L` (Currier language), `$H` (hand), and `$I` (illustration type). Locus lines carry the transcription. In ZL-style input, `.` and `,` can preserve certain versus uncertain separators, and alternate readings such as `[e:o]` are accepted by the parser. |
| |
| ```text |
| {IVTFF_EXAMPLE.rstrip()} |
| ``` |
| |
| The loader detects IVTFF automatically. Metadata is excluded from glyph statistics; plain text is analyzed directly after whitespace normalization. |
| """ |
|
|
|
|
| with gr.Blocks(title=APP_TITLE) as demo: |
| gr.Markdown(f"# {APP_TITLE}") |
| gr.Markdown( |
| "Start with a built-in transcript or provide your own. The bundled IVTFF demo is loaded automatically " |
| "when the app opens, so the analysis tabs are immediately usable." |
| ) |
|
|
| with gr.Group(): |
| gr.Markdown("## Transcript source") |
| with gr.Row(): |
| with gr.Column(scale=1): |
| preset = gr.Dropdown( |
| choices=preset_names(), |
| value=DEFAULT_PRESET, |
| label="Built-in transcript", |
| ) |
| preset_info = gr.Markdown(preset_description(DEFAULT_PRESET)) |
| load_builtin = gr.Button("Load selected built-in transcript", variant="primary") |
| with gr.Column(scale=1): |
| pasted = gr.Textbox( |
| label="Or paste your own transcription", |
| lines=8, |
| placeholder="Paste EVA-style plain text or IVTFF here…", |
| ) |
| upload = gr.File( |
| label="Or upload your own transcription", |
| file_types=[".txt", ".eva", ".ivtff"], |
| type="filepath", |
| ) |
| gr.Markdown("If both are supplied, pasted text takes precedence over the uploaded file.") |
| load_custom = gr.Button("Load pasted / uploaded transcript") |
|
|
| with gr.Accordion("Format examples and input rules", open=False): |
| gr.Markdown(FORMAT_HELP) |
|
|
| source_status = gr.Textbox(label="Loaded source", lines=4, interactive=False) |
| source_preview = gr.Textbox( |
| label="Analysis-stream preview (metadata removed)", |
| lines=8, |
| interactive=False, |
| ) |
|
|
| with gr.Group(): |
| gr.Markdown("## Run the full pipeline") |
| gr.Markdown( |
| "One click runs the main analysis stack in sequence. IVTFF-only stages are included when metadata is " |
| "available and skipped cleanly for plain text. Individual stage failures do not stop later stages. " |
| "ZeroGPU, generator playgrounds, and Hugging Face sync remain manual by design." |
| ) |
| with gr.Row(): |
| pipeline_profile = gr.Dropdown( |
| choices=profile_names(), |
| value="Standard", |
| label="Pipeline profile", |
| info="Quick is best for previews; Standard is the default; Thorough increases Monte Carlo/bootstrap depth.", |
| ) |
| pipeline_seed = gr.Number(value=0, precision=0, label="Pipeline seed") |
| pipeline_btn = gr.Button("Run full pipeline", variant="primary") |
| pipeline_status = gr.Textbox(label="Pipeline status", interactive=False) |
| pipeline_summary = gr.Markdown() |
| pipeline_stages = gr.Dataframe( |
| headers=["stage", "status", "seconds", "detail"], |
| datatype=["str", "str", "number", "str"], |
| label="Stage status", |
| interactive=False, |
| ) |
| with gr.Accordion("Full pipeline JSON report", open=False): |
| pipeline_json = gr.Code(language="json", label="Detailed results") |
|
|
| gr.Markdown("---\n## Analysis workspace") |
| legacy.demo.render() |
|
|
| pipeline_source_label = gr.State(DEFAULT_PRESET) |
| source_outputs = [ |
| legacy.raw_state, |
| legacy.text_state, |
| source_status, |
| source_preview, |
| legacy.load_status, |
| legacy.analysis_preview, |
| pipeline_source_label, |
| ] |
| load_builtin.click(load_builtin_ui, preset, source_outputs) |
| load_custom.click(load_custom_ui, [upload, pasted], source_outputs) |
| preset.change(preset_info_ui, preset, preset_info, queue=False) |
| pipeline_btn.click( |
| run_full_pipeline_ui, |
| [legacy.raw_state, legacy.text_state, pipeline_profile, pipeline_seed, pipeline_source_label], |
| [pipeline_status, pipeline_stages, pipeline_summary, pipeline_json], |
| ) |
| demo.load(load_builtin_ui, preset, source_outputs, queue=False) |
|
|
|
|
| if __name__ == "__main__": |
| demo.queue(default_concurrency_limit=2).launch() |
|
|
