| |
| """ |
| Render polished PNG overview figures for the Ropedia project page. |
| |
| The generated assets are used only as text-free visual backgrounds. All |
| labels, dimensions, task names, and metrics are read from committed result |
| files via scripts/generate_visualizations.py so the final figures stay |
| traceable. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import base64 |
| import html |
| import subprocess |
| import tempfile |
| from pathlib import Path |
|
|
| from generate_visualizations import collect_summary, task_architecture_rows |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| ASSETS = ROOT / "docs/assets" |
| DEFAULT_PIPELINE_BASE = ASSETS / "pipeline_diagram_base.png" |
| DEFAULT_ARCHITECTURE_BASE = ASSETS / "task_architectures_base.png" |
| DEFAULT_PIPELINE_OUTPUT = ASSETS / "pipeline_diagram.png" |
| DEFAULT_ARCHITECTURE_OUTPUT = ASSETS / "task_architectures.png" |
|
|
| PIPELINE_WIDTH = 1800 |
| PIPELINE_HEIGHT = 1120 |
| ARCHITECTURE_WIDTH = 1800 |
| ARCHITECTURE_HEIGHT = 2450 |
|
|
|
|
| COLORS = { |
| "blue": "#9bdfff", |
| "teal": "#7ae5c3", |
| "green": "#ccffa0", |
| "amber": "#d8f4a5", |
| "orange": "#b7ff91", |
| "red": "#ff8f7a", |
| "ink": "#f4f8ef", |
| "muted": "#a5afa2", |
| "line": "#2b4428", |
| } |
|
|
|
|
| TASK_GROUPS = [ |
| ("Label + State", "#9bdfff", ["timeline_action", "timeline_subtask", "next_action"]), |
| ( |
| "Prediction + Reconstruction", |
| "#ccffa0", |
| ["hand_trajectory_forecast", "modality_reconstruction", "contact_prediction"], |
| ), |
| ("Grounding + Retrieval", "#7ae5c3", ["caption_grounding", "cross_modal_retrieval", "object_relevance"]), |
| ("Temporal Diagnostics", "#d8f4a5", ["transition_detection", "temporal_order", "misalignment_detection"]), |
| ] |
|
|
|
|
| def data_uri(path: Path) -> str: |
| if not path.exists(): |
| return "" |
| encoded = base64.b64encode(path.read_bytes()).decode("ascii") |
| return f"data:image/png;base64,{encoded}" |
|
|
|
|
| def esc(value: object) -> str: |
| return html.escape(str(value), quote=True) |
|
|
|
|
| def build_base_layer(path: Path, opacity: float) -> str: |
| uri = data_uri(path) |
| if not uri: |
| return "" |
| return f'<div class="base-layer" style="background-image:url({uri});opacity:{opacity};"></div>' |
|
|
|
|
| def stage_card(number: str, title: str, lines: list[str], color: str) -> str: |
| detail = "".join(f"<li>{esc(line)}</li>" for line in lines) |
| return f""" |
| <article class="stage" style="--accent:{color}"> |
| <div class="stage-number">{esc(number)}</div> |
| <h3>{esc(title)}</h3> |
| <ul>{detail}</ul> |
| </article> |
| """ |
|
|
|
|
| def arrow() -> str: |
| return '<div class="flow-arrow" aria-hidden="true">-></div>' |
|
|
|
|
| def build_pipeline_html(summary: dict, base_path: Path) -> str: |
| suite = summary["suite"] |
| task_count = len(suite["tasks"]) |
| neural_count = len(suite.get("neural_tasks", {})) |
| stage_rows = [ |
| [ |
| stage_card( |
| "01", |
| "Raw public sample", |
| ["annotation.hdf5", "6 MP4 videos with audio", f"{suite['num_frames']:,} aligned frames"], |
| COLORS["blue"], |
| ), |
| arrow(), |
| stage_card( |
| "02", |
| "HOMIE loader", |
| ["video, depth, pose", "mocap, IMU, language", "audio features"], |
| COLORS["teal"], |
| ), |
| arrow(), |
| stage_card( |
| "03", |
| "Window builder", |
| [ |
| f"{suite['window_frames']}-frame windows", |
| f"{suite['stride_frames']}-frame stride", |
| f"{suite['num_windows']:,} windows", |
| ], |
| COLORS["green"], |
| ), |
| arrow(), |
| stage_card( |
| "04", |
| "Feature vector", |
| [f"{suite['feature_dim']:,} dimensions", "18 named blocks incl. audio", "manifested slice indices"], |
| COLORS["orange"], |
| ), |
| ], |
| [ |
| stage_card( |
| "05", |
| "Baseline models", |
| ["motion-only classifiers", "current all-feature classifiers", "neural MLP task heads"], |
| COLORS["blue"], |
| ), |
| arrow(), |
| stage_card( |
| "06", |
| "Ropedia Xperience-10M suite", |
| [f"{task_count} minimal + {neural_count} neural results", "forecast, retrieval, alignment", "chronological evaluation"], |
| COLORS["teal"], |
| ), |
| arrow(), |
| stage_card( |
| "07", |
| "Published artifacts", |
| ["metrics.json / csv / npz / pt", "GitHub Pages dashboard", "NN comparison charts"], |
| COLORS["green"], |
| ), |
| ], |
| ] |
| rows_html = "".join(f'<section class="flow-row">{"".join(row)}</section>' for row in stage_rows) |
| checks = [ |
| "Reproduction check: rerunning scripts in a temporary local workspace reproduced the committed metrics exactly.", |
| "Modality check: sample covers video, audio, depth, pose/SLAM, mocap, IMU, and language annotation.", |
| "Feature check: current baseline manifest has synchronized video/audio/depth/pose/mocap/IMU/language blocks.", |
| "Neural check: lightweight PyTorch MLP heads are reported beside the minimal task heads under neural_mlp/.", |
| "Scope check: this validates one public sample episode, not cross-episode generalization.", |
| ] |
| checks_html = "".join(f"<li>{esc(line)}</li>" for line in checks) |
| base_layer = build_base_layer(base_path, 0.42) |
| return f"""<!doctype html> |
| <html lang="en"> |
| <head> |
| <meta charset="utf-8"> |
| <style> |
| * {{ box-sizing: border-box; }} |
| body {{ margin: 0; background: #020502; font-family: "Inter Tight", "Space Grotesk", Arial, sans-serif; }} |
| .canvas {{ |
| position: relative; |
| width: {PIPELINE_WIDTH}px; |
| height: {PIPELINE_HEIGHT}px; |
| overflow: hidden; |
| color: #f4f8ef; |
| background: |
| radial-gradient(circle at 78% 24%, rgba(167,240,120,0.18), transparent 24%), |
| radial-gradient(circle, rgba(167,240,120,0.16) 1px, transparent 2px), |
| #020502; |
| background-size: auto, 18px 18px, auto; |
| }} |
| .base-layer {{ |
| position: absolute; |
| inset: 0; |
| background-size: cover; |
| background-position: center; |
| filter: saturate(1.08) contrast(1.05) brightness(0.48); |
| }} |
| .wash {{ |
| position: absolute; |
| inset: 0; |
| background: linear-gradient(180deg, rgba(2,5,2,0.76), rgba(2,5,2,0.94)); |
| }} |
| .content {{ |
| position: relative; |
| padding: 66px 82px; |
| height: 100%; |
| }} |
| header {{ |
| display: grid; |
| grid-template-columns: 1fr auto; |
| gap: 44px; |
| align-items: start; |
| margin-bottom: 42px; |
| }} |
| .kicker {{ |
| font: 700 17px "SF Mono", Menlo, monospace; |
| color: #ccffa0; |
| text-transform: uppercase; |
| letter-spacing: 0.09em; |
| margin-bottom: 14px; |
| }} |
| h1 {{ |
| margin: 0; |
| font-size: 56px; |
| line-height: 0.98; |
| letter-spacing: 0; |
| }} |
| .subtitle {{ |
| margin: 18px 0 0; |
| max-width: 1010px; |
| color: #dce8d7; |
| font-size: 24px; |
| line-height: 1.42; |
| font-weight: 520; |
| }} |
| .metrics {{ |
| display: grid; |
| grid-template-columns: repeat(2, 150px); |
| gap: 12px; |
| margin-top: 2px; |
| }} |
| .metric {{ |
| background: rgba(7,18,7,0.86); |
| border: 1px solid rgba(167,240,120,0.26); |
| border-radius: 8px; |
| padding: 13px 15px 12px; |
| box-shadow: 0 16px 44px rgba(0,0,0,0.42); |
| }} |
| .metric strong {{ |
| display: block; |
| font: 850 24px "SF Mono", Menlo, monospace; |
| color: #f4f8ef; |
| line-height: 1; |
| font-variant-numeric: tabular-nums; |
| }} |
| .metric span {{ |
| display: block; |
| margin-top: 7px; |
| color: #a5afa2; |
| font-size: 14px; |
| font-weight: 650; |
| }} |
| .flow-row {{ |
| display: flex; |
| align-items: center; |
| gap: 18px; |
| margin-top: 30px; |
| }} |
| .flow-row:nth-of-type(2) {{ |
| width: 78%; |
| margin-left: auto; |
| margin-right: auto; |
| margin-top: 38px; |
| }} |
| .stage {{ |
| min-width: 0; |
| flex: 1 1 0; |
| height: 182px; |
| position: relative; |
| background: rgba(7,18,7,0.86); |
| border: 1px solid rgba(167,240,120,0.24); |
| border-radius: 8px; |
| padding: 24px 24px 22px 30px; |
| box-shadow: 0 24px 62px rgba(0,0,0,0.40); |
| backdrop-filter: blur(12px); |
| }} |
| .stage::before {{ |
| content: ""; |
| position: absolute; |
| inset: 0 auto 0 0; |
| width: 8px; |
| border-radius: 8px 0 0 8px; |
| background: var(--accent); |
| }} |
| .stage-number {{ |
| color: var(--accent); |
| font: 850 16px "SF Mono", Menlo, monospace; |
| letter-spacing: 0.04em; |
| margin-bottom: 10px; |
| }} |
| .stage h3 {{ |
| margin: 0 0 13px; |
| font-size: 24px; |
| line-height: 1.08; |
| letter-spacing: 0; |
| }} |
| .stage ul {{ |
| margin: 0; |
| padding: 0; |
| list-style: none; |
| color: #dce8d7; |
| font-size: 17px; |
| line-height: 1.48; |
| font-weight: 560; |
| }} |
| .flow-arrow {{ |
| width: 54px; |
| flex: 0 0 54px; |
| height: 54px; |
| display: grid; |
| place-items: center; |
| border-radius: 999px; |
| border: 1px solid rgba(167,240,120,0.26); |
| background: rgba(7,18,7,0.78); |
| color: #ccffa0; |
| font: 850 22px "SF Mono", Menlo, monospace; |
| box-shadow: 0 14px 34px rgba(0,0,0,0.36); |
| }} |
| .audit {{ |
| position: absolute; |
| left: 82px; |
| right: 82px; |
| bottom: 62px; |
| display: grid; |
| grid-template-columns: 190px 1fr; |
| gap: 26px; |
| align-items: center; |
| background: rgba(7,18,7,0.88); |
| border: 1px solid rgba(167,240,120,0.24); |
| border-radius: 8px; |
| padding: 24px 28px; |
| box-shadow: 0 22px 52px rgba(0,0,0,0.42); |
| }} |
| .audit strong {{ |
| color: #f4f8ef; |
| font-size: 23px; |
| line-height: 1.1; |
| }} |
| .audit ul {{ |
| margin: 0; |
| padding: 0; |
| list-style: none; |
| color: #dce8d7; |
| font-size: 17px; |
| line-height: 1.55; |
| font-weight: 560; |
| }} |
| </style> |
| </head> |
| <body> |
| <main class="canvas"> |
| {base_layer} |
| <div class="wash"></div> |
| <div class="content"> |
| <header> |
| <div> |
| <div class="kicker">verified single-episode pipeline</div> |
| <h1>From Ropedia Xperience-10M episode to reproducible artifacts</h1> |
| <p class="subtitle">The figure follows the actual code path and includes minimal heads plus neural MLP results. Next milestone: Qwen3-Omni fine-tuning with sensor-bridge evaluation on held-out multi-episode splits.</p> |
| </div> |
| <div class="metrics"> |
| <div class="metric"><strong>{suite['num_frames']:,}</strong><span>frames</span></div> |
| <div class="metric"><strong>{suite['num_windows']:,}</strong><span>windows</span></div> |
| <div class="metric"><strong>{suite['feature_dim']:,}</strong><span>features</span></div> |
| <div class="metric"><strong>{task_count}+{neural_count}</strong><span>min + NN tasks</span></div> |
| </div> |
| </header> |
| {rows_html} |
| <section class="audit"> |
| <strong>Reproducibility checks</strong> |
| <ul>{checks_html}</ul> |
| </section> |
| </div> |
| </main> |
| </body> |
| </html> |
| """ |
|
|
|
|
| def family_label(family: str) -> str: |
| return { |
| "softmax": "linear softmax", |
| "ridge": "ridge regression", |
| "ridge+rank": "ridge + cosine rank", |
| "multilabel": "multi-label logistic", |
| }.get(family, family) |
|
|
|
|
| def build_task_card(row: dict, color: str) -> str: |
| return f""" |
| <article class="task-card" style="--accent:{color}"> |
| <div class="chip">{esc(family_label(row['family']))}</div> |
| <h3>{esc(row['task'])}</h3> |
| <dl> |
| <dt>Input</dt><dd>{esc(row['input'])}</dd> |
| <dt>Head</dt><dd>{esc(row['head'])}</dd> |
| <dt>Output</dt><dd>{esc(row['output'])}</dd> |
| </dl> |
| <div class="metric-line"><span>Metric</span><strong>{esc(row['metric'])}</strong></div> |
| </article> |
| """ |
|
|
|
|
| def build_architecture_html(summary: dict, base_path: Path) -> str: |
| suite = summary["suite"] |
| neural_count = len(suite.get("neural_tasks", {})) |
| rows_by_task = {row["task"]: row for row in task_architecture_rows(summary)} |
| group_html = [] |
| for title, color, task_names in TASK_GROUPS: |
| cards = "".join(build_task_card(rows_by_task[name], color) for name in task_names) |
| group_html.append( |
| f""" |
| <section class="task-group" style="--accent:{color}"> |
| <div class="group-head"> |
| <span></span> |
| <h2>{esc(title)}</h2> |
| </div> |
| <div class="group-cards">{cards}</div> |
| </section> |
| """ |
| ) |
|
|
| family_cards = [ |
| ("Linear softmax", "Minimal classifier for action, subtask, transition, contact, order, and alignment tasks.", COLORS["blue"]), |
| ("Ridge regression", "Minimal closed-form projection for forecasting, reconstruction, and retrieval spaces.", COLORS["green"]), |
| ("Multi-label logistic", "Minimal one-vs-rest sigmoid heads over the object vocabulary with top-1 fallback.", COLORS["orange"]), |
| ("Neural MLP", "Optional PyTorch nonlinear classifier/regressor over the same features, splits, and metrics.", COLORS["red"]), |
| ] |
| families = "".join( |
| f""" |
| <article class="family" style="--accent:{color}"> |
| <h3>{esc(title)}</h3> |
| <p>{esc(desc)}</p> |
| </article> |
| """ |
| for title, desc, color in family_cards |
| ) |
| base_layer = build_base_layer(base_path, 0.36) |
| return f"""<!doctype html> |
| <html lang="en"> |
| <head> |
| <meta charset="utf-8"> |
| <style> |
| * {{ box-sizing: border-box; }} |
| body {{ margin: 0; background: #020502; font-family: "Inter Tight", "Space Grotesk", Arial, sans-serif; }} |
| .canvas {{ |
| position: relative; |
| width: {ARCHITECTURE_WIDTH}px; |
| height: {ARCHITECTURE_HEIGHT}px; |
| overflow: hidden; |
| color: #f4f8ef; |
| background: |
| radial-gradient(circle at 76% 18%, rgba(167,240,120,0.16), transparent 24%), |
| radial-gradient(circle, rgba(167,240,120,0.13) 1px, transparent 2px), |
| #020502; |
| background-size: auto, 18px 18px, auto; |
| }} |
| .base-layer {{ |
| position: absolute; |
| inset: 0; |
| background-size: cover; |
| background-position: center; |
| filter: saturate(1.08) contrast(1.05) brightness(0.48); |
| }} |
| .wash {{ |
| position: absolute; |
| inset: 0; |
| background: linear-gradient(180deg, rgba(2,5,2,0.76), rgba(2,5,2,0.94)); |
| }} |
| .content {{ |
| position: relative; |
| height: 100%; |
| padding: 58px 74px 64px; |
| }} |
| header {{ |
| display: grid; |
| grid-template-columns: 1fr auto; |
| gap: 42px; |
| align-items: start; |
| margin-bottom: 28px; |
| }} |
| .kicker {{ |
| font: 700 16px "SF Mono", Menlo, monospace; |
| color: #ccffa0; |
| text-transform: uppercase; |
| letter-spacing: 0.09em; |
| margin-bottom: 13px; |
| }} |
| h1 {{ |
| margin: 0; |
| font-size: 52px; |
| line-height: 1; |
| letter-spacing: 0; |
| }} |
| .subtitle {{ |
| margin: 15px 0 0; |
| max-width: 1060px; |
| color: #dce8d7; |
| font-size: 22px; |
| line-height: 1.42; |
| font-weight: 520; |
| }} |
| .summary-pill {{ |
| display: grid; |
| place-items: center; |
| min-width: 188px; |
| min-height: 112px; |
| border: 1px solid rgba(167,240,120,0.26); |
| border-radius: 8px; |
| background: rgba(7,18,7,0.86); |
| box-shadow: 0 18px 44px rgba(0,0,0,0.42); |
| text-align: center; |
| }} |
| .summary-pill strong {{ |
| font: 850 36px "SF Mono", Menlo, monospace; |
| line-height: 1; |
| }} |
| .summary-pill span {{ |
| display: block; |
| margin-top: 8px; |
| color: #a5afa2; |
| font-size: 15px; |
| font-weight: 700; |
| }} |
| .shared {{ |
| display: grid; |
| grid-template-columns: repeat(4, 1fr); |
| gap: 18px; |
| margin-bottom: 24px; |
| }} |
| .shared article {{ |
| min-height: 110px; |
| border: 1px solid rgba(167,240,120,0.24); |
| border-radius: 8px; |
| background: rgba(7,18,7,0.86); |
| padding: 20px 22px; |
| box-shadow: 0 18px 44px rgba(0,0,0,0.36); |
| }} |
| .shared h2 {{ |
| margin: 0 0 9px; |
| font-size: 22px; |
| line-height: 1.08; |
| }} |
| .shared p {{ |
| margin: 0; |
| color: #dce8d7; |
| font-size: 16px; |
| line-height: 1.38; |
| font-weight: 560; |
| }} |
| .families {{ |
| display: grid; |
| grid-template-columns: repeat(4, 1fr); |
| gap: 18px; |
| margin-bottom: 28px; |
| }} |
| .family {{ |
| min-height: 124px; |
| border: 1px solid rgba(167,240,120,0.24); |
| border-radius: 8px; |
| background: rgba(7,18,7,0.82); |
| padding: 20px 20px 18px; |
| box-shadow: 0 16px 40px rgba(0,0,0,0.34); |
| }} |
| .family h3 {{ |
| margin: 0 0 10px; |
| color: var(--accent); |
| font-size: 21px; |
| line-height: 1.08; |
| }} |
| .family p {{ |
| margin: 0; |
| color: #dce8d7; |
| font-size: 15px; |
| line-height: 1.42; |
| font-weight: 560; |
| }} |
| .task-groups {{ |
| display: grid; |
| grid-template-columns: repeat(2, minmax(0, 1fr)); |
| gap: 24px; |
| }} |
| .task-group {{ |
| border: 1px solid rgba(167,240,120,0.22); |
| border-radius: 8px; |
| background: rgba(7,18,7,0.74); |
| padding: 22px; |
| box-shadow: 0 22px 54px rgba(0,0,0,0.42); |
| backdrop-filter: blur(10px); |
| }} |
| .group-head {{ |
| display: flex; |
| align-items: center; |
| gap: 11px; |
| margin-bottom: 18px; |
| }} |
| .group-head span {{ |
| width: 12px; |
| height: 34px; |
| border-radius: 999px; |
| background: var(--accent); |
| }} |
| .group-head h2 {{ |
| margin: 0; |
| font-size: 24px; |
| line-height: 1.08; |
| color: var(--accent); |
| }} |
| .group-cards {{ |
| display: grid; |
| gap: 14px; |
| }} |
| .task-card {{ |
| min-height: 230px; |
| position: relative; |
| border: 1px solid color-mix(in srgb, var(--accent), #020502 66%); |
| border-radius: 8px; |
| background: rgba(7,18,7,0.92); |
| padding: 18px 20px 17px; |
| overflow: hidden; |
| }} |
| .task-card::before {{ |
| content: ""; |
| position: absolute; |
| inset: 0 auto 0 0; |
| width: 6px; |
| background: var(--accent); |
| opacity: 0.92; |
| }} |
| .chip {{ |
| display: inline-flex; |
| border: 1px solid color-mix(in srgb, var(--accent), #ffffff 35%); |
| border-radius: 6px; |
| padding: 4px 8px; |
| color: var(--accent); |
| font: 850 11px "SF Mono", Menlo, monospace; |
| text-transform: uppercase; |
| letter-spacing: 0.03em; |
| background: rgba(7,18,7,0.72); |
| }} |
| .task-card h3 {{ |
| margin: 13px 0 12px; |
| color: #f4f8ef; |
| font-size: 21px; |
| line-height: 1.08; |
| overflow-wrap: anywhere; |
| }} |
| dl {{ |
| display: grid; |
| grid-template-columns: 54px 1fr; |
| gap: 5px 9px; |
| margin: 0; |
| color: #dce8d7; |
| font-size: 13px; |
| line-height: 1.32; |
| font-weight: 560; |
| }} |
| dt {{ |
| color: var(--accent); |
| font: 850 10px "SF Mono", Menlo, monospace; |
| text-transform: uppercase; |
| letter-spacing: 0.04em; |
| }} |
| dd {{ margin: 0; }} |
| .metric-line {{ |
| display: flex; |
| justify-content: space-between; |
| gap: 12px; |
| align-items: center; |
| margin-top: 12px; |
| border-top: 1px solid rgba(167,240,120,0.16); |
| padding-top: 12px; |
| font-size: 13px; |
| font-weight: 700; |
| }} |
| .metric-line span {{ |
| color: #a5afa2; |
| font: 850 11px "SF Mono", Menlo, monospace; |
| text-transform: uppercase; |
| }} |
| .metric-line strong {{ |
| color: var(--accent); |
| font: 850 15px "SF Mono", Menlo, monospace; |
| text-align: right; |
| font-variant-numeric: tabular-nums; |
| }} |
| </style> |
| </head> |
| <body> |
| <main class="canvas"> |
| {base_layer} |
| <div class="wash"></div> |
| <div class="content"> |
| <header> |
| <div> |
| <div class="kicker">minimal + neural verified model architectures</div> |
| <h1>12 Ropedia Xperience-10M tasks, minimal and NN heads</h1> |
| <p class="subtitle">Each task uses the same aligned episode-window contract. The figure shows minimal heads beside neural MLP metrics; next milestone is Qwen3-Omni fine-tuning with sensor-bridge evaluation.</p> |
| </div> |
| <div class="summary-pill"><strong>{len(suite['tasks'])}+{neural_count}</strong><span>min + NN tasks</span></div> |
| </header> |
| <section class="shared"> |
| <article><h2>Shared windows</h2><p>{suite['num_frames']:,} frames to {suite['num_windows']:,} windows over video, audio, depth, pose, mocap, inertial, and language features.</p></article> |
| <article><h2>Feature vector</h2><p>X_all is {suite['feature_dim']:,} dimensions with 18 named modality blocks.</p></article> |
| <article><h2>Reusable heads</h2><p>Minimal softmax/ridge/logistic heads plus optional PyTorch MLP heads cover the whole suite.</p></article> |
| <article><h2>Artifacts</h2><p>Metrics, predictions, model weights, neural checkpoints, manifests, and the source summary report are committed.</p></article> |
| </section> |
| <section class="families">{families}</section> |
| <section class="task-groups">{"".join(group_html)}</section> |
| </div> |
| </main> |
| </body> |
| </html> |
| """ |
|
|
|
|
| def render_html(html_text: str, output_path: Path, width: int, height: int, keep_html: Path | None = None) -> None: |
| output_path.parent.mkdir(parents=True, exist_ok=True) |
| if keep_html is None: |
| with tempfile.NamedTemporaryFile("w", suffix=".html", encoding="utf-8", delete=False) as handle: |
| handle.write(html_text) |
| html_path = Path(handle.name) |
| else: |
| html_path = keep_html |
| html_path.parent.mkdir(parents=True, exist_ok=True) |
| html_path.write_text(html_text, encoding="utf-8") |
| subprocess.run( |
| [ |
| "npx", |
| "--yes", |
| "playwright", |
| "screenshot", |
| "--full-page", |
| f"--viewport-size={width},{height}", |
| html_path.resolve().as_uri(), |
| str(output_path), |
| ], |
| check=True, |
| ) |
| print(f"Wrote image: {output_path}") |
| print(f"Wrote render HTML: {html_path}") |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--pipeline-base", type=Path, default=DEFAULT_PIPELINE_BASE) |
| parser.add_argument("--architecture-base", type=Path, default=DEFAULT_ARCHITECTURE_BASE) |
| parser.add_argument("--pipeline-output", type=Path, default=DEFAULT_PIPELINE_OUTPUT) |
| parser.add_argument("--architecture-output", type=Path, default=DEFAULT_ARCHITECTURE_OUTPUT) |
| parser.add_argument("--html-dir", type=Path, help="Optional directory for the intermediate render HTML files.") |
| parser.add_argument("--only", choices=["pipeline", "architecture", "both"], default="both") |
| args = parser.parse_args() |
|
|
| summary = collect_summary() |
| if args.only in {"pipeline", "both"}: |
| pipeline_html = build_pipeline_html(summary, args.pipeline_base) |
| html_path = args.html_dir / "pipeline_diagram.html" if args.html_dir else None |
| render_html(pipeline_html, args.pipeline_output, PIPELINE_WIDTH, PIPELINE_HEIGHT, html_path) |
| if args.only in {"architecture", "both"}: |
| architecture_html = build_architecture_html(summary, args.architecture_base) |
| html_path = args.html_dir / "task_architectures.html" if args.html_dir else None |
| render_html(architecture_html, args.architecture_output, ARCHITECTURE_WIDTH, ARCHITECTURE_HEIGHT, html_path) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
