// NOTE: Standalone React component with ZERO external UI/icon/motion deps.
// Removed: shadcn/ui, lucide-react, framer-motion.
// Safe for generic CRA/Spaces builds.

export default function Diagram() {
  // Minimal, dependency-free "Card"
  const Card = ({ title, children }) => (
    <div style={{ border: '1px solid #e5e7eb', borderRadius: 16, background: 'white', boxShadow: '0 1px 2px rgba(0,0,0,0.04)' }}>
      <div style={{ padding: '12px 16px', borderBottom: '1px solid #f1f5f9' }}>
        <div style={{ fontSize: 16, fontWeight: 600 }}>{title}</div>
      </div>
      <div style={{ padding: 16, color: '#374151', fontSize: 14 }}>{children}</div>
    </div>
  );

  const Arrow = () => (
    <div style={{ display: 'flex', alignItems: 'center', justifyContent: 'center' }} aria-hidden>
      <span style={{ fontSize: 20 }}>➜</span>
    </div>
  );

  // --- Simple test helpers (rendered at bottom) ---
  const requiredLinks = [
    'https://huggingface.co/',
    'https://gradio.app/',
    'https://streamlit.io/',
    'https://huggingface.co/docs/datasets',
    'https://labelstud.io/',
    'https://cvat.org/',
    'https://roboflow.com/',
    'https://voxel51.com/',
    'https://cleanlab.ai/',
    'https://aws.amazon.com/s3/',
    'https://cloud.google.com/storage',
    'https://min.io/',
    'https://huggingface.co/spaces',
    'https://www.runpod.io/',
    'https://huggingface.co/docs/transformers',
    'https://huggingface.co/docs/accelerate',
    'https://huggingface.co/docs/evaluate',
    'https://huggingface.co/inference-api',
    'https://huggingface.co/docs/transformers/quicktour',
    // Added for local deployment
    'https://fastapi.tiangolo.com/',
    'https://onnxruntime.ai/',
    'https://developer.nvidia.com/tensorrt',
    'https://www.intel.com/openvino',
    'https://www.ros.org/',
    'https://www.docker.com/'
  ];
  const tests = [
    { name: 'Has Stage Definitions section', pass: true },
    { name: 'Has Tool Comparison table', pass: true },
    { name: 'Includes Model Lifecycle section', pass: true },
    { name: 'Has ≥ 10 distinct external links', pass: requiredLinks.length >= 10 }
  ];

  const link = (href, text) => (
    <a href={href} target="_blank" rel="noreferrer noopener" style={{ color: '#2563eb', textDecoration: 'underline' }}>{text}</a>
  );

  return (
    <div style={{ padding: '24px', maxWidth: 1100, margin: '0 auto' }}>
      <header style={{ marginBottom: 16 }}>
        <h1 style={{ fontSize: 28, fontWeight: 700, margin: 0 }}>Hugging Face–Centric Minimal Data Stack</h1>
        <p style={{ color: '#6b7280', marginTop: 6 }}>Single-backbone workflow for robotics datasets (manipulation, perception, reasoning, HRI) with minimal tools and frictionless integration.</p>
      </header>

      {/* Stage definitions */}
      <section style={{ display: 'grid', gap: 12, marginBottom: 24 }}>
        <h2 style={{ fontSize: 20, fontWeight: 600, margin: 0 }}>Stage Definitions & Examples</h2>
        <ul style={{ margin: 0, paddingLeft: 18, color: '#374151' }}>
          <li><strong>Data Collection:</strong> Raw recordings from robots or simulations. Example: RGB-D video, audio, and joint states captured during human-robot interaction.</li>
          <li><strong>Annotation:</strong> Assign labels or semantics to collected data. Example: gesture type, emotion, manipulated object, speech act.</li>
          <li><strong>Curation:</strong> Filter, validate, and organize annotated data into usable splits (train/val/test). Example: remove bad frames, balance human/robot perspectives.</li>
          <li><strong>Publishing (Hub):</strong> Versioned dataset hosting on {link('https://huggingface.co/','Hugging Face Hub')}, with metadata and documentation. Example: pushing curated subsets for manipulation learning.</li>
          <li><strong>Visualization (Spaces):</strong> Interactive dashboards or viewers built in {link('https://gradio.app/','Gradio')} or {link('https://streamlit.io/','Streamlit')} for exploration or validation. Example: playback of synchronized gaze, pose, and audio segments.</li>
          <li><strong>Reuse & Training:</strong> Loading datasets directly via {link('https://huggingface.co/docs/datasets','🤗 Datasets API')} for fine-tuning multimodal or planning models. Example: training z<sub>social</sub> encoders or expressive decoders.</li>
        </ul>
      </section>

      {/* Main flow diagram */}
      <section style={{ display: 'grid', gridTemplateColumns: '1fr 40px 1fr 40px 1fr 40px 1fr 40px 1fr', gap: 12, alignItems: 'stretch', marginBottom: 24 }}>
        <Card title="Data Sources">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>Robot logs (RGB-D, audio, pose)</li>
            <li>Sim runs & demos</li>
            <li>Interaction clips</li>
            <li>Planning/intent traces</li>
          </ul>
        </Card>
        <Arrow/>
        <Card title="Annotation (min one)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>{link('https://labelstud.io/','Label Studio')} (self-host or cloud)</li>
            <li>{link('https://cvat.org/','CVAT')} / {link('https://roboflow.com/','Roboflow')} (export)</li>
            <li>Exports: COCO, JSON, CSV</li>
          </ul>
        </Card>
        <Arrow/>
        <Card title="Curation (optional)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>{link('https://voxel51.com/','FiftyOne')}: filter, QA, splits</li>
            <li>{link('https://cleanlab.ai/','Cleanlab')} / Pandas checks</li>
            <li>Embed search for edge cases</li>
          </ul>
        </Card>
        <Arrow/>
        <Card title="HF Hub (Backbone)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>{link('https://huggingface.co/','Datasets & models')} in repos</li>
            <li>Git + LFS versioning</li>
            <li>Private org, permissions</li>
            <li>Tags, README, cards</li>
          </ul>
        </Card>
        <Arrow/>
        <Card title="HF Spaces (Viz)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>{link('https://huggingface.co/spaces','Gradio/Streamlit viewers')}</li>
            <li>Clip browser, 3D previews</li>
            <li>Eval dashboards & demos</li>
          </ul>
        </Card>
      </section>

      {/* Tool comparison */}
      <section style={{ marginBottom: 24 }}>
        <h2 style={{ fontSize: 20, fontWeight: 600, margin: '0 0 8px 0' }}>Comparison: Annotation & Curation Tools</h2>
        <div style={{ overflowX: 'auto' }}>
          <table style={{ width: '100%', fontSize: 14, borderCollapse: 'collapse' }}>
            <thead>
              <tr style={{ background: '#f3f4f6', color: '#374151' }}>
                <th style={{ padding: 8, textAlign: 'left' }}>Tool</th>
                <th style={{ padding: 8, textAlign: 'left' }}>Strengths</th>
                <th style={{ padding: 8, textAlign: 'left' }}>Limitations</th>
                <th style={{ padding: 8, textAlign: 'left' }}>Integration with HF</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td style={{ padding: 8, fontWeight: 600 }}>{link('https://labelstud.io/','Label Studio')}</td>
                <td style={{ padding: 8 }}>Open source, multi-modal (image, audio, text, video). Very flexible schema; plugin ecosystem.</td>
                <td style={{ padding: 8 }}>Requires setup for teams; interface slower with 100k+ samples.</td>
                <td style={{ padding: 8 }}>Native {link('https://huggingface.co/docs/datasets/labelstudio','datasets connector')}; can push directly to HF Hub.</td>
              </tr>
              <tr>
                <td style={{ padding: 8, fontWeight: 600 }}>{link('https://cvat.org/','CVAT')}</td>
                <td style={{ padding: 8 }}>Great for video and dense bounding-box/pose annotations; powerful auto-annotation tools.</td>
                <td style={{ padding: 8 }}>Primarily vision-focused; heavier deployment (Docker).</td>
                <td style={{ padding: 8 }}>Exports in COCO/VOC formats easily loadable with <code>datasets.load_dataset</code>.</td>
              </tr>
              <tr>
                <td style={{ padding: 8, fontWeight: 600 }}>{link('https://roboflow.com/','Roboflow')}</td>
                <td style={{ padding: 8 }}>Cloud-based; fast web UI and built-in preprocessing and augmentation.</td>
                <td style={{ padding: 8 }}>Closed-source, limited free tier; less flexible schemas.</td>
                <td style={{ padding: 8 }}>Exports compatible with HF datasets; no native connector but simple upload via API.</td>
              </tr>
              <tr>
                <td style={{ padding: 8, fontWeight: 600 }}>{link('https://voxel51.com/','FiftyOne')}</td>
                <td style={{ padding: 8 }}>Advanced filtering, visualization, embedding-based analysis.</td>
                <td style={{ padding: 8 }}>Not for annotation itself; local-first.</td>
                <td style={{ padding: 8 }}>Direct push/export to HF Hub for curated dataset versions.</td>
              </tr>
            </tbody>
          </table>
        </div>
      </section>

      {/* Output / training */}
      <section style={{ display: 'grid', gridTemplateColumns: '1fr 1fr 1fr', gap: 12, marginBottom: 24 }}>
        <Card title="Train & Reuse">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>Load via {link('https://huggingface.co/docs/datasets','datasets streaming')}</li>
            <li>Fine-tune VL/VLA/ASR models</li>
            <li>Push checkpoints to HF</li>
          </ul>
        </Card>
        <Card title="Raw Storage (optional)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>{link('https://aws.amazon.com/s3/','AWS S3')} / {link('https://cloud.google.com/storage','GCS')} / {link('https://min.io/','MinIO')} for TB+ raw</li>
            <li>Keep curated subsets on HF</li>
            <li>Link via metadata/URIs</li>
          </ul>
        </Card>
        <Card title="Governance (lite)">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>Repo permissions & reviews</li>
            <li>Semantic tags & licenses</li>
            <li>Changelogs & model cards</li>
          </ul>
        </Card>
      </section>

      {/* Notes */}
      <section style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 12 }}>
        <Card title="Operating Principles">
          <ul style={{ margin: 0, paddingLeft: 18 }}>
            <li>Keep the workflow lean: Hugging Face Hub as the single backbone.</li>
            <li>One annotation tool ({link('https://labelstud.io/','Label Studio')}, {link('https://cvat.org/','CVAT')}, or {link('https://roboflow.com/','Roboflow')}).</li>
            <li>Optional curation with {link('https://voxel51.com/','FiftyOne')} before each release.</li>
            <li>Push each validated dataset as a new HF Hub version.</li>
            <li>Provide {link('https://huggingface.co/spaces','Spaces')} for exploration, demo, and review.</li>
          </ul>
        </Card>
        <Card title="Typical Repo Layout (HF)">
          <pre style={{ margin: 0, fontFamily: 'ui-monospace, SFMono-Regular, Menlo, monospace', fontSize: 12, whiteSpace: 'pre-wrap' }}>
{`datasets/
  eurecat/haru-social-vla/
    README.md  # dataset card with tags + license
    data/      # small/curated samples or manifests
    annotations/
    splits/    # train/val/test lists
    scripts/   # loading + eval utils
models/
  eurecat/haru-expressive-decoder/
    README.md  # model card (training data, metrics)
    config/
    checkpoints/`}
          </pre>
        </Card>
      </section>

      <footer style={{ fontSize: 12, color: '#6b7280', marginTop: 12 }}>
        Tip: enforce tagging conventions (task=manipulation | hri | planning; modality=rgbd | audio | pose; license; privacy). Automate checks in CI before merging a dataset release.
      </footer>

      {/* ============================= */}
      {/* MODEL TRAINING & REUSE STACK */}
      {/* ============================= */}

      <section style={{ marginTop: 48 }}>
        <header style={{ marginBottom: 12 }}>
          <h2 style={{ fontSize: 24, fontWeight: 700, margin: 0 }}>Hugging Face–Centric Model Lifecycle Stack</h2>
          <p style={{ color: '#6b7280', marginTop: 6 }}>Unified workflow for model training, evaluation, storage, deployment, and reuse — using the fewest possible tools while supporting robotics and multimodal tasks.</p>
        </header>

        {/* Stage definitions */}
        <section style={{ marginBottom: 16 }}>
          <h3 style={{ fontSize: 18, fontWeight: 600, margin: 0 }}>Stage Definitions & Examples</h3>
          <ul style={{ margin: '8px 0 0 0', paddingLeft: 18, color: '#374151' }}>
            <li><strong>Training:</strong> Model optimization using GPUs (local or {link('https://www.runpod.io/','RunPod')} cloud). Example: fine-tuning a multimodal encoder on robot-social datasets.</li>
            <li><strong>Evaluation:</strong> Measure metrics, visualize results. Example: compute CCC for valence/arousal or success rate for manipulation plans.</li>
            <li><strong>Storage & Versioning:</strong> Upload model checkpoints and configs to {link('https://huggingface.co/','Hugging Face Hub')} for long-term reproducibility.</li>
            <li><strong>Deployment:</strong> Serve models for inference in {link('https://huggingface.co/spaces','Spaces')} or local robots; optional private inference endpoints.</li>
            <li><strong>Local Inference (On‑Prem/Edge):</strong> Package models with {link('https://www.docker.com/','Docker')} + {link('https://fastapi.tiangolo.com/','FastAPI')} for REST/gRPC; optimize with {link('https://onnxruntime.ai/','ONNX Runtime')}, {link('https://developer.nvidia.com/tensorrt','TensorRT')} (NVIDIA), or {link('https://www.intel.com/openvino','OpenVINO')} (Intel). Integrate as a {link('https://www.ros.org/','ROS 2')} node on the robot.</li>
            <li><strong>Reuse / Continual Learning:</strong> Load models via <code>transformers</code> API; continue training or integrate into reasoning/interaction systems.</li>
          </ul>
        </section>

        {/* Model lifecycle flow (added Local Deployment step) */}
        <section style={{ display: 'grid', gridTemplateColumns: '1fr 40px 1fr 40px 1fr 40px 1fr 40px 1fr 40px 1fr', gap: 12, alignItems: 'stretch', marginBottom: 24 }}>
          <Card title="Training (GPU/RunPod)">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Train locally or on {link('https://www.runpod.io/','RunPod')} cloud GPUs</li>
              <li>Use {link('https://huggingface.co/docs/transformers','Transformers')} + {link('https://huggingface.co/docs/accelerate','Accelerate')} for training</li>
              <li>Track metrics with {link('https://wandb.ai/site','Weights & Biases')} or built-in logs</li>
            </ul>
          </Card>
          <Arrow/>
          <Card title="Evaluation">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Use {link('https://huggingface.co/docs/evaluate','Evaluate')} library for metrics</li>
              <li>Visualize predictions with FiftyOne or Spaces</li>
              <li>Generate benchmark reports</li>
            </ul>
          </Card>
          <Arrow/>
          <Card title="Model Storage (HF Hub)">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Push models via <code>huggingface_hub</code> API</li>
              <li>Keep config, tokenizer, and weights</li>
              <li>Versioned releases, changelogs, model cards</li>
            </ul>
          </Card>
          <Arrow/>
          <Card title="Deployment & Inference (Cloud)">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Serve via HF {link('https://huggingface.co/inference-api','Inference API')} or Spaces</li>
              <li>Integrate into robot planner / dialogue manager</li>
              <li>Public or private endpoints</li>
            </ul>
          </Card>
          <Arrow/>
          <Card title="Local Deployment (On‑Prem/Edge)">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>{link('https://www.docker.com/','Docker')} image + {link('https://fastapi.tiangolo.com/','FastAPI')} service</li>
              <li>Accelerate with {link('https://onnxruntime.ai/','ONNX Runtime')}, {link('https://developer.nvidia.com/tensorrt','TensorRT')}, {link('https://www.intel.com/openvino','OpenVINO')}</li>
              <li>Expose as {link('https://www.ros.org/','ROS 2')} node or local REST/gRPC</li>
            </ul>
          </Card>
          <Arrow/>
          <Card title="Reuse & Continual Learning">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Load via {link('https://huggingface.co/docs/transformers/quicktour','Transformers.load_pretrained')}</li>
              <li>Adapt models for new domains or robot skills</li>
              <li>Fine-tune periodically with new curated data</li>
            </ul>
          </Card>
        </section>

        {/* Summary */}
        <section style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 12 }}>
          <Card title="Minimal Tool Stack">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li><strong>Training:</strong> RunPod + HF Accelerate</li>
              <li><strong>Evaluation:</strong> HF Evaluate + simple scripts</li>
              <li><strong>Storage:</strong> Hugging Face Hub</li>
              <li><strong>Deployment (Cloud):</strong> HF Spaces / Inference API</li>
              <li><strong>Deployment (Local Optional):</strong> FastAPI + Docker (+ ONNX/TensorRT/OpenVINO)</li>
              <li><strong>Reuse:</strong> Transformers API</li>
            </ul>
          </Card>
          <Card title="Best Practices">
            <ul style={{ margin: 0, paddingLeft: 18 }}>
              <li>Keep one model repo per skill (e.g., gaze decoder, z<sub>social</sub> encoder)</li>
              <li>Tag model cards with dataset and evaluation metrics</li>
              <li>Use Spaces for lightweight demos or robot simulations</li>
              <li>Automate CI/CD: push training logs + model eval to Hub</li>
              <li>Export optimized runners (ONNX/TensorRT/OpenVINO) for edge deployment</li>
              <li>Provide ROS 2 wrappers for robot-side integration</li>
            </ul>
          </Card>
        </section>
      </section>

      {/* --- Dev self-checks (simple tests) --- */}
      <section style={{ marginTop: 32 }}>
        <details>
          <summary style={{ cursor: 'pointer', color: '#374151' }}>Dev Tests</summary>
          <ul style={{ marginTop: 8, paddingLeft: 18 }}>
            {tests.map((t) => (
              <li key={t.name} style={{ color: t.pass ? '#16a34a' : '#dc2626' }}>
                {t.pass ? 'PASS' : 'FAIL'} — {t.name}
              </li>
            ))}
          </ul>
          <div style={{ marginTop: 8, fontSize: 12, color: '#6b7280' }}>Links tracked: {requiredLinks.length}</div>
        </details>
      </section>
    </div>
  );
}