Deploy the 1.1.0 bundle as a Live Space

.dockerignore

@@ -1,4 +0,0 @@
-demo_data/
-__pycache__/
-*.pyc
-.DS_Store

.gitattributes

@@ -0,0 +1,4 @@
+bundle/restore/spaces/embed-anything__openai_clip-vit-base-patch32__4771034973d8/vectors.npz filter=lfs diff=lfs merge=lfs -text
+bundle/restore/spaces/embed-anything__openai_clip-vit-base-patch32__8da42c3ae90c/vectors.npz filter=lfs diff=lfs merge=lfs -text
+bundle/restore/spaces/hyper-models__hyper3-clip-v0_5__33d32a431c15/vectors.npz filter=lfs diff=lfs merge=lfs -text
+bundle/restore/spaces/hyper-models__hyper3-clip-v0_5__42052c955756/vectors.npz filter=lfs diff=lfs merge=lfs -text

.hyperview/extensions/fashion-search-readout/panel.jsx

@@ -1,128 +0,0 @@
-const sdk = globalThis.HyperViewPanelSDK;
-if (!sdk || sdk.version !== "2") {
-  throw new Error("HyperViewPanelSDK v2 is not available on window.");
-}
-
-const { React, hooks } = sdk;
-const { usePanelActions, usePanelState, useSampleResults, useSamples, useSelection } = hooks;
-
-function mediaUrl(sample) {
-  const raw = sample?.media_url || sample?.thumbnail_url || sample?.thumbnail;
-  return typeof raw === "string" && raw ? raw : null;
-}
-
-function percentage(value) {
-  return Number.isFinite(Number(value)) ? `${(Number(value) * 100).toFixed(1)}%` : "—";
-}
-
-function ModelCard({ name, checkpoint, color, rank, readout }) {
-  return (
-    <article className="fs-model">
-      <span className="fs-model-top"><span><i style={{background:color}} />{name}</span><strong style={{color}}>#{rank}</strong></span>
-      <small>{checkpoint} · first matching product</small><p>{readout}</p>
-    </article>
-  );
-}
-
-export default function FashionSearchPanel() {
-  const { props = {}, state = {}, patchState } = usePanelState();
-  const { updateProps } = usePanelActions();
-  const { showResults } = useSampleResults();
-  const { setSelection } = useSelection();
-  const photoCases = Array.isArray(props.photoCases) ? props.photoCases : [];
-  const textCases = Array.isArray(props.cases) ? props.cases : [];
-  const mode = state.activeMode === "text" ? "text" : "photo";
-  const activePhoto = photoCases.find((item) => item.id === state.activePhotoCaseId) || photoCases[0] || null;
-  const activeText = textCases.find((item) => item.id === state.activeTextCaseId) || textCases[0] || null;
-  const active = mode === "photo" ? activePhoto : activeText;
-  const samplePage = useSamples(props.collectionId, { pageSize: 800 });
-  const sampleById = React.useMemo(() => new Map(samplePage.samples.map((sample) => [sample.id, sample])), [samplePage.samples]);
-  const [busy, setBusy] = React.useState(false);
-  const [error, setError] = React.useState(null);
-
-  React.useEffect(() => {
-    if (samplePage.hasMore && !samplePage.loading) samplePage.loadMore();
-  }, [samplePage.hasMore, samplePage.loading, samplePage.loadMore]);
-
-  const present = (item, nextMode) => {
-    if (!item || busy) return;
-    setBusy(true);
-    setError(null);
-    void (async () => {
-      try {
-        const hyper3Results = await showResults(
-          item.results.hyper3.map((result) => result.sampleId),
-          { focus: false, source: `${item.label} · Hyper3-CLIP` },
-        );
-        const anchorSampleId = nextMode === "photo" ? item.anchorSampleId : null;
-        const hyper3CollectionId = hyper3Results?.result?.collection_id || item.collectionIds.hyper3;
-        await updateProps("samples", { mode: "results", collectionId: hyper3CollectionId, anchorSampleId });
-        await updateProps(props.clipSamplesPanelId || "fashion-clip-results", { mode: "results", collectionId: item.collectionIds.clip, anchorSampleId });
-        await patchState(nextMode === "photo" ? { activeMode: "photo", activePhotoCaseId: item.id } : { activeMode: "text", activeTextCaseId: item.id });
-        await setSelection([nextMode === "photo" ? item.anchorSampleId : item.target.sampleId]);
-      } catch (reason) {
-        setError(reason instanceof Error ? reason.message : String(reason));
-      } finally {
-        setBusy(false);
-      }
-    })();
-  };
-
-  if (!active) return <main className="fs-root">No Fashion cases are available.</main>;
-
-  const visualSampleId = mode === "photo" ? active.anchorSampleId : active.target.sampleId;
-  const visual = sampleById.get(visualSampleId);
-  const visualUrl = mediaUrl(visual);
-  const photoBenchmark = props.photoBenchmark || {};
-  const typed = props.aggregate || {};
-  const models = props.models || {};
-
-  return (
-    <main className="fs-root">
-      <style>{`
-        .fs-root,.fs-root *{box-sizing:border-box}.fs-root{height:100%;overflow:auto;padding:14px;background:var(--hv-color-background);color:var(--hv-color-foreground);font:11px/1.42 system-ui,-apple-system,BlinkMacSystemFont,sans-serif}.fs-kicker{display:block;color:var(--hv-color-muted-foreground);font-size:9px;font-weight:800;letter-spacing:.09em;text-transform:uppercase}.fs-header h2{margin:3px 0 0;font-size:18px;line-height:1.15;letter-spacing:-.02em}.fs-header p{margin:6px 0 0;color:var(--hv-color-muted-foreground)}.fs-tabs{display:grid;grid-template-columns:1fr 1fr;gap:7px;margin-top:11px}.fs-tab{border:1px solid var(--hv-color-border);border-radius:8px;padding:9px 8px;background:var(--hv-color-surface);color:var(--hv-color-muted-foreground);cursor:pointer;font-weight:760}.fs-tab:hover{border-color:color-mix(in srgb,var(--hv-color-accent) 55%,var(--hv-color-border));color:var(--hv-color-foreground)}.fs-tab.is-active{border-color:var(--hv-color-accent);background:color-mix(in srgb,var(--hv-color-accent) 20%,var(--hv-color-surface));color:var(--hv-color-foreground);box-shadow:inset 0 0 0 1px var(--hv-color-accent)}.fs-query{margin:10px 0 0;padding:8px 9px;border-left:2px solid var(--hv-color-accent);background:var(--hv-color-surface-muted);font-size:12px;font-weight:660}.fs-visual{display:grid;grid-template-columns:104px minmax(0,1fr);gap:9px;align-items:center;width:100%;margin-top:9px;border:1px solid var(--hv-color-border);border-radius:8px;padding:7px;background:var(--hv-color-surface);color:var(--hv-color-foreground);text-align:left;cursor:pointer}.fs-visual img{width:104px;height:96px;object-fit:contain;border-radius:5px;background:var(--hv-color-surface-muted)}.fs-visual strong,.fs-visual small{display:block}.fs-visual strong{margin-top:3px;font-size:12px}.fs-visual small{margin-top:3px;color:var(--hv-color-muted-foreground);font-size:9px}.fs-models{display:grid;grid-template-columns:1fr 1fr;gap:7px;margin-top:9px}.fs-model{border:1px solid var(--hv-color-border);border-radius:8px;padding:9px;background:var(--hv-color-surface)}.fs-model-top{display:flex;justify-content:space-between;gap:6px;font-weight:750}.fs-model-top span{display:flex;align-items:center;gap:5px}.fs-model-top i{width:7px;height:7px;border-radius:99px}.fs-model-top strong{font-size:17px}.fs-model>small{display:block;color:var(--hv-color-muted-foreground);font-size:8px}.fs-model p{margin:6px 0 0;color:var(--hv-color-muted-foreground);font-size:9px}.fs-cases{display:grid;grid-template-columns:1fr 1fr;gap:7px;margin-top:9px}.fs-case{border:1px solid var(--hv-color-border);border-radius:8px;padding:8px;background:var(--hv-color-surface);color:var(--hv-color-foreground);text-align:left;cursor:pointer}.fs-case:hover{border-color:color-mix(in srgb,var(--hv-color-accent) 55%,var(--hv-color-border))}.fs-case[aria-pressed=true]{border-color:var(--hv-color-accent);background:color-mix(in srgb,var(--hv-color-accent) 16%,var(--hv-color-surface));box-shadow:inset 0 0 0 1px var(--hv-color-accent)}.fs-case strong,.fs-case small{display:block}.fs-case small{margin-top:2px;color:var(--hv-color-muted-foreground);font-size:8px}.fs-benchmark{margin-top:13px;border-top:1px solid var(--hv-color-border);padding-top:10px}.fs-benchmark h3{margin:3px 0 2px;font-size:12px}.fs-caption{margin:0 0 7px;color:var(--hv-color-muted-foreground);font-size:9px}.fs-table{width:100%;border-collapse:collapse;font-size:9px}.fs-table th,.fs-table td{padding:5px 4px;border-bottom:1px solid var(--hv-color-border);text-align:right}.fs-table th:first-child,.fs-table td:first-child{text-align:left}.fs-table th{color:var(--hv-color-muted-foreground);font-size:8px;text-transform:uppercase}.fs-note{margin:7px 0 0;color:var(--hv-color-muted-foreground);font-size:9px}.fs-error{color:#ef4444}.fs-busy{opacity:.62}@media(max-width:390px){.fs-root{padding:10px}.fs-models{grid-template-columns:1fr}.fs-visual{grid-template-columns:82px minmax(0,1fr)}.fs-visual img{width:82px;height:78px}}
-      `}</style>
-
-      <header className="fs-header">
-        <span className="fs-kicker">Catalog search</span>
-        <h2>{mode === "photo" ? "Same product, another photo. Can search find it?" : `Does search find ${active.label.toLowerCase()}?`}</h2>
-        <p>{mode === "photo" ? "Use one product photo to find its other catalog views." : "Compare the same detailed request across both models."}</p>
-      </header>
-
-      <nav className="fs-tabs" aria-label="Search type">
-        <button type="button" className={`fs-tab${mode === "photo" ? " is-active" : ""}`} onClick={() => present(activePhoto, "photo")}>Photo match</button>
-        <button type="button" className={`fs-tab${mode === "text" ? " is-active" : ""}`} onClick={() => present(activeText, "text")}>Typed search</button>
-      </nav>
-
-      {mode === "text" ? <blockquote className="fs-query">“{active.query}”</blockquote> : null}
-
-      <button type="button" className="fs-visual" onClick={() => void setSelection([visualSampleId])}>
-        {visualUrl ? <img src={visualUrl} alt={active.label} /> : null}
-        <span><span className="fs-kicker">{mode === "photo" ? "Starting photo" : "Matching product"}</span><strong>{active.label}</strong><small>{mode === "photo" ? active.context : "Any view of this product counts as a match."}</small>{mode === "photo" ? <small>Correct means the same product ID, not a similar style.</small> : null}</span>
-      </button>
-
-      <section className="fs-models" aria-label="First matching product rank">
-        <ModelCard name="Hyper3-CLIP" checkpoint={models.hyper3 || "v0.5"} color="#60a5fa" rank={active.target.hyper3Rank} readout={active.modelReadout.hyper3} />
-        <ModelCard name="OpenAI CLIP" checkpoint={models.clip || "ViT-B/32"} color="#f59e0b" rank={active.target.clipRank} readout={active.modelReadout.clip} />
-      </section>
-
-      {mode === "photo" ? <nav className="fs-cases" aria-label="Photo matching examples">{photoCases.map((item) => <button key={item.id} type="button" className={`fs-case${busy ? " fs-busy" : ""}`} aria-pressed={item.id === active.id} disabled={busy} onClick={() => present(item, "photo")}><strong>{item.label}</strong><small>{item.context}</small></button>)}</nav> : textCases.length > 1 ? <nav className="fs-cases" aria-label="Typed search examples">{textCases.map((item) => <button key={item.id} type="button" className={`fs-case${busy ? " fs-busy" : ""}`} aria-pressed={item.id === active.id} disabled={busy} onClick={() => present(item, "text")}><strong>{item.label}</strong><small>{item.target.hyper3Rank === 1 ? "Hyper3 first" : item.target.clipRank === 1 ? "CLIP first" : "Compare ranks"}</small></button>)}</nav> : null}
-
-      <section className="fs-benchmark" aria-label="Full benchmark results">
-        <span className="fs-kicker">How the models compare</span>
-        {mode === "photo" ? <>
-          <h3>{photoBenchmark.queryCount} queries · {photoBenchmark.candidateCount} catalog photos</h3><p className="fs-caption">Correct means another view of the same product.</p>
-          <table className="fs-table"><thead><tr><th>Correct product</th><th style={{color:"#60a5fa"}}>Hyper3</th><th style={{color:"#f59e0b"}}>CLIP</th></tr></thead><tbody><tr><td>Ranked first</td><td>{percentage(photoBenchmark.hyper3R1)}</td><td>{percentage(photoBenchmark.clipR1)}</td></tr><tr><td>Within top ten</td><td>{percentage(photoBenchmark.hyper3R10)}</td><td>{percentage(photoBenchmark.clipR10)}</td></tr><tr><td>Mean average precision</td><td>{Number(photoBenchmark.hyper3Map).toFixed(3)}</td><td>{Number(photoBenchmark.clipMap).toFixed(3)}</td></tr></tbody></table>
-          <p className="fs-note">Cases use the largest first-match rank gap in each direction, so the CLIP win is shown too. Photo matching and typed retrieval are separate evaluations; their scores are not interchangeable.</p>
-        </> : <>
-          <h3>{typed.queryCount} written requests · {typed.candidateCount} photos</h3><p className="fs-caption">Correct means any view of the described product.</p>
-          <table className="fs-table"><thead><tr><th>Correct product</th><th style={{color:"#60a5fa"}}>Hyper3</th><th style={{color:"#f59e0b"}}>CLIP</th></tr></thead><tbody><tr><td>Ranked first</td><td>{percentage(typed.hyper3Hit1)}</td><td>{percentage(typed.clipHit1)}</td></tr><tr><td>Within top ten</td><td>{percentage(typed.hyper3Hit10)}</td><td>{percentage(typed.clipHit10)}</td></tr><tr><td>Within top fifty</td><td>{percentage(typed.hyper3Hit50)}</td><td>{percentage(typed.clipHit50)}</td></tr></tbody></table>
-          <p className="fs-note">This is one illustrative Hyper3 win. Across the full 1,120-photo benchmark, the models are close and OpenAI CLIP is ahead at fifty results. The visible workspace map contains {props.workspaceSampleCount || 741} catalog photos.</p>
-        </>}
-      </section>
-
-      {samplePage.error ? <p className="fs-error">{samplePage.error}</p> : null}
-      {error ? <p className="fs-error" role="alert">{error}</p> : null}
-    </main>
-  );
-}

Dockerfile

@@ -1,10 +1,7 @@
 FROM python:3.11-slim
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
     curl \
-    libssl-dev \
-    pkg-config \
     && rm -rf /var/lib/apt/lists/*
 
 RUN useradd -m -u 1000 user
@@ -16,47 +13,40 @@ ENV HOME=/home/user \
     PYTHONUNBUFFERED=1 \
     PIP_NO_CACHE_DIR=1
 
-WORKDIR $HOME/app
+WORKDIR /home/user/app
 
 RUN pip install --upgrade pip
-
-ARG HYPERVIEW_VERSION=1.0.0
-ARG HYPER_MODELS_VERSION=0.3.1
-
-# Install CPU-only PyTorch first so the Space does not pull the default CUDA bundle.
 RUN pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
-RUN echo "Installing hyperview==${HYPERVIEW_VERSION}" \
-    && pip install "hyperview==${HYPERVIEW_VERSION}" \
-    && python - <<'PY'
-import inspect
-import hyperview as hv
-
-print("hyperview", hv.__version__, inspect.signature(hv.launch))
-PY
 RUN pip install \
-    "hyper-models[ml]==${HYPER_MODELS_VERSION}" \
+    "hyperview==1.1.0" \
+    "hyper-models[ml]==0.3.1" \
     "datasets>=4.5.0" \
     "Pillow>=12.0.0"
 
-COPY --chown=user . .
+# The bundle is the unit of delivery: `hyperview serve --from` restores the
+# exported workspace, dataset, and layouts from it at startup.
+COPY --chown=user bundle /home/user/app/bundle
+RUN mkdir -p /home/user/app/data/datasets /home/user/app/data/media
 
-# HyperView 1.0 mints a session token and rejects unauthenticated runtime
+# HyperView mints a session token and rejects unauthenticated runtime
 # commands. A public Space has no way to hand visitors that token, so panel
 # creation and case switching 401 without this. It marks the server public:
 # visitors get the viewer commands and nothing else, so provider registration,
 # extension install, tool execution and compute stay closed.
 ENV HYPERVIEW_NO_AUTH=1
-ENV HYPERVIEW_HOST=0.0.0.0 \
-    HYPERVIEW_PORT=7860 \
-    HYPERVIEW_WORKSPACE_ID=fashion-typed-search-business-evidence-v3 \
-    HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
-    HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media \
-    HF_HUB_ETAG_TIMEOUT=30 \
-    HF_HUB_DOWNLOAD_TIMEOUT=120
+ENV HYPERVIEW_DATASETS_DIR=/home/user/app/data/datasets \
+    HYPERVIEW_MEDIA_DIR=/home/user/app/data/media
 
 EXPOSE 7860
 
-HEALTHCHECK --interval=30s --timeout=10s --start-period=2700s --retries=3 \
-    CMD curl -f http://localhost:7860/__hyperview__/health || exit 1
-
-CMD ["python", "demo.py"]
+HEALTHCHECK --interval=30s --timeout=10s --start-period=300s --retries=3 \
+    CMD curl -f http://localhost:7860/api/runtime || exit 1
+
+# The image carries the bundle and never moves it, so the restored dataset
+# points at the bundle's own media instead of copying it a second time.
+CMD ["hyperview", "serve", \
+     "--from", "/home/user/app/bundle", \
+     "--link-media", \
+     "--host", "0.0.0.0", \
+     "--port", "7860", \
+     "--public"]

PHOTO_EVIDENCE.md

@@ -1,20 +0,0 @@
-# Fashion photo-matching evidence
-
-This readout documents `deepfashion-same-product-persisted-spaces-2026-08-12`,
-the photo-to-photo evidence shown by the Fashion Products Space.
-
-- Dataset: the persisted 741-image DeepFashion catalog used by this workspace.
-- Gallery: all 741 catalog photos.
-- Queries: 710 photos whose DeepFashion product key has at least one other view.
-- Positive: another photo with the same DeepFashion product key.
-- Self-match: excluded.
-- Metrics: first matching-product rank, recall at 1 and 10, and average precision.
-- Case selection: the largest first-match rank gap in each direction, so the
-  walkthrough includes both a Hyper3-CLIP win and an OpenAI CLIP win.
-
-The canonical JSON projection `{photoBenchmark, photoCases}` has SHA-256
-`958017f729f73990b392530b37cbdbe8812c923141573077dbdc0c3f65213c4b`.
-`demo.py` recomputes and validates this hash before launching.
-
-This 741-image catalog probe is separate from the 1,120-image typed-search
-benchmark in the same Space. Their numbers are not interchangeable.

README.md

@@ -2,74 +2,24 @@
 title: HyperView DeepFashion Text Search
 emoji: 👖
 colorFrom: blue
-colorTo: indigo
+colorTo: green
 sdk: docker
 app_port: 7860
 pinned: false
+short_description: HyperView Live Space for deepfashion_text_search_clip_hyper3
 ---
 
-# HyperView — DeepFashion typed-search audit
+# HyperView DeepFashion Text Search
 
-This demo is designed for an ecommerce search or merchandising lead asking two
-questions: **does an attribute-heavy shopper request put the exact SKU on the
-first results screen, and does the model organize the broader catalog into
-coherent product categories?**
+A HyperView **Live Space**: the container runs the HyperView server over an
+exported workspace bundle, so visitors can browse the prepared view and also
+run new text queries, layouts, and model jobs.
 
-It presents three prepared shopper requests from a bounded DeepFashion
-text-to-image probe. The compact query panel controls a native ranked Samples
-panel, while a native Hyper3-CLIP scatter shows the full 741-product catalog
-topology with standard pan, zoom, lasso, and shared selection.
+## Contents
 
-The aggregate result is intentionally framed as modest: across 180
-metadata-generated queries and 1,120 candidate images, Hit@1 is 24.4% vs 23.3%
-and Hit@10 is 57.2% vs 55.0%. Hyper3 has 13 strong wins under the documented
-cutoff and CLIP has 9. The selected case illustrates a real failure mode; it is
-not evidence of a universal text-search advantage.
+- Workspace: `fashion-typed-search-business-evidence-v3`
+- Dataset: `deepfashion_text_search_clip_hyper3clip`
+- Exported with HyperView `1.1.0`
+- Capabilities: browse_samples, lasso_2d, layouts, selection
 
-## Static behavior
-
-The static export keeps the full HyperView shell and allows visitors to switch
-among three prepared requests, compare the stored Hyper3-CLIP and CLIP Top 6,
-browse the catalog, inspect exact targets, and explore the catalog topology. It
-does not show an arbitrary text box because running a new query requires model
-inference in a hosted HyperView Space.
-
-The workspace uses the persisted 741-row demo catalog. The custom panel uses
-normal JSX and public `HyperViewPanelSDK` hooks to orchestrate native panels;
-it does not reimplement result grids or scatter controls.
-
-## Run locally
-
-From the HyperView repository:
-
-```bash
-uv run python hyperview-spaces/demos/fashion-deepfashion-text-search-clip-hyper3clip/demo.py
-```
-
-Use `HYPERVIEW_PORT=<port>` to choose another local port.
-
-To run the full Live Space with arbitrary shopper text instead of the prepared
-model-comparison walkthrough:
-
-```bash
-uv run python hyperview-spaces/demos/fashion-deepfashion-text-search-clip-hyper3clip/live_demo.py
-```
-
-The native Samples panel discovers text-capable indexes from the runtime and
-routes new queries only to providers that can actually encode text.
-
-## Claim boundary
-
-- Dataset: `Marqo/deepfashion-inshop`, split `data`.
-- Corpus: 1,120 images from 260 products.
-- Query set: 180 queries generated from dataset metadata and visual attributes,
-  not real shopper logs.
-- Positive: any image view with the same DeepFashion product key.
-- This comparison does not include newer SigLIP, OpenCLIP, Jina-CLIP, NV-CLIP,
-  or Gemini baselines.
-
-## Deploy source
-
-This folder is intended to deploy to
-`hyper3labs/HyperView-DeepFashion-Text-Search` from the `hyperview-spaces`
-deployment repository.
+Built with [HyperView](https://github.com/Hyper3Labs/HyperView).

bundle/.assetsignore

@@ -0,0 +1,2 @@
+wrangler.jsonc
+.assetsignore

bundle/404.html

@@ -0,0 +1 @@
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