Deploy hyper3labs/HyperView-ABO-Catalog from Hyper3Labs/hyperview-spaces@67594d4

.hyperview/extensions/abo-catalog-readout/panel.js

@@ -35,7 +35,6 @@ function normalizeModels(value) {
       key: String(model.key || `model-${index}`),
       displayName: String(model.displayName || model.display_name || model.key || `Model ${index + 1}`),
       layoutKey: model.layoutKey || model.layout_key || null,
-      spaceKey: model.spaceKey || model.space_key || null,
     }))
     .filter((model) => model.layoutKey);
 }
@@ -732,7 +731,6 @@ export default function CatalogComparisonPanel() {
       await commands.showSimilar({
         sampleId: item.queryId,
         layoutKey: model.layoutKey,
-        spaceKey: model.spaceKey,
         k: 10,
         source: `abo-demo:${model.key}`,
         focus: "samples",

Dockerfile

@@ -20,7 +20,7 @@ WORKDIR $HOME/app
 
 RUN pip install --upgrade pip
 
-ARG HYPERVIEW_VERSION=0.6.1
+ARG HYPERVIEW_VERSION=0.6.2
 ARG HYPER_MODELS_VERSION=0.3.0
 
 # Install CPU-only PyTorch first so the Space does not pull the default CUDA bundle.

README.md

@@ -62,7 +62,7 @@ JavaScript.
 This folder is intended to deploy to `hyper3labs/HyperView-ABO-Catalog` from
 the `hyperview-spaces` deployment repository.
 
-The Dockerfile installs `hyperview==0.6.1` and `hyper-models[ml]==0.3.0` from
+The Dockerfile installs `hyperview==0.6.2` and `hyper-models[ml]==0.3.0` from
 PyPI. The released HyperView wheel includes the built frontend assets, so this
 Space does not carry a local `static/` bundle or copy frontend files into the
 installed package.

demo.py

@@ -12,11 +12,11 @@ from pathlib import Path
 from typing import Any
 
 from datasets import load_dataset
+from hyperview.core.sample import Sample
 from PIL import Image, ImageOps
 
 import hyperview as hv
 
-
 SPACE_DIR = Path(__file__).resolve().parent
 SPACE_HOST = os.environ.get("HYPERVIEW_HOST", "127.0.0.1")
 SPACE_PORT = int(os.environ.get("HYPERVIEW_PORT", "6262"))
@@ -379,10 +379,9 @@ def prepare_catalog_records() -> list[dict]:
 def add_abo_samples(dataset: hv.Dataset) -> None:
     existing_ids = {sample.id for sample in dataset.samples}
     media_dir = media_root()
-    added = 0
-    updated = 0
     skipped = 0
     product_counts: Counter[str] = Counter()
+    samples: list[Sample] = []
     records = prepare_catalog_records()
     expected_ids = {
         safe_sample_id(str(record["item_id"]), str(record["image_id"])) for record in records
@@ -395,7 +394,7 @@ def add_abo_samples(dataset: hv.Dataset) -> None:
     if not FORCE_SAMPLE_REFRESH and not missing_ids and not missing_media:
         print(
             f"ABO samples already prepared ({len(records)} products). "
-            "Refreshing sample metadata and media paths.",
+            "Existing sample rows will be reused.",
             flush=True,
         )
 
@@ -406,31 +405,39 @@ def add_abo_samples(dataset: hv.Dataset) -> None:
             skipped += 1
             continue
 
-        sample_exists = sample_id in existing_ids
-
         metadata = dict(record)
         metadata["hierarchy"] = f"{record['department']} -> {record['product_type_readable']}"
 
-        dataset.add_image(
-            str(destination),
-            label=record["product_type"],
-            metadata=metadata,
-            sample_id=sample_id,
+        samples.append(
+            Sample(
+                id=sample_id,
+                filepath=str(destination),
+                label=record["product_type"],
+                metadata=metadata,
+            )
         )
-        if sample_exists:
-            updated += 1
-        else:
-            existing_ids.add(sample_id)
-            added += 1
         product_counts[record["product_type"]] += 1
 
         if index == 1 or index % 50 == 0 or index == len(records):
             print(
-                f"Prepared {index}/{len(records)} products "
-                f"({added} added, {updated} updated, {skipped} skipped).",
+                f"Prepared media for {index}/{len(records)} products "
+                f"({skipped} skipped).",
                 flush=True,
             )
 
+    upserted, skipped_existing = dataset.add_samples(
+        samples,
+        skip_existing=not FORCE_SAMPLE_REFRESH,
+    )
+    updated = (
+        sum(1 for sample in samples if sample.id in existing_ids)
+        if FORCE_SAMPLE_REFRESH
+        else 0
+    )
+    added = upserted - updated
+    if skipped_existing:
+        print(f"Skipped {skipped_existing} existing ABO sample rows.", flush=True)
+    print(f"Prepared ABO samples ({added} added, {updated} updated, {skipped} media skipped).", flush=True)
     print(f"Product-type counts: {dict(product_counts)}", flush=True)
 
 
@@ -471,7 +478,6 @@ def model_panel_props(layouts: dict[str, str]) -> list[dict[str, Any]]:
                 "key": spec["key"],
                 "displayName": spec["display_name"],
                 "layoutKey": layout_key,
-                "spaceKey": layout_key.split("__euclidean_umap", 1)[0].split("__poincare_umap", 1)[0],
             }
         )
     return props