fix: move Imagenette precompute to runtime, increase health start-period

LanceDB stored in Docker overlay filesystem layers is extremely slow to
reopen on free-tier CPUs, causing a 2h+ delay at container startup and
a 30-minute health check timeout.

Move dataset/embedding/layout computation to runtime (same approach as
Jaguar Space) and increase HEALTHCHECK --start-period to 600s.

Author: mnm-matin

spaces/imagenette-clip-hycoclip/Dockerfile

@@ -33,12 +33,12 @@ COPY --chown=user demo.py ./demo.py
 ENV HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
     HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media
 
-# Precompute at build time so the Space starts fast.
-RUN python -c "from demo import build_dataset; build_dataset()"
-
 EXPOSE 7860
 
-HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
+HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
     CMD curl -f http://localhost:7860/__hyperview__/health || exit 1
 
+# Dataset is built at first startup (model download + embedding computation).
+# Precomputing at Docker build time causes LanceDB to be stored in overlay
+# filesystem layers, which are extremely slow to reopen on free-tier CPUs.
 CMD ["python", "demo.py"]

spaces/imagenette-clip-hycoclip/README.md

@@ -19,8 +19,8 @@ This example runs HyperView with:
 - CLIP embeddings (`openai/clip-vit-base-patch32`) for Euclidean layout
 - HyCoCLIP embeddings (`hycoclip-vit-s`) for Poincaré layout
 
-The Docker image installs released HyperView packages from PyPI and precomputes
-the dataset, embeddings, and layouts during build for fast runtime startup.
+The Docker image installs released HyperView packages from PyPI. The dataset,
+embeddings, and layouts are computed at first startup.
 
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