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# =============================================================================
# HyperView - Hugging Face Spaces Dockerfile
# =============================================================================
# Multi-stage build for deploying HyperView to HuggingFace Spaces.
#
# Features:
# - CLIP embeddings (Euclidean) via embed-anything
# - HyCoCLIP embeddings (Hyperbolic) via hyper-models ONNX
# - Pre-computed demo dataset (300 CIFAR-10 samples)
# - Torch-free runtime for minimal image size
#
# Deploy: https://huggingface.co/spaces/Hyper3Labs/HyperView
# =============================================================================
# -----------------------------------------------------------------------------
# Stage 1: Build Frontend (Next.js static export)
# -----------------------------------------------------------------------------
FROM node:20-slim AS frontend-builder
WORKDIR /app/frontend
# Install dependencies first (better caching)
COPY frontend/package.json frontend/package-lock.json ./
RUN npm ci --prefer-offline
# Build hyper-scatter (installed from source tarball, dist-lib not prebuilt)
RUN cd node_modules/hyper-scatter \
&& npm install \
&& npm run build:lib
# Copy frontend source and build
COPY frontend/ ./
RUN npm run build
# Verify output exists
RUN ls -la out/ && echo "Frontend build complete"
# -----------------------------------------------------------------------------
# Stage 2: Python Runtime
# -----------------------------------------------------------------------------
FROM python:3.11-slim AS runtime
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
curl \
git \
libssl-dev \
pkg-config \
&& rm -rf /var/lib/apt/lists/*
# HuggingFace Spaces requirement: create user with UID 1000
RUN useradd -m -u 1000 user
# Switch to user
USER user
# Set environment variables
ENV HOME=/home/user \
PATH=/home/user/.local/bin:$PATH \
HF_HOME=/home/user/.cache/huggingface \
PYTHONUNBUFFERED=1 \
PIP_NO_CACHE_DIR=1
WORKDIR $HOME/app
# Upgrade pip
RUN pip install --upgrade pip
# Copy Python package files
COPY --chown=user pyproject.toml README.md LICENSE ./
COPY --chown=user src/ ./src/
COPY --chown=user scripts/ ./scripts/
# Install Python package (without ML extras - we use ONNX)
RUN pip install -e .
# Copy built frontend to static directory
COPY --from=frontend-builder --chown=user /app/frontend/out ./src/hyperview/server/static/
# Verify frontend is in place
RUN ls -la src/hyperview/server/static/ && echo "Frontend copied successfully"
# -----------------------------------------------------------------------------
# Stage 3: Pre-compute Demo Dataset
# -----------------------------------------------------------------------------
# Create output directories
RUN mkdir -p $HOME/app/demo_data/datasets $HOME/app/demo_data/media
# Set environment for precomputation
ENV HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media \
DEMO_SAMPLES=300
# Pre-download HuggingFace models and compute embeddings
# This runs during build to ensure fast startup
RUN python scripts/precompute_hf_demo.py
# Verify dataset was created
RUN ls -la demo_data/ && echo "Demo dataset pre-computed successfully"
# -----------------------------------------------------------------------------
# Final Configuration
# -----------------------------------------------------------------------------
# Copy entrypoint
COPY --chown=user app_hf.py ./
# Set runtime environment
ENV HOST=0.0.0.0 \
PORT=7860 \
DEMO_DATASET=cifar10_hf_demo \
HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media
# Expose port (HuggingFace Spaces default)
EXPOSE 7860
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
CMD curl -f http://localhost:7860/__hyperview__/health || exit 1
# Start server
CMD ["python", "app_hf.py"]
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