Add PyPI release workflow with trusted publishing

- Add .github/workflows/release.yml for automated releases
- Build frontend in CI (remove committed static assets)
- Use uv publish with OIDC trusted publishing
- Smoke test wheel and sdist before publishing
- Trigger on v* tags and workflow_dispatch

Author: mnm-matin

.github/workflows/release.yml

@@ -0,0 +1,94 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - "v*"  # Publish on any tag starting with v, e.g., v0.1.0
+  workflow_dispatch:
+
+jobs:
+  build:
+    name: Build distributions
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0  # Required for hatch-vcs
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      # Build frontend
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: npm
+          cache-dependency-path: frontend/package-lock.json
+
+      - name: Build frontend
+        run: |
+          cd frontend
+          npm ci
+          npm run build
+          rm -rf ../src/hyperview/server/static
+          mkdir -p ../src/hyperview/server/static
+          cp -r out/* ../src/hyperview/server/static/
+
+      # Build Python package (includes frontend assets)
+      - name: Build
+        run: uv build --no-sources
+
+      # Smoke test: verify the built package can be imported
+      - name: Smoke test (wheel)
+        run: |
+          uv run --isolated --no-project --with dist/*.whl \
+            python -c "import hyperview; print(f'hyperview {hyperview.__version__}')"
+
+      - name: Smoke test (sdist)
+        run: |
+          uv run --isolated --no-project --with dist/*.tar.gz \
+            python -c "import hyperview; print(f'hyperview {hyperview.__version__}')"
+
+      - name: Upload dist artifacts
+        uses: actions/upload-artifact@v5
+        with:
+          name: python-package-distributions
+          path: dist/
+          if-no-files-found: error
+
+  pypi:
+    name: Publish to PyPI
+    needs: build
+    if: startsWith(github.ref, 'refs/tags/v')
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/hyperview
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - name: Download dist artifacts
+        uses: actions/download-artifact@v6
+        with:
+          name: python-package-distributions
+          path: dist/
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      - name: Publish to PyPI
+        run: uv publish

.gitignore

@@ -31,9 +31,8 @@ frontend/node_modules/
 frontend/.next/
 frontend/out/
 
-# Bundled frontend in Python package (built with scripts/export_frontend.sh)
-# Not ignored - needed for pip install from git / sdist
-# src/hyperview/server/static/
+# Bundled frontend in Python package (built in CI during release)
+src/hyperview/server/static/
 
 # Python package build
 dist/
@@ -71,4 +70,7 @@ TESTS.md
 AGENTS.md
 **/AGENTS.md
 .github/agents/
-.specstory/
+.specstory/
+
+# Generated version file (hatch-vcs)
+src/hyperview/_version.py

pyproject.toml

@@ -1,12 +1,12 @@
 [project]
 name = "hyperview"
-version = "0.1.0"
+dynamic = ["version"]
 description = "Open-source dataset curation with hyperbolic embeddings visualization"
 readme = "README.md"
 license = { text = "MIT" }
 requires-python = ">=3.10"
 authors = [
-    { name = "HyperView Team" }
+    { name = "hyper3labs" }
 ]
 keywords = ["embeddings", "visualization", "hyperbolic", "dataset", "curation", "machine-learning"]
 classifiers = [
@@ -60,12 +60,18 @@ Repository = "https://github.com/Hyper3Labs/HyperView"
 Issues = "https://github.com/Hyper3Labs/HyperView/issues"
 
 [build-system]
-requires = ["hatchling"]
+requires = ["hatchling", "hatch-vcs"]
 build-backend = "hatchling.build"
 
 [tool.hatch.metadata]
 allow-direct-references = true
 
+[tool.hatch.version]
+source = "vcs"
+
+[tool.hatch.build.hooks.vcs]
+version-file = "src/hyperview/_version.py"
+
 [tool.hatch.build.targets.wheel]
 packages = ["src/hyperview"]
 # Include frontend static assets (pre-built before packaging)

src/hyperview/__init__.py

@@ -1,18 +1,14 @@
 """HyperView - Open-source dataset curation with hyperbolic embeddings visualization."""
 
-from hyperview.api import Dataset, launch
-from hyperview.embeddings.engine import (
-    EmbeddingSpec,
-    get_provider_info,
-    list_embedding_providers,
-)
+from . import _version as _version
+from . import api as _api
+
+Dataset = _api.Dataset
+launch = _api.launch
+__version__ = _version.__version__
 
-__version__ = "0.1.0"
 __all__ = [
     "Dataset",
-    "EmbeddingSpec",
-    "get_provider_info",
     "launch",
-    "list_embedding_providers",
     "__version__",
 ]

src/hyperview/core/dataset.py

@@ -369,7 +369,7 @@ def compute_embeddings(
                 hyper-models name (e.g. 'hycoclip-vit-s') for hyperbolic embeddings.
             provider: Explicit provider identifier. If not specified, auto-detected:
                 'hyper-models' if model matches a hyper-models name, else 'embed-anything'.
-                Available providers: `hyperview.list_embedding_providers()`.
+                Available providers: `hyperview.embeddings.list_embedding_providers()`.
             checkpoint: Checkpoint path/URL (hf://... or local path) for weight-only models.
             batch_size: Batch size for processing.
             show_progress: Whether to show progress bar.
@@ -384,7 +384,7 @@ def compute_embeddings(
         if not model:
             raise ValueError(
                 "model is required. Examples: 'openai/clip-vit-base-patch32' (CLIP), "
-                "'hycoclip-vit-s' (hyperbolic). See hyperview.list_embedding_providers()."
+                "'hycoclip-vit-s' (hyperbolic). See hyperview.embeddings.list_embedding_providers()."
             )
 
         from hyperview.embeddings.engine import EmbeddingSpec