Publish NapistuDataStore:

README.md

@@ -1,5 +1,5 @@
 ---
-tags: ['napistu', 'biological-networks', 'graph-neural-networks', 'napistu-data-store']
+tags: ['napistu', 'napistu-torch', 'graph-neural-networks', 'biological-networks', 'pytorch', 'napistu-data-store']
 library_name: napistu-torch
 license: mit
 ---
@@ -8,18 +8,21 @@ license: mit
 
 This dataset contains a complete NapistuDataStore with all artifacts published as a read-only store.
 
+## Source Data
+
+This store was created from GCS asset: **human_consensus_no_rxns** (version: **20251218**)
+
 ## Store Contents
 
-- **NapistuData artifacts**: 2
+- **NapistuData artifacts**: 1
 - **VertexTensor artifacts**: 1
 - **pandas DataFrame artifacts**: 5
-- **Total artifacts**: 8
+- **Total artifacts**: 7
 
 ## Artifacts
 
 ### NapistuData
 - `relation_prediction`
-- `edge_prediction`
 
 ### VertexTensors
 - `comprehensive_pathway_memberships`
@@ -35,27 +38,117 @@ This dataset contains a complete NapistuDataStore with all artifacts published a
 
 ### Load from HuggingFace Hub
 
+The easiest way to load this dataset is using the `from_huggingface` class method:
+
+```python
+from napistu_torch.napistu_data_store import NapistuDataStore
+from pathlib import Path
+
+# Load read-only store from HuggingFace Hub
+store = NapistuDataStore.from_huggingface(
+    repo_id="seanhacks/relation_prediction",
+    store_dir=Path("./local_store"),
+    revision="main"
+)
+
+# Use the store (read-only)
+napistu_data = store.load_napistu_data("edge_prediction")
+```
+
+### Configure DataConfig
+
+You can also use this dataset in your `DataConfig` for PyTorch Lightning experiments:
+
+```python
+from napistu_torch.configs import DataConfig
+from pathlib import Path
+
+# Configure DataConfig to load from HuggingFace Hub
+config = DataConfig(
+    store_dir=Path("./local_store"),
+    hf_repo_id="seanhacks/relation_prediction",
+    hf_revision="main",
+    napistu_data_name="edge_prediction",
+)
+
+# Use with NapistuDataStore.from_config()
+from napistu_torch.napistu_data_store import NapistuDataStore
+store = NapistuDataStore.from_config(config)
+```
+
+### Load Raw Data from GCS (Optional)
+
+If you need to create new artifacts, you can convert this read-only store to a non-read-only store
+by loading the raw data from GCS and enabling artifact creation:
+
 ```python
-from huggingface_hub import hf_hub_download
 from napistu_torch.napistu_data_store import NapistuDataStore
+from napistu.gcs.downloads import load_public_napistu_asset
+from napistu.gcs.constants import GCS_SUBASSET_NAMES
+from pathlib import Path
 import tempfile
-import shutil
-
-# Download registry.json and create a temporary store directory
-with tempfile.TemporaryDirectory() as temp_dir:
-    # Download registry.json
-    registry_path = hf_hub_download(
-        repo_id="username/repo-name",
-        filename="registry.json",
-        repo_type="dataset",
-        local_dir=temp_dir
+
+# Download raw data from GCS
+with tempfile.TemporaryDirectory() as temp_data_dir:
+    sbml_dfs_path = load_public_napistu_asset(
+        "human_consensus_no_rxns",
+        temp_data_dir,
+        subasset=GCS_SUBASSET_NAMES.SBML_DFS,
+        version="20251218",
+    )
+    napistu_graph_path = load_public_napistu_asset(
+        "human_consensus_no_rxns",
+        temp_data_dir,
+        subasset=GCS_SUBASSET_NAMES.NAPISTU_GRAPH,
+        version="20251218",
+    )
+    
+    # Load store from HuggingFace Hub
+    store = NapistuDataStore.from_huggingface(
+        repo_id="seanhacks/relation_prediction",
+        store_dir=Path("./local_store"),
+    revision="main"
     )
     
-    # Create store from downloaded registry
-    store = NapistuDataStore(temp_dir)
+    # Convert to non-read-only by enabling artifact creation
+    store.enable_artifact_creation(sbml_dfs_path, napistu_graph_path)
+    
+    # Now you can create new artifacts
+    store.ensure_artifacts(["new_artifact_name"])
+```
+
+Alternatively, you can use `from_huggingface` with paths directly:
+
+```python
+from napistu_torch.napistu_data_store import NapistuDataStore
+from napistu.gcs.downloads import load_public_napistu_asset
+from napistu.gcs.constants import GCS_SUBASSET_NAMES
+from pathlib import Path
+import tempfile
+
+# Download raw data from GCS
+with tempfile.TemporaryDirectory() as temp_data_dir:
+    sbml_dfs_path = load_public_napistu_asset(
+        "human_consensus_no_rxns",
+        temp_data_dir,
+        subasset=GCS_SUBASSET_NAMES.SBML_DFS,
+        version="20251218",
+    )
+    napistu_graph_path = load_public_napistu_asset(
+        "human_consensus_no_rxns",
+        temp_data_dir,
+        subasset=GCS_SUBASSET_NAMES.NAPISTU_GRAPH,
+        version="20251218",
+    )
     
-    # Use the store (read-only)
-    napistu_data = store.load_napistu_data("edge_prediction")
+    # Load and convert in one step
+    store = NapistuDataStore.from_huggingface(
+        repo_id="seanhacks/relation_prediction",
+        store_dir=Path("./local_store"),
+    revision="main",
+        sbml_dfs_path=sbml_dfs_path,
+        napistu_graph_path=napistu_graph_path,
+    )
 ```
 
 ## Note