Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.10
+
+ARG SPOTLIGHT_VERSION=1.5.0
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+ENV SPOTLIGHT_VERSION=$SPOTLIGHT_VERSION
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel
+RUN pip install --no-cache-dir --upgrade "renumics-spotlight==${SPOTLIGHT_VERSION}"
+
+COPY --chown=user --chmod=0755 run.py .
+
+CMD ["./run.py"]

README.md

@@ -1,10 +1,14 @@
 ---
-title: Spotlight Mnist
-emoji: 🚀
-colorFrom: purple
-colorTo: red
+# title:
+emoji: 🔬
+colorFrom: indigo
+colorTo: green
 sdk: docker
+app_port: 7860
+datasets: [mnist, renumics/spotlight-mnist-enrichment]
+tags: [renumics, spotlight, EDA, enriched, data-centric-ai, viewer]
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Explore mnist with [Renumics Spotlight](https://github.com/renumics/spotlight)!

run.py

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+#!/usr/bin/env python3
+"""
+Serve a Hugging Face dataset.
+"""
+
+import dataclasses
+import os
+from typing import Optional
+
+import datasets
+import huggingface_hub
+from renumics import spotlight  # type: ignore
+
+
+def login() -> None:
+    """
+    Login to Hugging Face Hub.
+    """
+    if token := os.environ.get("HF_TOKEN"):
+        huggingface_hub.login(token)
+
+
+@dataclasses.dataclass
+class HFSettings:
+    """
+    Hugging Face settings.
+    """
+
+    dataset: str
+    subset: Optional[str] = None
+    split: Optional[str] = None
+    revision: Optional[str] = None
+
+    enrichment: Optional[str] = None
+    enrichment_revision: Optional[str] = None
+
+    @classmethod
+    def from_environ(cls) -> "HFSettings":
+        """
+        Parse Hugging Face settings from environment.
+        """
+        dataset = os.environ.get("HF_DATASET") or None
+        if dataset is None:
+            raise RuntimeError(
+                "Desired Hugging Face dataset must be set as `HF_DATASET` "
+                "environment variable."
+            )
+        return cls(
+            dataset,
+            os.environ.get("HF_SUBSET") or None,
+            os.environ.get("HF_SPLIT") or None,
+            os.environ.get("HF_REVISION") or None,
+            os.environ.get("HF_ENRICHMENT") or None,
+            os.environ.get("HF_ENRICHMENT_REVISION") or None,
+        )
+
+    def __str__(self) -> str:
+        return f"{self.dataset}[subset={self.subset},split={self.split},revision={self.revision}]"
+
+
+if __name__ == "__main__":
+    """
+    Load and serve the given Hugging Face dataset.
+    """
+    login()
+
+    hf_settings = HFSettings.from_environ()
+    print(f"Loading Hugging Face dataset {hf_settings}.")
+    ds = datasets.load_dataset(
+        hf_settings.dataset,
+        hf_settings.subset,
+        split=hf_settings.split,
+        revision=hf_settings.revision,
+    )
+    if hf_settings.enrichment is not None:
+        ds_enrichment = datasets.load_dataset(
+            hf_settings.enrichment,
+            hf_settings.subset,
+            split=hf_settings.split,
+            revision=hf_settings.enrichment_revision,
+        )
+        if len(ds_enrichment) != len(ds):
+            raise RuntimeError(
+                f"Length of the enrichment dataset ({len(ds_enrichment)}) "
+                f"mismatches length of the original dataset ({len(ds)})"
+            )
+        ds = datasets.concatenate_datasets([ds, ds_enrichment], split=ds.split, axis=1)
+
+    dtypes = {}
+    for col in ds.column_names:
+        if "embedding" in col and isinstance(ds.features[col], datasets.Sequence):
+            dtypes[col] = spotlight.dtypes.embedding_dtype
+
+    if not isinstance(ds, datasets.Dataset):
+        raise TypeError(
+            f"Loaded Hugging Face dataset is of type {type(ds)} instead of "
+            "`datasets.Dataset`. Did you forget to specify subset and/or split "
+            "(use environment variables `HF_SUBSET` and `HF_SPLIT` respective)?"
+        )
+    print(f"Serving Hugging Face dataset {hf_settings}.")
+    spotlight.show(
+        ds, host="0.0.0.0", port=7860, wait="forever", dtype=dtypes, analyze=True
+    )