Using Hugging Science Spaces (interactive demos)

A Hugging Face Space is a hosted web app — usually Gradio or Streamlit — that wraps a model behind a UI. The hugging-science org maintains ~27 of these, and many catalog entries point to a Space rather than (or in addition to) raw weights. Spaces are the fastest way to get scientific output without managing models or GPUs yourself.

Spaces are not just web pages — every Gradio Space exposes a programmatic API. You can call them from Python with gradio_client and parse the result as a normal Python value.

When to call a Space (vs. running the model locally)

Reach for a Space when:

• The user wants a one-shot result, not a fine-tuning loop.
• The model is huge (40B+) and the user has no GPU.
• The model has private weights and the Space is the only public interface.
• The Space already implements complex orchestration (tokenization, sampling, post-processing) you'd otherwise reimplement.

Reach for local execution instead when:

• You'll call it many times in a loop (Spaces have rate limits and queues).
• You're fine-tuning, batching at scale, or need offline reproducibility.
• The Space is private/gated and you can't get access.

Setup

uv pip install gradio_client python-dotenv    # or: uv add gradio_client python-dotenv

For private/gated Spaces, store the token in .env and load it at startup:

# .env (gitignored)
HF_TOKEN=hf_...

from dotenv import load_dotenv
load_dotenv()    # gradio_client picks up HF_TOKEN automatically

The general pattern

from gradio_client import Client

client = Client("hugging-science/<space-name>")

# Find the API endpoints exposed by this Space:
print(client.view_api())

# Call the endpoint named in view_api(), e.g. "/predict":
result = client.predict(
    "argument_one",
    42,
    api_name="/predict",
)
print(result)

view_api() is the discovery step — it prints every exposed endpoint with parameter names and types. Always run it once when wrapping a new Space; the function signature varies between Spaces and isn't always obvious from the UI.

Worked example: BoltzGen (protein/peptide/nanobody binder design)

BoltzGen is one of the flagship Spaces in the hugging-science org. It generates designed binders against a target protein.

from gradio_client import Client, file

client = Client("hugging-science/boltzgen-demo")
print(client.view_api())   # inspect first

# Typical call shape (verify against view_api() — endpoint names evolve):
result = client.predict(
    target_pdb=file("/path/to/target.pdb"),
    binder_type="protein",         # or "peptide", "nanobody"
    n_designs=8,
    api_name="/generate",
)
# result is usually a list of generated sequences/structures or a path
# to a downloadable file inside the Space's tmp dir.

When the Space returns a file path, gradio_client downloads the file to a local temp location and returns the path — handy for pipelines that need the actual output (.pdb, .fasta).

File inputs

Many scientific Spaces take structured file inputs (PDB, CIF, FASTA, NIfTI, FITS). Wrap them with gradio_client.file(...):

from gradio_client import file
result = client.predict(file("target.pdb"), api_name="/predict")

Don't pass raw paths as strings — Gradio uploads files differently from text and the type wrapper signals which is which.

Other notable Spaces in hugging-science

These are good defaults to know about. Always check view_api() for the current signature.

The full live list lives at huggingface.co/hugging-science (Spaces tab). If a Space name 404s, the org may have renamed it — search the org page or check the catalog entry.

Rate limits and queue behavior

Free Spaces share a community GPU queue. For interactive use this is fine; for any kind of batching:

• Expect occasional queue is full or timeout errors. Add retry-with-backoff.
• For large workloads, duplicate the Space into your own account (the "Duplicate" button on the Space page) to get private compute.
• Or: run the underlying model locally if weights are public — usually preferable for >10s of calls.

When the Space has no API

A small minority of Spaces disable the API or are Streamlit-based without a clean programmatic interface. In that case, fall back to local model execution (using-models.md) or surface the limitation to the user — don't try to scrape the UI.