Python SDK

pip install strand-sdk
# with bioinformatics extras (AnnData / zarr) for downloading results:
pip install "strand-sdk[anndata]"

pip install "git+https://github.com/Strand-AI/strand-sdk-python.git"

from strand import Client

client = Client()                      # picks up STRAND_API_KEY from env
client = Client(api_key="sk-strand-…") # explicit

result = client.predict(
    "biopsy.ome.tiff",
    markers=["PanCK", "CD8", "PDL1"],
    output_dir="./outputs/",
)
print(f"used {result.credits_used} credits; "
      f"wrote {len(result.marker_outputs)} markers")

upload = client.uploads.upload_file(
    "biopsy.svs",
    progress=lambda done, total: print(f"{done}/{total}"),
)
print(upload.id, upload.status)

# List or retrieve uploads from a later process.
page = client.uploads.list(limit=50)
upload = client.uploads.get(upload.id)

upload = client.uploads.upload_file("slide.svs", if_not_exists=True)

page = client.samples.list(scope="all", limit=48, tag="trial-042")
for item in page.items:
    print(item.ownership, item.id)

if page.next_cursor:
    page = client.samples.list(scope="all", cursor=page.next_cursor)

sample = client.samples.get(item.id)
if sample.ownership == "mine":
    print(sample.name, sample.job_count)
    for job in sample.jobs:
        print(job.id, job.status, job.results_available)
else:
    print(sample.title, sample.markers)
    sample.download_to("./public-sample.zarr")

client.samples.patch(
    upload.id,
    name="Trial 42 baseline",
    tags=["trial-042", "baseline"],
    mpp=0.26,
)

client.samples.patch(upload.id, name=None)  # revert to filename
client.samples.patch(upload.id, tags=[])    # clear editable tags

state = client.samples.segmentation(sample_id)
print(state.status, state.retryable, state.credit_cost)  # credit_cost is always 0

if state.status in {"not_started", "failed"}:
    state = client.samples.segment(sample_id)

manifest = client.samples.segmentation_manifest(sample_id, state.layer["id"])
cells = client.samples.segmentation_file(sample_id, state.layer["id"], "features/instances.parquet")

# Validate and price the exact request without reserving credits.
est = client.predict.submit(
    upload.id,
    markers=["CD8", "PanCK", "Ki67"],
    dry_run=True,
)
print(est.estimated_credits, est.org_balance)

# Submit the same request. Reserves credits atomically and returns a Job.
job = client.predict.submit(upload.id, markers=["CD8", "PanCK", "Ki67"])

result = client.predict(
    "slide.svs",
    markers=["CD8", "Ki67", "PanCK"],
    model="v0.7",
)

job = client.predict("slide.svs", markers=["CD3e", "CD8"], wait=False)
print(f"submitted {job.id}")

# ...do other work, or shut down the process. The job runs server-side.

# Later (same process or a fresh one via `client.jobs.get(job_id)`):
job.wait()
adata = job.download_results()

# Or stream events:
for event in job.stream_events():
    print(event.status, event.progress)

# Block until terminal. Uses SSE with polling fallback:
status = job.wait(timeout=1800)

# Cancel an in-flight job and refund its reserved credits:
status = job.cancel()

# Or stream events yourself:
for event in job.stream_events():
    print(event.status, event.progress)

# Download results as AnnData (requires the `anndata` extra):
adata = job.download_results()

# Or write the raw OME-Zarr store to disk:
path = job.download_results(path="./results-zarr")

# Select native stored data plus H&E and true cell outputs without conversion:
export = job.request_export("ome-zarr", include_he=True, include_segmentation=True)

# Or render and download a generated single-file format:
path = job.download_export("ome-tiff", "./result.ome.tiff", timeout=1800)

from strand import (
    InsufficientCreditsError,
    JobFailedError, JobTimeoutError,
)

try:
    job = client.predict.submit(upload.id, markers=["CD8"])
    job.wait(timeout=600)
except InsufficientCreditsError as e:
    print(f"need {e.required} credits; top up first")
except JobFailedError as e:
    print(f"job failed: {e}")

from strand import UnknownMarkerError

try:
    client.predict.submit(upload.id, markers=["CD3e", "MysteryMarker"])
except UnknownMarkerError as e:
    print("unknown:", e.unknown)              # ["MysteryMarker"]
    print("try one of:", e.known_subset[:5])  # sample of valid names

from strand import JobFailedError

try:
    result = client.predict("slide.svs", markers=["CD3e", "CD8"])
except JobFailedError as e:
    if e.upload_id:
        # Re-submit against the same upload; no re-upload needed.
        job = client.predict.submit(e.upload_id, markers=["CD3e", "CD8"])
        job.wait()

from strand import (
    Client, Job, JobEvent, JobStatus, JobResults,
    Upload, UploadList, Estimate, ModelId,
    MineSampleSummary, PublicSample, PublicSampleSummary,
    Sample, SampleJob, SampleList, SampleScope, UNSET,
    PredictResult, ResultExport,
    AuthError, BadRequestError, InsufficientCreditsError,
    JobFailedError, JobTimeoutError, NotFoundError,
    StrandError, UnknownMarkerError, UploadError,
)