SDK quickstart

pip install "strand-sdk[anndata]"

install.packages(
  "strandai",
  repos = c("https://strand-ai.r-universe.dev", "https://cloud.r-project.org")
)
BiocManager::install("SpatialExperiment")

import time

from strand import Client

client = Client()  # reads STRAND_API_KEY
upload = client.uploads.upload_file("biopsy.svs", mpp=0.26)
markers = ["CD8", "PanCK", "Ki67"]

# Automatic ingest preprocessing has started. Optional de-identification may
# also run when enabled for the organization. Wait for prediction readiness.
while True:
    sample = client.samples.get(upload.id)
    if sample.ownership == "mine" and sample.status == "ready":
        break
    if sample.ownership == "mine" and sample.status.endswith("failed"):
        raise RuntimeError(f"ingest failed: {sample.status}")
    time.sleep(2)

estimate = client.predict.submit(upload.id, markers, dry_run=True)
print(estimate.estimated_credits, estimate.org_balance)

library(strandai)

client <- strand_client()  # reads STRAND_API_KEY
upload <- strand_upload_file(client, "biopsy.svs", progress = TRUE, mpp = 0.26)
markers <- c("CD8", "PanCK", "Ki67")

# Automatic ingest has started, but the sample may not be prediction-ready.
repeat {
  sample <- strand_samples_get(client, upload$id)
  if (identical(sample$status, "ready")) break
  if (grepl("failed$", sample$status)) stop("ingest failed: ", sample$status)
  Sys.sleep(2)
}

estimate <- strand_predict(client, upload$id, markers, dry_run = TRUE)
message(estimate$estimated_credits, " credits; balance ", estimate$org_balance)

job = client.predict.submit(upload.id, markers)
status = job.wait(timeout=1800)
print(status.status)

job <- strand_predict(client, upload$id, markers)
status <- strand_job_wait(job, timeout = 1800, progress = TRUE)
message(status$status)

adata = job.download_results()
job.download_export("ome-tiff", "result.ome.tiff", timeout=1800)

spe <- strand_download_results(job)
strand_export_download(job, "ome-tiff", "result.ome.tiff")