The causal engine, headless.

# 1. Mint an Auth0 M2M token (omitted) → token

import requests, uuid, time

# 2. Upload a seed dataset
files = {"file": open("tracker_q3.csv", "rb")}
headers = {"Authorization": f"Bearer {token}",
           "Idempotency-Key": str(uuid.uuid4())}
r = requests.post("https://api.simulacra-data.com/v1/datasets", files=files, headers=headers)
dataset_id = r.json()["dataset_id"]
job_id = r.json().get("job_id")

# 3. Wait for training
while True:
    s = requests.get(f"https://api.simulacra-data.com/v1/jobs/{job_id}", headers=headers).json()
    if s["status"] in ("ready", "failed"): break
    time.sleep(2)

# 4. Inspect cleaned schema
schema = requests.get(f"https://api.simulacra-data.com/v1/datasets/{dataset_id}/schema",
                     headers=headers).json()

# 5. Generate scenario-conditioned rows
body = {
    "row_count": 2000,
    "conditions": {
        "categorical": {"segment": {"genz_switcher": 0.30}},
        "numeric": {"price": {"min": 5.49, "max": 5.49}}
    }
}
g = requests.post(f"https://api.simulacra-data.com/v1/datasets/{dataset_id}/generations",
                 json=body, headers=headers).json()

# 6. Poll, download artifact_url

# 1. Mint M2M token (omitted)
library(httr2)

req <- request("https://api.simulacra-data.com/v1/datasets") |>
  req_auth_bearer_token(token) |>
  req_headers(`Idempotency-Key` = uuid::UUIDgenerate()) |>
  req_body_multipart(file = curl::form_file("tracker_q3.csv"))

resp <- req |> req_perform() |> resp_body_json()
dataset_id <- resp$dataset_id
job_id     <- resp$job_id

# 2. Poll, fetch schema, generate (mirrors the Python flow)

# Upload seed
curl -X POST https://api.simulacra-data.com/v1/datasets \
  -H "Authorization: Bearer $TOKEN" \
  -H "Idempotency-Key: $(uuidgen)" \
  -F "file=@tracker_q3.csv"

# Generate scenario
curl -X POST https://api.simulacra-data.com/v1/datasets/$DSID/generations \
  -H "Authorization: Bearer $TOKEN" \
  -H "Idempotency-Key: $(uuidgen)" \
  -H "Content-Type: application/json" \
  -d '{ "row_count": 2000,
        "conditions": {
          "categorical": { "segment": { "genz_switcher": 0.30 } },
          "numeric": { "price": { "min": 5.49, "max": 5.49 } }
        }
      }'