ML Models

from cyberwave import Cyberwave

cw = Cyberwave()

# --- Catalog: discover what models exist (optional client-side filters) ---
for m in cw.models.list(filters=["edge", "image"]):
    print(m.slug, m.deployment, m.sdk_load_id)

# --- Runtime: load directly from a catalog entry ---
entry = cw.models.list(deployment="edge")[0]
model = cw.models.load(entry)   # resolves sdk_load_id → slug → uuid
pred = model.predict(image, confidence=0.25)

# Structured summary covers detection, classification, pose, segmentation, OBB, etc.
print(pred.describe())

pip install cyberwave[ml]

pip install -e '.[ml]'

# All models visible in your workspace
models = cw.models.list()

# Filter by deployment target (server-side query param)
edge_models = cw.models.list(deployment="edge")
cloud_models = cw.models.list(deployment="cloud")

# Extra client-side shorthands (ANDed): edge + image-capable catalog rows
edge_vision = cw.models.list(filters=["edge", "image"])

# Unknown filter strings → tag substring match (case-insensitive)

# Public models (no workspace membership required)
public_models = cw.models.list_public()

# By slug
m = cw.models.get("acme/models/yolov8-nano")

# By UUID
m = cw.models.get_by_uuid("3f2a1b4c-…")

# By slug (explicit)
m = cw.models.get_by_slug("acme/models/yolov8-nano")

result = cw.models.delete("model-uuid-here")

# Preferred: pass the entry from list() or get() directly
entry = cw.models.list(deployment="edge")[0]
model = cw.models.load(entry)          # SDK resolves sdk_load_id automatically

# Also accepted: explicit string IDs
model = cw.models.load("yolo26n.pt")               # edge — local filename
model = cw.models.load("acme/models/my-yolo")      # cloud — slug

# Cascade: independent predictions keyed by catalog name
edges = cw.models.list(filters=["edge", "image"])
cascade = cw.models.load([edges[0], edges[1]], store_input=True)
multi = cascade.predict(image)

handle = cw.models.playground("acme/models/gemini-robotics-er")
sync_or_queued = handle.run(
    image="scene.jpg",
    prompt="cups",
    structured_task="detect_points",
)

from PIL import Image

img = Image.open("frame.jpg").convert("RGB")
pred = model.predict(img, confidence=0.25)

# Summary for whichever output type the runtime produced
print(pred.describe())

# Bounding-box-only shorthand (delegates to detection output); prefer describe()
print(pred.describe_detections_text())

# Iterate bbox detections when output is DetectionResult-shaped
for d in pred:
    print(d.label, d.confidence, d.bbox)

cw = Cyberwave()

# 1. Find an edge-compatible model
models = cw.models.list(deployment="edge")
entry  = models[0]
print(f"Loading '{entry.name}'  sdk_load_id={entry.sdk_load_id}")

# 2. Load by passing the entry — SDK resolves sdk_load_id automatically
model = cw.models.load(entry)
pred = model.predict(image, confidence=0.3)
print(pred.describe())

cloud_entry = cw.models.list(deployment="cloud")[0]
model = cw.models.load(cloud_entry)   # routes via slug to playground-backed inference where applicable
pred = model.predict(image)
print(pred.describe())