Overview

from cyberwave import Cyberwave

client = Cyberwave()

# Register a vision model
model = client.api.create_mlmodel({
    "name": "Object Detector",
    "description": "YOLOv8 model for detecting warehouse items",
    "model_external_id": "yolov8n",
    "model_provider_name": "local",
    "can_take_image_as_input": True,
    "can_take_video_as_input": True,
    "visibility": "private",
    "tags": ["vision", "detection", "warehouse"]
})

print(f"Model UUID: {model.uuid}")

from cyberwave import Cyberwave

client = Cyberwave()

# Register GPT-4 Vision for robot commands
vlm = client.api.create_mlmodel({
    "name": "Robot Commander",
    "description": "Interprets visual scenes and generates robot commands",
    "model_external_id": "gpt-4-vision-preview",
    "model_provider_name": "openai",
    "can_take_image_as_input": True,
    "can_take_text_as_input": True,
    "visibility": "private",
    "metadata": {
        "max_tokens": 1000,
        "temperature": 0.2
    }
})

from cyberwave import Cyberwave

client = Cyberwave()

# List all models in your workspace
models = client.api.list_mlmodels()

for model in models:
    print(f"{model.name} ({model.model_provider_name})")
    print(f"  Video: {model.can_take_video_as_input}")
    print(f"  Image: {model.can_take_image_as_input}")
    print(f"  Text: {model.can_take_text_as_input}")

# Via workflow nodes or API
response = client.api.vlm_generation({
    "model_uuid": vlm.uuid,
    "prompt": "What objects do you see? How should the robot pick them up?",
    "image_url": "https://..."
})

import asyncio
from cyberwave import Cyberwave

async def run_local_inference():
    client = Cyberwave()
    
    # Your local model
    import torch
    model = torch.hub.load('ultralytics/yolov8', 'yolov8n')
    
    twin_uuid = "your-twin-uuid"
    
    def on_frame(frame_data):
        """Process each incoming frame."""
        # Run detection
        results = model(frame_data)
        
        # Publish results back to Cyberwave
        detections = results.pandas().xyxy[0].to_dict()
        client.mqtt.publish(
            f"twins/{twin_uuid}/detections",
            detections
        )
    
    # Subscribe to video frames
    client.mqtt.subscribe(
        f"twins/{twin_uuid}/video/frames",
        on_frame
    )
    
    while True:
        await asyncio.sleep(1)

asyncio.run(run_local_inference())