Register for Corn Yield Predictor

export HOKUSAI_API_KEY="your-hokusai-api-key-here"

import os
import mlflow
from hokusai.core import ModelRegistry

# Set up MLflow tracking URI
mlflow.set_tracking_uri("https://registry.hokus.ai/api/mlflow")

# IMPORTANT: Use your Hokusai API key, NOT an MLflow token
# The Hokusai API key authenticates both the registry and MLflow
os.environ["MLFLOW_TRACKING_TOKEN"] = os.getenv("HOKUSAI_API_KEY")

# Initialize registry
registry = ModelRegistry()

# Register your model
with mlflow.start_run() as run:
    # Log your model (replace with your actual model)
    mlflow.sklearn.log_model(
        your_trained_model,
        "model",
        registered_model_name="Corn Yield Predictor"
    )

    # Register with Hokusai
    model_uri = f"runs:/{run.info.run_id}/model"
    registered_model = registry.register_tokenized_model(
        model_uri=model_uri,
        model_name="Corn Yield Predictor",
        token_id="HCORN",
        metric_name="Mae",
        baseline_value=6.5 bu/acre,
        additional_tags={"proposal_id": "15", "author": "your-name", "version": "1.0"}
    )

    print(f"✅ Model registered successfully: {registered_model.name}")