import joblib
import json
import os

# Azure ML will call init() once when the container is started
def init():
    global model
    model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR", "."), "model.joblib")
    model = joblib.load(model_path)

# Azure ML will call run() for every request
def run(raw_data):
    try:
        input_data = json.loads(raw_data)
        predictions = model.predict(input_data["data"])
        return predictions.tolist()
    except Exception as e:
        return {"error": str(e)}