+ score file sample

Author: brown9804

azML-modelcreation/src/score.py

@@ -0,0 +1,55 @@
+import logging
+import joblib
+import numpy as np
+from azureml.core.model import Model
+
+# Configure logging at INFO level
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+def init():
+    """
+    Initialize the global model variable.
+    This function is called once when the service starts.
+    """
+    global model
+    try:
+        model_path = Model.get_model_path("my_model_RegressionModel")
+        model = joblib.load(model_path)
+        logger.info("Model loaded successfully from: %s", model_path)
+    except Exception as e:
+        logger.error("Failed to load model due to: %s", e)
+        raise
+
+def run(data: dict) -> dict:
+    """
+    Run scoring on the input data.
+
+    Parameters:
+      data (dict): A dictionary containing the key "data" mapped to the input features.
+    
+    Returns:
+      dict: A dictionary with key 'result' containing model predictions,
+            or key 'error' with an error message.
+    """
+    try:
+        # Validate input
+        if not isinstance(data, dict):
+            raise ValueError("Input must be a dictionary with key 'data'.")
+        raw_data = data.get("data")
+        if raw_data is None:
+            raise ValueError("Missing 'data' key in input.")
+        
+        # Convert to numpy array
+        input_data = np.array(raw_data)
+        logger.info("Input data converted to numpy array with shape: %s", input_data.shape)
+        
+        # Generate predictions
+        predictions = model.predict(input_data)
+        logger.info("Prediction successful.")
+        
+        # Return results in a structured format
+        return {"result": predictions.tolist()}
+    except Exception as e:
+        logger.error("Error during run: %s", e)
+        return {"error": str(e)}