MediumBlogging-Vertex AI custom container deployment-prediction_input.py

prediction_input.py

from google.cloud import aiplatform
import json

def prepare_test_data():
    test_data = {
        "Street": "Pave",
        "LotFrontage": 80.0,
        "OverallQual": 5,
        "GarageCond": "TA",
        "1stFlrSF": 896,
        "TotalBsmtSF": 882.0,
        "LotArea": 11622,
        "BsmtFinType2": "LwQ"
    }
    return json.dumps(test_data)

def predict_custom_trained_model(instances, project_number, endpoint_id):
    """
    Uses Vertex AI endpoint to make predictions
    Args:
        instances (str): JSON-encoded instances.
        project_number (str): Google Cloud project number.
        endpoint_id (str): Vertex AI endpoint ID.
    Returns:
        dict: Prediction results
    """
    endpoint = aiplatform.Endpoint(
        endpoint_name=f"projects/{project_number}/locations/us-central1/endpoints/{endpoint_id}"
    )
    result = endpoint.predict(instances=[instances])
    return result.predictions

if __name__ == "__main__":
    test_data = prepare_test_data()

    # Make predictions using the custom trained model
    prediction_result = predict_custom_trained_model(
        instances=test_data,
        project_number="65143767267",
        endpoint_id="1431064961085341696"
    )
   
    print(prediction_result)