sklearn_cloud_function.py

from google.cloud import storage
import joblib
import sklearn

def predict(request):
    """Responds to any HTTP request.
    Args:
        request (flask.Request): HTTP request object.
    Returns:
        The response text or any set of values that can be turned into a
        Response object using
        `make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`.
    """
    request_json = request.get_json()
    message = request_json['message']
    
    # message = "im on my way home"
    model = load_model()
    vectorizer = load_vectorizer()

    print("model",model)
    print("vectorizer", vectorizer)

    message = vectorizer.transform([message])
    message = message.toarray()

    prediction = model.predict(message)[0]
    print("prediction",prediction)
    return prediction


def load_model():
    storage_client = storage.Client()
    bucket_name="spam_classifier"
    model_bucket='model.joblib'
    model_local='/tmp/local.joblib'

    bucket = storage_client.get_bucket(bucket_name)
    #select bucket file
    blob = bucket.blob(model_bucket)
    #download that file and name it 'local.joblib'
    blob.download_to_filename(model_local)
    #load that file from local file
    job=joblib.load(model_local)
    return job

def load_vectorizer():
    storage_client = storage.Client()
    bucket_name="spam_classifier"
    model_bucket='vectorizer.joblib'
    model_local='/tmp/vectorizer.joblib'

    bucket = storage_client.get_bucket(bucket_name)
    #select bucket file
    blob = bucket.blob(model_bucket)
    #download that file and name it 'local.joblib'
    blob.download_to_filename(model_local)
    #load that file from local file
    job=joblib.load(model_local)
    return job