black_friday_challenge_predict.py

import googleapiclient.discovery
import argparse
import pandas as pd
import json

import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('--csv-data-path',
                        dest='csv_data_path', required=True)    
    parser.add_argument('--model-name',
                        dest='model_name', required=True)
    parser.add_argument('--project-id',
                        dest='project_id', required=True)
    parser.add_argument('--version-name',
                        dest='version_name', required=True)
    args = parser.parse_args()    

    df_raw = pd.read_csv(args.csv_data_path)    

    logger.info(df_raw.head())
    instances = df_raw.head().to_dict(orient='lines')    

    service = googleapiclient.discovery.build('ml', 'v1', cache_discovery=False)
    name = 'projects/{}/models/{}/versions/{}'.format(
        args.project_id, args.model_name, args.version_name)    

    request_body = {'instances': instances}    

    response = service.projects().predict(
        name=name,
        body=request_body
    ).execute()

    if 'error' in response:
        raise RuntimeError(response['error'])
    else:
        logger.info(response['predictions'])