Model/lookalike-model/lookalike_model/application/rest_client.py

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"""
THE script gets the data, process it and send the request to
the rest client and print out the response from the the rest API
"""
import requests
import json
import argparse
import yaml


def flatten(lst):
    f = [y for x in lst for y in x]
    return f


def str_to_intlist(table):
    ji = []
    for k in [table[j].split(",") for j in range(len(table))]:
        s = []
        for a in k:
            b = int(a.split(":")[0])
            s.append(b)
        ji.append(s)
    return ji


def inputData(record, keyword, length):
    if len(record['show_counts']) >= length:
        hist = flatten(record['show_counts'][:length])
        instance = {'hist_i': hist, 'u': record['did'], 'i': keyword, 'j': keyword, 'sl': len(hist)}
    else:
        hist = flatten(record['show_counts'])
        # [hist.extend([0]) for i in range(length - len(hist))]
        instance = {'hist_i': hist, 'u': record['did'], 'i': keyword, 'j': keyword, 'sl': len(hist)}
    return instance


def predict(serving_url, record, length, new_keyword):
    body = {"instances": []}
    for keyword in new_keyword:
        instance = inputData(record, keyword, length)
        body['instances'].append(instance)
    body_json = json.dumps(body)
    result = requests.post(serving_url, data=body_json).json()
    if 'error' in result.keys():
        predictions = result['error']
    else:
        predictions = result['predictions']
    return predictions


def run(cfg):
    length = cfg['input']['din_model_length']
    url = cfg['input']['din_model_tf_serving_url']
    ##time_interval, did, click_counts, show_counts, media_category, net_type_index, gender, age, keyword
    record = {"did": 0, "show_counts": ['25:3', '29:6,25:2', '29:1,25:2,14:2', '14:1,29:2,25:2',
                                          '29:1', '26:1,14:2,25:4', '14:1,25:3'], "show_clicks": [], "age": '10', "gender": '3'}
    record['show_counts'] = str_to_intlist(record['show_counts'])
    new_keyword = [26, 27, 29]
    response = predict(serving_url=url, record=record, length=length, new_keyword=new_keyword)

    print(response)


if __name__ == '__main__':  # record is equal to window size
    parser = argparse.ArgumentParser(description='Prepare data')
    parser.add_argument('config_file')
    args = parser.parse_args()

    with open(args.config_file, 'r') as ymlfile:
        cfg = yaml.safe_load(ymlfile)

    run(cfg)