Resume-Rater/main.py at master · heet2201/Resume-Rater · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
import argparse
import random
import sys
from src.model import RatingModel
from src.utils import *
from src.info_extractor import InfoExtractor

y=None
rate=None
def res(a,b,c):


    RANDOM_INT_STR = str(random.randint(100000, 1000000))
    parser = argparse.ArgumentParser(description="Train or test documents on models")


    ### GENERAL
    parser.add_argument(
        "--type",
        dest="type",
        type=str,
        default="fixed",
        nargs="?",
        help="the type of model to use",
    )
    ### FOR TEST
    parser.add_argument(
        "path_to_resume",
        metavar="path_to_resume",
        nargs="?",
        default=None,
        help="the path to a resume",
    )
    parser.add_argument(
        "--model_path",
        dest="model_path",
        type=str,
        default=None,  # since there are two
        nargs="?",
        help="the pre-trained model to use",
    )
    parser.add_argument(
        "--no_info",
        dest="no_info",
        action="store_true",
        help="don't show extracted info and don't open document"
    )
    ### FOR TRAIN
    parser.add_argument(
        "--train",
        dest="train",
        type=str,
        default=None,
        nargs="?",
        help="the training directory for training",
    )
    parser.add_argument(
        "--model_name",
        dest="model_name",
        type=str,
        default="model_" + RANDOM_INT_STR,
        nargs="?",
        help="the model name to save as",
    )

    parser.add_argument(
        "--keywords",
        dest="keywords",
        type=str,
        nargs="*",
        help="keywords to use for training a fixed model",
    )


    #args = vars(parser.parse_args())
    args={
        "type":a,
        "path_to_resume":b,
        "model_path":None,
        "no_info":False,
        "train":None,
        "model_name":c,
        "keywords":None
    }
    #print(args)
    class MainArgParseException(Exception):
        pass

    # Testing--

    _type = args["type"]
    if _type not in ("fixed", "lda"):
        raise MainArgParseException("--type should be either 'fixed' or 'lda'")

    if args["train"] is None:
        path_to_resume = args["path_to_resume"]
        if path_to_resume is None:
            raise MainArgParseException("No path/to/resume provided")

        train_type = args["type"]
        model_path = args["model_path"]
        # if no model path specified, use model name to derive it instead
        if model_path is None:
            model_name = args["model_name"]
            dirname = os.path.dirname(os.path.abspath(__file__))
            model_path = os.path.join(
                dirname, "src/models/model_" + _type, model_name + ".json"
            )

        r = RatingModel(_type, model_path)
        if args["no_info"]:
            infoExtractor = None
        else:
            infoExtractor = InfoExtractor(r.nlp, r.parser)
        r.test(path_to_resume, infoExtractor)
    else:
        training_dir = args["train"]
        model_name = args["model_name"]
        keywords = args["keywords"]
        r = RatingModel()
        if _type == "fixed":
            if len(keywords) < 1:
                raise MainArgParseException("No keywords supplied in --keywords")
            r.train(training_dir, _type, model_name, keywords)
        else:
            r.train(training_dir, _type, model_name)

    return [y,rate]