predict.py

# SWAMI KARUPPASWAMI THUNNAI

import glob
import csv
import numpy as np
from keras.models import load_model

def initialize():
    with open("dataset/header.txt", "r") as file:
        headers = file.readlines()
        headers = [header.strip() for header in headers]
        headers = list(filter(None, headers))
        return headers



def predict(file):
    global classifier
    row = [0 for i in range(len(headers))]
    with open(file, "r") as func_file:
        functions = func_file.readlines()
        functions = [function.strip() for function in functions]
        functions = list(filter(None, functions))
        if len(functions) > 5:
            for function in functions:
                if function[-1] == "W":
                    function = function[:-1]
                elif function[-2] == "A":
                    function = function[:-1]
                try:
                    row[headers.index(function)] = 1
                except ValueError:
                    pass
    return classifier.predict_classes(np.array([row]))[0]



if __name__ == "__main__":
    classifier = load_model("adam.h5")
    headers = initialize()
    files = glob.glob("dataset/test/*.txt")
    for file in files:
        print(predict(file))