feel-it | Sentiment analysis and emotion classification | Predictive Analytics library

def build_policy():
    Exp = {
        "expression" : "resource.matchTagId('org-id-from-gcp/disableSerialAccess', 'yes')",
        "title" : "this is the title",
        "description" : "this is a description",
    }

    rule1 = types.Po

import glob
import os.path

def list_json_files():
    path_of_directory = 'C:\\JSON_Test\\json_files'
    ext = ('*.json')
    list_of_files = glob.glob(os.path.join(path_of_directory, ext))
    return list_of_files

def write_csv():
    

    def DFS(start, end, budget, adj_list, dist_list, cost_list):
        visited = {start: True}
        parent = {}
        path = []
        pathFound = False
        stack = [(start, 0, False)] #node, energy, waiting_for_adj_list
      

pipeline = 'udpsrc port=5000 ! application/x-rtp,media=video,encoding-name=H264 ! rtph264depay ! decodebin ! nvvidconv ! video/x-raw,format=BGRx ! videoconvert ! video/x-raw,format=BGR ! appsink drop=1'
cam = cv2.VideoCapture(pipeline, cv2

t1 = threading.Timer(10, childFunction, args=(var1,var2,number))
t1.name = t1.name + "_timer" + str(number)
t1.start()

for timerthread in threading.enumerate():
    if timerthread.name.endswith('timer' + str(number

import re
stopwords = ["Ford", "Hyundai", "Toyota", "Volkswagen", "Volvo"]
tests = ["Something about a Ford doing some car stuff",
         "Hyundai is another car manufacturer",
         "Not everyone buys cars. Some people buy trucks fro