mnguyenngo

from flask import Flask
from flask_restful import reqparse, abort, Api, Resource
import pickle
import numpy as np
from model import NLPModel

app = Flask(__name__)
api = Api(app)

model = NLPModel()

Kiwibp

#facebook marketplace 
from selenium import webdriver
from time import sleep
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from pymongo import MongoClient

class App:

nguyenkims

import logging
import sys
from logging.handlers import TimedRotatingFileHandler

FORMATTER = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
LOG_FILE = "my_app.log"

def get_console_handler():
	console_handler = logging.StreamHandler(sys.stdout)
	console_handler.setFormatter(FORMATTER)

gbaman

# An example to get the remaining rate limit using the Github GraphQL API.

import requests

headers = {"Authorization": "Bearer YOUR API KEY"}


def run_query(query): # A simple function to use requests.post to make the API call. Note the json= section.
    request = requests.post('https://api.github.com/graphql', json={'query': query}, headers=headers)
    if request.status_code == 200:

xameeramir

# Author: Zameer Ansari
# You should look at the following URL's in order to grasp a solid understanding
# of Nginx configuration files in order to fully unleash the power of Nginx.
# http://wiki.nginx.org/Pitfalls
# http://wiki.nginx.org/QuickStart
# http://wiki.nginx.org/Configuration
#
# Generally, you will want to move this file somewhere, and start with a clean
# file but keep this around for reference. Or just disable in sites-enabled.
#

chadmayfield

#!/bin/bash
git clone https://github.com/hashcat/hashcat.git
mkdir -p hashcat/deps
git clone https://github.com/KhronosGroup/OpenCL-Headers.git hashcat/deps/OpenCL
cd hashcat/ && make
./hashcat --version
./hashcat -b -D 1,2
./example0.sh

vickyqian

import nltk
training_set = nltk.classify.util.apply_features(extract_features, tweets)
# Train the classifier Naive Bayes Classifier
NBClassifier = nltk.NaiveBayesClassifier.train(training_set)
#ua is a dataframe containing all the united airline tweets
ua['sentiment'] = ua['tweets'].apply(lambda tweet: NBClassifier.classify(extract_features(getFeatureVector(processTweet2(tweet)))))

vickyqian

def getFeatureVector(tweet):
    featureVector = []
    #split tweet into words
    words = tweet.split()
    for w in words:
        #replace two or more with two occurrences
        w = replaceTwoOrMore(w)
        #strip punctuation
        w = w.strip('\'"?,.')
        #check if the word stats with an alphabet

vickyqian

###Preprocess tweets
def processTweet2(tweet):
    # process the tweets

    #Convert to lower case
    tweet = tweet.lower()
    #Convert www.* or https?://* to URL
    tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))','URL',tweet)
    #Convert @username to AT_USER
    tweet = re.sub('@[^\s]+','AT_USER',tweet)

vickyqian

import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)