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// jQuery snippet for changing HTML form into JSON
	 (function ($) {

		 $.fn.serializeFormJSON = function () {
			 var o = {};
			 var a = this.serializeArray();
			 $.each(a, function () {
				 if (o[this.name]) {
					 if 	(!o[this.name].push) { o[this.name] = [o[this.name]]; }

mzmmoazam

import csv
from reportlab.platypus import Paragraph,SimpleDocTemplate
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.graphics.charts.piecharts import Pie
from reportlab.lib.colors import brown,blue
from reportlab.graphics.charts.legends import Legend
from reportlab.graphics.shapes import Drawing
from reportlab.lib.colors import black

# THIS FUNCTION CALCULATES THE PERCENTILE

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class Graph:
  def __init__(self):
    self.nodes = set()
    self.edges = defaultdict(list)
    self.distances = {}

  def add_node(self, value):
    self.nodes.add(value)

  def add_edge(self, from_node, to_node, distance):

mzmmoazam

import sqlite3

class database(object):
    def __init__(self):
        self.filename = "help.db"
        self.table = "help_data"
        self.sql_do('create table if not exists '+self.table+' ( key TEXT,data TEXT)')
    def sql_do(self, sql, *params):
        self._db.execute(sql, params)
        self._db.commit()

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#!/usr/bin/python

import psycopg2

class database(object):
    def __init__(self,database,table):
        self.database = database
        self.table = table
        self.sql_do('create table if not exists '+self.table+' ( key TEXT,data TEXT)')
    def sql_do(self, sql, *params):

mzmmoazam

import requests
from urllib.parse import urlparse,urljoin
from bs4 import BeautifulSoup

all_urls = set()

class url_tree():
    def __init__(self, base_url,page_no=1 ,children=None):
        self.url = base_url
        self.page_no =page_no

mzmmoazam

import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.data_utils import to_categorical
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter
import glob

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import speech_recognition as sr, pyttsx
from googletrans import Translator

# pyttsx engine config
speech_engine = pyttsx.init('sapi5')  # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
speech_engine.setProperty('rate', 150)


# translator object
translator = Translator()

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import Algorithmia, speech_recognition as sr, pyttsx

# Algorthmia Api key and end point
client = Algorithmia.client('simCM+BqJzKSw/iMrQsR/OTRwVE1')
algo = client.algo('translation/GoogleTranslate/0.1.1')

# pyttsx engine config
speech_engine = pyttsx.init('sapi5')  # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
speech_engine.setProperty('rate', 150)

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import csv, random


class NaiveBayes(object):
    def __init__(self, filename, split_ratio):
        '''

        :param filename: a csv filename with absolute or full path
        :param split_ratio: test to train ratio
        '''