bowbowbow

import utils
import nltk

from pprint import pprint

def find_document_by_keyword(keyword):
    df = utils.data_load()

    count = 0
    for index, row in df.iterrows():

bowbowbow

import json, re, datetime
import pandas as pd
import numpy as np
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin

bowbowbow

import json, re, datetime
import pandas as pd
import numpy as np
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin

bowbowbow

import json, re, datetime
import pandas as pd
import numpy as np
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin

bowbowbow

import json, re, datetime
import pandas as pd
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion, Pipeline

bowbowbow

import json, re, datetime
import pandas as pd
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion, Pipeline

bowbowbow

import json, re, datetime
import pandas as pd
import nltk
from nltk.stem.snowball import SnowballStemmer

stopwords = nltk.corpus.stopwords.words('english')
stemmer = SnowballStemmer("english")

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion, Pipeline

bowbowbow

import requests

filenames = ['10_1_mic1.wav','10_1_mic2.wav']

files = dict()
for filename in filenames:
    f = open(filename, 'rb')
    files[filename] = f

res = requests.post('http://localhost:8080/upload', files=files)

bowbowbow

import numpy as np
import pandas as pd
import nltk
import re, json, os, codecs, mpld3, datetime
from sklearn import feature_extraction
from sklearn.externals import joblib


def data_load(year):
    data = list()

bowbowbow

const express = require('express');
const http = require('http');
const app = express();
const exec = require('child_process').exec;

function run(command) {
  return new Promise((resolve, reject) => {
    exec(command, (error, stdout, stderr) => {
      console.log('stdout: ' + stdout);
      console.log('stderr: ' + stderr);