new_contents: import math
  import os
  from random import sample
  import cPickle as pickle
  
  from scipy.spatial.distance import cdist
  import json
  import numpy as np
  import indicoio
  from texttable import Texttable
  indicoio.config.api_key = "YOUR_API_KEY_HERE"
  
  '''
  description
  '''
  
  faqs = {
      'Where can I find my API Key?':'Hi there! You receive an API key upon sign up. After you confirm your email you will be able to log in to your dashboard at indico.io/dashboard and see your API key on the top of the screen.',
      'Can indico be downloaded as a package and used offline?':'Unfortunately, no. However we do have a paid option for on premise deployment for enterprise clients.',
      'What is indico API credit?':'Hello! indico API credit is what we use to keep track of usage. If you send in 100 bits of text into our API you are charged 100 credits, essentially one credit is consumed per datapoint analyzed. Every user gets 10,000 free API credits per month.',
      'Would I be able to set up a Pay as You Go account and have it stop if I reach 10,000 calls so that I won\'t be charged if I accidentally go over the limit?':'Hi there! Yep, the best way for you to do this would be to sign up for a pay as you go account and don\'t put in a credit card (we don\'t require you to). When you hit 10,000 you will be locked out of your account and unable to make more calls until you put a credit card in or you can wait until the first of the month when it resets to 10,000.',
      'Hello! When I try to install indico with pip, I get this error on Windows. Do you know why?':'Hello, please try following the steps listed here: https://indico.io/blog/getting-started-indico-tutorial-for-beginning-programmers/#windows and let us know if you still continue to have problems.'
  }
  
  def make_feats(data):
      """
      Send our text data throught the indico API and return each text example's text vector representation
      """
      # TODO
  
  def calculate_distances(feats):
      # cosine distance is the most reasonable metric for comparison of these 300d vectors
      distances = cdist(feats, feats, 'cosine')
      return distances
  
  def similarity_text(idx, distance_matrix, data, n_similar=5):
      """
      idx: the index of the text we're looking for similar questions to
           (data[idx] corresponds to the actual text we care about)
      distance_matrix: an m by n matrix that stores the distance between
                       document m and document n at distance_matrix[m][n]
      data: a flat list of text data
      """
      t = Texttable()
      t.set_cols_width([50, 20])
  
      # these are the indexes of the texts that are most similar to the text at data[idx]
      # note that this list of 10 elements contains the index of text that we're comparing things to at idx 0
      sorted_distance_idxs = np.argsort(distance_matrix[idx])[:n_similar] # EX: [252, 102, 239, ...]
      # this is the index of the text that is most similar to the query (index 0)
      most_sim_idx = sorted_distance_idxs[1]
  
      # header for texttable
      t.add_rows([['Text', 'Similarity']])
      print t.draw()
  
      # set the variable that will hold our matching FAQ
      faq_match = None
  
      for similar_idx in sorted_distance_idxs:
          # actual text data for display
          datum = data[similar_idx]
  
          # distance in cosine space from our text example to the similar text example
          distance = distance_matrix[idx][similar_idx]
  
          # how similar that text data is to our input example
          similarity =  1 - distance
  
          # add the text + the floating point similarity value to our Texttable() object for display
          t.add_rows([[datum, str(round(similarity, 2))]])
          print t.draw()
  
          # set a confidence threshold
          # TODO
  
      # print the appropriate answer to the FAQ, or bring in a human to respond
      # TODO
  
  def input_question(data, feats):
      # TODO
  
  def run():
      # TODO
  
  
  if __name__ == "__main__":
      run()
  
subject: Create faqs_bot.py