Building a Customized

Personal Assistant with Python

PyCon JP 2017

2017.09.08 (Fri) Room 201

Iskandar Setiadi

#pyconjp_201

Iskandar Setiadi

On the Map: Indonesia

Why Python?

Prologue

Primitive

Available at https://github.com/fogleman/primitive

Deep Photo Style Transfer

Available at https://github.com/luanfujun/deep-photo-styletransfer

Primitive: Configure Once, Execute Everywhere

def create_primitive_image(image_path, filename):
    for i in xrange(0, 3):  # Generate 3 different images
        output_path = os.path.join(image_path, "output{}.png".format(i))
        args = [
            'primitive',
            '-i',  # input file
            os.path.join(image_path, filename),
            '-o',  # output file
            output_path,
            '-n',  # number of iteration
            '175'
        ]
        # Execute
        p = subprocess.Popen(args, stdout=subprocess.PIPE, shell=False)
        (output, err) = p.communicate()
        if err:
            logging.error(err)
    return generate_gif_file_from_primitive(image_path, filename)

Showcase

Learning Basic Japanese

Step 1: Prepare the Data

Download it from http://gakuran.com/files/japanese_jlpt1_lite.xls

Step 2: Retrieve the Data

Python csv module to help!

with open(filename, 'rb') as fobj:
    reader = csv.reader(fobj, delimiter=',', encoding='utf-8')
	...  # get random Kanji & Vocabulary based on level choice here

Step 3: Create a Generic Daily Scheduler

while True:
    current_mt = int(time.time())
    metadata = redis_client.get_schedules()
    if metadata["next_mt"] < current_mt:
        metadata["kanji_n4"] = get_random_kanji(level=4)
        ...  # Store any other daily information
        metadata["next_mt"] += 86400  # every 1 day
        redis_client.set_schedules(metadata)

Subscribe

Showcase

Language Preference

Examples

Examples

soimort/translate-shell

Available at https://github.com/soimort/translate-shell

shlex - Simple Lexical Analysis

  import shlex
  try:
      elements = shlex.split(query.encode('utf-8'))
  except:
      elements = [query.encode('utf-8')]

  # Input  : Translate "I am going to Pycon" to 日本語
  # Output : ['Translate', 'I am going to Pycon', 'to', '日本語']

trans-shell to Google Translate

  ...
  # Use trans online language detection
  if not source_language_key:
      source_language_key = get_trans_language_prediction(source_text)  # "trans -id {text}" handling
  if not target_language_key:
      target_language_key = Constants.TRANSLATION_MAP[source_language_key]

  args = ["trans", "-b", "{}:{}".format(source_language_key, target_language_key), source_text]
  p = subprocess.Popen(args, stdout=subprocess.PIPE, shell=False)
  (output, _) = p.communicate()
  return output

Train Accident

requests + BeautifulSoup + Google Translate

  import requests
  from bs4 import BeautifulSoup
  
  # Retrieve page
  r = requests.get("https://transit.yahoo.co.jp/traininfo/area/4/")  # Yahoo Page
  soup = BeautifulSoup(r.content, "html.parser")
  soup_attrs = soup.select('div.trouble table tr td')

Showcase

Showcase

Showcase

Showcase

ChatterBot

$ pip install chatterbot

  from chatterbot import ChatBot

  chatbot = ChatBot(
      'Ron Obvious',
      trainer='chatterbot.trainers.ChatterBotCorpusTrainer'
  )

  # Train based on the english corpus
  chatbot.train("chatterbot.corpus.english")

  # Get a response to an input statement
  chatbot.get_response("Hello, how are you today?")
  # Response: "I am doing well."

ChatterBot

Available at https://github.com/gunthercox/ChatterBot

Custom Corpus

  self.chatbot = ChatBot(
      'Maid-chan',
      trainer='chatterbot.trainers.ChatterBotCorpusTrainer',  # Can also be trained with Twitter or Ubuntu dialog corpus
      output_adapter="chatterbot.output.OutputFormatAdapter",  # Output can be integrated with other framework (MS Bot)
      output_format='text',
      storage_adapter="chatterbot.storage.MongoDatabaseAdapter"  # Can be stored in JSON file, MongoDB, or even SQL Alchemy
  )

  self.chatbot.train(
      "chatterbot.corpus.indonesia.conversations",
      "chatterbot.corpus.indonesia.greetings",
      "chatterbot.corpus.english.conversations",
      "chatterbot.corpus.english.greetings",
      "chatterbot.corpus.maidcorpus"  # Train from a custom corpus
  )

Examples

Examples

feedparser

$ pip install feedparser  # https://github.com/kurtmckee/feedparser

  import feedparser

  response = feedparser.parse(url)
  if 'bozo_exception' in response:  # invalid response, not a valid RSS
      return False
  for entry in response.get("entries", {}):
      title = entry.get("title")
      ...

FB Messenger Bot: Configure

Documentation: https://developers.facebook.com/docs/messenger-platform/getting-started

Infinite Possibilities

Optimus Prime Bot (Example: Location x Yelp) https://github.com/hungtraan/FacebookBot

Closing

Project available at https://github.com/freedomofkeima/messenger-maid-chan

Presentation slide available at https://freedomofkeima.com/pyconjp2017/