tweet.py

#!/usr/bin/env python

# This is a simple script to set up a Twitter 'bot' based on a character-level recurrent neural network. Clone sherjilozair's
# char-rnn-tensorflow (https://github.com/sherjilozair/char-rnn-tensorflow) and train it on the material of your choice.
# Then drop this script into the main directory, create a Twitter account and Twitter app for the bot and enter the
# relevant authentication information at the commented points below. Run this script and whenever somebody
# @mentions the bot it will reply with a sample from your neural network.

# Louis Goddard <[email protected]> 

import numpy as np
import tensorflow as tf

import argparse
import time
import os
import re
import cPickle

from utils import TextLoader
from model import Model

from twython import Twython

APP_KEY = ''  # consumer key
APP_SECRET = ''  # consumer secret
OAUTH_TOKEN = ''  # access token
OAUTH_TOKEN_SECRET = ''  # access token secret

twitter = Twython(APP_KEY, APP_SECRET, OAUTH_TOKEN, OAUTH_TOKEN_SECRET)

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--save_dir', type=str, default='save',
                       help='model directory to store checkpointed models')
    parser.add_argument('-n', type=int, default=140,
                       help='number of characters to sample')
    parser.add_argument('--prime', type=str, default=' ',
                       help='prime text')
    args = parser.parse_args()
    sample(args)

def sample(args):
    with open(os.path.join(args.save_dir, 'config.pkl')) as f:
        saved_args = cPickle.load(f)
    with open(os.path.join(args.save_dir, 'chars_vocab.pkl')) as f:
        chars, vocab = cPickle.load(f)
    model = Model(saved_args, True)
    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        saver = tf.train.Saver(tf.all_variables())
        ckpt = tf.train.get_checkpoint_state(args.save_dir)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)

            latest = twitter.get_home_timeline(count = 1)
            ident = latest[0]['id']

            while True:
                mentions = twitter.get_mentions_timeline(contributor_details = True, since_id = ident)
                for mention in mentions:
                    ident = mention['id']
                    target = mention['user']['screen_name']
                    incoming = re.sub('[^A-Za-z0-9]+', '', mention['text'])
                    output = str(model.sample(sess, chars, vocab, len(mention['text'])+(140-len(target)), incoming))
                    twitter.update_status(status = '@' + target + ' ' + output[len(incoming):139], in_reply_to_status_id = ident)
                time.sleep(60)

if __name__ == '__main__':
    main()