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from argparse import ArgumentParser
from random import randint, choice
from torch import float16
from transformers import AutoTokenizer, AutoModelForCausalLM
parser = ArgumentParser()
parser.add_argument('-b', '--backend', choices=['mastodon', 'misskey', 'matrix', 'none'], default='mastodon',
help='fediverse server type')
parser.add_argument('-i', '--instance', help='Mastodon instance hosting the bot')
parser.add_argument('-t', '--token', help='Mastodon application access token')
parser.add_argument('-n', '--input', help='initial input text')
parser.add_argument('-d', '--data', default='data',
help='data for automatic input generation')
parser.add_argument('-m', '--model', default='model',
help='path to load saved model')
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
model = AutoModelForCausalLM.from_pretrained(args.model).to('cuda')
if args.input is None:
# Create random input
if randint(0, 1) == 0:
args.input = choice([
'I am',
'My life is',
'Computers are',
'This is',
'My',
'I\'ve',
'No one',
'I love',
'I will die of',
'I',
'The',
'Anime',
'I\'m going to die',
'Hello',
'@ta180m@exozy.me',
'Life',
'My favorite',
'I\'m not',
'I hate',
'I think',
'In my opinion',
'Breaking news:',
'Have I ever told you that',
'I read on the news that',
'I never knew that',
'My dream is',
'It\'s terrible that'
])
else:
with open(args.data, 'r') as f:
# Get a line with at least two words
lines = f.readlines()
line = choice(lines).split()
while len(line) < 2:
line = choice(lines).split()
# Remove mentions
if line[0].count('@') > 1:
line[0] = '@'.join(line[0].split('@')[0:2])
if line[1].count('@') > 1:
line[1] = '@'.join(line[1].split('@')[0:2])
args.input = line[0] + ' ' + line[1]
# Run the input through the model
print(args.input)
inputs = tokenizer.encode(args.input, return_tensors='pt').to('cuda')
output = tokenizer.decode(model.generate(
inputs, do_sample=True, max_length=150, top_p=0.9)[0])
print(output)
# Prepare the post
output = output.split('\n')
post = output[0]
if len(post) < 200 and len(output) > 1:
post = output[0] + '\n' + output[1]
post = post[:500]
# Post it!
if args.backend == 'mastodon':
from mastodon import Mastodon
mastodon = Mastodon(
access_token=args.token,
api_base_url=args.instance
)
mastodon.status_post(post)
elif args.backend == 'misskey':
from Misskey import Misskey
misskey = Misskey(args.instance, i=args.token)
misskey.notes_create(post)
elif args.backend == 'matrix':
import simplematrixbotlib as botlib
creds = botlib.Creds(args.instance, 'ebooks', args.token)
bot = botlib.Bot(creds)
@bot.listener.on_startup
async def room_joined(room_id):
await bot.api.send_text_message(room_id=room_id, message=post)
bot.run()
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