91 lines
2.4 KiB
Python
91 lines
2.4 KiB
Python
from telegram import Bot
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from telegram import Update
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from telegram.ext import Updater
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from telegram.ext import MessageHandler
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from telegram.ext import Filters
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from pickle import load
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from keras.models import load_model
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from keras.utils import to_categorical
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from keras.preprocessing.sequence import pad_sequences
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def generate_seq(model, mapping, seq_length, seed_text, n_chars):
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in_text = seed_text
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# generate a fixed number of characters
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for _ in range(n_chars):
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# encode the characters as integers
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encoded = [mapping[char] for char in in_text]
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# truncate sequences to a fixed length
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encoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')
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# one hot encode
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encoded = to_categorical(encoded, num_classes=len(mapping))
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# predict character
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yhat = model.predict_classes(encoded, verbose=0)
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# reverse map integer to character
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out_char = ''
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for char, index in mapping.items():
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if index == yhat:
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out_char = char
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break
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# append to input
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if char == ' ':
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char = '_'
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in_text += char
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return in_text
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TG_TOKEN = "1011115574:AAHLaC4jgtkYGxL9wILnMjmTxsHLIqsGDZE"
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BUFF = ''
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def message_handler(bot: Bot, update: Update):
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sim = 5
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model = load_model('model.h5')
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global BUFF
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# load the mapping
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mapping = load(open('mapping.pkl', 'rb'))
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user = update.effective_user
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bot.send_message(chat_id=update.effective_message.chat_id,
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text="Введи начало никнейма")
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text = update.effective_message.text
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text_in = BUFF + text
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nike = generate_seq(model, mapping, 10, text_in, sim)
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nik = ''
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iterator = (sim + len(text))*-1
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while iterator != 0:
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nik += nike[iterator]
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iterator += 1
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replay_text = f'{nik}'
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bot.send_message(chat_id=update.effective_message.chat_id,
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text=replay_text)
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BUFF += nik
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return
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def main():
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bot = Bot(
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token=TG_TOKEN,
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)
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updater = Updater(
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bot=bot,
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)
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hendler = MessageHandler(Filters.all, message_handler)
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updater.dispatcher.add_handler(hendler)
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updater.start_polling()
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updater.idle()
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if __name__ == '__main__':
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main()
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