├── sample_config.py ├── requirements.txt ├── model.hdf5 ├── telegram.jpg ├── .gitignore ├── telegram.py ├── README.md ├── helpers.py ├── chatbot.py └── LICENSE /sample_config.py: -------------------------------------------------------------------------------- 1 | class Config: 2 | telegram_token = "" 3 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | keras 2 | tensorflow 3 | requests 4 | h5py 5 | pyaml 6 | -------------------------------------------------------------------------------- /model.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EibrielInv/ice-cream-truck/HEAD/model.hdf5 -------------------------------------------------------------------------------- /telegram.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EibrielInv/ice-cream-truck/HEAD/telegram.jpg -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | bobo/ 85 | 86 | # Spyder project settings 87 | .spyderproject 88 | 89 | # Rope project settings 90 | .ropeproject 91 | 92 | config.py 93 | telegram.json 94 | 95 | -------------------------------------------------------------------------------- /telegram.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import requests 4 | 5 | class telegram: 6 | def __init__ (self, botname, token, useragent): 7 | self.botname = botname 8 | self.token = token 9 | self.savefile = "telegram.json" 10 | self.useragent = useragent 11 | 12 | def send_to_bot(self, access_point, data=None): 13 | headers = {'user-agent': self.useragent} 14 | try: 15 | r = requests.get('https://api.telegram.org/bot{0}/{1}'.format(self.token, access_point), data=data, timeout=40, headers=headers) 16 | except requests.exceptions.ConnectionError: 17 | return None 18 | except requests.exceptions.Timeout: 19 | return None 20 | return r 21 | 22 | def get_update(self): 23 | if self.data["last_update"] != 0: 24 | self.data["last_update"] += 1 25 | r = self.send_to_bot('getUpdates?timeout=30&offset={0}'.format(self.data["last_update"])) 26 | if not r: 27 | return None 28 | r_json = r.json() 29 | if not r_json["ok"]: 30 | return None 31 | if not "result" in r_json: 32 | return None 33 | if len(r_json["result"]) > 0: 34 | self.data["last_update"] = r_json["result"][-1]["update_id"] 35 | return r 36 | 37 | def open_session(self): 38 | if not self.get_telegram_data(): 39 | self.data = { 40 | "last_update": 0 41 | } 42 | 43 | def close_session(self): 44 | self.set_telegram_data() 45 | 46 | def get_telegram_data(self): 47 | if not os.path.exists(self.savefile): 48 | return False 49 | else: 50 | #with open(self.savefile, encoding='utf-8') as data_file: 51 | with open(self.savefile) as data_file: 52 | try: 53 | self.data = json.load(data_file) 54 | except: 55 | raise 56 | return False 57 | return True 58 | 59 | def set_telegram_data(self): 60 | #with open(self.savefile, 'w', encoding='utf-8') as data_file: 61 | with open(self.savefile, 'w') as data_file: 62 | json.dump(self.data, data_file, sort_keys=True, indent=4, separators=(',', ': ')) 63 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Ice Cream Truck bot 2 | This chatbot allows you to place an order of Ice Cream in a [Random Access Navigation](https://medium.com/assist/theres-a-dozen-ways-to-order-a-coffee-why-do-dumb-bots-only-allow-one-27230542636d) way, using a Memory Neural Network. 3 | 4 | Entry for machine learning tutorial [How to Make a Chatbot - Intro to Deep Learning #12](https://www.youtube.com/watch?v=t5qgjJIBy9g) 5 | 6 | 7 | ## Dependencies 8 | - python 3 9 | - tensorflow 10 | - keras 11 | - h5py (For saving and loading the model) 12 | - requests (For Telegram support) 13 | 14 | ## Running 15 | To run the command line interface, just type: 16 | `python chatbot.py` 17 | The bot will use the included pre trained model, if you want to train your own remove the file `model.hdf5` 18 | 19 | ## CLI interface 20 | Interaction example: 21 | 22 | ``` 23 | >add chocolate 24 | >change chocolate for cherry 25 | >add coffee 26 | >done 27 | 28 | 29 | Your order: cherry, coffee 🍦 30 | ``` 31 | 32 | ## Telegram interface 33 | In order to use a Telegram bot as your interface: 34 | 35 | - Install [Telegram](https://telegram.org) 36 | - Add BotFather to your contancts: https://t.me/BotFather 37 | - Use the `/newbot` command and follow the instructions 38 | - On the code duplicate the file `sample_config.py` to `config.py` 39 | - From BotFather copy the bot token to `config.py` 40 | - Run the chatbot with the following parameter `python chatbot.py telegram` 41 | 42 | Done! You can now add your bot to your contacts and order your Ice Cream 43 | 44 | My demo on Telegram: https://t.me/eibriel_icecream_bot 45 | 46 | ![Telegram Demo](telegram.jpg) 47 | 48 | 49 | ## Installing 50 | 51 | If you are familiar with virtualenv create a new one after cloning the repo. 52 | 53 | I am naming my env venv: 54 | 55 | Python2: 56 | ```bash 57 | virtualenv -p /usr/local/bin/python venv 58 | ``` 59 | 60 | Python3: 61 | ```bash 62 | virtualenv -p /usr/local/bin/python3 venv 63 | ``` 64 | 65 | If you are using bash activate your env: 66 | 67 | ```bash 68 | . venv/bin/activate 69 | ``` 70 | 71 | Fish users just use the fish version. 72 | 73 | ```fish 74 | . venv/bin/activate.fish 75 | ``` 76 | 77 | After activating you get a similar prompt: 78 | 79 | ```fish 80 | (venv) ~/c/s/ice-cream-truck (master ⚡☡=) 81 | ``` 82 | 83 | Installing libraries: 84 | 85 | ```bash 86 | pip install -r requirements.txt 87 | ``` 88 | 89 | Startung up the bot: 90 | 91 | ```fish 92 | (venv) ~/c/s/ice-cream-truck (master ⚡→☡=) python chatbot.py 93 | Using TensorFlow backend. 94 | Missing config.py, no Telegram support 95 | - 96 | ('Vocab size:', 22, 'unique words') 97 | ('Story max length:', 30, 'words') 98 | ... 99 | ``` 100 | 101 | -------------------------------------------------------------------------------- /helpers.py: -------------------------------------------------------------------------------- 1 | from functools import reduce 2 | from keras.preprocessing.sequence import pad_sequences 3 | 4 | import numpy as np 5 | 6 | import re 7 | 8 | def tokenize(sent): 9 | '''Return the tokens of a sentence including punctuation. 10 | >>> tokenize('Bob dropped the apple. Where is the apple?') 11 | ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?'] 12 | ''' 13 | return [x.strip() for x in re.split('(\W+)?', sent) if x.strip()] 14 | 15 | 16 | def parse_stories(lines, only_supporting=False): 17 | '''Parse stories provided in the bAbi tasks format 18 | If only_supporting is true, only the sentences 19 | that support the answer are kept. 20 | ''' 21 | data = [] 22 | story = [] 23 | for line in lines: 24 | #line = line.decode('utf-8').strip() 25 | line = line.strip() 26 | nid, line = line.split(' ', 1) 27 | nid = int(nid) 28 | if nid == 1: 29 | story = [] 30 | if '\t' in line: 31 | q, a, supporting = line.split('\t') 32 | q = tokenize(q) 33 | substory = None 34 | if only_supporting: 35 | # Only select the related substory 36 | supporting = map(int, supporting.split()) 37 | substory = [story[i - 1] for i in supporting] 38 | else: 39 | # Provide all the substories 40 | substory = [x for x in story if x] 41 | data.append((substory, q, a)) 42 | story.append('') 43 | else: 44 | sent = tokenize(line) 45 | story.append(sent) 46 | return data 47 | 48 | 49 | def get_stories(f, only_supporting=False, max_length=None): 50 | '''Given a file name, read the file. 51 | ''' 52 | return get_stories_from_list(f.readlines(), only_supporting, max_length) 53 | 54 | 55 | def get_stories_from_list(l, only_supporting=False, max_length=None): 56 | '''Given a file name, read the file, 57 | retrieve the stories, 58 | and then convert the sentences into a single story. 59 | If max_length is supplied, 60 | any stories longer than max_length tokens will be discarded. 61 | ''' 62 | data = parse_stories(l, only_supporting=only_supporting) 63 | flatten = lambda data: reduce(lambda x, y: x + y, data) 64 | data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length] 65 | return data 66 | 67 | 68 | def vectorize_stories(data, word_idx, story_maxlen, query_maxlen): 69 | X = [] 70 | Xq = [] 71 | Y = [] 72 | for story, query, answer in data: 73 | x = [word_idx[w] for w in story] 74 | xq = [word_idx[w] for w in query] 75 | # let's not forget that index 0 is reserved 76 | y = np.zeros(len(word_idx) + 1) 77 | y[word_idx[answer]] = 1 78 | X.append(x) 79 | Xq.append(xq) 80 | Y.append(y) 81 | return (pad_sequences(X, maxlen=story_maxlen), 82 | pad_sequences(Xq, maxlen=query_maxlen), np.array(Y)) 83 | 84 | 85 | def find_nearest_vector(array, value): 86 | idx = np.array([np.linalg.norm(x+y) for (x,y) in array-value]).argmin() 87 | return array[idx] 88 | 89 | 90 | def np_softmax(x): 91 | """Compute softmax values for each sets of scores in x.""" 92 | e_x = np.exp(x - np.max(x)) 93 | return e_x / e_x.sum() 94 | 95 | def list_to_string(text_array, vocab): 96 | input_list=[] 97 | for idx in text_array: 98 | if idx != 0: 99 | input_list.append(vocab[idx-1]) 100 | return " ".join(input_list) 101 | -------------------------------------------------------------------------------- /chatbot.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | '''Trains a memory network on the bAbI dataset. 5 | References: 6 | - Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush, 7 | "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks", 8 | http://arxiv.org/abs/1502.05698 9 | - Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, 10 | "End-To-End Memory Networks", 11 | http://arxiv.org/abs/1503.08895 12 | Reaches 98.6% accuracy on task 'single_supporting_fact_10k' after 120 epochs. 13 | Time per epoch: 3s on CPU (core i7). 14 | 15 | Code by Eibriel, Siraj Raval and fchollet. 16 | 17 | ''' 18 | 19 | import os 20 | import sys 21 | import random as rd 22 | 23 | from keras.layers import add 24 | from keras.layers import dot 25 | from keras.layers import LSTM 26 | from keras.layers import Dense 27 | from keras.layers import Input 28 | from keras.layers import Dropout 29 | from keras.layers import Permute 30 | from keras.layers import Activation 31 | from keras.layers import concatenate 32 | 33 | from keras.models import Model 34 | from keras.models import Sequential 35 | from keras.layers.embeddings import Embedding 36 | 37 | from helpers import * 38 | 39 | from telegram import telegram 40 | 41 | try: 42 | # Attempts to load Telegram configuration 43 | from config import Config 44 | except: 45 | print ("Missing config.py, no Telegram support") 46 | 47 | # Generate the dataset on the fly 48 | 49 | # Commands to add an item 50 | add_commands = [ 51 | 'add', 52 | 'i would like', 53 | 'i want' 54 | ] 55 | # Commands to remove an item 56 | remove_commands = [ 57 | 'remove', 58 | 'i dont want' 59 | ] 60 | # Commands to change an item for another 61 | change_commands = [ 62 | 'change:for' 63 | ] 64 | # List of ice cream flavors 65 | flavors = [ 66 | 'chocolate', 67 | 'lemon', 68 | 'cherry', 69 | 'coffee' 70 | ] 71 | generated_dataset = [] 72 | # Ammount of stories to generate 73 | # will be splitted by half on training and testing set 74 | stories_count = 40000 75 | for n in range(stories_count): 76 | is_flavor = [False, False, False, False] # Holds flavor selection status 77 | sentences = [] 78 | for n in range(rd.randint(1, 6)): 79 | random_action = rd.randint(0, 2) # Selects a random action between add, remove or change 80 | random_flavor = rd.randint(0, 3) # Selects a random flavor 81 | random_flavor_b = rd.randint(0, 3) # Selects a random flavor 82 | if random_action==0: #add 83 | is_flavor[random_flavor] = True 84 | text = "{} {} .".format(rd.choice(add_commands), flavors[random_flavor]) 85 | elif random_action==1: #remove 86 | is_flavor[random_flavor] = False 87 | text = "{} {} .".format(rd.choice(remove_commands), flavors[random_flavor]) 88 | elif random_action==2: #change 89 | is_flavor[random_flavor] = False 90 | is_flavor[random_flavor_b] = True 91 | command_text = rd.choice(change_commands) 92 | command_text = command_text.split(':') 93 | text = "{} {} {} {} .".format(command_text[0], flavors[random_flavor], command_text[1], flavors[random_flavor_b]) 94 | sentences.append(text) 95 | sentences = " ".join(sentences) 96 | random_flavor = rd.randint(0, 3) # Select a random flavor for the question 97 | question = "is {} in the order ?".format(flavors[random_flavor]) # Generates the question 98 | answer = "yes" if is_flavor[random_flavor] else "no" # Generates the answer 99 | generated_dataset.append( (sentences.split(" "), question.split(" "), answer) ) 100 | 101 | # The dataset is divided by half for training and testing 102 | # Given the way the data is created the training and testing set 103 | # might have some duplicated stories, so the validation is not accurated 104 | split_idx = int(stories_count/2) 105 | train_stories = generated_dataset[:split_idx] 106 | test_stories = generated_dataset[split_idx:] 107 | 108 | # Generates the vocabulary 109 | vocab = set() 110 | for story, q, answer in train_stories + test_stories: 111 | vocab |= set(story + q + [answer]) 112 | vocab = sorted(vocab) 113 | 114 | # Reserve 0 for masking via pad_sequences 115 | vocab_size = len(vocab) + 1 116 | story_maxlen = max(map(len, (x for x, _, _ in train_stories + test_stories))) 117 | query_maxlen = max(map(len, (x for _, x, _ in train_stories + test_stories))) 118 | 119 | print('-') 120 | print('Vocab size:', vocab_size, 'unique words') 121 | print('Story max length:', story_maxlen, 'words') 122 | print('Query max length:', query_maxlen, 'words') 123 | print('Number of training stories:', len(train_stories)) 124 | print('Number of test stories:', len(test_stories)) 125 | print('-') 126 | print('Here\'s what a "story" tuple looks like (input, query, answer):') 127 | print(train_stories[0]) 128 | print('-') 129 | print('Vectorizing the word sequences...') 130 | 131 | word_idx = dict((c, i + 1) for i, c in enumerate(vocab)) 132 | inputs_train, queries_train, answers_train = vectorize_stories(train_stories, 133 | word_idx, 134 | story_maxlen, 135 | query_maxlen) 136 | inputs_test, queries_test, answers_test = vectorize_stories(test_stories, 137 | word_idx, 138 | story_maxlen, 139 | query_maxlen) 140 | 141 | print('-') 142 | print('inputs: integer tensor of shape (samples, max_length)') 143 | print('inputs_train shape:', inputs_train.shape) 144 | print('inputs_test shape:', inputs_test.shape) 145 | print('-') 146 | print('queries: integer tensor of shape (samples, max_length)') 147 | print('queries_train shape:', queries_train.shape) 148 | print('queries_test shape:', queries_test.shape) 149 | print('-') 150 | print('answers: binary (1 or 0) tensor of shape (samples, vocab_size)') 151 | print('answers_train shape:', answers_train.shape) 152 | print('answers_test shape:', answers_test.shape) 153 | print('-') 154 | print('Compiling...') 155 | 156 | 157 | #story_maxlen = 1 158 | 159 | embed_dim=64 160 | keep_prob = 0.3 161 | 162 | # placeholders 163 | input_sequence = Input((story_maxlen,)) 164 | question = Input((query_maxlen,)) 165 | 166 | 167 | # encoders 168 | # embed the input sequence into a sequence of vectors 169 | input_encoder_m = Sequential() 170 | input_encoder_m.add(Embedding(input_dim=vocab_size, 171 | output_dim=embed_dim)) 172 | input_encoder_m.add(Dropout(keep_prob)) 173 | # output: (samples, story_maxlen, embedding_dim) 174 | 175 | # embed the input into a sequence of vectors of size query_maxlen 176 | input_encoder_c = Sequential() 177 | input_encoder_c.add(Embedding(input_dim=vocab_size, 178 | output_dim=query_maxlen)) 179 | input_encoder_c.add(Dropout(keep_prob)) 180 | # output: (samples, story_maxlen, query_maxlen) 181 | 182 | # embed the question into a sequence of vectors 183 | question_encoder = Sequential() 184 | question_encoder.add(Embedding(input_dim=vocab_size, 185 | output_dim=embed_dim, 186 | input_length=query_maxlen)) 187 | question_encoder.add(Dropout(keep_prob)) 188 | # output: (samples, query_maxlen, embedding_dim) 189 | 190 | 191 | # encode input sequence and questions (which are indices) 192 | # to sequences of dense vectors 193 | input_encoded_m = input_encoder_m(input_sequence) 194 | input_encoded_c = input_encoder_c(input_sequence) 195 | question_encoded = question_encoder(question) 196 | 197 | 198 | # compute a 'match' between the first input vector sequence 199 | # and the question vector sequence 200 | # shape: `(samples, story_maxlen, query_maxlen)` 201 | match = dot([input_encoded_m, question_encoded], axes=(2, 2)) 202 | match = Activation('softmax')(match) 203 | 204 | # add the match matrix with the second input vector sequence 205 | response = add([match, input_encoded_c]) # (samples, story_maxlen, query_maxlen) 206 | response = Permute((2, 1))(response) # (samples, query_maxlen, story_maxlen) 207 | 208 | # concatenate the match matrix with the question vector sequence 209 | answer = concatenate([response, question_encoded]) 210 | 211 | # the original paper uses a matrix multiplication for this reduction step. 212 | # we choose to use a RNN instead. 213 | answer = LSTM(32)(answer) # (samples, 32) 214 | 215 | # one regularization layer -- more would probably be needed. 216 | answer = Dropout(keep_prob)(answer) 217 | answer = Dense(vocab_size)(answer) # (samples, vocab_size) 218 | # we output a probability distribution over the vocabulary 219 | answer = Activation('softmax')(answer) 220 | 221 | 222 | # build the final model 223 | model = Model([input_sequence, question], answer) 224 | model.compile(optimizer='rmsprop', loss='categorical_crossentropy', 225 | metrics=['accuracy']) 226 | 227 | model_filepath = 'model.hdf5' 228 | if not os.path.isfile(model_filepath): 229 | # train 230 | model.fit([inputs_train, queries_train], answers_train, 231 | batch_size=32*8, 232 | epochs=120, 233 | validation_data=([inputs_test, queries_test], answers_test)) 234 | model.save_weights(model_filepath) 235 | else: 236 | model.load_weights(model_filepath) 237 | 238 | # Returns an order, given a story 239 | def order_from_story(story): 240 | story_int = [0 for n in range(30-len(story))] 241 | story_int = story_int + [word_idx[word] for word in story] 242 | 243 | order = [] 244 | for f in flavors: 245 | query = ["is", f, "in", "the", "order", "?"] 246 | query_int = [word_idx[word] for word in query] 247 | prediction = model.predict([np.array(story_int).reshape(1, -1), np.array(query_int).reshape(1, -1)]) 248 | if vocab[np.argmax(prediction)-1]=="yes": 249 | order.append(f) 250 | order = ", ".join(order) 251 | return "Your order: *{}* 🍦\n\n(restarting order)\n\n".format(order) 252 | 253 | # Sends a message to Telegram 254 | def send_to_telegram(chat_id, answer): 255 | msg = { 256 | 'chat_id': chat_id, 257 | 'parse_mode': 'Markdown', 258 | 'text': answer, 259 | } 260 | r = telegram_conection.send_to_bot('sendMessage', data = msg) 261 | 262 | # Checks if a list contains only known words from the vocabulary 263 | def known_words(sentence): 264 | for word in sentence: 265 | if not word in word_idx or word in ["order", "yes", "no", "is", "in", "the"]: 266 | return False 267 | return True 268 | 269 | welcome_text = """ 270 | 271 | *Welcome to the End-to-End Ice Cream Truck, please place your order.* 272 | I understand the following commands: 273 | *add flavor* / *i would like flavor* / *i want flavor* 274 | To select a new flavor 275 | 276 | *remove flavor* / *i dont want flavor* 277 | To remove a selected flavor 278 | 279 | *change flavor for flavor* 280 | To change one flavor to another 281 | 282 | *done* - To print your current order 283 | *quit* - To exit 284 | 285 | Today flavors: _chocolate_ - _lemon_ - _cherry_ - _coffee_ 286 | """ 287 | 288 | # Select the proper UI (cli or telegram) 289 | if "telegram" in sys.argv: 290 | ui="telegram" 291 | else: 292 | ui="cli" 293 | 294 | # If UI is cli 295 | if ui=="cli": 296 | input_text = "" 297 | print (welcome_text.replace("*", "").replace("_", "")) 298 | while 1: 299 | story = [] # Restart story 300 | while 1: 301 | input_text = input(">") # Read the inpur 302 | if input_text in ["done", "quit", "order"]: # If is a stop command 303 | break 304 | sentence = input_text.split(" ") 305 | if sentence[-1] != ".": 306 | sentence.append(".") 307 | if not known_words(sentence): # If contains unknown words 308 | print ("Unknown command") 309 | continue 310 | story = story + sentence 311 | if input_text == "quit": 312 | break 313 | if len(story) == 0: 314 | continue 315 | # Print order 316 | print ("\n") 317 | print (order_from_story(story).replace("*", "").replace("_", "")) 318 | # if ui is Telegram 319 | elif ui=="telegram": 320 | print ("\nListening for Telegram messages") 321 | # Configure Telegram connection 322 | telegram_conection = telegram("eibriel_icecream_bot", Config.telegram_token, "8979") 323 | chat_history = {} # Holds the story of the Telegram users 324 | while 1: 325 | telegram_conection.open_session() 326 | r = telegram_conection.get_update() # Listen for new messages 327 | if not r: 328 | continue # If no messages continue loop 329 | r_json = r.json() 330 | telegram_conection.close_session() 331 | for result in r_json["result"]: 332 | answer = "" 333 | if not ("message" in result and "text" in result["message"]): 334 | continue # Sanity check on the message 335 | 336 | chat_id = result["message"]["chat"]["id"] # Get user id 337 | sentence = result["message"]["text"].lower() # Get input text 338 | print (sentence) 339 | 340 | if sentence == "/restart": 341 | # If restart command, empty user story 342 | chat_history[chat_id] = [] 343 | send_to_telegram(chat_id, "Order restarted") 344 | continue 345 | 346 | if sentence in ["done", "quit", "order"]: 347 | # If quit command print order 348 | if chat_id not in chat_history: 349 | continue 350 | if len(chat_history[chat_id]) == 0: 351 | continue 352 | answer = order_from_story(chat_history[chat_id]) 353 | send_to_telegram(chat_id, answer) 354 | chat_history[chat_id] = [] 355 | continue 356 | 357 | # Input text to list, the model expect period 358 | # at the end of a sentence 359 | sentence = sentence.split(" ") 360 | if sentence[-1] != ".": 361 | sentence.append(".") 362 | 363 | # If the sentence includes unknown words abort 364 | if not known_words(sentence): 365 | send_to_telegram(chat_id, welcome_text) 366 | continue 367 | 368 | # Add the new sentence to the story 369 | if not chat_id in chat_history: 370 | chat_history[chat_id] = [] 371 | chat_history[chat_id] += sentence 372 | send_to_telegram(chat_id, "Ok!") 373 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------