def suggest(alp): autocomplete.load() l = autocomplete.predict('the', alp) i = 0 while (i < 5 and i < len(l)): print(l[i][0]) i = i + 1
def test_fnc(seed_word = 'the',roughly_number_words=100 ): import autocomplete reload(autocomplete) from random import randint import string import random autocomplete.load() #the seed for the poem prior_word = seed_word #the prose you generate poem = prior_word for i in range(0,roughly_number_words): random_seed = random.choice(string.letters) x = randint(0,9) if x <= 1: poem = poem + '\n' else: new_prediction=autocomplete.predict(prior_word, random_seed) if len(new_prediction)>=1: y = randint(0,len(new_prediction)-1) new_word = new_prediction[y][0] poem = poem + ' ' + new_word print "\n\n****Words of inspiration:****\n\n" print poem
def __init__(self) -> None: # with open('../../data/PhraseSets/big_mackenzie2.txt', 'r') as myfile: # autocomplete.models.train_models(myfile.read()) autocomplete.load() super().__init__() self.checking_char_dict2list = { 'q': ['a', 'z'], 'a': ['q', 'z'], 'z': ['a', 'q'], 'w': ['s', 'x'], 's': ['w', 'x'], 'x': ['s', 'w'], 'e': ['d', 'c'], 'd': ['e', 'c'], 'c': ['d', 'e'], 'r': ['f', 'v'], 'f': ['r', 'v'], 'v': ['f', 'r'], 't': ['g', 'b'], 'g': ['t', 'b'], 'b': ['g', 't'], 'y': ['h', 'n'], 'h': ['y', 'n'], 'n': ['h', 'y'], 'u': ['j', 'm'], 'j': ['u', 'm'], 'm': ['j', 'u'], 'i': ['k'], 'k': ['i'], 'p': [], 'o': ['l'], 'l': ['o'] }
def lastwords(self): autocomplete.load() if len(self._label.cget("text").split("_")) > 1: a = self._label.cget("text").split("_")[-2:] print(autocomplete.predict(a[0], a[1])) return autocomplete.predict(a[0], a[1]) else: return [("", 1), ("", 1), ("", 1)]
def autocomplete_word(text): try: autocomplete.load() autoword = autocomplete.predict_currword(text) autoword = re.sub(r'[^\w]', ' ', str(autoword[0])) autoword = re.sub(r'\d+', ' ', autoword) return True,autoword.strip() except: return False,"Can not predict word"
def suggest_word(previous, current): """Given the previous word and the current partially typed word, suggest some possibilities for the word being typed. Returns a list of tuples containing the suggested word and the number of times it showed up as a followup in the training corpus. """ if not suggest_word.is_loaded: #Don't train more than once autocomplete.load() suggest_word.is_loaded = True return autocomplete.predict(previous, current)
def startup(): autocomplete.load() return DEFAULTPREDICTION
from kivy.uix.boxlayout import BoxLayout from kivy.uix.gridlayout import GridLayout from kivy.uix.popup import Popup from kivy.uix.button import Button from kivy.uix.togglebutton import ToggleButton from kivy.uix.textinput import TextInput from kivy.uix.progressbar import ProgressBar from kivy.properties import NumericProperty, ObjectProperty, StringProperty, \ BooleanProperty, OptionProperty, ListProperty, \ DictProperty from collections import OrderedDict import json import autocomplete autocomplete.load() from src.components.LabTextInput import LabTextInput __all__ = ('KeyboardToggleButton', 'NumericKeyboard', 'KeyboardButton') class KeyboardButton(Button): ''' Track Keyboard buttons. Color and other styles defined in `settings.kv`. ''' pass class KeyboardToggleButton(ToggleButton):
def test_load_models(self): import autocomplete is_loaded = autocomplete.load() self.assertTrue(is_loaded)
if __name__ == '__main__': # setup logging logging.basicConfig(filename='logs/main_execution.log', filemode='w', level=logging.DEBUG) # init Arduino adapter my_adapter = ArduinoAdapter() # connect adapter and arduino my_adapter.connect() # init trie t9 = pred.Trie() # train trie training_set_location = "./training_sets/smsCorpus_en_2015.03.09_all.json" t9.json_train_adapter(training_set_location) # train swapnil model ac.load() # init tkinter main window root = tk.Tk() # init a text entry to store already typed text T = tk.Text(root, height=10, width=50) # init interactive entry entry = AutocompleteEntry(root, # matchesFunction=t9.predict, matchesFunction=autocomplete_predictor_wrapper, arduino_adapter=my_adapter, width=32) entry.grid(row=0, column=0) T.grid(column=0) root.mainloop()
def test_autocomplete(beginning, letters): autocomplete.load() print(autocomplete.predict(beginning, letters))