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_load_models(self):
import autocomplete
is_loaded = autocomplete.load()
self.assertTrue(is_loaded)
def test_autocomplete(beginning, letters):
autocomplete.load()
print(autocomplete.predict(beginning, letters))
