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neodict.py
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neodict.py
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import random
from functools import partial
from kivy.app import App
from kivy.clock import Clock
from kivy.graphics.texture import Texture
from kivy.lang import Builder
from kivy.uix.screenmanager import ScreenManager, Screen
from kivymd.uix.button import MDRaisedButton
from kivy.uix.widget import Widget
from kivy.uix.gridlayout import GridLayout
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.button import Button
from kivy.graphics import Color
from kivy.graphics import Rectangle
from kivy.core.window import Window
from kivymd.app import MDApp
from kivymd.uix.label import MDLabel, MDIcon
from kivy.uix.textinput import TextInput
from kivy.properties import ObjectProperty
from kivy.utils import get_color_from_hex
import pandas as pd
import csv
import numpy as np
import random
import io
from tensorflow import keras
from tensorflow.keras import layers
import threading
from time import time
from kivy.animation import Animation
from nltk.corpus import words
# creating a screen manager
class Manager(ScreenManager):
pass
# creating child classes of screen
# for splash screen on startup
class SplashScreen(Screen):
pass
# for the main game
class DefineScreen(Screen):
pass
class ExampleScreen(Screen):
pass
# loading screen for while words are generating
class GeneratingScreen(Screen):
pass
# and a screen to show previous definitions
class DefinitionsScreen(Screen):
pass
# made the window roughly phone sized to check how it will look there
Window.size = (400,700)
class neoDict(MDApp):
prompt_word = ""
generated_words = []
# building the app from kv file and screen class instances
def build(self):
# on close event for potentially ending threads etc
Window.bind(on_request_close=self.on_request_close)
#changing window name from default
self.title = 'Vorpal Dictionary'
# setting some colour themes
self.theme_cls.primary_palette = "Blue"
self.theme_cls.theme_style = "Dark"
# adding screens to the screen manager
sm = ScreenManager()
self.splash_screen = SplashScreen()
sm.add_widget(self.splash_screen)
self.define_screen = DefineScreen()
sm.add_widget(self.define_screen)
self.example_screen = ExampleScreen()
sm.add_widget(self.example_screen)
self.gen_screen = GeneratingScreen()
sm.add_widget(self.gen_screen)
# starting thread for neural network
threading.Thread(target=self.generate_words, daemon=True).start()
# loading kv file with app components
kv_file = Builder.load_file('app_ui.kv')
#returning the loaded app with screen manager as root
return kv_file
def load_words(self):
#method to optionally load the pregenerated words from a file, not in use currently/not the way words are given
generated_words = open('generated_adjs.txt', 'r')
generated_words = generated_words.read()
self.words_list = list([i for i in set(generated_words.split(" ")) if len(i) > 0])
random.shuffle(self.words_list)
self.definitions_dict = {}
def set_word(self):
self.prompt_word = self.generated_words[0]
prompt_text = "What does \n {} \n mean?".format(self.prompt_word)
self.root.get_screen('Define').ids.prompttext.text = prompt_text
# convert to full dictionary entry
# makes a fake phonetic version with direct substitution of letters
def make_phonetic(self, word):
letters_phon = "æɓçɖɘɸɡʜɪjkɭɰɲøpqʁstʊʋwχyʒ"
letters_alph = "abcdefghijklmnopqrstuvwxyz"
replaced = [letters_phon[letters_alph.index(i)] for i in word if i in letters_alph]
return "/{}/".format("".join(replaced))
def update_definition_screen(self):
text_to_display = ""
for key, value in self.definitions_dict.items():
text_to_display += key + " \n " + "[adjective]" + "\n" + " \n ".join(value[1:]) + "\n\n"
self.root.get_screen('Definitions').ids.definitions_text.text = text_to_display
def submit(self, definition):
self.root.get_screen('Example').ids.exampleprompttext.text = 'Ok. And what is an example of {} in a sentence'.format(self.prompt_word)
phonetic = self.make_phonetic(self.prompt_word)
self.definitions_dict[self.prompt_word] = [phonetic]
self.definitions_dict[self.prompt_word].append(definition)
self.root.current = 'Example'
def example_submit(self, example):
# need to add to dict which will be class property
self.definitions_dict[self.prompt_word].append(example)
print(self.definitions_dict.get(self.prompt_word))
# logic to go on to the next word - new word and back to the definition screen
self.skip()
self.root.current = 'Define'
self.update_definition_screen()
print('Submitted example of use')
self.check_not_out_of_words()
def skip(self):
self.generated_words = self.generated_words[1:]
self.check_not_out_of_words()
if self.root.current !='Generating':
self.set_word()
def generate_words(self):
# loading training data and doing some setup
adjs_df = pd.read_csv('Adjectives.csv', usecols=[1], header = 0, delimiter=",", quoting=csv.QUOTE_NONE,
encoding='utf-8')
adjs_df.columns = ['word']
adjs = adjs_df.word.tolist()
adjs = [i.replace(" ", "") for i in adjs]
text = " ".join(adjs).lower().replace("\"", "")
chars = sorted(list(set(text)))
# making dicts for encoding of characters
char_indices = dict((v, c) for c,v in enumerate(chars))
indices_char = dict((c, v) for c,v in enumerate(chars))
maxlen = 30
step = 3
sequences = []
next_char = []
for i in range(0, len(text) - maxlen, step):
sequences.append(text[i : i + maxlen])
next_char.append(text[i + maxlen])
x = np.zeros((len(sequences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sequences), len(chars)), dtype=np.bool)
for i, sequence in enumerate(sequences):
for t, char in enumerate(sequence):
x[i, t, char_indices[char]] = 1
y[i, char_indices[next_char[i]]] = 1
model = keras.models.load_model('adj_generate_model.h5', custom_objects=None, compile=True, options=None)
epochs = 40
batch_size = 128
for epoch in range(epochs):
model.fit(x, y, batch_size=batch_size, epochs = 1)
start_index = random.randint(0, len(text) - maxlen - 1)
for diversity in [0.2, 0.5, 1.0, 1.2]:
generated = ""
sentence = text[start_index : start_index + maxlen]
for i in range(400):
x_pred = np.zeros((1, maxlen, len(chars)))
for t, char in enumerate(sentence):
x_pred[0, t, char_indices[char]] = 1.0
preds = model.predict(x_pred, verbose=0)[0]
next_index = self.sample(preds, diversity)
next_char = indices_char[next_index]
sentence = sentence[1:] + next_char
generated += next_char
self.generated_words = list(set([i for i in self.generated_words + generated.split(" ") if len(i) > 0 and i not in words.words()]))
random.shuffle(self.generated_words)
if self.root.current == 'Generating':
self.generating_ani.join()
self.root.current = 'Define'
self.set_word()
def sample(self, preds, temperature=1.0):
preds = np.asarray(preds).astype("float64")
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
def on_request_close(self, *args):
print("closing")
def animate_generating_txt(self):
gen_label = self.root.get_screen('Generating').ids.generating_label
anim = Animation(color=(0, 0, 0, 1), duration=.5) + Animation(color=(1, 1, 1, 1), duration=.5)
anim.repeat = True
anim.start(gen_label)
def start_animation(self):
self.generating_ani = threading.Thread(target=self.animate_generating_txt, daemon=True)
self.generating_ani.start()
def save_to_file(self, filename='definitions_test'):
#todo: maybe give a dialog box style prompt to ask if they want to change the file name
with open(filename + '.txt', 'w', encoding="utf-8") as file:
for key, value in self.definitions_dict.items():
file.write(key + " \n " + "[adjective]" + "\n" + " \n ".join(value) + "\n\n")
def check_not_out_of_words(self):
if len(self.generated_words) == 0:
# go to generate screen and logic already in place to switch back as soon as there are words again
self.root.current = 'Generating'
# running the app
if __name__ == '__main__':
neoDict().run()