EnkrateiaLucca

for i,url in enumerate(df["links"][last_index:]):
    webbrowser.open(url)
    start_session = time.time()
    date = get_date()
    df["date"][i] = date
    att_level = float(input("Input the attention level for this
                            input (0-10):"))
    df["attention_level"][i] = att_level
    next_inp = input("Press enter to go to next link or q to quit
                      training for now")

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def set_up_dataset(csv_file="dataset.csv",text_file='dataset.txt'):
    """
    Creates a Dataframe from a text file with urls. 
    The columns are: 
    attention_level -> input a number representing how much attention the user will give to that source
    session_time -> How long it took to finish studying that source
    date -> The date of this session
    session_score -> The subjective score the user gives to its own performance on that content.
    """
    df = pd.read_csv(text_file, sep=" ", header=None)

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for i,url in enumerate(df["links"][last_index:]): # Looping over the links
        webbrowser.open(url) # Opens each link on the default browser 
        start_session = time.time() 
        date = get_date()
        df["date"][i] = date # Stores the date
        att_level = float(input("Input the attention level for this input (0-10):")) # Requests a score of attention 
        df["attention_level"][i] = att_level
        next_inp = input("Press enter to go to next link or q to quit training for now") # To go to the next link
        if next_inp == "q":
            df["last_index"] = i

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# source for the stopwatch: https://stackoverflow.com/questions/31995804/stopwatch-on-tkinter-creating-a-class
import tkinter as tk
from tkinter import messagebox
import numpy as np
import os
import natsort
import shutil
import random
import sys
import time

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from nltk import word_tokenize
# Loading my article in the article.txt file
with open("article.txt", "r", encoding="utf8") as intro: 
  text = intro.readlines()
# Selecting a sentence as a string
sentence = text[3]
print(word_tokenize(sentence))

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from nltk.stem import PorterStemmer, LancasterStemmer
#Instantiating the Stemmer classes from nltk
porter = PorterStemmer() 
lancaster = LancasterStemmer()
# Selecting a Sentence
sentence = text[3]
# Making a nice print output
print("Sentence:")
print("'" + sentence + "'")
print("{0:20} {1:20}".format("Ported Stemmer", "Lancaster Stemmer"))

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import nltk
from nltk.stem import WordNetLemmatizer
# Instantiating the lemmatizer class from nltk
wordnet_lemmatizer = WordNetLemmatizer()
# List of verbs and adjectives to feed to the lemmatizer
verbs = ["thinking", "wondering", "reflecting"]
adjectives = ["better", "brighter", "smarter"]
# Making a nice print for both
print("Verbs")
print("{0:20}{1:20}".format("Word","Lemma"))

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import nltk
from nltk.stem import WordNetLemmatizer
# Instantiating the lemmatizer class from nltk
wordnet_lemmatizer = WordNetLemmatizer()

# List of verbs and adjectives to feed to the lemmatizer
verbs = ["thinking", "wondering", "reflecting"]
adjectives = ["better", "brighter", "smarter"]

# Print them in parallel 

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import spacy
sp = spacy.load("en_core_web_sm")
sentence = text[-7]
sent_tokens = word_tokenize(sentence)
pos_tagged = nltk.pos_tag(text)
sen = sp(u"What matters most is using it")
spacy.displacy.render(sen, style='dep', jupyter=True, options={'distance': 100})

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import spacy 
sp = spacy.load("en_core_web_sm")

sentence = "Only accept truths that are absolutely evident"
#Tokenizing the sentence
sentence_token = word_tokenize(sentence)
# Tagging the tokens of the sentence
sentence_tagged = nltk.pos_tag(sentence_token)
print(sentence_tagged)
# Stablishing a pattern to give to the chunker