def intialize_model():
'''Feed the data to a markov chain and return the object of it'''
poems = fetch_poem()
mc = MarkovChain()
for poem in poems:
mc.add_string(poems[poem])
return mc
def main(args):
mc = MarkovChain()
for a in args[1::]:
fd = FetchData(a)
mc.add_string(fd.fetch_data())
chain = mc.generate_text()
out = (" ").join(chain)
print out
def write_song(doc1, doc2):
mc = MarkovChain()
verse = 1
mc.add_file(doc1)
with open(doc2, "w") as f:
while verse < 5:
f.write(("Verse %s" + "\n") % (verse))
for x in range(4):
line = mc.generate_text()
join_line = " ".join(line)
f.write(join_line.capitalize() + "\n")
verse += 1
def write_song(doc1, doc2):
mc = MarkovChain()
verse = 1
mc.add_file(doc1)
with open(doc2, 'w') as f:
while verse < 5:
f.write(('Verse %s' + '\n') % (verse))
for x in range(4):
line = mc.generate_text()
join_line = ' '.join(line)
f.write(join_line.capitalize() + '\n')
f.write('\n')
verse += 1
def get_chain(text, num_words):
#grab the text from the web. Data shouldn't contain HTML.
#initialize the markov chain with text
chain = MarkovChain()
chain.add_string(text)
#ask user for the number of words
#generate the markov chain using the number of words provided
#by the user and the text from the website entered.
chain_out = chain.generate_text(num_words)
stripped_chain = format_chain(chain_out)
return add_punct(stripped_chain)
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file('/Users/mobpro/desktop/winereviews.txt')
mc.add_string("red")
print str(mc.generate_text(10))
def generate_data():
raw_text = read_data(data_file)
mc = MarkovChain()
mc.add_string(raw_text)
mcg = mc.generate_text()
return mcg
from markov_python.cc_markov import MarkovChain
import sqlite3
from datetime import datetime
mk = MarkovChain()
conn = sqlite3.connect('tweets.db')
c = conn.cursor()
c.execute("SELECT COUNT(DISTINCT tweet) from home_tweets")
num_rows = c.fetchone()[0]
c.execute("SELECT DISTINCT tweet from home_tweets")
tweets = c.fetchmany(num_rows)
for tweet in tweets:
mk.add_string(list(tweet)[0])
all_status = []
for i in range(int(num_rows / 50)):
all_status.append(" ".join(mk.generate_text(20)))
with open("data/update_status_" + str(datetime.today()) + ".txt",
'wt',
encoding='utf-8') as f:
for status in all_status:
f.write(status)
f.write("\n")
pass
from markov_python.cc_markov import MarkovChain
import random
mc = MarkovChain(3)
mc.add_file('phrase_bank.txt')
generated_song = ""
def generate_verse():
new_verse = ""
for lyric in mc.generate_text(random.randint(5, 16)):
if lyric == 'i' or lyric == "i'm" or lyric == "i'ma" or lyric == "i'mma":
lyric = lyric.capitalize()
if new_verse == "":
lyric = lyric.capitalize()
new_verse += lyric + ' '
return new_verse
def generate_song():
generated_song = ""
for i in range(0, random.randint(45, 55)):
generated_song += (generate_verse() + ('\n'))
return generated_song
print generate_song()
#runs the project and outputs the text
from markov_python.cc_markov import MarkovChain
import fetch_data
mc = MarkovChain()
mc.add_file("sermon_output.txt")
initial = mc.generate_text()
def convert_to_file(initial):
sermon_string = ""
for n in initial:
sermon_string += (n + " ")
with open("Markov_Sermon.txt", "w") as f:
f.write(sermon_string)
convert_to_file(initial)
from markov_python.cc_markov import MarkovChain
import requests
from bs4 import BeautifulSoup, Tag
r = requests.get('https://www.familyfriendpoems.com/print/poem/NDA3ODk=')
response = r.content
soup = BeautifulSoup(response, 'html.parser')
tag = soup.find_all('p') + soup.find_all('script') + soup.find_all(
'b') + soup.find_all('title') + soup.find_all('a')
for i in tag:
i.decompose()
markov_input = soup.get_text()
mc = MarkovChain()
mc.add_string(markov_input)
a = mc.generate_text()
b = mc.generate_text()
c = mc.generate_text()
d = mc.generate_text()
print(' '.join(a))
print(' '.join(b))
print(' '.join(c))
print(' '.join(d))
print(markov_input)
'''
Created on June 2, 2018
@author: vanessa
'''
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file('songtexte.txt')
lyric = mc.generate_text()
lyric = ' '.join(lyric)
soup = BeautifulSoup(r2.content, "html.parser")
# This section pulls out the lyrics and puts into a list
rough = soup.find_all(itemprop='description')
results2 = list(map(str, rough[0].contents))
stripped_results2 = []
for line in results2[:-5]:
if line != "<br/>":
if line:
stripped_results2.append(line.strip())
song2 = ''.join(stripped_results2)
print("*** New song with more Cowbell!", )
#print (stripped_results2)
mc = MarkovChain()
mc.add_string(song1)
mc.add_string(song2)
x = mc.generate_text()
x = ' '.join(x)
print(x)
# This section pulls out the breaks
# new_rough = rough.replace("<br/>", "")
# print(new_rough)
# This printed the lyrics but included div class and <br> print(soup.find_all(itemprop='description'))
# This found null print(soup.find_all('lyrics'))
# from jerry. Said worked on 2.7 -- print ("".join(map(str, div.contents)))
#div = soup.find('div', id='lyrics')
#soup = BeautifulSoup(r.content, builder=HTMLParserTreeBuilder())
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file("text.txt")
print(mc.generate_text(15))
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file("story.txt")
print mc.generate_text()
from fetch_data import train
from markov_python.cc_markov import MarkovChain
# The above comes from https://github.com/ReagentX/markov_python
import re
# Get the text from a webpage
data = train('http://www.gutenberg.org/files/135/135-h/135-h.htm')
# Clean the page to remove any pesky bits
data = re.sub('<.*?>', '', data)
data = re.sub('&rsquo', '\'', data)
data = re.sub('&rdquo|&ldquo', '\"', data)
# Make a markov chain object
mc = MarkovChain(4)
# Add the data
mc.add_string(data)
text = mc.generate_text(30)
result = ''
for word in text:
result += word + ' '
print(result)
import os from markov_python.cc_markov import MarkovChain mc = MarkovChain() dirname = os.path.dirname(os.path.abspath(__file__)) book = os.path.join(dirname, 'Monologo_do_Vaqueiro_Gil_Vicente.txt') mc.add_file(book) sentence = mc.generate_text() print(sentence)
from markov_python.cc_markov import MarkovChain
q = MarkovChain()
a = MarkovChain()
q.add_file('/users/agaro/desktop/Talk_bot/questions.txt')
a.add_file('/users/agaro/desktop/Talk_bot/answers.txt')
counter = 0
while counter <= 10:
question = q.generate_text(13)
question = ' '.join(question)
print('Bot A: ')
print question
answer = a.generate_text(14)
print('Bot B: ')
answer = ' '.join(answer)
print answer
counter += 1
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file('C:/Users/dell/PycharmProjects/markov_chain/Wine Reviews.txt')
mc.add_string("red")
word_lst_1 = mc.generate_text(10)
print (word_lst_1)
word_lst_2 = []
for word in word_lst_1:
if word.isdigit() == False:
word_lst_2.append(word)
print(word_lst_2)
not_allowed = ["cases", "made", "has", "while"]
not_all_end = ["with", "and", "or", "on", "direct", "that", "are", "now", "through", "the", "supports", "hard", "a"]
word_lst_3 = []
for word in word_lst_2:
if word not in not_allowed:
word_lst_3.append(word)
print(word_lst_3)
while word_lst_3[len(word_lst_3) - 1] in not_all_end:
del word_lst_3[len(word_lst_3) - 1]
print(word_lst_3)
str1 = ' '.join(word_lst_3)
'''
Created on 17/02/2019
@author: dinis
'''
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file("texto.txt")
words_list = mc.generate_text()
words_list = " ".join(words_list)
print (words_list)
from markov_python.cc_markov import MarkovChain
from fetch_data import fetch_data
lyrics = fetch_data()
mc = MarkovChain()
for song in lyrics:
mc.add_string(song)
print mc
"""text = mc.generate_text(400)
for i in range(0,400,5):
for j in text[i:i+5]:
print j,
print ""
if i % 4 == 0:
print "\n"""
"""
Script for running the project and outputting data
"""
from markov_python.cc_markov import MarkovChain
import fetch_data
"""
"""
# fetch_data.get_text()
open_text = open('concatenated.txt')
base_text = open_text.read()
# base_text = fetch_data.set_text()
# base_text = open('concatenated.txt')
# print (base_text)
# base_text.close()
mc = MarkovChain()
mc.add_string(base_text)
mctext = mc.generate_text()
print(mctext)
from markov_python.cc_markov import MarkovChain
import scrapy
mc = MarkovChain()
#add filepath into mc.add_file(filepath) for files created by scrapy
mc.add_file('fetched_text')
#.generate_text() should generate a list of words
listofwords = []
listofwords = mc.generate_text(5)
listofwords = " ".join(listofwords)
print listofwords
from markov_python.cc_markov import MarkovChain
import fetch_data
link = 'http://www.e-reading.club/bookreader.php/1020088/Fomina_-_Pritchi._Daosskie%2C_kitayskie%2C_dzenskie.html'
#link='http://www.krotov.info/acts/01/joseph/filon_02.htm'
vocabul = fetch_data.load_web_to_text(link, 'voc.txt')
print type(vocabul)
fetch_data.show_param(str(vocabul))
out_fileS = 'out_text_string.txt'
out_file = 'out_text.txt'
text_object = MarkovChain(3)
text_object.add_file('voc.txt')
out_text = text_object.generate_text(200)
for i in out_text:
try:
print type(i), i.decode('utf8').encode('cp866')
except:
print 'Error'
out_text_j = ' '.join(out_text)
#with open(out_fileS,'w+') as ofile:
# for i in out_text: ofile.write(i+'\n')
with open(out_file, 'w+') as ofile:
for i in out_text:
ofile.write(i + ' ')
""" This is the launcher to run the New Shakespeare Sonnet Generator """ $ git init $ git status $ git add . $ git status $ git commit -m "Initial commit" from fetchshakespeare import fetch_data from markov_python.cc_markov import MarkovChain import textwrap #creates and adds the randomly generated data to the string def get_words(): mc.add_string(fetch_data()) #generator mc = MarkovChain() get_words() poem_length = 100 output = mc.generate_text(poem_length) output = " ".join(output) print textwrap.fill(output,width=35).capitalize()
"""
This program prompts you to insert lyric urls from azlyrics.
It mixes up the lyrics and gives you a new song in the same words by using a Markov Chain Generator
"""
from fetch_data import get_data
from markov_python.cc_markov import MarkovChain
import textwrap
num = int(raw_input("How many songs do you want to mash up? "))
def get_songs():
for i in range(num):
mc.add_string(get_data())
mc = MarkovChain()
get_songs()
song_length = int(raw_input('How many words do you want in this song? '))
output = mc.generate_text(song_length)
output = " ".join(output)
print textwrap.fill(output,20)
import fetch_data
from markov_python.cc_markov import MarkovChain
print "Welcom to MarkovChain"
url = raw_input("Provide an URL:")
text1 = fetch_data.getURL(url)
mc = MarkovChain()
mc.add_string(text1)
output = mc.generate_text()
print output
Learn Python
Final Project
Markov Chain Text Generator
28 October 2018
"""
"""
import necessary code (including fetch_data module and
a slightly modified Codecademy Markov Chain generator module)
"""
import fetch_data
from markov_python.cc_markov import MarkovChain
from random import randint
from time import sleep
# prepare Markov Chain generator
mc = MarkovChain()
# links to relevant works
wagnerian = {
"https://www.gutenberg.org/files/51356/51356-0.txt":
"The Birth of Tragedy",
"http://www.gutenberg.org/cache/epub/5652/pg5652.txt":
"Untimely Meditations(I)",
"http://www.gutenberg.org/cache/epub/38226/pg38226.txt":
"Untimely Meditations(II)"
}
positivistic = {
"https://www.gutenberg.org/files/51935/51935-0.txt":
"Human, All-Too-Human (I)",
"https://www.gutenberg.org/files/37841/37841-0.txt":
#test markov chain text gnerator
from markov_python.cc_markov import MarkovChain
import urllib2
from bs4 import BeautifulSoup
import os
#testing markov chain from file path
text = urllib2.urlopen('http://www.challies.com/')
html = text.read()
html_soup = BeautifulSoup(html, 'html.parser')
html_soup = (html_soup.get_text())
print html_soup
text_file = open("test_2", "w+")
text_file.write(html_soup)
mc = MarkovChain()
file_path = os.getcwd()
#print file_path
#mc.add_file("%s/test_2" % (file_path))
#final = mc.generate_text()
#print final
text_file.close()
#testing markov chain from string
response = urllib2.urlopen(url_path)
return response.read()
def display_on_console(output):
print "\nHere's " + str(
len(output)) + " significant words we found in your lyrics:"
for word in output:
print word,
print "\n\n"
def write_to_file(output):
filename = "lyrics_on_" + str(datetime.now())
file = open(filename, "w")
wrdCnt = 0
for word in output:
if wrdCnt % WORD_BREAK == 0:
file.write("\n")
file.write(word + " ")
wrdCnt += 1
print "Done. You can see the output at " + filename + "\n\n"
generateText = MarkovChain()
def run_markov_chain(input):
generateText.add_string(input)
return generateText.generate_text(len(input))
