def main():
try:
mc = MarkovChain()
text = mc.add_file('ascii.txt')
mc.generate_text(text)
except ValueError:
print "Oops! There's been an issue"
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 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)
def generateLyrics(artists): allLyrics = [] if USE_DATABASE == True: # TODO return None else: for artist in artists: allLyrics += [item[1] for item in getAllLyrics(artist)] # end for #print allLyrics mc = MarkovChain(2) for song in allLyrics: mc.add_string(song) # end newLyrics = mc.generate_text(80) print newLyrics return newLyrics
'''
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)
from markov_python.cc_markov import MarkovChain
import fetch_data
hounds = MarkovChain()
hounds.add_file('D:\Projects\holmes_markov\Hounds.txt')
output = hounds.generate_text()
output = ' '.join(output)
output = output.capitalize() + "."
print output
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file("story.txt")
print mc.generate_text()
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))
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)
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
from markov_python.cc_markov import MarkovChain
import fetch_data
lyrics_5 =fetch_data.lyrics_4
#blink_data= open('')
'''with open('/Users/lennykogosov/Desktop/markovproject/text.txt', 'r+') as f:
text=f.read()'''
my_file= open('/Users/lennykogosov/Desktop/markovproject/text.txt', 'r+')
for x in lyrics_5:
my_file.write(x)
#my_file.close()
mc= MarkovChain()
mc.add_file('/Users/lennykogosov/Desktop/markovproject/text.txt')
first_sample= mc.generate_text(20)
print first_sample
my_file.close()
#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
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
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)
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 + ' ')
#print out_text.decode('utf-8')
except ValueError:
print
print("Sorry, that's not a valid length.")
print
print("Please, try again!")
length = 0
print
"""
generate text from the Markov Chain (call multiple times to generate additional data)
and convert output to the required format
"""
for current in range(number):
while True:
try:
temporary = mc.generate_text(length)
formatted = " ".join(temporary)
print("Passage #" + str(current + 1) + ": " + formatted)
print
break
except UnicodeEncodeError:
pass
# ending message if merely generating quotes
if desired_activity == "1":
print
print("That's all for now! Enjoy the quotes!")
"""
if playing a game
user tries to make an educated guess regarding
the period from which the mimic quotes originate
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())
#rough=soup.find_all(itemprop='description')
#print(''.join(map(str, rough[0].contents)))
from markov_python.cc_markov import MarkovChain
mc = MarkovChain()
mc.add_file('Romeo.txt')
lines = (mc.generate_text(20))
line = " ".join(lines)
newLine = line[0].upper()
for char in line[1:]:
newLine = newLine + char
print(newLine)
from markov_python.cc_markov import MarkovChain
if __name__ == '__main__':
mc = MarkovChain()
mc.add_file('lyrics.txt')
lyric = mc.generate_text()
lyric = ' '.join(lyric)
lyric = ''.join([i for i in lyric if not i.isdigit()])
print '---------------------------'
print '---------------------------'
print lyric
print '---------------------------'
print '---------------------------'
from markov_python.cc_markov import MarkovChain
#standard parser
mc = MarkovChain()
#mc.add_file('C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/carols.txt')
#mc.add_file('C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/farie tales.txt')
#mc.add_file('C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/50Shades.txt')
#mc.add_file('C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/lovecraft.txt')
#mc.add_file('C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/songdata.txt')
mc.add_file(
'C:/Users/Chris/PycharmProjects/CodeCademy/venv/text files/bass_guitar_tabs.txt'
)
lyrics = mc.generate_text(20)
def printChain(L):
s = "\n"
while len(L) > 0:
s = s + L.pop(0) + " "
return s
print printChain(lyrics)
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
This file combines the functions of the get_raw_data and cc_markov files to output the final program. '''
# combine the code to collect database information and run the Markov chain program.
from markov_python.cc_markov import MarkovChain
from get_raw_data import combine_end_users, combine_data, fetch_data
from time import sleep
# aggregate the raw data into a Markov chain and initialize a MarkovChain class instance
raw_data = combine_end_users()
emile_mc = MarkovChain()
# Run the interactive program. The initial steps are hard coded to reflect the "onboarding" process
print "Emile: Hi! I'm Emile, a self improvement AI. Would you like to work on sleep or exercise? \n"
user_input = raw_input("User entry (or q to Quit): ")
while user_input != 'q':
if user_input.lower() == "sleep":
print "\nEmile: Do you want to focus on your bed time, your wake time, or simply the number of hours of sleep you get per night? \n"
elif user_input.lower() == "exercise":
print "\nEmile: Great! As a baseline, how many days did you exercise last week, and what's your ideal number of exercise days?\n"
else:
emile_ans = emile_mc.generate_text(user_input, raw_data)
if emile_ans == "no response": # Emile will only respond to input he has "seen" in the past
print "\n","No known response. Try again!","\n"
else:
print "\n","Emile:",emile_ans[0][0],"\n"
if len(emile_ans[0]) > 1: # If Emile's response contains 2+ continuous texts, he will respond again before prompting for user input
sleep(1)
print "Emile:",emile_ans[0][1],"\n"
user_input = raw_input("User entry (or q to Quit): ") # continue until user quits out of the program
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 fetch_data
text = fetch_data
mc = MarkovChain()
mc.add_file("C:\Users\Wojtek\Desktop\Python Programming\markov_chain\Data_separate.txt")
start = True
while start == True:
user_input = raw_input("You: ")
if user_input == "bye":
print "Judy: OK, byee!"
start = False
else:
print "Judy: " + " ".join(mc.generate_text())
key_word_initializer=int(raw_input("How many keywords would you like"\
+ "use when reinitializing the Markov Chain: "))
if(key_word_initializer == 0):
my_markov_chain = MarkovChain()
else:
my_markov_chain = MarkovChain(key_word_initializer)
print("The Markov Chain has now been reset.")
elif( menu_selection == 'a' ):
input_processing_selection=raw_input("Do you want to add a (w)ebpage"\
+ " or a (f)ile: ")
if(input_processing_selection == 'w'):
webpage_upload_processing(my_markov_chain)
elif(input_processing_selection == 'f'):
file_upload_processing(my_markov_chain)
elif( menu_selection == 'g'):
markov_length=int(raw_input("How many words to you want the markov"\
+ " chain to be. Default is 20: "))
if(markov_length > 0):
markov_output=my_markov_chain.generate_text(markov_length)
else:
markov_output=my_markov_chain.generate_text()
print(markov_output)
program_exit=raw_input("Do you want to e(x)it or (r)epeat: ")
if(program_exit == 'x'):
break
from fetch import get_data
from markov_python.cc_markov import MarkovChain
import textwrap
hms = int(input('How many songs do you want to mash up?'))
def get_songs():
for i in range(hms):
str = get_data()
#mark.add_string(get_data())
mark = MarkovChain()
get_songs()
hmw = int(input('How many words do you want in this song?'))
output = mark.generate_text(hmw)
output = ' '.join(output)
print(output, 20)
def generate_data():
raw_text = read_data(data_file)
mc = MarkovChain()
mc.add_string(raw_text)
mcg = mc.generate_text()
return mcg
content = request.text
soup = BeautifulSoup(content, "html.parser")
comments = soup.find_all(id="mid-song-discussion")
for comment in comments:
comment.extract()
return soup.find_all(id="lyrics-body-text")[0].get_text()
def grab_hitler(url):
request = requests.get(url)
content = request.text
soup = BeautifulSoup(content, "html.parser")
results = soup.find_all('span','bqQuoteLink')
for result in results:
yield result.get_text()
for url in taytay_urls:
mc.add_string(grab_taytay(url))
output = mc.generate_text(max_length=20)
print "PRE HITLER LYRICS: "
print " ".join(output)
hitlers = grab_hitler(hitler_urls[0])
for hitler in hitlers:
mc.add_string(hitler)
h_output = mc.generate_text(max_length=20)
print "POST HITLER LYRICS: "
print " ".join(h_output)
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 file = "pg1661.txt" mc = MarkovChain() mc.add_file(file) print mc.generate_text().items
'''
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 lyric_links
lyric_links()
markov_lyrics = open("lyrics.txt", "r")
mc = MarkovChain()
mc.add_string(markov_lyrics.read())
markov_lyrics.close()
answer = " ".join(mc.generate_text())
answer = answer.capitalize()
print answer
""" 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()
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)
"""
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
mc = MarkovChain()
mc.add_file("text.txt")
print(mc.generate_text(15))
"""
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)
"""
This module will tie together code from fetch_data.py and cc_markov.py.
"""
from markov_python.cc_markov import MarkovChain
import fetch_data
my_chain = MarkovChain()
my_chain.add_string(fetch_data.get_content('http://wiersze.juniora.pl/tuwim/tuwim_l01.html'))
# 3 years
my_text = my_chain.generate_text()
for word in my_text:
print u"word: {}".format(word)
