SimpleChains.py

import random

# Most of this code is more advanced than what I'd expect from the students. Lots of list comprehension that can be replaced with loops

def removePunctuation(s):
    '''
    Remove all the punctuation and replace with spaces.
    '''
    return ''.join(ch if ch not in string.punctuation else ' ' for ch in s)

def textToMarkovModel(text):
    '''
    Make a dictionary that is a pair of words as a key with an array of any words that follow it as the value
    '''
    text = removePunctuation(text).lower().split()
    samples = {}
    for i in range(len(text) - 2):
        key = (text[i], text[i+1])
        if key in samples:
            samples[(text[i], text[i+1])] += [text[i+2]]
        else:
            sample[key] = [text[i+2]]
    return samples

def getNextWord(markovModel, text):
    '''
    Return a random word that comes from the last 2 words in the model. Else with the last word.
    '''
    words = text.lower().split()
    k = (words[-2], words[-1])
    if k in markovModel: 
        return random.choice(markovModel[k])
    possibilities = [word for ((_, w), word) in markovModel.items() if words[-1] == w]
    if len(possibilities) > 0:
        return random.choice(possibilities)
    possibilities = [word for (key, word) in markovModel.items() if words[-1] in key or words[-2] in key]   
    if len(possibilities) > 0:
        return random.choice(possibilities)
    possibilities =  [word for (_, word) in markovModel.items()]
    return random.choice(possibilities)

def makeTweet(seed, baseText):
    m = textToMarkovModel(baseText)
    t = seed
    while len(t) < 140:
        w = getNextWord(m, t)
        t += " " + w
    return t