Allow generating poems from raw text

The filename of the raw text is supplied via the first command line argument to
the python script call.

generate_poem.py

@@ -1,3 +1,4 @@
+import codecs
 import nltk
 import random
 import re
@@ -32,22 +33,31 @@ class PoemGenerator():
         self.words = []
         self.all_words = []
         self.inflect_engine = inflect.engine()
-        with open('/var/www/buzzfeed-haiku-generator/buzzfeed_facebook_statuses.csv', newline='', encoding='utf-8') as statuses:
-            reader = csv.reader(statuses, delimiter=',')
-            for row in reader:
-                if 'via buzzfeed ' not in row[1].lower():  # only English
-                    # split title into a list of words and punctuation
-                    title = self.spaces_and_punctuation.findall(row[2])
-                    # spell out digits into ordinal words for syllable counting
-                    title = [string.capwords(
-                             self.inflect_engine.number_to_words(int(word)))
-                             if word.isdigit() else word for word in title]
-                    self.sents.append(title)
-                    self.words.extend(title)
-                    # all_words only contains words, no punctuation
-                    self.all_words.extend([word for word in title
-                                           if not
-                                           self.only_punctuation.match(word)])
+        #  with open('/var/www/buzzfeed-haiku-generator/buzzfeed_facebook_statuses.csv', newline='', encoding='utf-8') as statuses:
+            #  reader = csv.reader(statuses, delimiter=',')
+            #  for row in reader:
+                #  if 'via buzzfeed ' not in row[1].lower():  # only English
+                    #  # split title into a list of words and punctuation
+                    #  title = self.spaces_and_punctuation.findall(row[2])
+                    #  # spell out digits into ordinal words for syllable counting
+                    #  title = [string.capwords(
+                             #  self.inflect_engine.number_to_words(int(word)))
+                             #  if word.isdigit() else word for word in title]
+                    #  self.sents.append(title)
+                    #  self.words.extend(title)
+                    #  # all_words only contains words, no punctuation
+                    #  self.all_words.extend([word for word in title
+                                           #  if not
+                                           #  self.only_punctuation.match(word)])
+        with codecs.open('trump.txt', 'r', 'utf-8') as corpus:
+            text = corpus.read()
+            sents = nltk.tokenize.sent_tokenize(text)
+            words = nltk.tokenize.word_tokenize(text)
+            self.sents.extend(sents)
+            self.words.extend(words)
+            self.all_words.extend([word for word in words
+                                   if not
+                                   self.only_punctuation.match(word)])
         self.bigrams = list(nltk.bigrams(self.words))
         self.cfd = nltk.ConditionalFreqDist(self.bigrams)
         #self.parser = Parser()
@@ -129,7 +139,8 @@ class PoemGenerator():
 
 
 if __name__ == '__main__':
-    generator = PoemGenerator(nltk.corpus.gutenberg)
+    generator = PoemGenerator('poop')
     #generator.generate_poem()
-    generator.generate_haiku()
+    haiku = generator.generate_haiku()
+    print haiku
     #generator.generate_endless_poem(None)

json_to_txt.py

@@ -0,0 +1,20 @@
+# Converts a json twitter dump to raw text file.
+import codecs
+import json
+import sys
+
+
+def get_text_from_json(filename):
+    with codecs.open(filename, 'r', 'utf-8') as f:
+        return [item['text'] for item in json.loads(f.read())]
+
+
+def write_text_to_file(filename, text_array, delimiter=' '):
+    text_to_write = delimiter.join(text_array)
+    with codecs.open(filename, 'w', 'utf-8') as f:
+        f.write(text_to_write)
+
+
+if __name__ == '__main__':
+    text_array = get_text_from_json(sys.argv[1])
+    write_text_to_file(sys.argv[2], text_array)

syntax_aware_generate.py

@@ -5,6 +5,7 @@ import pickle
 import random
 import re
 import codecs
+import sys
 from nltk.tree import Tree
 from collections import defaultdict
 from tqdm import tqdm
@@ -28,13 +29,13 @@ Tree.__hash__ = tree_hash
 # corpora. Shitty bus wifi makes it hard to download spacy data and look up the docs.
 
 
-def generate():
+def generate(filename):
     global syntaxes
     parser = Parser()
     if not os.path.exists(SYNTAXES_FILE):
         #  sents = nltk.corpus.gutenberg.sents('results.txt')
         # NOTE: results.txt is a big file of raw text not included in source control, provide your own corpus.
-        with codecs.open('results.txt', encoding='utf-8') as corpus:
+        with codecs.open(filename, encoding='utf-8') as corpus:
             sents = nltk.sent_tokenize(corpus.read())
             sents = [sent for sent in sents if len(sent) < 150][0:1500]
             for sent in tqdm(sents):
@@ -50,8 +51,7 @@ def generate():
             syntaxes = pickle.load(pickle_file)
 
     if not os.path.exists(CFDS_FILE):
-        #  corpus = nltk.corpus.gutenberg.raw('results.txt')
-        with codecs.open('results.txt', encoding='utf-8') as corpus:
+        with codecs.open(filename, encoding='utf-8') as corpus:
             cfds = [make_cfd(corpus.read(), i, exclude_punctuation=False, case_insensitive=True) for i in range(2, 5)]
             with open(CFDS_FILE, 'wb+') as pickle_file:
                 pickle.dump(cfds, pickle_file)
@@ -165,4 +165,4 @@ def get_most_common(search, cfds, most_common=None):
 
 
 if __name__ == '__main__':
-    generate()
+    generate(sys.argv[1])