curr += 1
tokenizer = Tokenizer()
model = Model()
# data = tokenizer.tokenize_sentence(corpus)
# clusters = model.fit(data)
# summarizer = Summarizer(clusters)
# import pdb; pdb.set_trace()
# print(summarizer.generate())
for k, v in module.items():
v = ' '.join(v)
data = tokenizer.tokenize_sentence(v)
clusters = model.fit(data)
summarizer = Summarizer(clusters)
print(summarizer.generate())
# print(k)
# if(re.match(ur'ΚΕΦΑΛΑΙΟ [1-9]ο :',text[i])):
# if(text[i] not in chapter):
# chapter[text[i]] = 1
# else:
# j = i+1
# para = ""
# while not(re.match(ur'ΚΕΦΑΛΑΙΟ [1-9]ο :',text[j])):
# if(text[j] == u"Βιβλιογραφικές Αναφορές" or text[j] == u"Βιβλιογραφία" or text[j] == u"ΒΙΒΛΙΟΓΡΑΦΙΚΕΣ ΑΝΑΦΟΡΕΣ"):
# break
# para += text[j]
# j = j+1
# subtext[text[i]] = para
from model import Model
from tokenizer import Tokenizer
from summarizer import Summarizer
import codecs
#Opening file to read
file_name = sys.argv[1]
#File Name: greekpdf-2.txt
with codecs.open(file_name, encoding='utf-8') as f:
text = f.read().split('\n')
chapter_dictionary = dict()
for i in range(len(text)):
if (text[i].isdigit()):
chapter_dictionary[text[i]] = text[i + 1]
for j in range(i + 2, len(text)):
if not (text[i].isdigit()):
chapter_dictionary[text[i]].append(text[j])
tokenizer = Tokenizer()
model = Model()
file_output = open("a.txt", "w")
for k, v in chapter_dictionary.items():
v = ''.join(v)
data = tokenizer.tokenize_sentence(v)
clusters = model.fit(data)
summarizer = Summarizer(clusters)
file_output.write(summarizer.generate() + "\n")
