import slate, subprocess, pyttsx
loc = raw_input('Enter location of pdf:')
with open(loc) as f:
doc = slate.PDF(f)
choice = input("Speak(0)/Save(1):")
if choice == 1:
page = input('Enter number of to save in 1 file(0 for all):')
if page == 0:
page = len(doc)
i = 0
while i < len(doc):
t = ""
for j in range(i, i + page):
if i + j >= len(doc):
break
t = t + doc[j - 1]
name = (str)(i) + " to " + (str)(i + page)
i = i + page
subprocess.call(["espeak", "-w" + name + ".mp3", t])
else:
page = input("Enter page number to speak:")
t = doc[page - 1]
engine = pyttsx.init()
engine.say(doc[page - 1])
engine.runAndWait()
#!/usr/bin/env python2
import slate
import sys
fb = (
'one two three four five six seven eight nine ten ' +
'eleven twelve thirteen fourteen fifteen sixteen seventeen eighteen nineteen '
+
'twenty twenty-one twenty-two twenty-three twenty-four twenty-five twenty-six twenty-seven'
)
lfn = fb.split()
txt = ''
for fx in lfn:
fn = 'page-%s.pdf' % fx
with open(fn) as f:
t = slate.PDF(f)
txt += t[0][0:-1]
with open(sys.argv[1], 'w') as f:
f.write(txt)
# python 03-pages.py > pages.txt
# base64 -d pages.txt > pages.png
# view the png "We like the moon" Joel and Alex Veitch rathergood.com
# Hmmm
# exiftool pages.png -> shows some text in the comment
def test_pdf_generation(self):
pdf = StringIO(generator(source=SAMPLE_TEXT, header='header'))
text = slate.PDF(pdf)
self.assertEqual(9, len(text))
self.assertIn('Lorem ipsum dolor', text[0])
self.assertIn('Sed egestas dui tellus', text[4])
def preprocess_pdf(pdf):
#had to edit the next line because there was an error.
#annotators = 'tokenize, ssplit, pos, lemma, ner, entitymentions, coref, sentiment, quote, openie'
annotators = 'tokenize, ssplit, pos, lemma, ner'
options = {'openie.resolve_coref': True}
nlp = StanfordCoreNLP(annotators=annotators, options=options)
with open(pdf, 'rb') as f:
doc = slate.PDF(f)
doc = ' '.join([' '.join(x.split()) for x in doc])
text_split = doc.split('Abstract')
if len(text_split) > 1:
text_no_title = ' '.join(text_split[1:])
text_no_title = str(text_no_title).encode('latin-1', 'ignore')
text_no_title = text_no_title[0:1000]
document = nlp(text_no_title)
first_sentence = document[0]
#only formats the first 2 sentences in the abstract
for token in first_sentence:
if str(token).isalpha():
if str(token).islower():
print(str(token).lower(),
'￨L￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
else:
print(str(token).lower(),
'￨U￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
else:
print(str(token).lower(),
'￨L￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
print('\n')
second_sentence = document[1]
for token in second_sentence:
if str(token).isalpha():
if str(token).islower():
print(str(token).lower(),
'￨L￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
else:
print(str(token).lower(),
'￨U￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
else:
print(str(token).lower(),
'￨L￨',
token.pos,
'￨',
token.ner,
'￨-',
end=" ",
sep='')
print('\n')