def line_handler(db: Database, line_queue: Queue, error_queue: Queue,
source_id: int) -> None:
session = get_session(db)
source = session.query(Source).filter(Source.id == source_id).one()
while True:
try:
line_pair = line_queue.get()
if line_pair == DONE_READING:
break
line_no, line = line_pair
stems = nlp.stem_sentence(line)
rhyme_sound = nlp.rhyme_sound(line)
syllables = nlp.count_syllables(line)
alliteration = nlp.has_alliteration(line)
phrase = Phrase(stems=stems,
raw=line,
alliteration=alliteration,
rhyme_sound=rhyme_sound,
syllables=syllables,
line_no=line_no,
source=source)
session.add(phrase)
except Exception as e:
error_queue.put(e)
log.error('Died while processing text, rolling back')
session.rollback()
session.close()
return
session.commit()
def line_handler(db: Database,
line_queue: Queue,
error_queue: Queue,
source_id: int) -> None:
session = get_session(db)
source = session.query(Source).filter(Source.id == source_id).one()
while True:
try:
line_pair = line_queue.get()
if line_pair == DONE_READING:
break
line_no, line = line_pair
stems = nlp.stem_sentence(line)
rhyme_sound = nlp.rhyme_sound(line)
syllables = nlp.count_syllables(line)
alliteration = nlp.has_alliteration(line)
phrase = Phrase(stems=stems, raw=line, alliteration=alliteration,
rhyme_sound=rhyme_sound,
syllables=syllables, line_no=line_no, source=source)
session.add(phrase)
except Exception as e:
error_queue.put(e)
log.error('Died while processing text, rolling back')
session.rollback()
session.close()
return
session.commit()
def process_sentence(sentence, source_name, line_no):
phonemes = list(map(nlp.word_to_phonemes, nlp.words(sentence)))
return {'stems': nlp.stem_sentence(sentence),
'source': source_name,
'tagged': nlp.tag(sentence),
'rhyme_sound': nlp.rhyme_sound(sentence),
'phonemes': phonemes,
'num_syllables': nlp.count_syllables(sentence),
'line_no': line_no,
'alliteration': nlp.has_alliteration(sentence),
'raw': sentence,
'blank': False,
}
def process_sentence(sentence, source_name, line_no):
phonemes = list(map(nlp.word_to_phonemes, nlp.words(sentence)))
return {
'stems': nlp.stem_sentence(sentence),
'source': source_name,
'tagged': nlp.tag(sentence),
'rhyme_sound': nlp.rhyme_sound(sentence),
'phonemes': phonemes,
'num_syllables': nlp.count_syllables(sentence),
'line_no': line_no,
'alliteration': nlp.has_alliteration(sentence),
'raw': sentence,
'blank': False,
}
