def get_corpus(lang:Text, chapter:Text):
dfjson = pd.read_json(f'~/pi/stack/crawlers/langcrs/all_{lang}.json')
ch=dfjson[dfjson['chapter'].str.match(chapter)]
rs=[]
for i, (sent,ref) in enumerate(zip(ch['translate'], ch['text'])):
rs.append(fix_sents(sent, lang))
return rs
def build_anal_tree(sents: Text,
lang: Text,
engine: Text,
nodecls=None,
docimpl=None):
"""
>>> from sagas.nlu.anal import build_anal_tree
>>> from anytree.search import findall, findall_by_attr
>>> f=build_anal_tree(sents, lang, engine)
>>> words = findall_by_attr(f, name='upos', value='VERB')
>>> objs = findall(words[0], filter_=lambda n: n.dependency_relation in ("obj"))
:param sents:
:param lang:
:param engine:
:return:
"""
from sagas.nlu.uni_remote import dep_parse
from sagas.nlu.utils import fix_sents
sents = fix_sents(sents, lang)
# doc is SentenceIntf
doc, resp = dep_parse(sents,
lang=lang,
engine=engine,
pipelines=['predicts'],
doc_impl=docimpl)
predicts = resp['predicts'] if resp and 'predicts' in resp else []
return from_doc(doc, lang, engine, nodecls, predicts)
def get_records(lang, chapter, field, fix=False):
from sagas.tool.intents_tool import intents_tool
text_list = [(
doc[field] if not fix else fix_sents(doc[field], lang),
doc['text']
if lang != 'en' else f"{doc['chapter'].lower()[:10]}_{doc['index']}",
doc['intent'] if 'intent' in doc else '',
) for doc in intents_tool.db.corpus.find({'chapter': chapter})]
return text_list
def parse_comps(sents, source):
sents = fix_sents(sents, source)
engine = cf.engine(source)
doc_jsonify, resp = dep_parse(sents, source, engine, ['predicts'])
if len(resp['predicts']) > 0:
rs = resp['predicts']
else:
rs = get_chunks(doc_jsonify)
return rs
def parse_deps(text, lang, translit=None):
text = fix_sents(text, lang)
engine = cf.engine(lang)
# g = sentence_view(lang, text, engine=engine, translit_lang=lang, enable_contrast=True)
doc_jsonify, resp = dep_parse(text, lang, engine, ['predicts'])
if doc_jsonify is not None:
list_chunks(doc_jsonify, resp, lang, enable_contrast=True)
g = display_doc_deps(doc_jsonify, resp, translit_lang=lang)
st.graphviz_chart(g)
if translit is not None:
st.text(f"♤ {translit}")
words = [word.text for word in doc_jsonify.words]
tools.contrast(text, lang, word_map=words)
def proc_corpus(lang, chapter):
"""
>>> proc_corpus('ko', 'At school')
:param lang:
:param chapter:
:return:
"""
dfjson = pd.read_json(f'~/pi/stack/crawlers/langcrs/all_{lang}.json')
ch = dfjson[dfjson['chapter'].str.match(chapter)]
rs_map = {}
for i, (sent, ref) in enumerate(zip(ch['translate'], ch['text'])):
text = fix_sents(sent, lang)
print('-', i, ref, '-' * 10)
# digest(text, 'spacy')
rs_map[i] = digest_verb(text, lang, 'stanza')
return len(ch), len([t for t in rs_map.values() if t])
def list_chapter_text(self, lang, chapter, fix=False):
"""
>>> intents_tool.list_chapter_text('fr', 'People', True)
>>> intents_tool.list_chapter_text('en', 'People')
:param lang:
:param chapter:
:param fix:
:return:
"""
field = f'lang_{lang}' if lang != 'en' else 'text'
print(*[
doc[field] if not fix else fix_sents(doc[field], lang)
for doc in self.db.corpus.find({'chapter': chapter})
],
sep='\n')
def clip_parse(self, source, sents='', specified='default', do_test=False):
"""
>> clip text: یک آبجو مى خواهم.
$ nlu clip_parse fa
$ engine='stanford' nluc ar
$ nlu clip_parse fi 'Tuolla ylhäällä asuu vanha nainen.'
$ nluc nl 'De vrouw heeft verschillende appels.'
$ nluc id 'Ini adalah judul buku yang saya baca.' aux
$ nluc fi 'Voiko täältä lainata aurinkovarjoa?' default True
:param source:
:return:
"""
from sagas.nlu.uni_remote import dep_parse
from sagas.nlu.common import get_from_clip
from sagas.conf.conf import cf
from sagas.nlu.uni_remote_viz import list_chunks
from sagas.nlu.utils import fix_sents
if sents=='':
sents = get_from_clip()
if sents.strip()=='':
tc.info('no text avaliable in clipboard.')
return
sents=fix_sents(sents, source)
tc.info(sents)
# Parse the sentence and display it's chunks, domains and contrast translations.
engine=cf.engine(source)
doc_jsonify, resp = dep_parse(sents, source, engine, ['predicts'])
if doc_jsonify is None:
raise Exception(f'Cannot parse sentence for lang {source}')
list_chunks(doc_jsonify, resp, source,
enable_contrast=True,
specified=None if specified=='default' else specified)
words = [word.text for word in doc_jsonify.words]
self.contrast(sents, source, word_map=words)
## visual tree
self.main_domains(sents, source, engine, False)
## add rulesets procs
from sagas.nlu.inferencer import do_infers
cli_cmd, pats = do_infers(sents, source)
if do_test:
for pat in pats:
self.check_rule(sents, source, pat)
def sents_summary(sents, source):
from sagas.nlu.uni_remote import dep_parse
from sagas.nlu.uni_remote_viz import list_contrast
from sagas.conf.conf import cf
from sagas.nlu.utils import fix_sents
from sagas.nlu.uni_parser import get_chunks
sents=fix_sents(sents, source)
engine=cf.engine(source)
doc_jsonify, resp = dep_parse(sents, source, engine, ['predicts'])
types=[]
if doc_jsonify is None:
raise Exception(f'Cannot parse sentence for lang {source}')
if len(resp['predicts']) > 0:
rs=resp['predicts']
else:
rs = get_chunks(doc_jsonify)
for serial, r in enumerate(rs):
print(f"{serial}. {r['type']} -> {r['word']}")
types.append(f"{source}:{r['type']}")
list_contrast(rs, source)
return types
def parse_sents(data: Dict):
sents, source, engine = data['sents'], data['lang'], data['engine']
sents = fix_sents(sents, source)
# engine=cf.engine(source)
return parse_and_cache(sents, source, engine)
def fix_data(data):
if 'engine' not in data:
data['engine'] = cf.engine(data['lang'])
data['sents']=fix_sents(data['sents'], data['lang'])
return data
def trans_clip(self,
source='auto',
targets='zh-CN;ja',
says=None,
details=True,
sents='',
deps=''):
"""
$ trans
$ trans auto en
$ trans ru en
$ trans ru 'zh-CN;ja'
$ trans-ru
$ trans-rus
$ alias sp="python -m sagas.tool.misc trans_clip pt 'en;it;ja' ja False"
$ sp 'O homem fica amarelo.'
$ sa 'أنا متأسف.'
$ sf "La similitude entre ces deux phrases" 'ja;zh;id'
$ sz '这两句话的相似程度' en
$ sz '这两句话的相似程度' 'en;fr;ar;ja;fa'
$ engine=spacy se 'I like to eat cucumber.'
$ sj '足にひどい痛みを感じました。' # multiple predicates
:return:
"""
import clipboard
from sagas.nlu.nlu_cli import NluCli
from sagas.nlu.nlu_tools import NluTools
ascii_incompatibles = ['zh', 'ja', 'ko', 'ar', 'fa']
if sents != '':
text = sents
interact_mode = False
else:
text = clipboard.paste()
text = text.replace("\n", "")
interact_mode = True
# remove spaces if lang is ja/zh
# if source in ('ja','zh'):
# text=text.replace(' ','')
text = fix_sents(text, source)
engine = cf.engine(source)
tc.emp('yellow', f".. parse with {engine}: ({text})")
# events
from sagas.nlu.events import init_reps
init_reps()
# add at 2019.9.15
ascii_gs = []
if self.enable_ascii_viz:
rt = NluCli().ascii_viz(text, source, engine=engine)
if source not in ascii_incompatibles:
ascii_gs.extend(rt.split('\n'))
print(rt)
# target_sents=[]
# sents_map={}
ctx = TransContext(source, targets, text, says, deps)
# print('❣', text)
if source != 'auto':
# text = fix_sents(source, text)
ctx.sents_map[source[:2]] = text
# addi_pronounce=[]
# succ=self.trans_google(ctx)
succ = self.translators[self.translator](ctx)
if not succ:
return
# if len(addi_pronounce)>0:
# target_sents.extend(addi_pronounce)
## addons
if self.enable_chunks_parse:
addons, result = self.parse_chunks(text,
source,
targets,
ctx,
details=details)
else:
addons = []
# result = '\n\t'.join([text] + ctx.target_sents)
lines = []
lines.append(f'\t.sent({source}="{text}"')
suffix = ") \\"
result = ', \n\t '.join(lines + ctx.target_sents + [suffix])
print(result)
# other langs dep-parse
if self.enable_ascii_viz and deps != '':
for t in deps.split(';'):
if t in ctx.sents_map:
rt = NluCli().ascii_viz(ctx.sents_map[t],
t,
engine=cf.engine(t))
# ascii_gs.extend(rt.split('\n'))
print(rt)
else:
color_print(
'red',
f".. the lang {t} for dep-parse is not available in translated list."
)
# do infers
from sagas.nlu.inferencer import do_infers
cli_cmd, pats = do_infers(text, source)
# tools
tools = NluTools()
# if cf.is_enabled('print_tree'): # move to events
# tools.main_domains(text, lang=source, engine=engine, print_domains=False)
# copy to clipboard
if interact_mode:
result = result + '\n\t'
if self.append_ascii_viz and len(ascii_gs) > 0:
result = result + '\n\t'.join(ascii_gs)
if len(addons) > 0:
# result=result+'\n\t'+'\n\t'.join(addons)
result = result + '\n\t'.join(addons)
if pats:
result = '\n\t'.join([result, cli_cmd, '\n\t'.join(pats)])
if self.enable_chunks_parse:
result = result + '\n'
# clipboard.copy(result+'\n')
clipboard.copy(result)
if interact_mode and says is not None:
tools.say(ctx.sents_map[says], says)
