def test_issue3882(en_vocab):
"""Test that displaCy doesn't serialize the doc.user_data when making a
copy of the Doc.
"""
doc = Doc(en_vocab, words=["Hello", "world"], deps=["dep", "dep"])
doc.user_data["test"] = set()
displacy.parse_deps(doc)
def visualize_parser(docs: List[spacy.tokens.Doc],
*,
title: Optional[str] = None,
key: Optional[str] = None) -> None:
st.header(title)
cols = st.beta_columns(2)
num_parses = cols[1].select_slider('Number of Sentences to Visualise:',
options=[0, 1, 2, 3, 4],
value=1)
vismode = cols[0].radio('Which Dependencies to Show',
('All', 'Collocation Candidates'))
if num_parses >= 1:
for num, sent in enumerate(docs):
if num < num_parses:
allparsed = displacy.parse_deps(sent)
colparsed = my_parser(sent, num)
html = displacy.render(
(allparsed if vismode == 'All' else colparsed),
style="dep",
manual=True)
# Double newlines seem to mess with the rendering
html = html.replace("\n\n", "\n")
if len(docs) > 1:
st.markdown(f"> {sent.text}")
st.write(get_svg(html), unsafe_allow_html=True)
def recognize_named_tag(event, context):
request_body = event['body']
text = json.loads(request_body)['text']
print('received test from http post: ', text)
if text is not None:
doc = MODEL(text)
parse = displacy.parse_deps(doc)
setting = {}
setting['lang'] = 'en'
setting['direction'] = 'ltr'
parse['setting'] = setting
print('parse after create: ', parse)
body = parse
response = {
"statusCode": 200,
"body": json.dumps(body),
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*'
}
}
return response
def test_displacy_parse_deps(en_vocab):
"""Test that deps and tags on a Doc are converted into displaCy's format."""
words = ["This", "is", "a", "sentence"]
heads = [1, 1, 3, 1]
pos = ["DET", "VERB", "DET", "NOUN"]
tags = ["DT", "VBZ", "DT", "NN"]
deps = ["nsubj", "ROOT", "det", "attr"]
doc = Doc(en_vocab,
words=words,
heads=heads,
pos=pos,
tags=tags,
deps=deps)
deps = displacy.parse_deps(doc)
assert isinstance(deps, dict)
assert deps["words"] == [
{
"lemma": None,
"text": words[0],
"tag": pos[0]
},
{
"lemma": None,
"text": words[1],
"tag": pos[1]
},
{
"lemma": None,
"text": words[2],
"tag": pos[2]
},
{
"lemma": None,
"text": words[3],
"tag": pos[3]
},
]
assert deps["arcs"] == [
{
"start": 0,
"end": 1,
"label": "nsubj",
"dir": "left"
},
{
"start": 2,
"end": 3,
"label": "det",
"dir": "left"
},
{
"start": 1,
"end": 3,
"label": "attr",
"dir": "right"
},
]
def test_displacy_parse_deps(en_vocab):
"""Test that deps and tags on a Doc are converted into displaCy's format."""
words = ["This", "is", "a", "sentence"]
heads = [1, 0, 1, -2]
pos = ["DET", "VERB", "DET", "NOUN"]
tags = ["DT", "VBZ", "DT", "NN"]
deps = ["nsubj", "ROOT", "det", "attr"]
doc = get_doc(en_vocab, words=words, heads=heads, pos=pos, tags=tags, deps=deps)
deps = displacy.parse_deps(doc)
assert isinstance(deps, dict)
assert deps["words"] == [
{"text": "This", "tag": "DET"},
{"text": "is", "tag": "VERB"},
{"text": "a", "tag": "DET"},
{"text": "sentence", "tag": "NOUN"},
]
assert deps["arcs"] == [
{"start": 0, "end": 1, "label": "nsubj", "dir": "left"},
{"start": 2, "end": 3, "label": "det", "dir": "left"},
{"start": 1, "end": 3, "label": "attr", "dir": "right"},
]
def get_spacy_parse(contexts):
all_parses = []
for context in contexts:
tokens = context["tokens"]
for index in range(len(tokens)):
tokens[index]["extract"] = context["extract_mask"][index]
tokens[index]["weight"] = context["weight_mask"][index]
sentences = get_sentence_tokens_from_token_list(tokens)
for sentence_tokens in sentences:
sentence_text = get_sentence_text_from_tokens(sentence_tokens)
parse = en_nlp(sentence_text)
for index, token in enumerate(parse):
# assuming same token index, as we'd already parsed it with the same spacy model before
if token.text.strip().lower(
) != sentence_tokens[index]["text"].strip().lower():
print(token.text.strip().lower(), "!=",
sentence_tokens[index]["text"].strip().lower())
print(parse[max(0, index -
7):min(len(parse) - 1, index + 7)])
print(" ".join([
x["text"] for x in
sentence_tokens[max(0, index -
7):min(len(parse) - 1, index + 7)]
]))
break
token._.extract = sentence_tokens[index]["extract"]
token._.weight = sentence_tokens[index]["weight"]
token._.dist_cit = sentence_tokens[index].get("dist_cit", 0)
# token._.dist_cit_norm = sentence_tokens[index][0].get("dist_cit_norm", 0)
display_dict = displacy.parse_deps(parse)
# for index, word in enumerate(display_dict["words"]):
# if word["text"] != parse[index].text:
# print("ARGh")
all_parses.append(parse)
return all_parses
def create_dep(text):
doc = MODEL(text)
return displacy.parse_deps(doc)
import spacy
import os
from spacy import displacy
import json
nlp = spacy.load("en_core_web_sm")
cwd = os.getcwd()
path = cwd + "/py/uploads/display-upload.txt"
#print(path)
with open(path, 'r') as f:
text = f.read()
doc = nlp(text)
sentence_spans = list(doc.sents)
parse_dict = displacy.parse_deps(doc)
parsed_json = json.dumps(parse_dict)
print(parsed_json)
def visualize_text(text):
language = settings.LANG_ID.classify(text)[0]
lang = settings.LANGUAGE_MODELS[language]
doc = lang(text)
return displacy.parse_deps(doc)
def tag_and_parse(text):
doc = MODEL(text)
return displacy.parse_deps(doc)
def displacy_service(text):
doc = nlp(text)
return displacy.parse_deps(doc)
def displacy_service(text):
"""Deploys a displaCy server in localhost"""
doc = nlp(text)
return displacy.parse_deps(doc)
def displacyService(self, text):
'Returns a graph object'
doc = self.nlp(text)
return displacy.parse_deps(doc)
