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backend_new
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import cherrypy
import cherrypy_cors
import json
import os
from util_new import *
import re
from time import time
import regex
# import < your_code >
from nltk import sent_tokenize, word_tokenize
special_char_list = ["'", ",", ";", ":", "-", "?", "!", "$", "%", "#", "_", "&", "~", "|", "^", "+", "*", "/", "<", "=", ">", "(", ")", "{", "}", "[", "]"]
def flatten(t):
return [item for sublist in t for item in sublist]
def get_ccg_SRL_tokens(input_sentence):
sentences = sent_tokenize(input_sentence)
headers = {'Content-type': 'application/json'}
tokens = list()
sentences_end = list()
last_end = 0
for s in sentences:
SRL_response = requests.post('http://leguin.seas.upenn.edu:4039/annotate/',
json={"sentence": s, "task": "tokenize"}, headers=headers)
if SRL_response.status_code != 200:
return None, None
SRL_result = json.loads(SRL_response.text)
tmp_tokens = [w[0] for w in SRL_result['tokens']]
last_end = last_end + len(tmp_tokens)
sentences_end.append(last_end)
tokens.append(tmp_tokens)
tokens = flatten(tokens)
# print("\ntokens: ", tokens)
SRL_tokens = tokens
SRL_sentences = {'generator': 'srl_pipeline', 'score': 1.0, 'sentenceEndPositions': sentences_end}
return SRL_tokens, SRL_sentences, sentences
def Get_CogComp_SRL_results2(input_sentence):
start_time = time()
sentences = sent_tokenize(input_sentence)
tokens = list()
sentences_end = list()
last_end = 0
for s in sentences:
tmp_tokens = word_tokenize(s)
last_end = last_end + len(tmp_tokens)
sentences_end.append(last_end)
tokens.append(tmp_tokens)
tokens = flatten(tokens)
SRL_sentences = {'generator': 'srl_pipeline', 'score': 1.0, 'sentenceEndPositions': sentences_end}
end_time = time()
print("***Processing Time new:", end_time-start_time)
return tokens, SRL_sentences, sentences
def Get_CogComp_SRL_results(input_sentence):
start_time = time()
# We then work on Celine's SRL system.
# print('Extracting the events.')
SRL_tokens = list()
SRL_sentences = list()
# SRL_response = requests.get('http://dickens.seas.upenn.edu:4039/annotate', data=input_sentence)
# start_time_all_srl = time()
SRL_response = requests.get('http://leguin.seas.upenn.edu:4039/annotate', data=input_sentence)
# print("Processing Time for SRL backend: ", time() - start_time_all_srl)
if SRL_response.status_code != 200:
return None, None
SRL_result = json.loads(SRL_response.text)
SRL_tokens = SRL_result['tokens']
SRL_sentences = SRL_result['sentences']
end_time = time()
print("***Processing Time (tokenization): ", end_time-start_time)
return SRL_tokens, SRL_sentences
# print('Match tokens.')
def preprocess_input_text(input_text="", multi=False, special_char="remove", char_list=[]):
# start_time = time()
input_text = input_text.encode('utf-8').decode("utf-8")
# special_char_list = ["!", "#", "$", "%", "&", "'", "(", ")", "*", "+", ",", "-", ".", "/", ":", ";", "<", "=", ">", "?", "@", "[", "]", "^", "_", "`", "{", "|", "}", "~"]
if multi:
if "\n" in input_text:
input_text = re.sub("\n+", " ", input_text)
if "\t" in input_text:
input_text = re.sub("\t+", " ", input_text)
if special_char=="remove":
input_text = regex.sub(r'[^\p{Latin}]', u' ', input_text)
elif special_char=="convert" :
if "’" in input_text:
input_text = re.sub("\’", " ' ", input_text)
# input_text = re.sub("\’", " ", input_text)
if "‘" in input_text:
input_text = re.sub("\‘", " ' ", input_text)
# input_text = re.sub("\‘", " ", input_text)
if "“" in input_text:
input_text = re.sub("“", " \" ", input_text)
# input_text = re.sub("“", " ", input_text)
if "”" in input_text:
input_text = re.sub("”", " \" ", input_text)
# input_text = re.sub("”", " ", input_text)
if "—" in input_text:
input_text = re.sub("—", " - ", input_text)
if "–" in input_text:
input_text = re.sub("–", " - ", input_text)
if "…" in input_text:
input_text = re.sub("…", " . ", input_text)
# if "." in input_text:
# input_text = re.sub(".", " . ", input_text)
if char_list:
if "'" in input_text and "'" in char_list:
input_text = re.sub("'", " ' ", input_text)
# input_text = re.sub("'", " ", input_text)
if "," in input_text and "," in char_list:
input_text = re.sub(",", " , ", input_text)
# input_text = re.sub(",", " ", input_text)
if ";" in input_text and ";" in char_list:
input_text = re.sub(";", " ; ", input_text)
# input_text = re.sub(";", " ", input_text)
if ":" in input_text and ":" in char_list:
input_text = re.sub(":", " : ", input_text)
# input_text = re.sub(":", " ", input_text)
if "-" in input_text and "-" in char_list:
input_text = re.sub("-+", " - ", input_text)
if "?" in input_text and "?" in char_list:
input_text = re.sub("\?+", " ? ", input_text)
if "!" in input_text and "!" in char_list:
input_text = re.sub("!+", " ! ", input_text)
if "$" in input_text and "$" in char_list:
input_text = re.sub("\$", " $ ", input_text)
if "%" in input_text and "%" in char_list:
input_text = re.sub("%", " % ", input_text)
if "#" in input_text and "#" in char_list:
input_text = re.sub("#", " # ", input_text)
if "_" in input_text and "_" in char_list:
input_text = re.sub("_", " _ ", input_text)
if "&" in input_text and "&" in char_list:
input_text = re.sub("&", " & ", input_text)
if "~" in input_text and "~" in char_list:
input_text = re.sub("~", " ~ ", input_text)
if "|" in input_text and "|" in char_list:
input_text = re.sub("|", " | ", input_text)
if "^" in input_text and "^" in char_list:
input_text = re.sub("\^", " ^ ", input_text)
if "+" in input_text and "+" in char_list:
input_text = re.sub("\+", " + ", input_text)
if "*" in input_text and "*" in char_list:
input_text = re.sub("\*", " * ", input_text)
if "/" in input_text and "/" in char_list:
input_text = re.sub("/", " / ", input_text)
if "<" in input_text and "<" in char_list:
input_text = re.sub("<", " < ", input_text)
if "=" in input_text and "=" in char_list:
input_text = re.sub("=", " = ", input_text)
if ">" in input_text and ">" in char_list:
input_text = re.sub(">", " > ", input_text)
if "(" in input_text and "(" in char_list:
input_text = re.sub("\(", " ( ", input_text)
if ")" in input_text and ")" in char_list:
input_text = re.sub("\)", " ) ", input_text)
if "{" in input_text and "{" in char_list:
input_text = re.sub("{", " { ", input_text)
if "}" in input_text and "}" in char_list:
input_text = re.sub("}", " } ", input_text)
if "[" in input_text and "[" in char_list:
input_text = re.sub("\[", " [ ", input_text)
if "]" in input_text and "]" in char_list:
input_text = re.sub("\]", " ] ", input_text)
# input_text = regex.sub(r'[^\p{Latin}]', u' ', input_text)
input_text = re.sub("\s+", " ", input_text).strip()
# print("input_text[-1] : ", input_text[-1])
# if not input_text[-1] == '.':
# input_text = input_text + "."
end_time = time()
# print("***Processing Time (preprocessing): ", time() - start_time)
return input_text
class MyWebService(object):
@cherrypy.expose
def index(self):
return open('html/index.html', encoding='utf-8')
@cherrypy.expose
@cherrypy.tools.json_out()
@cherrypy.tools.json_in()
def info(self, **params):
return {"status":"online"}
@cherrypy.expose
def halt(self, **params):
cherrypy.engine.exit()
@cherrypy.expose
@cherrypy.tools.json_out()
@cherrypy.tools.json_in()
def annotate(self):
start_time = time()
hasJSON = True
result = {"status": "false"}
try:
# get input JSON
data = cherrypy.request.json
except:
hasJSON = False
result = {"error": "invalid input"}
if hasJSON:
# process input
input_paragraph = data['text']
input_paragraph = preprocess_input_text(input_paragraph, multi=True, special_char="convert", char_list=special_char_list)
# input_paragraph = preprocess_input_text(input_paragraph, multi=True, special_char="convert", char_list=["'", ","])
print("input_paragraph: ", input_paragraph)
###
# headers = {'Content-type': 'application/json'}
# input_paragraph = re.sub(r'[\n]', ' ', input_paragraph)
# NER_response = requests.post('http://dickens.seas.upenn.edu:4022/ner/',
# json={"task": "ner", "text": "Hello world."}, headers=headers)
# if NER_response.status_code != 200:
# return {'error': 'The NER service is down.'}
# SRL_response = requests.get('http://leguin.seas.upenn.edu:4039/annotate', data=input_paragraph)
# SRL_response = requests.post('http://leguin.seas.upenn.edu:4039/annotate',
# json={'sentence': "Hello world."})
# if SRL_response.status_code != 200:
# return {'error': 'The SRL service is down.'}
# SRL_tokens, SRL_sentences = Get_CogComp_SRL_results(input_paragraph)
SRL_tokens, SRL_sentences, sentences2 = get_ccg_SRL_tokens(input_paragraph)
# print("SRL_tokens: ", SRL_tokens)
# if (not SRL_tokens) or (not SRL_sentences):
# return {'error': 'The SRL service is down.'}
# print(SRL_sentences['sentenceEndPositions'])
sentences = list()
sentences_by_char = list()
# sentences = sentences2
# sentences_by_char = SRL_tokens
for i, tmp_s_end_token in enumerate(SRL_sentences['sentenceEndPositions']):
if i == 0:
sentences.append(' '.join(SRL_tokens[:tmp_s_end_token]))
sentences_by_char.append(SRL_tokens[:tmp_s_end_token])
else:
sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token]))
sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token])
if SRL_sentences['sentenceEndPositions'][-1] < len(SRL_tokens):
sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:]))
sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:])
# sentences = input_paragraph.split('\n')
print('Number of sentences:', len(sentences))
# print('Number of sentences:', len(SRL_sentences['sentenceEndPositions']))
previous_char = 0
tmp_view_data = dict()
tmp_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.PredicateArgumentView'
tmp_view_data['viewName'] = 'event_extraction'
tmp_view_data['generator'] = 'cogcomp_kairos_event_ie_v1.0'
tmp_view_data['score'] = 1.0
tmp_view_data['constituents'] = list()
tmp_view_data['relations'] = list()
all_tokens = list()
sentence_positions = list()
previous_char = 0
for s_id, tmp_s in enumerate(sentences):
# print(s_id, " : ", tmp_s)
# start_time_event = time()
extracted_events, tmp_tokens, SRL_sentences2 = extractor.extract(tmp_s, include_all_verbs=False)
# print("Processing Time for 1 sentence event extraction: ", time() - start_time_event)
print(extracted_events)
# if len(extracted_events) > 0:
# tmp_tokens = extracted_events[0]['tokens']
# else:
# tmp_tokens = tmp_s.split(' ')
all_tokens += tmp_tokens
sentence_positions.append(len(all_tokens))
for tmp_event in extracted_events:
trigger_start_token_id = tmp_event['trigger']['position'][0]
trigger_end_token_id = tmp_event['trigger']['position'][1]
trigger_consituent_position = len(tmp_view_data['constituents'])
tmp_view_data['constituents'].append(
{'label': tmp_event['trigger']['type'], 'score': 1.0, 'start': (trigger_start_token_id + previous_char),
'end': (trigger_end_token_id + previous_char), 'properties': {
'SenseNumber': '01', 'sentence_id': s_id,
'predicate': tmp_tokens[
trigger_start_token_id:trigger_end_token_id]}})
for tmp_argument in tmp_event['arguments']:
argument_start_token_id = tmp_argument['position'][0] + previous_char
argument_end_token_id = tmp_argument['position'][1] + previous_char
tmp_view_data['relations'].append(
{'relationName': tmp_argument['role'], 'srcConstituent': trigger_consituent_position,
'targetConstituent': len(tmp_view_data['constituents'])})
tmp_view_data['constituents'].append(
{'label': tmp_argument['role'], 'score': 1.0, 'start': argument_start_token_id,
'end': argument_end_token_id, 'entity_type': tmp_argument['entity_type']})
previous_char += len(sentences_by_char[s_id])
event_ie_view = dict()
event_ie_view['viewName'] = 'Event_extraction'
event_ie_view['viewData'] = [tmp_view_data]
token_view = dict()
token_view['viewName'] = 'TOKENS'
tmp_token_view_data = dict()
tmp_token_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.TokenLabelView'
tmp_token_view_data['viewName'] = 'TOKENS'
tmp_token_view_data['generator'] = 'Cogcomp-SRL'
tmp_token_view_data['score'] = 1.0
tmp_token_view_data['constituents'] = list()
for i, tmp_token in enumerate(all_tokens):
tmp_token_view_data['constituents'].append({'label': tmp_token, 'score': 1.0, 'start': i, 'end': i+1})
token_view['viewData'] = tmp_token_view_data
result = dict()
result['corpusId'] = ''
result['id'] = ''
result['text'] = input_paragraph
result['tokens'] = all_tokens
result['sentences'] = SRL_sentences
result['views'] = [token_view, event_ie_view]
# result['tokens'] = all_tokens
# result['sentences'] = {'generator': 'srl_pipeline', 'score': 1.0, 'sentenceEndPositions': SRL_sentences['sentenceEndPositions']}
# return resulting JSON
end_time = time()
print("***Processing Time for Event Extraction: ", end_time - start_time)
print("***Average Time for Event Extraction : ", (end_time - start_time)/len(SRL_sentences['sentenceEndPositions']))
# print(result)
# print("\n")
return result
@cherrypy.expose
@cherrypy.tools.json_out()
@cherrypy.tools.json_in()
def annotate_multi(self):
start_time = time()
hasJSON = True
result = {"status": "false"}
try:
# get input JSON
data = cherrypy.request.json
except:
hasJSON = False
result = {"error": "invalid input"}
if hasJSON:
# process input
input_paragraph = data['text']
input_paragraph = preprocess_input_text(input_paragraph, multi=True, special_char="convert", char_list=special_char_list)
# input_paragraph = preprocess_input_text(input_paragraph, multi=True, special_char="convert", char_list=["'", ","])
print("input_paragraph: ", input_paragraph)
###
# headers = {'Content-type': 'application/json'}
# input_paragraph = re.sub(r'[\n]', ' ', input_paragraph)
# NER_response = requests.post('http://dickens.seas.upenn.edu:4022/ner/',
# json={"task": "ner", "text": "Hello world."}, headers=headers)
# if NER_response.status_code != 200:
# return {'error': 'The NER service is down.'}
# SRL_response = requests.get('http://leguin.seas.upenn.edu:4039/annotate', data=input_paragraph)
# SRL_response = requests.post('http://leguin.seas.upenn.edu:4039/annotate',
# json={'sentence': "Hello world."})
# if SRL_response.status_code != 200:
# return {'error': 'The SRL service is down.'}
# SRL_tokens, SRL_sentences = Get_CogComp_SRL_results(input_paragraph)
# SRL_tokens, SRL_sentences, sentences2 = get_ccg_SRL_tokens(input_paragraph)
# print("SRL_tokens: ", SRL_tokens)
# if (not SRL_tokens) or (not SRL_sentences):
# return {'error': 'The SRL service is down.'}
# print(SRL_sentences['sentenceEndPositions'])
# sentences = list()
# sentences_by_char = list()
# sentences = sentences2
# sentences_by_char = SRL_tokens
# for i, tmp_s_end_token in enumerate(SRL_sentences['sentenceEndPositions']):
# if i == 0:
# sentences.append(' '.join(SRL_tokens[:tmp_s_end_token]))
# sentences_by_char.append(SRL_tokens[:tmp_s_end_token])
# else:
# sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token]))
# sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token])
# if SRL_sentences['sentenceEndPositions'][-1] < len(SRL_tokens):
# sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:]))
# sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:])
# sentences = input_paragraph.split('\n')
# print('Number of sentences:', len(sentences))
# print('Number of sentences:', len(SRL_sentences['sentenceEndPositions']))
previous_char = 0
tmp_view_data = dict()
tmp_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.PredicateArgumentView'
tmp_view_data['viewName'] = 'event_extraction'
tmp_view_data['generator'] = 'cogcomp_kairos_event_ie_v1.0'
tmp_view_data['score'] = 1.0
tmp_view_data['constituents'] = list()
tmp_view_data['relations'] = list()
all_tokens = list()
sentence_positions = list()
# for s_id, tmp_s in enumerate(sentences):
# print(s_id, " : ", tmp_s)
# start_time_event = time()
extracted_events, tmp_tokens, SRL_sentences = extractor.extract(input_paragraph, include_all_verbs=False)
# print("Processing Time for 1 sentence event extraction: ", time() - start_time_event)
print(extracted_events)
# if len(extracted_events) > 0:
# tmp_tokens = extracted_events[0]['tokens']
# else:
# tmp_tokens = tmp_s.split(' ')
all_tokens += tmp_tokens
# sentence_positions.append(len(all_tokens))
for tmp_event in extracted_events:
trigger_start_token_id = tmp_event['trigger']['position'][0]
trigger_end_token_id = tmp_event['trigger']['position'][1]
# print(SRL_sentences['sentenceEndPositions'])
s_id = -1
for i in range(len(SRL_sentences['sentenceEndPositions'])):
if trigger_end_token_id < SRL_sentences['sentenceEndPositions'][i]:
s_id = i-1
break
if s_id==-1:
s_id = len(SRL_sentences['sentenceEndPositions']) - 1
trigger_consituent_position = len(tmp_view_data['constituents'])
tmp_view_data['constituents'].append(
{'label': tmp_event['trigger']['type'], 'score': 1.0, 'start': (trigger_start_token_id),
'end': (trigger_end_token_id), 'properties': {
'SenseNumber': '01', 'sentence_id': s_id,
'predicate': tmp_tokens[
trigger_start_token_id:trigger_end_token_id]}})
for tmp_argument in tmp_event['arguments']:
argument_start_token_id = tmp_argument['position'][0]
argument_end_token_id = tmp_argument['position'][1]
tmp_view_data['relations'].append(
{'relationName': tmp_argument['role'], 'srcConstituent': trigger_consituent_position,
'targetConstituent': len(tmp_view_data['constituents'])})
tmp_view_data['constituents'].append(
{'label': tmp_argument['role'], 'score': 1.0, 'start': argument_start_token_id,
'end': argument_end_token_id, 'entity_type': tmp_argument['entity_type']})
# previous_char += len(sentences_by_char[s_id])
event_ie_view = dict()
event_ie_view['viewName'] = 'Event_extraction'
event_ie_view['viewData'] = [tmp_view_data]
token_view = dict()
token_view['viewName'] = 'TOKENS'
tmp_token_view_data = dict()
tmp_token_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.TokenLabelView'
tmp_token_view_data['viewName'] = 'TOKENS'
tmp_token_view_data['generator'] = 'Cogcomp-SRL'
tmp_token_view_data['score'] = 1.0
tmp_token_view_data['constituents'] = list()
for i, tmp_token in enumerate(all_tokens):
tmp_token_view_data['constituents'].append({'label': tmp_token, 'score': 1.0, 'start': i, 'end': i+1})
token_view['viewData'] = tmp_token_view_data
result = dict()
result['corpusId'] = ''
result['id'] = ''
result['text'] = input_paragraph
result['tokens'] = all_tokens
result['sentences'] = SRL_sentences
result['views'] = [token_view, event_ie_view]
# result['tokens'] = all_tokens
# result['sentences'] = {'generator': 'srl_pipeline', 'score': 1.0, 'sentenceEndPositions': SRL_sentences['sentenceEndPositions']}
# return resulting JSON
end_time = time()
print("***Processing Time for Event Extraction: ", end_time - start_time)
print("***Average Time for Event Extraction : ", (end_time - start_time)/len(SRL_sentences['sentenceEndPositions']))
# print(result)
# print("\n")
return result
if __name__ == '__main__':
print("")
# INITIALIZE YOUR MODEL HERE
parser = argparse.ArgumentParser()
parser.add_argument("--gpu", default='1', type=str, required=False,
help="choose which gpu to use")
parser.add_argument("--representation_source", default='nyt', type=str, required=False,
help="choose which gpu to use")
parser.add_argument("--model", default='bert-large', type=str, required=False,
help="choose which gpu to use")
parser.add_argument("--pooling_method", default='final', type=str, required=False,
help="choose which gpu to use")
parser.add_argument("--weight", default=100, type=float, required=False,
help="weight assigned to triggers")
parser.add_argument("--argument_matching", default='exact', type=str, required=False,
help="weight assigned to triggers")
parser.add_argument("--eval_model", default='joint', type=str, required=False,
help="weight assigned to triggers")
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('current device:', device)
extractor = CogcompKairosEventExtractorTest(device, 'mbert')
# IN ORDER TO KEEP IT IN MEMORY
print("Starting rest service...")
cherrypy_cors.install()
config = {
'global': {
'server.socket_host': 'leguin.seas.upenn.edu',
'server.socket_port': 4023,
'cors.expose.on': True
},
'/': {
'tools.sessions.on': True,
'cors.expose.on': True,
'tools.staticdir.root': os.path.abspath(os.getcwd())
},
'/static': {
'tools.staticdir.on': True,
'tools.staticdir.dir': './html'
},
'/html': {
'tools.staticdir.on': True,
'tools.staticdir.dir': './html',
'tools.staticdir.index': 'index.html',
'tools.gzip.on': True
}
}
cherrypy.config.update(config)
# cherrypy.config.update({'server.socket_port': 4036})
cherrypy.quickstart(MyWebService(), '/', config)
# import cherrypy
#
#
# class demoExample:
# @cherrypy.expose
# def index(self):
#
# return "Hello World!!!"
#
#
#
# cherrypy.quickstart(demoExample())