def before_all(context):
config = ConfigHandler('app.ini')
app_host = config.get_config_option('bottle', 'host')
app_port = config.get_config_option('bottle', 'port')
context.app_uri = 'http://%s:%s/serve' % (app_host, app_port)
context.neo4j_conf = {
'server': config.get_config_option('neo4j', 'server'),
'user': config.get_config_option('neo4j', 'user'),
'password': config.get_config_option('neo4j', 'password'),
}
context.neo4j_adapter = Neo4jAdapter(context.neo4j_conf)
nlp_host = config.get_config_option('nlp', 'host')
nlp_port = config.get_config_option('nlp', 'port')
context.nlp_uri = 'http://%s:%s/?properties={"annotators":"sentiment","outputFormat":"json"}' % (
nlp_host, nlp_port)
] + ['"%s.%s_constraints": "%s"' % (TOPIC_PREFIX, prefix, statement)])
text_file.write(SINK_CONFIG %
(topic_list, host_conf['host'], host_conf['username'],
host_conf['password'], topics))
print('%s topics created.' % len(topics_conf))
if __name__ == '__main__':
if len(sys.argv) < 4:
print('python extractor.py <config> <source_dir> <target_dir>\n')
exit(1)
source_dir = sys.argv[2]
target_dir = sys.argv[3]
config_handler = ConfigHandler(sys.argv[1])
prefix = config_handler.get_config_option('info', 'prefix')
sources = config_handler.get_eval_option('extraction', 'sources')
for source in sources:
tsv_files = source['tsv_files']
for tsv_file in tsv_files:
file_name = '%s/%s' % (source_dir, tsv_file['file_name'])
lines = load_lines(file_name)
ent_conf = tsv_file['entities']
for entity_name, entity_info in ent_conf.items():
file_name = '%s/%s.tsv' % (target_dir, entity_name)
rows = create_rows(lines, entity_info)
count = write_rows(rows, file_name, entity_info)
print('%s [%s] entities extracted.' % (count, entity_name))
from queue import Queue
import signal
from threading import Thread, Event, get_ident
from time import sleep
from bottle import HTTPError, run, route, request, response
from config_handler import ConfigHandler
from neo4j_adapter import Neo4jAdapter
fileConfig('logging.ini')
logger = logging.getLogger('appLogger')
config = ConfigHandler('app.ini')
bottle_conf = {
'host': config.get_config_option('bottle', 'host'),
'port': int(config.get_config_option('bottle', 'port')),
'server': config.get_config_option('bottle', 'server'),
'threads': int(config.get_config_option('bottle', 'threads')),
}
neo4j_conf = {
'server': config.get_config_option('neo4j', 'server'),
'user': config.get_config_option('neo4j', 'user'),
'password': config.get_config_option('neo4j', 'password'),
}
in_queue = Queue()
out_queue_dict = dict()
stop_event = Event()
__email__ = "*****@*****.**"
__status__ = "Development"
from itertools import groupby
from typing import List
from fastapi import FastAPI
from pydantic import BaseModel
from requests import Session
from config_handler import ConfigHandler
####################
# Reading configuration from given file in relative path
config = ConfigHandler('conf/extractor.ini')
tika_url = config.get_config_option('tika', 'url')
file_dir = config.get_config_option('tika', 'dir')
####################
# Define the document model that the webapp receives from submission:
# It is a json format:
# [
# "u": the file name of the document, the webapp retains and returns it
# ]
class Item(BaseModel):
u: str
####################
# Create an instance of ASPI webapp provided by FastAPI
from post_nlp import PostProcessor
####################
# Reading configuration from given file in relative path
config = ConfigHandler('conf/nlp.ini')
####################
# Create an instance of the post processor
post_processor = PostProcessor(config)
####################
# Start `stanza`:
# - obtain the language string
# - (optional) uncomment if the language's model was not pre-downloaded
# - creates a `stanza` NLP processing pipeline, namely `nlp`
language = config.get_config_option('stanza', 'language')
# stanza.download(language)
nlp = stanza.Pipeline(language)
####################
# Define the document model that the webapp receives from submission:
# It is a json format:
# [
# "u": the uid of the document, the webapp retains and returns it
# "c": the textual content of the document.
# ]
class Item(BaseModel):
u: str
c: str
producer.flush()
print('. %s' % count)
if __name__ == "__main__":
if len(sys.argv) < 3:
print("Usage: python producer.py <config> <tsv_dir> <remote>")
exit(1)
handler = ConfigHandler(sys.argv[1])
tsv_dir = sys.argv[2]
remote = len(sys.argv) == 4
config = handler.get_eval_option('yggdrasil', 'conf')
prefix = handler.get_config_option('info', 'prefix')
broker = config['broker']
schema_registry = config['schema_registry']
schema = handler.get_config_option('avro', 'schema')
avro_schema = avro.loads(schema)
avro_producer = AvroProducer(
{
'bootstrap.servers': broker,
'schema.registry.url': schema_registry,
},
default_value_schema=avro_schema)
# if remote:
# schema_topic = '%s_constraints' % prefix