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sqg_webserver.py
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sqg_webserver.py
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#!flask/bin/python
import flask
from gevent.pywsgi import WSGIServer
import argparse
from orchestrator import Orchestrator
from common.container.uri import Uri
from common.container.linkeditem import LinkedItem
from parser.lc_quad import LC_QaudParser
from parser.webqsp import WebQSPParser
from learning.classifier.svmclassifier import SVMClassifier
from learning.classifier.naivebayesclassifier import NaiveBayesClassifier
import logging
import common.utility.utility as utility
import sys
import os
import hashlib
from interruptingcow import timeout
app = flask.Flask(__name__)
queryBuilder = None
kb = None
classifier = None
cache_path = "./caches/"
utility.makedirs(cache_path)
hash_file = os.path.join(cache_path, "sqg_webserver.cache")
if os.path.exists(hash_file):
hash_list = utility.PersistanceDict.load(hash_file)
else:
hash_list = utility.PersistanceDict()
def hash(input):
return hashlib.md5(input).hexdigest()
@app.route('/qg/api/v1.0/query', methods=['POST'])
def generate_query():
if not flask.request.json:
flask.abort(400)
question = flask.request.json['question']
force_count_query = flask.request.json['force_count'] if 'force_count' in flask.request.json else False
force_bool_query = flask.request.json['force_bool'] if 'force_bool' in flask.request.json else False
force_list_query = flask.request.json['force_list'] if 'force_list' in flask.request.json else False
raw_entities = flask.request.json['entities']
raw_relations = flask.request.json['relations']
h1_threshold = int(flask.request.json['h1_threshold']) if 'h1_threshold' in flask.request.json else 9999999
timeout_threshold = int(flask.request.json['timeout']) if 'timeout' in flask.request.json else 9999999
use_cache = bool(flask.request.json['use_cache']) if 'use_cache' in flask.request.json else True
hash_key = hash((str(question) + str(raw_entities) + str(raw_relations) + str(h1_threshold)
+ str(force_count_query) + str(force_bool_query) + str(force_list_query)).encode('utf-8'))
if use_cache and hash_key in hash_list:
return flask.jsonify(hash_list[hash_key]), 201
logger.info(question)
entities = []
for item in raw_entities:
uris = [Uri(uri["uri"], kb.parse_uri, uri["confidence"]) for uri in item["uris"]]
entities.append(LinkedItem(item["surface"], uris))
relations = []
for item in raw_relations:
uris = [Uri(uri["uri"], kb.parse_uri, uri["confidence"]) for uri in item["uris"]]
relations.append(LinkedItem(item["surface"], uris))
try:
if len(entities) == 0 or len(relations) == 0:
raise Exception('Wrong number of input entity/relation!')
with timeout(timeout_threshold):
question_type = None
if force_list_query:
question_type = 0
elif force_bool_query:
question_type = 1
elif force_count_query:
question_type = 2
print('POST generate_query efetuado')
queries, question_type, type_confidence = queryBuilder.generate_query(question, entities, relations,
h1_threshold,
question_type)
question_type_str = "list"
ask_query = False
count_query = False
if question_type == 2:
question_type_str = "count"
count_query = True
elif question_type == 1:
question_type_str = "boolean"
ask_query = True
queries = [
{"query": kb.sparql_query(item["where"], "?u_" + str(item["suggested_id"]), count_query, ask_query),
"confidence": item["confidence"]} for item in
queries]
result = {'queries': queries, 'type': question_type_str, 'type_confidence': type_confidence}
if use_cache:
hash_list[hash_key] = result
hash_list.save(hash_file)
return flask.jsonify(result), 201
except RuntimeError as expt:
logger.error(expt)
return flask.jsonify({'error': str(expt)}), 408
except Exception as expt:
logger.error(expt)
return flask.jsonify({'error': str(expt)}), 422
@app.errorhandler(404)
def not_found(error):
return flask.make_response(flask.jsonify({'error': 'Command Not found'}), 404)
if __name__ == '__main__':
logger = logging.getLogger(__name__)
utility.setup_logging()
# Get initialization parameters
parser = argparse.ArgumentParser(description='Generate SPARQL query')
parser.add_argument("--port", help="port", default=5000, type=int, dest="port")
parser.add_argument("--kb", help="'dbpedia' (default) or 'freebase'", default="dbpedia", dest="kb")
parser.add_argument("--classifier", help="'svm' (default) or 'naivebayes'", default="svm", dest="classifier")
args = parser.parse_args()
# Create a dataset (parser) object. They have internally a KB object.
if args.kb == "dbpedia":
parser = LC_QaudParser()
elif args.kb == "freebase":
parser = WebQSPParser()
kb = parser.kb
if not kb.server_available:
logger.error("Server is not available. Please check the endpoint at: {}".format(kb.endpoint))
sys.exit(0)
# Instantiate classifiers
# Turns out that the SVM Classifier is actually a Logistic Regression
base_dir = "./output"
question_type_classifier_path = os.path.join(base_dir, "question_type_classifier")
double_relation_classifier_path = os.path.join(base_dir, "double_relation_classifier")
utility.makedirs(question_type_classifier_path)
utility.makedirs(double_relation_classifier_path)
if args.classifier == "svm":
question_type_classifier = SVMClassifier(os.path.join(question_type_classifier_path, "svm.model"))
double_relation_classifier = SVMClassifier(os.path.join(double_relation_classifier_path, "svm.model"))
elif args.classifier == "naivebayes":
question_type_classifier = NaiveBayesClassifier(os.path.join(question_type_classifier_path, "naivebayes.model"))
double_relation_classifier = NaiveBayesClassifier(
os.path.join(double_relation_classifier_path, "naivebayes.model"))
# Creates an orchestrator with classifiers and KB parser
queryBuilder = Orchestrator(logger, question_type_classifier, double_relation_classifier, parser)
logger.info("Starting the HTTP server")
http_server = WSGIServer(('', args.port), app)
http_server.serve_forever()