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from __future__ import unicode_literals, print_function
def add_parse_parser(subparsers, formatter_class):
subparser = subparsers.add_parser(
"parse", formatter_class=formatter_class,
help="Load a trained NLU engine and perform parsing")
subparser.add_argument("training_path", type=str,
help="Path to a trained engine")
subparser.add_argument("-q", "--query", type=str,
help="Query to parse. If provided, it disables the "
"interactive behavior.")
subparser.add_argument("-v", "--verbosity", action="count", default=0,
help="Increase output verbosity")
subparser.add_argument("-f", "--intents-filter", type=str,
help="Intents filter as a comma-separated list")
subparser.set_defaults(func=_parse)
return subparser
def _parse(args_namespace):
return parse(args_namespace.training_path, args_namespace.query,
args_namespace.verbosity, args_namespace.intents_filter)
def parse(training_path, query, verbose=False, intents_filter=None):
"""Load a trained NLU engine and play with its parsing API interactively"""
import csv
import logging
from builtins import input, str
from snips_inference_agl import SnipsNLUEngine
from snips_inference_agl.cli.utils import set_nlu_logger
if verbose == 1:
set_nlu_logger(logging.INFO)
elif verbose >= 2:
set_nlu_logger(logging.DEBUG)
if intents_filter:
# use csv in order to properly handle commas and other special
# characters in intent names
intents_filter = next(csv.reader([intents_filter]))
else:
intents_filter = None
engine = SnipsNLUEngine.from_path(training_path)
if query:
print_parsing_result(engine, query, intents_filter)
return
while True:
query = input("Enter a query (type 'q' to quit): ").strip()
if not isinstance(query, str):
query = query.decode("utf-8")
if query == "q":
break
print_parsing_result(engine, query, intents_filter)
def print_parsing_result(engine, query, intents_filter):
from snips_inference_agl.common.utils import unicode_string, json_string
query = unicode_string(query)
json_dump = json_string(engine.parse(query, intents_filter),
sort_keys=True, indent=2)
print(json_dump)
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