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# SPDX-License-Identifier: Apache-2.0
#
# Copyright (c) 2023 Malik Talha
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from typing import Text
from snips_inference_agl import SnipsNLUEngine
class SnipsInterface:
"""
SnipsInterface is a class for interacting with the Snips Natural Language Understanding Engine (Snips NLU).
"""
def __init__(self, model_path: Text):
"""
Initialize the SnipsInterface instance with the provided Snips NLU model.
Args:
model_path (Text): The path to the Snips NLU model.
"""
self.engine = SnipsNLUEngine.from_path(model_path)
def preprocess_text(self, text):
"""
Preprocess the input text by converting it to lowercase, removing leading/trailing spaces,
and removing special characters and punctuation.
Args:
text (str): The input text to preprocess.
Returns:
str: The preprocessed text.
"""
# text to lower case and remove trailing and leading spaces
preprocessed_text = text.lower().strip()
# remove special characters, punctuation, and extra whitespaces
preprocessed_text = re.sub(r'[^\w\s]', '', preprocessed_text).strip()
# replace % with " precent"
preprocessed_text = re.sub(r'%', ' percent', preprocessed_text)
# replace ° with " degrees"
preprocessed_text = re.sub(r'°', ' degrees ', preprocessed_text)
return preprocessed_text
def extract_intent(self, text: Text):
"""
Extract the intent from preprocessed text using the Snips NLU engine.
Args:
text (Text): The preprocessed input text.
Returns:
dict: The intent extraction result as a dictionary.
"""
preprocessed_text = self.preprocess_text(text)
result = self.engine.parse(preprocessed_text)
return result
def process_intent(self, intent_output):
"""
Extract intent and slot values from Snips NLU output.
Args:
intent_output (dict): The intent extraction result from Snips NLU.
Returns:
tuple: A tuple containing the intent name (str) and a dictionary of intent actions (entity-value pairs).
"""
intent_actions = {}
intent = intent_output['intent']['intentName']
slots = intent_output.get('slots', [])
for slot in slots:
action = slot['entity']
value = slot['value']['value']
intent_actions[action] = value
return intent, intent_actions
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