Add gRPC-based voice agent service for AGL

Introducing a gRPC-based voice agent service for Automotive Grade Linux
(AGL) that leverages GStreamer, Vosk, Snips, and RASA. It seamlessly
processes user voice commands, converting spoken words to text,
extracting intents, and performing actions via the Kuksa interface (WIP).

Bug-AGL: SPEC-4906
Signed-off-by: Malik Talha <talhamalik727x@gmail.com>
Change-Id: I47e61c66149c67bb97fecc745e4c3afd79f447a5

1 files changed, 46 insertions, 0 deletions

diff --git a/agl_service_voiceagent/nlu/snips_interface.py b/agl_service_voiceagent/nlu/snips_interface.py
new file mode 100644
index 0000000..f0b05d2
--- /dev/null
+++ b/agl_service_voiceagent/nlu/snips_interface.py
@@ -0,0 +1,46 @@
+# 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:
+    def __init__(self, model_path: Text):
+        self.engine = SnipsNLUEngine.from_path(model_path)
+
+    def preprocess_text(self, 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()
+        return preprocessed_text
+
+    def extract_intent(self, text: Text):
+        preprocessed_text = self.preprocess_text(text)
+        result = self.engine.parse(preprocessed_text)
+        return result
+    
+    def process_intent(self, intent_output):
+        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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