def test_parse(self):
# Given / When
train(BEVERAGE_DATASET_PATH, str(self.tmp_file_path), config_path=None)
# When
with self.fail_if_exception("Failed to parse using CLI script"):
parse(str(self.tmp_file_path), "Make me two cups of coffee")
def test_parse(self):
# Given / When
dataset_stream = io.StringIO(u"""
---
type: intent
name: MakeTea
utterances:
- make me a [beverage_temperature:Temperature](hot) cup of tea
- make me [number_of_cups:snips/number](five) tea cups
---
type: intent
name: MakeCoffee
utterances:
- brew [number_of_cups:snips/number](one) cup of coffee please
- make me [number_of_cups] cups of coffee""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
nlu_engine = SnipsNLUEngine().fit(dataset)
nlu_engine.persist(self.tmp_file_path)
# When / Then
output_target = io.StringIO()
with self.fail_if_exception("Failed to parse using CLI script"):
with redirect_stdout(output_target):
parse(str(self.tmp_file_path), "Make me two cups of coffee")
output = output_target.getvalue()
# Then
expected_output = """{
"input": "Make me two cups of coffee",
"intent": {
"intentName": "MakeCoffee",
"probability": 1.0
},
"slots": [
{
"entity": "snips/number",
"range": {
"end": 11,
"start": 8
},
"rawValue": "two",
"slotName": "number_of_cups",
"value": {
"kind": "Number",
"value": 2.0
}
}
]
}
"""
self.assertEqual(expected_output, output)