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This file contains tests for the API of your model. You can run these tests by installing test requirements:
```bash
pip install -r requirements-test.txt
```
Then execute `pytest` in the directory of this file.
- Change `NewModel` to the name of the class in your model.py file.
- Change the `request` and `expected_response` variables to match the input and output of your model.
"""
import pytest
import json
from model import BertClassifier
@pytest.fixture
def client():
from _wsgi import init_app
app = init_app(model_class=BertClassifier)
app.config['TESTING'] = True
with app.test_client() as client:
yield client
def test_predict(client):
request = {
'tasks': [{
'data': {
'text': 'Today is a great day to play football.'
}
}],
# Your labeling configuration here
'label_config':
'<View>'
'<Text name="text" value="$text" />'
'<Choices name="topic" toName="text" choice="single">'
'<Choice value="sports" />'
'<Choice value="politics" />'
'<Choice value="technology" />'
'</Choices>'
'</View>'
}
expected_response_results = [{
'result': [{
'from_name': 'topic',
'to_name': 'text',
'type': 'choices',
'value': {'choices': ['sports']}
}]
}]
response = client.post('/predict', data=json.dumps(request), content_type='application/json')
assert response.status_code == 200
response = json.loads(response.data)
assert len(expected_response_results) == len(response['results'])
# TODO
# Implement test_fit()
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