def postgresql_status(self):
try:
from data_models import Prediction
Prediction.first()
return 'OK'
except:
return 'failure'
def test_predictions_weak(self, client):
picture = Picture.create(image_id='foobar.jpg')
algorithm = Algorithm.create(name='DeevioNet', version='1.1')
prediction = Prediction.create(picture_id=picture.id, algorithm_id=algorithm.id, proba=0.69)
prediction = Prediction.create(picture_id=picture.id, algorithm_id=algorithm.id, proba=0.7)
body = json.loads(client.get('/v1/predictions/weak').data)
assert len(body) == 1
def post(self):
vector = parser.parse_args()
prediction = float(
ml_model.predict(
np.array([v for v in vector.values()]).reshape(1, -1)))
db_log = Prediction(input_data=json.dumps(vector),
prediction=prediction,
predicted_at=datetime.datetime.now())
db.session.add(db_log)
db.session.commit()
return {'prediction': prediction}
def test_predictions_by_image_id(self, client):
picture = Picture.create(image_id='foobar.jpg')
algorithm = Algorithm.create(name='DeevioNet', version='1.1')
prediction = Prediction.create(picture_id=picture.id, algorithm_id=algorithm.id, proba=0.99, status='complete', label='nail', result='good', bbox=[100, 200, 500, 500])
body = json.loads(client.get('/v1/predictions/%s' % 'foobar.jpg').data)
assert body['probability'] == prediction.proba
assert body['status'] == prediction.status
assert body['label'] == prediction.label
assert body['result'] == prediction.result
assert body['bbox'] == prediction.bbox
assert body['algorithm']['type'] == 'GET'
assert body['algorithm']['rel'] == 'algorithm'
assert body['algorithm']['href'] == '/v1/algorithm/DeevioNet?version=1.1'
assert body['picture']['type'] == 'GET'
assert body['picture']['rel'] == 'picture'
assert body['picture']['href'] == '/v1/picture/foobar.jpg'
def test_prediction_create_algorithm(self):
algorithm = Algorithm.create()
prediction = Prediction.create(algorithm_id=algorithm.id)
assert prediction.algorithm == algorithm
assert algorithm.predictions[-1] == prediction
def test_prediction_create_result(self, result):
assert Prediction.create(result=result).result == result
def test_prediction_create_bbox(self, bbox):
assert Prediction.create(bbox=bbox).bbox == bbox
def test_prediction_create_proba(self, proba):
assert Prediction.create(proba=proba).proba == proba
def test_prediction_init(self, label):
assert Prediction(label=label).label == label
def test_prediction_create_from_mq(self):
payload = '{"algorithm": {"name": "DeevioNet","version": "1.0"},"status":"complete","imagePath":"20180907/1536311270718.jpg","imageId":"1536311270718","output":[{"bbox":[1008.8831787109375,280.6226501464844,1110.0245361328125,380.72021484375],"probability":0.9725130796432495,"label":"nail","result":"good"} ]}'
Prediction.create_from_mq(payload)
def test_prediction_create_picture(self):
picture = Picture.create()
prediction = Prediction.create(picture_id=picture.id)
assert prediction.picture == picture
assert picture.predictions[-1] == prediction
def on_message(client, userdata, msg):
print('New message: %s' % msg.payload)
Prediction.create_from_mq(msg.payload)
def predictions_weak():
predictions = Prediction.weak()
return jsonify(PredictionsPresenter(predictions).to_dict())
def predictions(image_id):
picture = Picture.find_by(image_id=image_id)
prediction = Prediction.find_by(picture_id=picture.id)
return jsonify(PredictionPresenter(prediction).to_dict())