def create(model_id: int, payload: dict) -> int:
"""
Validate and add predictions from a model to the database
:params model_id, payload
:raises DataError
:returns ID of the prediction
"""
version = payload['version']
try:
semver.VersionInfo.parse(version)
except Exception as e:
raise "Version Must be SemVer"
prediction_dto = PredictionDTO()
prediction_dto.model_id = model_id
prediction_dto.version = payload['version']
prediction_dto.tile_zoom = payload['tileZoom']
prediction_dto.inf_list = payload['infList']
prediction_dto.inf_type = payload['infType']
prediction_dto.inf_binary = payload['infBinary']
prediction_dto.inf_supertile = payload['infSupertile']
prediction_dto.validate()
new_prediction = Prediction()
try:
new_prediction.create(prediction_dto)
except sqlalchemy.exc.IntegrityError as e:
if isinstance(e.orig, UniqueViolation):
raise VersionExists
else:
raise e
return new_prediction.id
def get(model_id: int, bbox: list, latest=False):
"""
Fetch latest predictions from a model for the given bbox
:params model_id, bbox
:raises PredictionsNotFound
:returns predictions
"""
if (latest):
# get the latest version
latest_version = MLModelVersion.get_latest_version(model_id)
if (latest_version is None):
raise PredictionsNotFound('Predictions not found')
else:
version_id = latest_version.id
predictions = Prediction.get_latest_predictions_in_bbox(
model_id, version_id, bbox)
else:
predictions = Prediction.get_all_predictions_in_bbox(
model_id, bbox)
if (len(predictions) == 0):
raise PredictionsNotFound('Predictions not found')
data = []
for prediction in predictions:
prediction_dto = Prediction.as_dto(prediction)
data.append(prediction_dto.to_primitive())
return data
def get_prediction_by_id(prediction_id: int):
"""
Get a prediction by ID
:params prediction_id
:returns prediction
"""
prediction = Prediction.get(prediction_id)
if prediction:
return Prediction.as_dto(prediction)
else:
raise PredictionsNotFound
def get_all_by_model(model_id: int):
"""
Fetch all predictions of the given model
:params model_id
:returns predictions
:raises PredictionsNotFound
"""
predictions = Prediction.get_predictions_by_model(model_id)
prediction_dtos = []
for prediction in predictions:
prediction_dtos.append(Prediction.as_dto(prediction).to_primitive())
return prediction_dtos
def export(prediction_id: int):
prediction = Prediction.get(prediction_id)
if (prediction):
stream = prediction.export()
return stream
else:
raise NotFound('Prediction does not exist')
def create(model_id: int, version_id: int, payload: dict) -> int:
"""
Validate and add predictions from a model to the database
:params model_id, version_id, payload
:raises DataError
:returns ID of the prediction
"""
prediction_dto = PredictionDTO()
prediction_dto.model_id = model_id
prediction_dto.version_id = version_id
prediction_dto.bbox = payload['bbox']
prediction_dto.tile_zoom = payload['tileZoom']
prediction_dto.validate()
new_prediction = Prediction()
new_prediction.create(prediction_dto)
return new_prediction.id
def tilejson(model_id, prediction_id):
"""
Get the TileJSON of the prediction id given
:params model_id
:params prediction_id
:returns dict
"""
tiles = PredictionTile.count(prediction_id)
if tiles.count == 0:
raise PredictionsNotFound('No Prediction Tiles exist')
ml_model = MLModel.get(model_id)
prediction = Prediction.get(prediction_id)
tilejson = {
"tilejson":
"2.1.0",
"name":
ml_model.name,
"description":
ml_model.project_url,
"inferences":
PredictionTile.inferences(prediction_id),
"token":
CONFIG.EnvironmentConfig.MAPBOX_TOKEN,
"attribution":
ml_model.source,
"version":
prediction.version,
"scheme":
"xyz",
"type":
"vector",
"tiles": [
"/v1/model/{0}/prediction/{1}/tiles/{{z}}/{{x}}/{{y}}.mvt".
format(model_id, prediction_id)
],
"minzoom":
0,
"maxzoom":
prediction.tile_zoom,
"bounds":
PredictionTile.bbox(prediction_id)
}
return tilejson
def patch(prediction_id: int, update: dict) -> int:
"""
Patch a prediction by ID
:params prediction_id
:params update
:returns prediction
"""
prediction = Prediction.get(prediction_id)
if (prediction):
prediction.link(update)
return prediction_id
else:
raise NotFound('Prediction does not exist')