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 delete_ml_model(model_id: int):
"""
Deletes ML model and associated predictions
:params model_id
"""
ml_model = Project.get(model_id)
if ml_model:
ml_model.delete()
else:
raise NotFound("Model does not exist")
def get_ml_model_by_id(model_id: int):
"""
Get an ML Model for a given ID
:params model_id
:raises NotFound
:returns ML Model
"""
ml_model = MLModel.get(model_id)
if ml_model:
return ml_model.as_dto()
else:
raise NotFound('Model does not exist')
def delete(uid: int, token_id: int):
"""
Delete an token
:params token
"""
token = Token.get(uid, token_id)
if token:
token.delete()
return {"status": "deleted"}
else:
raise NotFound("Token Not Found")
def update_ml_model(updated_ml_model_dto: MLModelDTO) -> int:
"""
Update an existing ML Model
:params model_id
:raises NotFound
:returns model_id
"""
ml_model = MLModel.get(updated_ml_model_dto.model_id)
if (ml_model):
ml_model.update(updated_ml_model_dto)
return updated_ml_model_dto.model_id
else:
raise NotFound('Model does not exist')
def get(uid: int, token_id: int):
"""
Fetch information about a given token
:params token
:raises NotFound
:returns imagery
"""
token = Token.get(uid, token_id).as_dto().to_primitive()
if token:
return token.to_primitive()
else:
raise NotFound("Token Not Found")
def get_all(uid: int, model_filter: str, model_archived: bool):
"""
Get all ML Models
:raises NotFound
:returns array of ML Models
"""
ml_models = Project.get_all(uid, model_filter, model_archived)
if ml_models:
model_collection = []
for model in ml_models:
model_collection.append(model.as_dto().to_primitive())
return model_collection
else:
raise NotFound("No models exist")
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')
def get_all():
"""
Get all ML Models
:raises NotFound
:returns array of ML Models
"""
ml_models = MLModel.get_all()
if (ml_models):
model_collection = []
for model in ml_models:
model_collection.append(model.as_dto().to_primitive())
return model_collection
else:
raise NotFound('No models exist')
def get_ml_model_by_id(model_id: int):
"""
Get an ML Model for a given ID
:params model_id
:raises NotFound
:returns ML Model
"""
ml_model = Project.get(model_id)
users = ProjectAccess.list(model_id)
if ml_model:
model = ml_model.as_dto(users=users)
return model
else:
raise NotFound("Model does not exist")
def update_ml_model(dto: ProjectDTO) -> int:
"""
Update an existing ML Model
:params model_id
:raises NotFound
:returns model_id
"""
ml_model = Project.get(dto.model_id)
if ml_model:
ml_model.update(dto)
if dto.users:
users = ProjectAccess.list(dto.model_id)
ProjectAccess.list_update(dto.model_id, users, dto.users)
return dto.model_id
else:
raise NotFound("Model does not exist")