def post(self, project_id):
"""
Store predictions for an ML Model
---
produces:
- application/json
parameters:
- in: body
name: body
required: true
type: string
description: JSON object of predictions
schema:
properties:
modelId:
type: integer
description: ML Model ID
required: true
version:
type: string
description: semver version of the Model
required: true
docker_url:
type: string
description: URL to docker image
required: false
bbox:
type: array of floats
description: BBOX of the predictions
required: true
responses:
200:
description: ID of the prediction
400:
description: Invalid Request
500:
description: Internal Server Error
"""
try:
payload = request.get_json()
# check if this model exists
ProjectService.get_ml_model_by_id(project_id)
# check if the version is registered
prediction_id = PredictionService.create(project_id, payload)
return {"prediction_id": prediction_id}, 200
except NotFound:
return err(404, "model not found"), 404
except VersionExists:
return err(400, "Version Exists"), 400
except DataError as e:
current_app.logger.error(f"Error validating request: {str(e)}")
return err(400, str(4)), 400
except Exception as e:
current_app.logger.error(traceback.format_exc())
error_msg = f"Unhandled error: {str(e)}"
return err(500, error_msg), 500
def post(self, model_id):
"""
Store predictions for an ML Model
---
produces:
- application/json
parameters:
- in: body
name: body
required: true
type: string
description: JSON object of predictions
schema:
properties:
modelId:
type: integer
description: ML Model ID
required: true
version:
type: string
description: semver version of the Model
required: true
dockerhub_hash:
type: string
description: dockerhub hash
required: false
bbox:
type: array of floats
description: BBOX of the predictions
required: true
responses:
200:
description: ID of the prediction
400:
description: Invalid Request
500:
description: Internal Server Error
"""
try:
payload = request.get_json()
version = payload['version']
# check if this model exists
ml_model_dto = MLModelService.get_ml_model_by_id(model_id)
# check if the version is registered
model_version = MLModelVersionService.get_version_by_model_version(
ml_model_dto.model_id, version)
prediction_id = PredictionService.create(model_id,
model_version.version_id,
payload)
return {"prediction_id": prediction_id}, 200
except VersionNotFound:
# if not, add it
try:
version_array = version_to_array(version)
version_dto = MLModelVersionDTO()
version_dto.model_id = model_id
version_dto.version_major = version_array[0]
version_dto.version_minor = version_array[1]
version_dto.version_patch = version_array[2]
version_id = MLModelVersionService.create_version(version_dto)
prediction_id = PredictionService.create(
model_id, version_id, payload)
return {"prediction_id": prediction_id}, 200
except DataError as e:
current_app.logger.error(f'Error validating request: {str(e)}')
return str(e), 400
except Exception as e:
error_msg = f'Unhandled error: {str(e)}'
current_app.logger.error(error_msg)
return {"error": error_msg}, 500
except NotFound:
return {"error": "model not found"}, 404
except DataError as e:
current_app.logger.error(f'Error validating request: {str(e)}')
return str(e), 400
except Exception as e:
error_msg = f'Unhandled error: {str(e)}'
current_app.logger.error(error_msg)
return {"error": error_msg}, 500