def predict():
if request.method == "POST":
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
# Step 2: Access the model prediction function (also validates data)
result = make_prediction(input_data=json_data)
# Step 3: Handle errors
errors = result.get("errors")
if errors:
return Response(json.dumps(errors), status=400)
# Step 4: Split out results
predictions = result.get("predictions").tolist()
version = result.get("version")
# Step 5: Save predictions
persistence = PredictionPersistence(db_session=current_app.db_session)
persistence.save_predictions(
inputs=json_data,
model_version=version,
predictions=predictions,
db_model=ModelType.GRADIENT_BOOSTING,
)
# Step 6: Prepare prediction response
return jsonify({
"predictions": predictions,
"version": version,
"errors": errors
})
def prev_predict():
if request.method == "POST":
# Extract data from the json
# get_json output is a str and json.loads outputs us a list(dict) that can be transformed
# into a dataframe and that is what the predict.make_prediction function is expecting as an input.
# NOT REALLY ANYMORE
json_data = request.get_json()
_logger.info(f"Inputs : {json_data}"
f"model : {ModelType.NEURALNET.name}"
f"model_version : {shadow_version}"
)
# Check if the data is valid
input_data,errors = validation.validate_data(json_data)
# Making the predictions
result = dl_make_prediction(input_data=input_data)
_logger.info(f"Outputs : {result}")
predictions = result.get("predictions").tolist()
version = result.get("version")
# Save predictions
persistence = PredictionPersistence(db_session=current_app.db_session)
persistence.save_predictions(
inputs=json_data,
model_version=version,
predictions=predictions,
db_model=ModelType.NEURALNET,
)
# Asynchronous shadow mode
if current_app.config.get("SHADOW_MODE_ACTIVE"):
_logger.debug(
f"Calling shadow model asynchronously: "
f"{ModelType.GRADIENT_BOOSTING.value}"
)
thread = threading.Thread(
target=persistence.make_save_predictions,
kwargs={
"db_model": ModelType.GRADIENT_BOOSTING,
"input_data": input_data,
"app": current_app._get_current_object(),
"json_data": json_data
},
)
thread.start()
return jsonify({"predictions": predictions,
"errors" : errors,
"version": version})