async def task_handler(data:Iris, background_tasks: BackgroundTasks):
data = data.dict()
print("in the prediction")
sepal_length = data['sepal_length']
sepal_width = data['sepal_width']
petal_length = data['petal_length']
petal_width = data['petal_width']
new_task = Job()
jobs[new_task.uid] = new_task
background_tasks.add_task(start_modelling_task, new_task.uid, [sepal_length, sepal_width, petal_length, petal_width])
return new_task
async def predict(
data: Iris
): # Declare it as a parameter after Iris data model been created
# convert Iris object into dictionary
data = data.dict()
print("in the prediction")
# There are different way of input data
# another way is to input as csv file using: fastapi.UploadFile
sepal_length = data['sepal_length']
sepal_width = data['sepal_width']
petal_length = data['petal_length']
petal_width = data['petal_width']
prediction = await model(
[sepal_length, sepal_width, petal_length, petal_width])
print("prediction is done")
# can control the return by specify the response_model inside @app.post() decorator
return {"prediction": str(prediction)}