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# -*- coding: utf-8 -*-
"""Utilities for task assignment
"""
from flask import current_app
from app.models import User, Dataset, Task
def generate_user_task(user):
"""
Generate new task for a given user.
This function assigns tasks to a given user and ensures that:
1) datasets that are nearly annotated with the desired number of
datasets get priority
2) users never are given more tasks than max_per_user
3) users never get the same dataset twice
"""
max_per_user = current_app.config["TASKS_MAX_PER_USER"]
num_per_dataset = current_app.config["TASKS_NUM_PER_DATASET"]
user_tasks = Task.query.filter_by(annotator_id=user.id).all()
user_tasks = [t for t in user_tasks if not t.dataset.is_demo]
# don't assign a new task if the user has assigned tasks
not_done = [t for t in user_tasks if not t.done]
if len(not_done) > 0:
return None
# don't assign a new task if the user has reached maximum
n_user_tasks = len(user_tasks)
if n_user_tasks >= max_per_user:
return None
potential_datasets = []
for dataset in Dataset.query.filter_by(is_demo=False).all():
dataset_tasks = Task.query.filter_by(dataset_id=dataset.id).all()
# check that this dataset needs more annotations
n_needed = num_per_dataset - len(dataset_tasks)
if n_needed <= 0:
continue
# check that this dataset is not already assigned to the user
task = Task.query.filter_by(
dataset_id=dataset.id, annotator_id=user.id
).first()
if not task is None:
continue
potential_datasets.append((n_needed, dataset))
if len(potential_datasets) == 0:
return None
# sort datasets so that the ones who need the least are at the front.
potential_datasets.sort(key=lambda x: x[0])
_, dataset = potential_datasets[0]
task = Task(annotator_id=user.id, dataset_id=dataset.id)
return task
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