def predict():
data = request.get_json()
task_id = data['task_id']
jobs = data.get('jobs', [PREDICT_ENTITIES])
document_id = data['document_id']
user_id = data.get('user_id', current_user.get_id())
current_prediction_user = prediction_user_for_user(user_id)
prediction_user_doc_id = load_user_doc_id(document_id,
current_prediction_user)
delete_user_document(prediction_user_doc_id)
document_data = json.loads(data.get('current_state', None))
if document_data is None:
document_data = load_document(document_id, user_id)
else:
# the current status has to be saved first in order to disambiguate the ids of the annotations
user_doc_id = load_user_doc_id(document_id, current_user.get_id())
successful = save_document(document_data, user_doc_id, document_id,
current_user.get_id(), task_id)
if not successful:
return "Could not save the document", 500
if PREDICT_ENTITIES in jobs:
cursor = get_connection().cursor()
cursor.execute(
'INSERT INTO "LTN_DEVELOP"."USER_DOCUMENTS" '
'VALUES (?, ?, ?, 0, current_timestamp, current_timestamp)', (
prediction_user_doc_id,
current_prediction_user,
document_id,
))
cursor.close()
get_connection().commit()
predict_entities(document_id, task_id, prediction_user_doc_id)
if PREDICT_RELATIONS in jobs:
if PREDICT_ENTITIES not in jobs:
save_document(document_data, prediction_user_doc_id, document_id,
current_prediction_user, task_id, False)
predicted_pairs = predict_relations(prediction_user_doc_id, task_id)
if PREDICT_ENTITIES not in jobs:
remove_entities_without_relations(predicted_pairs, document_data,
prediction_user_doc_id)
document_data = load_document(document_id, current_user.get_id(), True)
return respond_with(document_data)
def export_document(document_id, users):
bcollection = bioc.BioCCollection()
for user_id in users:
document = load_document(document_id, user_id)
bdocument = create_bioc_document_from_document_json(document)
bcollection.add_document(bdocument)
result = bcollection.tobioc()
response = Response(result, mimetype='text/xml')
response.headers["Content-Disposition"] = "attachment; filename=" + document_id + ".xml"
return response
def export_document(document_id, users):
bcollection = bioc.BioCCollection()
for user_id in users:
document = load_document(document_id, user_id)
bdocument = create_bioc_document_from_document_json(document)
bcollection.add_document(bdocument)
result = bcollection.tobioc()
response = Response(result, mimetype='text/xml')
response.headers[
"Content-Disposition"] = "attachment; filename=" + document_id + ".xml"
return response
def predict():
data = request.get_json()
task_id = data['task_id']
jobs = data.get('jobs', [PREDICT_ENTITIES])
document_id = data['document_id']
user_id = data.get('user_id', current_user.get_id())
current_prediction_user = prediction_user_for_user(user_id)
prediction_user_doc_id = load_user_doc_id(document_id, current_prediction_user)
delete_user_document(prediction_user_doc_id)
document_data = json.loads(data.get('current_state', None))
if document_data is None:
document_data = load_document(document_id, user_id)
else:
# the current status has to be saved first in order to disambiguate the ids of the annotations
user_doc_id = load_user_doc_id(document_id, current_user.get_id())
successful = save_document(document_data, user_doc_id, document_id, current_user.get_id(), task_id)
if not successful:
return "Could not save the document", 500
if PREDICT_ENTITIES in jobs:
cursor = get_connection().cursor()
cursor.execute('INSERT INTO "LTN_DEVELOP"."USER_DOCUMENTS" '
'VALUES (?, ?, ?, 0, current_timestamp, current_timestamp)',
(prediction_user_doc_id, current_prediction_user, document_id,))
cursor.close()
get_connection().commit()
predict_entities(document_id, task_id, prediction_user_doc_id)
if PREDICT_RELATIONS in jobs:
if PREDICT_ENTITIES not in jobs:
save_document(document_data, prediction_user_doc_id, document_id, current_prediction_user, task_id, False)
predicted_pairs = predict_relations(prediction_user_doc_id, task_id)
if PREDICT_ENTITIES not in jobs:
remove_entities_without_relations(predicted_pairs, document_data, prediction_user_doc_id)
document_data = load_document(document_id, current_user.get_id(), True)
return respond_with(document_data)