def import_document():
user_id = current_user.get_id()
if user_id is None:
return "No user is logged in", 401
req = request.get_json()
doc_type = req.get('type', TYPE_PLAINTEXT)
task = req['task']
documents = []
if doc_type == TYPE_PLAINTEXT:
documents.append(req)
elif doc_type == TYPE_BIOC:
documents = extract_documents_from_bioc(req['text'],
req['document_id'], task)
else:
return "Document type not supported", 400
for document in documents:
document_id = document['document_id']
message, code = create_document_in_database(
document_id, document['text'], int(document.get('visibility', 1)),
task)
if code == 201 and doc_type == TYPE_BIOC:
save_document(document, load_user_doc_id(document_id, user_id),
document_id, user_id,
int(document.get('visibility', 1)))
if code != 201:
return message, code
return "Successfully imported", 201
def import_document():
user_id = current_user.get_id()
if user_id is None:
return "No user is logged in", 401
req = request.get_json()
doc_type = req.get('type', TYPE_PLAINTEXT)
task = req['task']
documents = []
if doc_type == TYPE_PLAINTEXT:
documents.append(req)
elif doc_type == TYPE_BIOC:
documents = extract_documents_from_bioc(req['text'], req['document_id'], task)
else:
return "Document type not supported", 400
for document in documents:
document_id = document['document_id']
message, code = create_document_in_database(document_id,
document['text'],
int(document.get('visibility', 1)),
task)
if code == 201 and doc_type == TYPE_BIOC:
save_document(document,
load_user_doc_id(document_id, user_id),
document_id,
user_id,
int(document.get('visibility', 1)))
if code != 201:
return message, code
return "Successfully imported", 201
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 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)
