def index(request):
template = loader.get_template('dataset_importer.html')
jobs = DatasetImport.objects.all()
archive_formats = collect_map_entries(extractor_map)
single_document_formats = collect_map_entries(entity_reader_map)
document_collection_formats = collect_map_entries(collection_reader_map)
database_formats = collect_map_entries(database_reader_map)
# preprocessors = collect_map_entries(preprocessor_map)
# enabled_preprocessors = [preprocessor for preprocessor in preprocessors if preprocessor['is_enabled'] is True]
datasets = Datasets().get_allowed_datasets(request.user)
language_models = Task.objects.filter(
task_type=TaskTypes.TRAIN_MODEL.value).filter(
status__iexact=Task.STATUS_COMPLETED).order_by('-pk')
analyzers = ES_Manager.get_analyzers()
context = {
# 'enabled_input_types': DATASET_IMPORTER_CONF['enabled_input_types'],
'archive_formats': archive_formats,
'single_document_formats': single_document_formats,
'document_collection_formats': document_collection_formats,
'database_formats': database_formats,
'language_models': language_models,
'allowed_datasets': datasets,
'jobs': jobs,
'analyzers': analyzers
# 'enabled_preprocessors': enabled_preprocessors
}
return HttpResponse(template.render(context, request))
def index(request):
template = loader.get_template('dataset_importer.html')
jobs = DatasetImport.objects.all()
archive_formats = collect_map_entries(extractor_map)
single_document_formats = collect_map_entries(entity_reader_map)
document_collection_formats = collect_map_entries(collection_reader_map)
database_formats = collect_map_entries(database_reader_map)
# preprocessors = collect_map_entries(preprocessor_map)
# enabled_preprocessors = [preprocessor for preprocessor in preprocessors if preprocessor['is_enabled'] is True]
datasets = Datasets().get_allowed_datasets(request.user)
language_models =Task.objects.filter(task_type=TaskTypes.TRAIN_MODEL.value).filter(status__iexact=Task.STATUS_COMPLETED).order_by('-pk')
analyzers = ES_Manager.get_analyzers()
context = {
# 'enabled_input_types': DATASET_IMPORTER_CONF['enabled_input_types'],
'archive_formats': archive_formats,
'single_document_formats': single_document_formats,
'document_collection_formats': document_collection_formats,
'database_formats': database_formats,
'language_models': language_models,
'allowed_datasets': datasets,
'jobs': jobs,
'analyzers': analyzers
# 'enabled_preprocessors': enabled_preprocessors
}
return HttpResponse(template.render(context, request))
def check_if_analyzer_exists(self):
ELASTICSEARCH_ANALYZERS = ES_Manager.get_analyzers()
user_sent_analyzer = self.post_dict["analyzer"]
available_analyzer_names = list(
map(lambda x: x["analyzer"], ELASTICSEARCH_ANALYZERS))
if user_sent_analyzer not in available_analyzer_names:
raise ValueError(
"Analyzer '{0}' not available. Available analyzers are: '{1}'".
format(user_sent_analyzer, available_analyzer_names))
def check_if_analyzer_exists(self):
ELASTICSEARCH_ANALYZERS = ES_Manager.get_analyzers()
user_sent_analyzer = self.post_dict["analyzer"]
available_analyzer_names = list(map(lambda x: x["analyzer"], ELASTICSEARCH_ANALYZERS))
if user_sent_analyzer not in available_analyzer_names:
raise ValueError("Analyzer '{0}' not available. Available analyzers are: '{1}'".format(user_sent_analyzer, available_analyzer_names))
def get_analyzer_names(request):
ELASTICSEARCH_ANALYZERS = ES_Manager.get_analyzers()
analyzer_names = list(map(lambda x: x["analyzer"], ELASTICSEARCH_ANALYZERS))
return JsonResponse({"analyzers": analyzer_names})
def get_analyzer_names(request):
ELASTICSEARCH_ANALYZERS = ES_Manager.get_analyzers()
analyzer_names = list(map(lambda x: x["analyzer"],
ELASTICSEARCH_ANALYZERS))
return JsonResponse({"analyzers": analyzer_names})
