def __init__(self, dataset="django"):
arg_parser = init_arg_parser()
if dataset == "django":
args = init_arg_parser().parse_args("--mode test \
--load_model src/external_repos/tranX/data/pretrained_models/django.bin \
--beam_size 15 \
--test_file src/external_repos/tranX/data/django/test.bin \
--save_decode_to 0.test.decode \
--decode_max_time_step 100 \
--example_preprocessor django_example_processor"
.split())
elif dataset == "conala":
args = init_arg_parser().parse_args("--mode test \
--load_model src/external_repos/tranX/data/pretrained_models/conala.bin \
--beam_size 15 \
--test_file src/external_repos/tranX/data/conala/test.bin \
--save_decode_to 0.test.decode \
--decode_max_time_step 100 \
--example_preprocessor conala_example_processor"
.split())
self.parser = StandaloneParser(args.parser,
args.load_model,
args.example_preprocessor,
beam_size=args.beam_size,
cuda=args.cuda)
def __init__(self, cuda=True, fields={'input_field':'input_text', 'output_field':'pred_text'}):
'''
This uses the model from Pengchen Yin preseented in:
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
It translates English to Python code
'''
self.fields = fields
parser = 'default_parser'
model_file = 'src/external_repos/external-knowledge-codegen/best_pretrained_models/finetune.mined.retapi.distsmpl.dr0.3.lr0.001.lr_de0.5.lr_da15.beam15.seed0.mined_100000.intent_count100k_topk1_temp5.bin'
processor = 'django_example_processor'
beam_size = 15
self.parser = StandaloneParser(parser,
model_file,
processor,
beam_size=beam_size,
cuda=cuda)
def interactive_mode(args):
"""Interactive mode"""
print('Start interactive mode', file=sys.stderr)
parser = StandaloneParser(args.parser,
args.load_model,
args.example_preprocessor,
beam_size=args.beam_size,
cuda=args.cuda)
while True:
utterance = input('Query:').strip()
hypotheses = parser.parse(utterance, debug=True)
for hyp_id, hyp in enumerate(hypotheses):
print('------------------ Hypothesis %d ------------------' % hyp_id)
print(hyp.code)
actions_repr = [action.__repr__(True) for action in hyp.action_infos]
hyp_entry = dict(id=hyp_id + 1,
value=hyp.code,
tree_repr=hyp.tree.to_string(),
score=hyp.rerank_score.item() if hasattr(
hyp, 'rerank_score') else hyp.score.item(),
actions=actions_repr)
responses['hypotheses'].append(hyp_entry)
return jsonify(responses)
if __name__ == '__main__':
args = init_arg_parser().parse_args()
config_dict = json.load(open(args.config_file))
for parser_id, config in config_dict.items():
parser = StandaloneParser(
parser_name=config['parser'],
model_path=config['model_path'],
example_processor_name=config['example_processor'],
beam_size=config['beam_size'],
reranker_path=config['reranker_path'],
cuda=args.cuda)
parsers[parser_id] = parser
app.run(host='0.0.0.0', port=args.port, debug=True)