lac_http_client.py

# encoding=utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=doc-string-missing

from paddle_serving_client import HttpClient
from paddle_serving_app.reader import LACReader
import sys
import os
import io
import numpy as np

client = HttpClient(ip='127.0.0.1', port='9393')
client.load_client_config(sys.argv[1])
#client.set_ip('127.0.0.1')
#client.set_port('9292')
''' 
if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...)
as for HTTP-client,set_use_grpc_client(False)(which is default)
or you can directly use client.http_client_predict(...)
'''
#client.set_use_grpc_client(True)
'''
if you want to enable Encrypt Module,uncommenting the following line
'''
#client.use_key("./key")
'''
if you want to compress,uncommenting the following line
'''
#client.set_response_compress(True)
#client.set_request_compress(True)
'''
we recommend use Proto data format in HTTP-body, set True(which is default)
if you want use JSON data format in HTTP-body, set False
'''
#client.set_http_proto(True)

reader = LACReader()
for line in sys.stdin:
    if len(line) <= 0:
        continue
    feed_data = reader.process(line)
    if len(feed_data) <= 0:
        continue
    print(feed_data)
    #fetch_map = client.predict(feed={"words": np.array(feed_data).reshape(len(feed_data), 1), "words.lod": [0, len(feed_data)]}, fetch=["crf_decode"], batch=True)
    fetch_map = client.predict(
        feed={
            "words": np.array(feed_data + feed_data).reshape(
                len(feed_data) * 2, 1),
            "words.lod": [0, len(feed_data), 2 * len(feed_data)]
        },
        fetch=["crf_decode"],
        batch=True)
    print(fetch_map)