refine senta demo

python/examples/senta/README.md

-# Chinese sentence sentiment classification
+# Chinese Sentence Sentiment Classification
 ([简体中文](./README_CN.md)|English)
-## Get model files and sample data
-```
-sh get_data.sh
-```
-## Install preprocess module
 
+## Get Model
 ```
-pip install paddle_serving_app
+python -m paddle_serving_app.package --get_model senta_bilstm
+python -m paddle_serving_app.package --get_model lac
 ```
 
-## Start http service
+## Start HTTP Service
 ```
-python senta_web_service.py senta_bilstm_model/ workdir 9292
+python -m paddle_serving_server.serve --model lac_model --port 9300
+python senta_web_service.py
 ```
-In the Chinese sentiment classification task, the Chinese word segmentation needs to be done through [LAC task] (../lac). Set model path by ```lac_model_path``` and dictionary path by ```lac_dict_path```. 
-In this demo, the LAC task is placed in the preprocessing part of the HTTP prediction service of the sentiment classification task. The LAC prediction service is deployed on the CPU, and the sentiment classification task is deployed on the GPU, which can be changed according to the actual situation.
+In the Chinese sentiment classification task, the Chinese word segmentation needs to be done through [LAC task] (../lac). 
+In this demo, the LAC task is placed in the preprocessing part of the HTTP prediction service of the sentiment classification task.
+
 ## Client prediction
 ```
 curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "天气不错"}], "fetch":["class_probs"]}' http://127.0.0.1:9292/senta/prediction

python/examples/senta/README_CN.md

 # 中文语句情感分类
 (简体中文|[English](./README.md))
-## 获取模型文件和样例数据
-```
-sh get_data.sh
-```
-## 安装数据预处理模块
+
+## 获取模型文件
 ```
-pip install paddle_serving_app
+python -m paddle_serving_app.package --get_model senta_bilstm
+python -m paddle_serving_app.package --get_model lac
 ```
 
 ## 启动HTTP服务
 ```
-python senta_web_service.py senta_bilstm_model/ workdir 9292
+python -m paddle_serving_server.serve --model lac_model --port 9300
+python senta_web_service.py
 ```
-中文情感分类任务中需要先通过[LAC任务](../lac)进行中文分词，在脚本中通过```lac_model_path```参数配置LAC任务的模型文件路径,```lac_dict_path```参数配置LAC任务词典路径。
-示例中将LAC任务放在情感分类任务的HTTP预测服务的预处理部分，LAC预测服务部署在CPU上，情感分类任务部署在GPU上,可以根据实际情况进行更改。
+中文情感分类任务中需要先通过[LAC任务](../lac)进行中文分词。
+示例中将LAC任务放在情感分类任务的HTTP预测服务的预处理部分。
 
 ## 客户端预测
 ```

python/examples/senta/senta_web_service.py

+#encoding=utf-8
 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,56 +13,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle_serving_server_gpu.web_service import WebService
+from paddle_serving_server.web_service import WebService
 from paddle_serving_client import Client
 from paddle_serving_app.reader import LACReader, SentaReader
 import os
 import sys
-from multiprocessing import Process
 
+#senta_web_service.py
+from paddle_serving_server.web_service import WebService
+from paddle_serving_client import Client
+from paddle_serving_app.reader import LACReader, SentaReader
 
-class SentaService(WebService):
-    def set_config(
-            self,
-            lac_model_path,
-            lac_dict_path,
-            senta_dict_path, ):
-        self.lac_model_path = lac_model_path
-        self.lac_client_config_path = lac_model_path + "/serving_server_conf.prototxt"
-        self.lac_dict_path = lac_dict_path
-        self.senta_dict_path = senta_dict_path
-
-    def start_lac_service(self):
-        if not os.path.exists('./lac_serving'):
-            os.mkdir("./lac_serving")
-        os.chdir('./lac_serving')
-        self.lac_port = self.port + 100
-        r = os.popen(
-            "python -m paddle_serving_server.serve --model {} --port {} &".
-            format("../" + self.lac_model_path, self.lac_port))
-        os.chdir('..')
-
-    def init_lac_service(self):
-        ps = Process(target=self.start_lac_service())
-        ps.start()
-        self.init_lac_client()
-
-    def lac_predict(self, feed_data):
-        lac_result = self.lac_client.predict(
-            feed={"words": feed_data}, fetch=["crf_decode"])
-        return lac_result
-
-    def init_lac_client(self):
-        self.lac_client = Client()
-        self.lac_client.load_client_config(self.lac_client_config_path)
-        self.lac_client.connect(["127.0.0.1:{}".format(self.lac_port)])
 
-    def init_lac_reader(self):
+class SentaService(WebService):
+    #初始化lac模型预测服务
+    def init_lac_client(self, lac_port, lac_client_config):
         self.lac_reader = LACReader()
-
-    def init_senta_reader(self):
         self.senta_reader = SentaReader()
+        self.lac_client = Client()
+        self.lac_client.load_client_config(lac_client_config)
+        self.lac_client.connect(["127.0.0.1:{}".format(lac_port)])
 
+    #定义senta模型预测服务的预处理，调用顺序：lac reader->lac模型预测->预测结果后处理->senta reader
     def preprocess(self, feed=[], fetch=[]):
         feed_data = [{
             "words": self.lac_reader.process(x["words"])
@@ -80,15 +53,9 @@ class SentaService(WebService):
 
 
 senta_service = SentaService(name="senta")
-senta_service.set_config(
-    lac_model_path="./lac_model",
-    lac_dict_path="./lac_dict",
-    senta_dict_path="./vocab.txt")
-senta_service.load_model_config(sys.argv[1])
-senta_service.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
-senta_service.init_lac_reader()
-senta_service.init_senta_reader()
-senta_service.init_lac_service()
+senta_service.load_model_config("senta_bilstm_model")
+senta_service.prepare_server(workdir="workdir")
+senta_service.init_lac_client(
+    lac_port=9300, lac_client_config="lac_model/serving_server_conf.prototxt")
 senta_service.run_rpc_service()
 senta_service.run_web_service()