Merge pull request #507 from MRXLT/senta-fix

fix senta demo

README.md

@@ -166,7 +166,7 @@ python image_classification_service_demo.py resnet50_serving_model
 <p>
 
 ``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg", "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
 ```
 - **Request result**: 
 ``` shell

README_CN.md

@@ -171,7 +171,7 @@ python image_classification_service_demo.py resnet50_serving_model
 <p>
 
 ``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg", "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
 ```
 - **返回结果示例**: 
 ``` shell

python/examples/senta/README.md

 # Chinese sentence sentiment classification
-
+([简体中文](./README_CN.md)|English)
 ## Get model files and sample data
 ```
 sh get_data.sh
@@ -12,5 +12,5 @@ In the Chinese sentiment classification task, the Chinese word segmentation need
 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.
 ## Client prediction
 ```
-curl -H "Content-Type:application/json" -X POST -d '{"words": "天气不错 | 0", "fetch":["sentence_feature"]}' http://127.0.0.1:9292/senta/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
@@ -13,5 +13,5 @@ python senta_web_service.py senta_bilstm_model/ workdir 9292
 
 ## 客户端预测
 ```
-curl -H "Content-Type:application/json" -X POST -d '{"words": "天气不错 | 0", "fetch":["sentence_feature"]}' http://127.0.0.1:9292/senta/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/get_data.sh

-#wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/senta_bilstm.tar.gz --no-check-certificate
-#tar -xzvf senta_bilstm.tar.gz
+wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/senta_bilstm.tar.gz --no-check-certificate
+tar -xzvf senta_bilstm.tar.gz
 wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/LexicalAnalysis/lac_model.tar.gz --no-check-certificate
 tar -xzvf lac_model.tar.gz
 wget https://paddle-serving.bj.bcebos.com/reader/lac/lac_dict.tar.gz  --no-check-certificate

python/examples/senta/senta_web_service.py

@@ -69,11 +69,8 @@ class SentaService(WebService):
     def init_senta_reader(self):
         self.senta_reader = SentaReader(vocab_path=self.senta_dict_path)
 
-    def preprocess(self, feed={}, fetch={}):
-        if "words" not in feed:
-            raise ("feed data error!")
-        feed_data = self.lac_reader.process(feed["words"])
-        fetch = ["crf_decode"]
+    def preprocess(self, feed=[], fetch=[]):
+        feed_data = self.lac_reader.process(feed[0]["words"])
         if self.show:
             print("---- lac reader ----")
             print(feed_data)
@@ -81,7 +78,7 @@ class SentaService(WebService):
         if self.show:
             print("---- lac out ----")
             print(lac_result)
-        segs = self.lac_reader.parse_result(feed["words"],
+        segs = self.lac_reader.parse_result(feed[0]["words"],
                                             lac_result["crf_decode"])
         if self.show:
             print("---- lac parse ----")
@@ -90,7 +87,6 @@ class SentaService(WebService):
         if self.show:
             print("---- senta reader ----")
             print("feed_data", feed_data)
-        fetch = ["class_probs"]
         return {"words": feed_data}, fetch
 
 
@@ -107,31 +103,4 @@ senta_service.init_lac_reader()
 senta_service.init_senta_reader()
 senta_service.init_lac_service()
 senta_service.run_server()
-#senta_service.run_flask()
-
-from flask import Flask, request
-
-app_instance = Flask(__name__)
-
-
-@app_instance.before_first_request
-def init():
-    global uci_service
-    senta_service._launch_web_service()
-
-
-service_name = "/" + senta_service.name + "/prediction"
-
-
-@app_instance.route(service_name, methods=["POST"])
-def run():
-    print("---- run ----")
-    print(request.json)
-    return senta_service.get_prediction(request)
-
-
-if __name__ == "__main__":
-    app_instance.run(host="0.0.0.0",
-                     port=senta_service.port,
-                     threaded=False,
-                     processes=4)
+senta_service.run_flask()