增加kw_predict

classify_server.py

@@ -18,12 +18,14 @@ class Classify(classify_pb2_grpc.classifyServicer):
 
 
 
 
     def Classify(self, request, context):
     def Classify(self, request, context):
-
         try:
         try:
             dic_data = json.loads(request.keyword)
             dic_data = json.loads(request.keyword)
             res = self.predictorfac.predict(dic_data)
             res = self.predictorfac.predict(dic_data)
             res['status'] = 1
             res['status'] = 1
-        except:
+        except json.decoder.JSONDecodeError:
+            res = self.predictorfac.predict(request.keyword)
+            res['status'] = 1
+        except :
             res = {
             res = {
                 'status': 0,
                 'status': 0,
                 'err_msg': '未知'
                 'err_msg': '未知'

predic_fac.py

@@ -14,10 +14,11 @@ class PredictorFac():
         self.dict_predictor = dict_predict.DicPredict('dict', extractor, classifier)
         self.dict_predictor = dict_predict.DicPredict('dict', extractor, classifier)
 
 
     def _get_predictor(self, data):
     def _get_predictor(self, data):
-        if isinstance(data, str):
-            return self.kw_predictor
-        elif isinstance(data, dict):
+
+        if isinstance(data, dict):
             return self.dict_predictor
             return self.dict_predictor
+        else:
+            return self.kw_predictor
 
 
     def predict(self, dic_data, predict_type='all'):
     def predict(self, dic_data, predict_type='all'):
         predictor = self._get_predictor(dic_data)
         predictor = self._get_predictor(dic_data)
@@ -29,7 +30,7 @@ if __name__ == "__main__":
     def get_test_data():
     def get_test_data():
         import pandas as pd
         import pandas as pd
         import json
         import json
-        df = pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\51AB0571_ CCTV ASST询价_SZIMS.xlsx', header=None, sheet_name='3')
+        df = pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\立创.xlsx', header=None)
         df.fillna('?', inplace=True)
         df.fillna('?', inplace=True)
         dic_dft = df.to_dict(orient='list')
         dic_dft = df.to_dict(orient='list')
         return json.dumps(dic_dft)
         return json.dumps(dic_dft)
@@ -40,6 +41,7 @@ if __name__ == "__main__":
     data = get_test_data()
     data = get_test_data()
     p = PredictorFac(model_config)
     p = PredictorFac(model_config)
     data = json.loads(data)
     data = json.loads(data)
-    print(data)
-    res = p.predict(data,predict_type='all')
+    pretty_col = {'第%s列' % k: v for k, v in data.items()}
+    print(pretty_col)
+    res = p.predict(pretty_col, predict_type='all')
     print(res)
     print(res)

predict/dict_predict.py

@@ -137,7 +137,7 @@ class DicPredict(BasePredictor):
         return model_id_res
         return model_id_res
 
 
     def predict(self, dic_data, predict_type='all'):
     def predict(self, dic_data, predict_type='all'):
-        self.info.info('预测类型为%s接收数据: ' % predict_type + str(dic_data))
+        self.info.info('预测类型为: %s, 接收数据: %s' % (predict_type, str(dic_data)))
         dic_data = self.pre_deal(dic_data)
         dic_data = self.pre_deal(dic_data)
 
 
         if predict_type == 'all':
         if predict_type == 'all':

predict/kw_predict.py

 #!/usr/bin/env python
 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
 # -*- coding:utf-8 -*-
 from predict.base_handler import BasePredictor
 from predict.base_handler import BasePredictor
-
+from static_config import EN_TO_ZH_MAP
 
 
 class KwPredict(BasePredictor):
 class KwPredict(BasePredictor):
 
 
-    def predict(self, key):
-        res, prab = self.get_single_predict(key)
+    def predict(self, string, predict_type='all'):
+        self.info.info('预测类型为: %s, 接收数据: %s' % (predict_type, string))
+        result, prab = self.get_single_predict(string)
+        zh_prob = {EN_TO_ZH_MAP[k]: v for k, v in prab.items()}
+
+        res = {'result': EN_TO_ZH_MAP[result], 'probably_dict': zh_prob}
         return res
         return res