修改response结构

nlp/chat.py

@@ -3,6 +3,7 @@
 # 测试脚本
 # 测试环境：tensorflow 2.5.3 + keras 2.3.1 + bert4keras 0.11
 import os
+
 os.environ['TF_KERAS'] = "1"
 
 import numpy as np
@@ -15,6 +16,7 @@ from bert4keras.snippets import AutoRegressiveDecoder
 class ChatBot(AutoRegressiveDecoder):
     """基于随机采样对话机器人
     """
+
     def __init__(self, start_id, end_id, maxlen, model, tokenizer):
         super().__init__(start_id, end_id, maxlen)
         self.model = model
@@ -53,11 +55,9 @@ def build_chat_model(model_path, tokenizer):
     chatbot = ChatBot(start_id=None, end_id=tokenizer._token_end_id, maxlen=32, model=model, tokenizer=tokenizer)
     return chatbot
 
+
 if __name__ == '__main__':
     tokenizer = Tokenizer("../models/nezha_gpt_dialog/vocab.txt", do_lower_case=True)
     chatbot = build_chat_model('../models/nezha_gpt_dialog', tokenizer)
     text_list = ['绿遍山原白满川', '子规声里雨如烟']
     print(chatbot.response(text_list))
-    
-
-

nlp/hanlp_tools.py

@@ -1,6 +1,7 @@
 import hanlp
 from logzero import logger
 from hanlp_common.document import Document
+
 tok = hanlp.load('./.hanlp/tok/coarse_electra_small_20220616_012050/')
 dep = hanlp.load('./.hanlp/dep/ctb9_dep_electra_small_20220216_100306/')
 sts = hanlp.load('./.hanlp/sts/sts_electra_base_zh_20210530_200109/')
@@ -17,6 +18,7 @@ def text_analysis(text):
     logger.info(rst)
     return rst
 
+
 def text_simi(src, tgt):
     score = sts([(src, tgt)])[0]
     result = ["negative", "positive"][round(score)]
@@ -25,4 +27,3 @@ def text_simi(src, tgt):
 
 if __name__ == '__main__':
     print(text_analysis("台湾省是中国不可分割的一部分。"))
-    

nlp/reading.py

@@ -3,6 +3,7 @@
 # (Accuracy=0.7282149325820084  F1=0.8207266829447049   Final=0.7744708077633566)
 
 import json, os, re
+
 os.environ['TF_KERAS'] = '1'
 import numpy as np
 from bert4keras.backend import keras, K
@@ -16,6 +17,7 @@ from keras.models import Model
 from tqdm import tqdm
 
 import tensorflow as tf
+
 config = tf.compat.v1.ConfigProto()
 config.gpu_options.allow_growth = True  # 按需分配显存
 tf_session = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
@@ -27,6 +29,7 @@ max_a_len = 32
 batch_size = 32
 epochs = 10
 
+
 # # bert配置
 # config_path = '../models/nezha_gpt/config.json'
 # checkpoint_path = '../models/nezha_gpt/gpt.ckpt'
@@ -113,7 +116,6 @@ def masked_cross_entropy(y_true, y_pred):
     return cross_entropy
 
 
-
 def build_reading_model(config_path: str, ckpt_path: str, keep_tokens: str, weight_path: str):
     model = build_transformer_model(
         config_path,
@@ -127,6 +129,7 @@ def build_reading_model(config_path:str, ckpt_path:str, keep_tokens:str, weight_
     model.load_weights(weight_path)
     return model
 
+
 def get_ngram_set(x, n):
     """生成ngram合集，返回结果格式是:
     {(n-1)-gram: set([n-gram的第n个字集合])}
@@ -221,7 +224,8 @@ if __name__ == '__main__':
     model = build_reading_model()
     model.load_weights('../models/qa/best_model.weights')
     questions = "嬴政出生在哪里？"
-    passages = ["秦始皇嬴政（前259年—前210年），嬴姓，赵氏 ，名政（一说名“正”），又称赵政 、祖龙 ，也有吕政一说（详见“人物争议-姓名之争”目录）。秦庄襄王和赵姬之子。中国古代杰出的政治家、战略家、改革家，首次完成中国大一统的政治人物，也是中国第一个称皇帝的君主。",
+    passages = [
+        "秦始皇嬴政（前259年—前210年），嬴姓，赵氏 ，名政（一说名“正”），又称赵政 、祖龙 ，也有吕政一说（详见“人物争议-姓名之争”目录）。秦庄襄王和赵姬之子。中国古代杰出的政治家、战略家、改革家，首次完成中国大一统的政治人物，也是中国第一个称皇帝的君主。",
         "公元前221年，秦统一六国之后，秦王嬴政认为自己“德兼三皇，功过五帝”，遂采用三皇之“皇”、五帝之“帝”构成“皇帝”的称号，是中国历史上第一个使用“皇帝”称号的君主，所以自称“始皇帝”。",
         "秦始皇有二十余子。长子扶苏，少子胡亥。",
         "嬴政出生在当时赵国的邯郸廓城（在今城内中街以东，丛台西南的朱家巷一带），是当时的秦国王孙异人之子。"]

nlp/text_classification.py

@@ -22,16 +22,15 @@ config.gpu_options.allow_growth=True #  按需分配显存
 tf_session = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
 tf.compat.v1.keras.backend.set_session(tf_session)
 
-
 from sklearn.model_selection import train_test_split
 
-
 set_gelu('tanh')  # 切换gelu版本
 
 MAX_LEN = 128
 BATCH_SIZE = 32
 AdamLR = extend_with_piecewise_linear_lr(Adam, name='AdamLR')
 
+
 # config_path = '/home/zhaojh/pretrain_models/roberta_base/bert_config.json'
 # checkpoint_path = '/home/zhaojh/pretrain_models/roberta_base/bert_model.ckpt'
 # dict_path = '/home/zhaojh/pretrain_models/roberta_base/vocab.txt'
@@ -191,8 +190,10 @@ def load_cls_model(config_path:str, ckpt_path:str, weight_path:str):
     model.load_weights(weight_path)
     return model
 
+
 if __name__ == '__main__':
     from keras.models import load_model
+
     dict_path = '../models/tokenizer/vocab.txt'
     tokenizer = Tokenizer(dict_path, do_lower_case=True)
     model = load_model('../models/text_classifier/best_model.h5')

nlp/text_gen.py

@@ -1,5 +1,6 @@
 #! -*- coding: utf-8 -*-
 import os
+
 os.environ["TF_KERAS"] = "1"
 import numpy as np
 from bert4keras.models import build_transformer_model
@@ -10,6 +11,7 @@ from bert4keras.snippets import AutoRegressiveDecoder
 class ArticleCompletion(AutoRegressiveDecoder):
     """基于随机采样的文章续写
     """
+
     def __init__(self, start_id, end_id, maxlen, minlen, config_path, ckpt_path):
         super().__init__(start_id, end_id, maxlen, minlen)
         self.model = build_transformer_model(

run.py

@@ -4,11 +4,11 @@ import sys
 import json
 from flask import Flask, request, make_response
 from logzero import logger
+
 # current_path = os.path.dirname(os.path.abspath(__file__)) # for local
 current_path = "/app"  # for docker
 logger.info(f"{current_path}")
 
-
 app = Flask(__name__)
 
 sys.path.append(f"{current_path}/nlp/")
@@ -16,7 +16,6 @@ os.environ["TF_KERAS"] = "1"
 os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-
 from bert4keras.tokenizers import Tokenizer, load_vocab
 from keras.models import load_model
 
@@ -27,7 +26,6 @@ from nlp.text_classification import run_cls as run_class, AdamLR
 from nlp.hanlp_tools import text_analysis, text_simi
 from nlp.reading import build_reading_model, gen_answer
 
-
 general_tokenizer = Tokenizer(f"{current_path}/models/tokenizer/vocab.txt", do_lower_case=True)  # 通用分词器
 dialog_tokenizer = Tokenizer(f"{current_path}/models/nezha_gpt_dialog/vocab.txt", do_lower_case=True)  # 对话分词器
 
@@ -66,49 +64,52 @@ reading_model = build_reading_model(
     f"{current_path}/models/qa/best_model.weights"
 )
 
+TEXT = "text"
+
 app = Flask(__name__)
 
+
 @app.route('/text_cls/', methods=["POST"])
 def run_cls():
-    resp = make_response()
+    resp_info = dict()
     if request.method == "POST":
         text = request.form.get('text')
         if text is not None and text != '':
-            resp.response = run_class(cls_model, general_tokenizer, text)
+            rst = run_class(cls_model, general_tokenizer, text)
+            resp_info["code"] = 200
+            resp_info["data"] = rst
+            resp_info["dtype"] = TEXT
+        else:
+            resp_info["msg"] = "Input is None, please check !"
+            resp_info["code"] = 406
+    resp = make_response(json.dumps(resp_info))
     resp.status_code = 200
     return resp
-        else:
+
-            resp.status_code = 406
-            return resp
-    else:
-        resp.status_code=405
-        return resp
 
 @app.route('/text_gen/', methods=["POST"])
 def run_gen():
-    resp = make_response()
+    resp_info = dict()
     if request.method == "POST":
         text = request.form.get('text')
         logger.info(f"将对文本'{text}'进行续写")
         if text != "":
             rest = gen_model.generate(text, general_tokenizer)
             logger.info(rest)
-            resp.response = rest
+            resp_info["code"] = 200
+            resp_info["data"] = rest
+            resp_info["dtype"] = TEXT
+        else:
+            resp_info["msg"] = "Input is None, please check !"
+            resp_info["code"] = 406
+    resp = make_response(json.dumps(resp_info))
     resp.status_code = 200
     return resp
-        else:
+
-            resp.status_code = 406
-            return resp
-    else:
-        resp.status_code=405
-        return resp
 
 @app.route('/chat/', methods=["POST"])
 def run_chat():
-    # todo: 这个模块可以用grpc流式服务做。
+    resp_info = dict()
-    # 如果用flask 就把历史对话都按照list的方式传进来
-    # 历史对话可以用json传
-    resp = make_response()
     if request.method == "POST":
         dialog_history = request.form.get("dialog_history")
         dialog_history = dialog_history.split('。')
@@ -116,15 +117,16 @@ def run_chat():
         if len(dialog_history) > 0:
             rest = chatbot.response(dialog_history)
             logger.info(rest)
-            resp.response = rest
+            resp_info["code"] = 200
+            resp_info["data"] = rest
+            resp_info["dtype"] = TEXT
+        else:
+            resp_info["msg"] = "请将历史对话以中文句号分隔"
+            resp_info["code"] = 406
+    resp = make_response(json.dumps(resp_info))
     resp.status_code = 200
     return resp
-        else:
+
-            resp.status_code = 406
-            return resp
-    else:
-        resp.status_code=405
-        return resp
 
 # @app.route('/translate/', methods=["POST"])
 # def run_translate():
@@ -144,48 +146,56 @@ def run_chat():
 
 @app.route('/simi/', methods=["POST"])
 def run_match():
-    resp = make_response()
+    resp_info = dict()
+
     if request.method == "POST":
         src = request.form.get('text_1')
         tgt = request.form.get('text_2')
-        resp.response = str(text_simi(src, tgt))
+        resp_info["code"] = 200
+        resp_info["data"] = str(text_simi(src, tgt))
+        resp_info["dtype"] = TEXT
+    resp = make_response(json.dumps(resp_info))
     resp.status_code = 200
     return resp
-    else:
-        resp.status_code=405
-        return resp
 
 
 @app.route('/dependency/', methods=["POST"])
 def run_depend():
-    resp = make_response()
+    resp_info = dict()
     if request.method == "POST":
         text = request.form.get('text')
         if text is None or text.strip() == "":
-            resp.status_code=406
+            resp_info["msg"] = "Input is None, please check !"
-            return resp
+            resp_info["code"] = 406
-        resp.response = str(text_analysis(text))
+        else:
+            resp_info["code"] = 200
+            resp_info["data"] = str(text_analysis(text))
+            resp_info["dtype"] = TEXT
+    resp = make_response(json.dumps(resp_info))
     resp.status_code = 200
     return resp
-    else:
+
-        resp.status_code=405
-        return resp
 
 @app.route('/reading/', methods=["POST"])
 def run_reading():
-    resp = make_response()
+    resp_info = dict()
     if request.method == "POST":
         question = request.form.get("question")
         passages = request.form.get("passages")
         passages = [x + '。' for x in passages.split('。')]
-        if len(passages) == 0 or question is None or question.strip() == "":
+        if question is None or question.strip() == "":
-            resp.status_code=406
+            resp_info["msg"] = "Question is None, please check!"
-            return resp
+            resp_info["code"] = 406
-        resp.response = json.dumps(gen_answer(question, passages, reading_model, reading_tokenizer))
+        elif len(passages) == 0:
-        resp.status_code = 200
+            resp_info["msg"] = "请将文章段落以中文句号分隔"
-        return resp
+            resp_info["code"] = 406
         else:
-        resp.status_code=405
+            rest = gen_answer(question, passages, reading_model, reading_tokenizer)
+            resp_info["code"] = 200
+            resp_info["data"] = rest
+            resp_info["dtype"] = TEXT
+    resp = make_response(json.dumps(resp_info))
+    resp.status_code = 200
     return resp