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将tensorflow训练好的模型部署成sercice服务,并做预测

分类: 文章 • 2025-02-06 14:18:10

web框架用的是tornado，模型加载在上一篇博客已经提到，下面写了一个service.py文件:



    

  1. import tornado.web
    2. 

  2. import logging
    3. 

  3. import string
    4. 

  4. import os
    5. 

  5. import sys
    6. 

  6. from predict import predict
    7. 

    8. 

  8. logger = logging.getLogger('mylogger')
    9. 

  9. logger.setLevel(logging.DEBUG)
    10. 

  10. # 再创建一个handler，用于输出到控制台
    11. 

  11. ch = logging.StreamHandler()
    12. 

  12. ch.setLevel(logging.DEBUG)
    13. 

  13. # 定义handler的输出格式
    14. 

  14. formatter = logging.Formatter('%(asctime)s - %(message)s')
    15. 

  15. ch.setFormatter(formatter)
    16. 

  16. # 给logger添加handler
    17. 

  17. logger.addHandler(ch)
    18. 

  18. def work(text):
    19. 

  19.  if text is None:
    20. 

  20.  return 0.
    21. 

  21.  else:
    22. 

  22.  return predict(text)
    23. 

    24. 

  24. class MainHandler(tornado.web.RequestHandler):
    25. 

  25.  def post(self):
    26. 

  26.  text = self.get_argument('text')
    27. 

  27.  p = work(text)
    28. 

  28.  self.write(p)
    29. 

    30. 

  30. class IndexHandler(tornado.web.RequestHandler):
    31. 

  31.  def get(self):
    32. 

  32.  text = self.get_argument('text')
    33. 

  33.  p = work(text)
    34. 

  34.  self.render('index.html',text=text,result='{0}'.format(p))
    35. 

    36. 

    37. 

  37. application=tornado.web.Application(
    38. 

  38. handlers=[(r"/", MainHandler),
    39. 

  39.  (r'/demo', IndexHandler)],
    40. 

  40.  template_path=os.path.join(os.path.dirname(__file__), "templates")
    41. 

  41. )
    42. 

    43. 

  43. if __name__=='__main__':
    44. 

  44.  if len(sys.argv)<2:
    45. 

  45.  port=8410
    46. 

  46.  else:
    47. 

  47.  port=string.atoi(sys.argv[1])
    48. 

  48.  application.listen(port)
    49. 

  49.  logger.info('Starting server:port=%d, use <Ctrl-C> to stop' % port)
    50. 

  50.  tornado.ioloop.IOLoop.instance().start()
    51. 

    52. 





















templates/index.html问价内容：










    

  1. <!DOCTYPE html>
    2. 

  2. <html>
    3. 

  3.  <head><title>text abumse server</title></head>
    4. 

  4.  <body>
    5. 

  5.  <form method="get" action="/demo">
    6. 

  6.  <p>text content: <input type="text" name="text" value="{{text}}" style="width: 800px"><input type="submit"></p>
    7. 

  7.  </form>
    8. 

  8.  <p>{{result}}</p>
    9. 

  9.  <p><img src="{{text}}" width="600px"></p>
    10. 

  10.  </body>
    11. 

  11. </html>
    12. 











启动service.py文件:








    

  1. /anaconda/bin/python3 /Users/shuubiasahi/Documents/python/credit-tftextclassify-abuse/service.py
    2. 

  2. 2017-10-13 15:58:20.625704: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
    3. 

  3. 2017-10-13 15:58:20.625723: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
    4. 

  4. 2017-10-13 15:58:20.625728: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
    5. 

  5. 2017-10-13 15:58:20.625733: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
    6. 

  6. 2017-10-13 15:58:21,732 - Starting server:port=8410, use <Ctrl-C> to stop
    7. 













监控的是8410端口，可以启动在浏览器输入http://localhost:8410/demo?text=我操，日狗了，如下：



将tensorflow训练好的模型部署成sercice服务,并做预测








将tensorflow训练好的模型部署成sercice服务,并做预测











将tensorflow训练好的模型部署成sercice服务,并做预测







将tensorflow训练好的模型部署成sercice服务,并做预测本身我是用的cnn做的辱骂识别模型，预测的概率还挺高，看到最后别喷啊 ，哈哈哈哈。。。。。




															

																					

								

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