Google 自然语言预测示例

问题描述

我是 Python 新手。已经训练了自定义的 Google 自然语言模型并尝试执行 google 提供的示例。

import sys
import os

from google.api_core.client_options import ClientOptions
from google.cloud import automl


os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="my_service_account.json"

def inline_text_payload(file_path):
  with open(file_path,'rb') as ff:
    content = ff.read()
  return {'text_snippet': {'content': content,'mime_type': 'text/plain'} }

def get_prediction(file_path,model_name):
  options = ClientOptions(api_endpoint='eu-automl.googleapis.com')
  prediction_client = automl.PredictionServiceClient(client_options=options)

  payload = inline_text_payload(file_path)

  params = {}
  request = prediction_client.predict(model_name,payload,params)
  return request  # waits until request is returned

if __name__ == '__main__':
  file_path = sys.argv[1]
  model_name = sys.argv[2]

  print(get_prediction(file_path,model_name))

通过执行此代码，我收到错误：

Traceback (most recent call last):
  File "predict.py",line 33,in <module>
    print(get_prediction(file_path,model_name))
  File "predict.py",line 26,in get_prediction
    request = prediction_client.predict(model_name,params)
TypeError: predict() takes from 1 to 2 positional arguments but 4 were given

我进行了多次搜索，但似乎无法找到问题所在。如果任何有经验的人可以看看并指出正确的方向，我将不胜感激。

解决方法

]更新]

不得不改写 prediction_client.predict 参数。工作代码：

import sys

from google.api_core.client_options import ClientOptions
from google.cloud import automl
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="my_service_account.json"

def inline_text_payload(file_path):
  with open(file_path,'rb') as ff:
    content = ff.read()
  return {'text_snippet': {'content': content,'mime_type': 'text/plain'} }

def pdf_payload(file_path):
  return {'document': {'input_config': {'gcs_source': {'input_uris': [file_path] } } } }

def get_prediction(file_path,model_name):
  options = ClientOptions(api_endpoint='eu-automl.googleapis.com')
  prediction_client = automl.PredictionServiceClient(client_options=options)

  payload = inline_text_payload(file_path)

  params = {}
  request = prediction_client.predict(name=model_name,payload=payload,params=params)
  return request  # waits until request is returned

if __name__ == '__main__':
  file_path = sys.argv[1]
  model_name = sys.argv[2]

  print(get_prediction(file_path,model_name))