# The script MUST contain a function named azureml_main
  # which is the entry point for this module.

  # imports up here can be used to 
  import pandas as pd
  import json
  import time
  import requests
  from io import StringIO
  
  # url for Microsoft's Cognitive Services - Vision API - OCR
  #_url = 'https://api.projectoxford.ai/vision/v1.0/ocr' # previous url, still work
  _url = 'https://westus.api.cognitive.microsoft.com/vision/v1.0/ocr' # latest url
  
  # maximum number of retries when posting a request
  _maxNumRetries = 10

  # The entry point function can contain up to two input arguments:
  #   Param<dataframe1>: a pandas.DataFrame
  #   Param<dataframe2>: a pandas.DataFrame
  def azureml_main(dataframe1 = None, dataframe2 = None):

      # Get the OCR key
      VISION_API_KEY = str(dataframe2['Col1'][0])
      
      # Load the file containing image url and label
      df_url_label = dataframe1
            
      # create an empty pandas data frame
      df = pd.DataFrame({'Text' : [], 'Category' : [], 'ReceiptID' : []})
      
      # extract image url, setting OCR API parameters, process request
      for index, row in df_url_label.iterrows():
          imageurl = row['Url']
          
          # setting OCR parameters
          params = { 'language': 'en', 'detectOrientation ': 'true'} 
          headers = dict()
          headers['Ocp-Apim-Subscription-Key'] =  VISION_API_KEY
          headers['Content-Type'] = 'application/json' 
          
          image_url = { 'url': imageurl } ; 
          image_file = None
          result = processRequest( image_url, image_file, headers, params )
          
          if result is not None:
              # extract text
              text = extractText(result); 
              
              # populate dataframe
              df.loc[index,'Text'] = text
          else:
              # populate dataframe
              df.loc[index,'Text'] = None
            
          # 'Category' is the label
          df.loc[index,'Category'] = row['Category']
          df.loc[index,'ReceiptID'] = imageurl[-17:-4]
          
      # Return value must be a sequence of pandas.DataFrame
      return df

  # Extract text only from OCR's response
  def extractText(result):
      text = ""
      for region in result['regions']:
          for line in region['lines']:
              for word in line['words']:
                  text = text + " " + word.get('text')
      return text

  # Process request
  def processRequest( image_url, image_file, headers, params ):

      """
      Ref: https://github.com/Microsoft/Cognitive-Vision-Python/blob/master/Jupyter%20Notebook/Computer%20Vision%20API%20Example.ipynb
      Helper function to process the request to Project Oxford
      Parameters:
      json: Used when processing images from its URL. See API Documentation
      data: Used when processing image read from disk. See API Documentation
      headers: Used to pass the key information and the data type request
      """

      retries = 0
      result = None

      while True:
          response = requests.request( 'post', _url, json = image_url, data = image_file, headers = headers, params = params )
          
          if response.status_code == 429: 
              print( "Message: %s" % ( response.json()['message'] ) )

              if retries <= _maxNumRetries: 
                  time.sleep(1) 
                  retries += 1
                  continue
              else: 
                  print( 'Error: failed after retrying!' )
                  break

          elif response.status_code == 200 or response.status_code == 201:
              if 'content-length' in response.headers and int(response.headers['content-length']) == 0: 
                  result = None 
              elif 'content-type' in response.headers and isinstance(response.headers['content-type'], str): 
                  if 'application/json' in response.headers['content-type'].lower(): 
                      result = response.json() if response.content else None 
                  elif 'image' in response.headers['content-type'].lower(): 
                      result = response.content
          else:
              print(response.json()) 
              print( "Error code: %d" % ( response.status_code ) ); 
              print( "Message: %s" % ( response.json()['message'] ) ); 

          break
          
      return result