CODEWITHSUNDEEP
pythonazurehttpurllib2azure-cognitive-services
Muhammed Eltabakh
Muhammed Eltabakh

Reputation: 497

How to Access Microsoft cognitive API (HTTPError: HTTP Error 400: Bad Request)

I'm trying to build a sentiment analysis model on a csv file on using the text analytics api on azure

This is the code I used: 

for j in range(0,num_of_batches): # this loop will add num_of_batches strings to input_texts
    input_texts.set_value(j,"")   # initialize input_texts string j
    for i in range(j*l//num_of_batches,(j+1)*l//num_of_batches): #loop through a window of rows from the dataset
        comment = str(mydata["tweet"][i])            #grab the comment from the current row
        comment = comment.replace("\"", "'") #remove backslashes (why? I don’t remember. #honestblogger)

        #add the current comment to the end of the string we’re building in input_texts string j   
        input_texts.set_value(j, input_texts[j] + '{"language":"' + "pt"',"id":"' + str(i) + '","text":"'+ comment + '"},')

    #after we’ve looped through this window of the input dataset to build this series, add the request head and tail
    input_texts.set_value(j, '{"documents":[' + input_texts[j] + ']}')

headers = {'Content-Type':'application/json', 'Ocp-Apim-Subscription-Key':account_key}

Sentiment = pd.Series()
batch_sentiment_url = "https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment"

until now every thing is fine, but when I try to get the data from the api I get an error in the final part

for j in range(0,num_of_batches):
    # Detect sentiment for the each batch.
    req = urllib2.Request(batch_sentiment_url, input_texts[j], headers) 
    response = urllib2.urlopen(req)
    result = response.read()
    obj = json.loads(result.decode('utf-8'))

    #loop through each result string, extracting the sentiment associated with each id
    for sentiment_analysis in obj['documents']:
        Sentiment.set_value(sentiment_analysis['id'], sentiment_analysis['score'])   

#tack our new sentiment series onto our original dataframe

mydata.insert(len(mydata.columns),'Sentiment',Sentiment.values)

This error

HTTPError: HTTP Error 400: Bad Request

Upvotes: 0

Views: 363

Answers (2)

cthrash
cthrash

Reputation: 2973

You're getting a 400 error because your JSON is malformed (mismatched quotes around 'pt'). I don't think you're doing yourself any favors by using the pandas module for the outgoing request, or attempting to hand-craft the JSON. In particular you are vulnerable to errant quote marks or escape characters screwing things up.

Here's how you might do it instead:

input_texts = []
for j in range(0,num_of_batches): # this loop will add num_of_batches strings to input_texts
  documents = []
  for i in range(j*l//num_of_batches,(j+1)*l//num_of_batches): #loop through a window of rows from the dataset
    documents.append({
      'language':'pt',
      'id': str(i),
      'text': str(mydata["tweet"][i])})
  input_texts.append({'documents':documents})

...
req = urllib2.Request(batch_sentiment_url, json.dumps(input_texts[j]), headers)

Upvotes: 1

BSalita
BSalita

Reputation: 8941

Always validate API calls using curl first. Afterwards insert into code. This curl line works for me:

curl -k -X POST -H "Ocp-Apim-Subscription-Key: <your ocp-apim-subscription-key>" -H "Content-Type: application/json" --data "{ 'documents': [ { 'id': '12345', 'text': 'now is the time for all good men to come to the aid of their party.' } ] }" "https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment"

Upvotes: 0

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