def azureKPE()

in src/plugins/utils/kpe.py [0:0]

• 41 lines of code
• 8 McCabe index (conditional complexity)

def azureKPE(KibbleBit, bodies):
    """ KPE using Azure Text Analysis API """
    if 'azure' in KibbleBit.config:
        headers = {
            'Content-Type': 'application/json',
            'Ocp-Apim-Subscription-Key': KibbleBit.config['azure']['apikey']
        }
        
        
        js = {
            "documents": []
          }
        
        # For each body...
        a = 0
        KPEs = []
        for body in bodies:
            # Crop out quotes
            lines = body.split("\n")
            body = trimBody(body)
            doc = {
                "language": "en",
                "id": str(a),
                "text": body
              }
            js['documents'].append(doc)
            KPEs.append({}) # placeholder for each doc, to be replaced
            a += 1
        try:
            rv = requests.post(
                "https://%s.api.cognitive.microsoft.com/text/analytics/v2.0/keyPhrases" % KibbleBit.config['azure']['location'],
                headers = headers,
                data = json.dumps(js)
            )
            jsout = rv.json()
        except:
            jsout = {} # borked sentiment analysis?
        
        if 'documents' in jsout and len(jsout['documents']) > 0:
            for doc in jsout['documents']:
                KPEs[int(doc['id'])] = doc['keyPhrases'][:5] # Replace KPEs[X] with the actual phrases, 5 first ones.
                
        else:
            KibbleBit.pprint("Failed to analyze email body.")
            print(jsout)
            # Depending on price tier, Azure will return a 429 if you go too fast.
            # If we see a statusCode return, let's just stop for now.
            # Later scans can pick up the slack.
            if 'statusCode' in jsout:
                KibbleBit.pprint("Possible rate limiting in place, stopping for now.")
                return False
        return KPEs