def test(request):
context = {}
client = IODClient(APIURL, APIKEY)
r=client.post('analyzesentiment',{'text':'I like cats'})
analyzesentiment=r.json()
sentiment = analyzesentiment['aggregate']['sentiment']
context['sentiment']=analyzesentiment
hightlight_sentiment = ''
for word in analyzesentiment[sentiment]:
hightlight_sentiment += '{},'.format(word['topic'])
print hightlight_sentiment + " here"
r=client.post('highlighttext',{'text':'I like cats', 'highlight_expression':'{}'.format(hightlight_sentiment), 'start_tag':'<b>', 'end_tag':'</b>', })
context['highlight']=r.json()['text']
index = client.getIndex('mailsift')
doc1={'reference':'doc1','title':'title1','content':'this is my content'}
doc2={'reference':'doc2','title':'title2','content':'this is another content'}
doc3={'reference':'doc3','title':'title2','content':'this is another content'}
doc4={'reference':'doc2','title':'titleNew','content':'this is another content alksdjflkjasdfkljaslkdf'}
docs = [doc1, doc2, doc3, doc4]
index.addDocs([doc1, doc2, doc3, doc4])
for doc in docs:
index.pushDoc(doc)
print index.size()
index.commit()
print index.size()
return render(request, 'parse/test.html', context)
def generate_highlight(request, feedback_pk):
context = {}
client = IODClient(APIURL, APIKEY)
feedback_email = Email.objects.get(pk=feedback_pk)
r = client.post('analyzesentiment', {'text': feedback_email.message})
analyzesentiment = r.json()
sentiment = analyzesentiment['aggregate']['sentiment']
context['sentiment'] = analyzesentiment
feedback_email.sentiment = 50 + 50 * analyzesentiment['aggregate']['score']
hightlight_sentiment = ''
for word in analyzesentiment[sentiment]:
hightlight_sentiment += '{},'.format(word['topic'])
r = client.post(
'highlighttext', {
'text': feedback_email.message,
'highlight_expression': '{}'.format(hightlight_sentiment),
'start_tag': '<b>',
'end_tag': '</b>',
})
feedback_email.content = r.json()['text']
feedback_email.priority = feedback_email.sentiment + 40
feedback_email.save()
context['test'] = feedback_email
return render(request, 'parse/test.html', context)
def generate_tags(request, feedback_pk):
context = {}
client = IODClient(APIURL, APIKEY)
feedback_email = Email.objects.get(pk=feedback_pk)
index = client.getIndex('mailsift')
r = client.post('findsimilar', {'text':feedback_email.message, 'indexes':'mailsift'})
similar_feedback = r.json()
if len(similar_feedback['documents']) > 0:
similar_email = Email.objects.get(pk=similar_feedback['documents'][0]['reference'])
feedback_email.tags = similar_email.tags
else:
feedback_email.tags = ['']
feedback_email.save()
doc1={'reference':feedback_email.pk,'title':feedback_email.subject, 'content':feedback_email.message}
docs = [doc1]
index.addDocs([doc1])
for doc in docs:
index.pushDoc(doc)
print index.size()
index.commit()
print index.size()
context['test'] = feedback_email
return render(request, 'parse/test.html', context)
def generate_highlight(request, feedback_pk):
context = {}
client = IODClient(APIURL, APIKEY)
feedback_email = Email.objects.get(pk=feedback_pk)
r=client.post('analyzesentiment',{'text':feedback_email.message})
analyzesentiment=r.json()
sentiment = analyzesentiment['aggregate']['sentiment']
context['sentiment']=analyzesentiment
feedback_email.sentiment = 50 + 50 * analyzesentiment['aggregate']['score']
hightlight_sentiment = ''
for word in analyzesentiment[sentiment]:
hightlight_sentiment += '{},'.format(word['topic'])
r=client.post('highlighttext',{'text':feedback_email.message, 'highlight_expression':'{}'.format(hightlight_sentiment), 'start_tag':'<b>', 'end_tag':'</b>', })
feedback_email.content=r.json()['text']
feedback_email.priority = feedback_email.sentiment + 40
feedback_email.save()
context['test'] = feedback_email
return render(request, 'parse/test.html', context)
class SentimentAnalyser:
def __init__(self):
self.client = IODClient("http://api.idolondemand.com/","07a106d0-ff07-496b-a1b5-288b752da744")
self.hashtags = {"#jeb": "Jeb Bush", "#clinton": "Hillary Clinton", "#trump": "Donald Trump", "#hilary": "Hillary Clinton", "#berniesanders": "Bernie Sanders"}
def generateSentiment(self, text):
r = self.client.post('analyzesentiment',{'text':text})
docs = r.json()
if(docs['aggregate']['score'] != 0):
dictJson = {"candidate": self.getCandidate(text)}
if docs['positive'] != []:
for doc in docs['positive']:
dictJson[doc['topic']] = doc['score']
if docs['negative'] != []:
for doc in docs['negative']:
dictJson[doc['topic']] = doc['score']
return dictJson
def getCandidate(self, tweet):
try:
x = next(value for key, value in self.hashtags.iteritems() if key in tweet)
except StopIteration:
x = "Jeb Bush"
return x
def call(answers,count2):
#Call client from IOD and pass in API key
client = IODClient('http://api.idolondemand.com', '#*APIKEY*#')
text = answers
#set data to text dictionary
data = {'text':text}
#tell the client what API to call
r = client.post('analyzesentiment', data)
#Return information from IOD
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print "********************************",'\n'
data = r.json()
count1 = 0
#Set lists to hold topics
global n_topics
global p_topics
n_topics = []
p_topics = []
for i in (data['negative']):
#If statment to check is there is a negative topic
if 'negative' not in data:
print('negative key is missing')
elif len(data['negative']) == 0:
print('no items in the negative list')
elif 'topic' not in data['negative'][count2]:
print('topic is missing')
elif data['negative'][count2]['topic'] == '':
print('topic is empty')
else:
#what to return from the negative topic
print "Text:", "'"+text+"'"
print "Sentiment: negative "
print "Topic:" , (data['negative'][count2]['topic'])
print "Score:", (data['negative'][count2]['score'])
n_topics.append(str(data['negative'][count2]['topic']))
if count2 == 1:
print "negative topic"
break
logging.warning('Watch out!')
print '\n',"********************************"
print '\n',"Negative topics:",n_topics
print '\n',"********************************", '\n'
#If statment to check is there is a positive topic
for i in (data['positive']):
if 'positive' not in data:
print('positive key is missing')
elif len(data['positive']) == 0:
print('no items in the positive list')
elif 'topic' not in data['positive'][count1]:
print('topic is missing')
elif data['positive'][count1]['topic'] == '':
print('topic is empty')
else:
#what to return from the positive topic
print "Text:", "'"+text+"'"
print "Sentiment: positive "
print "Topic:" , (data['positive'][count1]['topic'])
print "Score:", (data['positive'][count1]['score'])
p_topics.append(str(data['positive'][count1]['topic']))
count1 += 1
print '\n',"********************************",'\n'
print "Positive topics:",p_topics,'\n'
# print statments to print the aggregate for the data_set
#print "Aggregate Result"
#print "sentiment: ", sentiment + '\n',"score: ", score , '\n'
print "********************************"
GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)
## initialize
print "Initializing..."
GPIO.output(LED1_PIN, True)
GPIO.output(LED2_PIN, True)
time.sleep(2)
GPIO.output(LED1_PIN, False)
GPIO.output(LED2_PIN, False)
time.sleep(2)
##
## repeat over and over again
while True:
camera.capture("image1.jpg")
r = client.post("detectfaces", files={"file": open("./image1.jpg", "rb")})
print "------------"
print "POST to IDOL"
if len(r.json()["face"]) > 0: # is there a face in the image
print "Face(s) detected"
GPIO.output(LED1_PIN, True) # turn light on
input_state = GPIO.input(BUTTON_PIN)
if input_state == False: # i.e. the button was pushed
print ("Button pressed")
print ("Saving to S3")
GPIO.output(LED2_PIN, True)
k = Key(b)
k.key = "image1_s3.jpg" # this is what you want to name it in S3
k.set_contents_from_filename("image1.jpg") # this is name of the lcoal file
k.set_acl("public-read")
time.sleep(2) # make sure it actually gets up there
