def Calculate():
try:
news = request.form['inputNews'].lower()
topic = request.form['inputTopic']
category = request.form['inputCategory']
print news + "\t" + topic + "\t" + category
from havenondemand.hodindex import HODClient
client = HODClient(apikey='6b1f8438-56c7-45e0-98a6-6742c1be0d65', apiversiondefault=1)
"""def get_bias(url):
print "Hello"
data = {'url': url}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print url + " | " + sentiment + " | " + str(score)
return score"""
paper = newspaper.build("http://" + news + ".com", language='en', memoize_articles=False)
url = []
for article in paper.articles:
url.append(article.url)
cumulative_score = 0.0
countNegative = 0
countPositive = 0
countNeutral = 0
"""import multiprocessing as mp
p = mp.Pool(3)
res = p.map(get_bias, url)"""
print newspaper.category
for u in url:
data = {'url': u}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print u + " | " + sentiment + " | " + str(score)
cumulative_score += score
if sentiment == 'positive':
countPositive += 1
elif sentiment == 'negative':
countNegative += 1
elif sentiment == 'neutral':
countNeutral += 1
print cumulative_score
print cumulative_score/len(url)
except Exception as e:
return json.dumps({'error':str(e)})
return news + topic + category
def wikipediagrabber(filepath):
#make API call, as outlined in https://github.com/HPE-Haven-OnDemand/havenondemand-python
client = HODClient("http://api.havenondemand.com/", "5e8a3841-5bec-43cc-9dac-5e5d0a90bbc9")
r = client.post('extractentities', data={'entity_type': ['people_eng'], 'unique_entities': 'true'},files={'file':open(filepath,'rb')} )
#set variables
myjson = r.json()
identifiers = []
dictionary={}
#iterate through each named entity
for i in range(0, len(myjson['entities'])):
#try statement that only stores named entries with wikipedia descriptions in dictionary
try:
#record duplicate named entities
identifier = myjson['entities'][i]['additional_information']['wikidata_id']
#only add to dictionary if named entity has not already appeared
if identifier not in identifiers:
identifiers.append(identifier)
entry = myjson['entities'][i]['original_text']
dictionary[myjson['entities'][i]['additional_information']['wikidata_id']] = [myjson['entities'][i]['original_text'], wikipedia.summary(entry, sentences = 5), myjson['entities'][i]['additional_information']['wikipedia_eng']]
#do not add to dictionary if they do not have wikipedia pages
except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e:
continue
return dictionary
def havenSentiment(text):
"""Takes a string as an input and runs it through the Haven API to gather sentiment analysis and returns it"""
from havenondemand.hodindex import HODClient
import os
key = os.environ.get('havenAPI')
client = HODClient(apikey=key, apiversiondefault=1)
data = {'text': text}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
# return text + " | " + sentiment + " | " + str(score)
return score
# if __name__ == "__main__":
# from twitterGrab import twitterUserGrab
# from twitterGrab import twitterTopicGrab
# print havenSentiment(twitterTopicGrab("python"))
# print havenSentiment(twitterUserGrab("kanyewest"))
def havenSentiment(text):
"""Takes a string as an input and runs it through the Haven API to gather sentiment analysis and returns it"""
from havenondemand.hodindex import HODClient
import os
key = os.environ.get('havenAPI')
client = HODClient(apikey=key, apiversiondefault=1)
data = {'text': text}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
# return text + " | " + sentiment + " | " + str(score)
return score
# if __name__ == "__main__":
# from twitterGrab import twitterUserGrab
# from twitterGrab import twitterTopicGrab
# print havenSentiment(twitterTopicGrab("python"))
# print havenSentiment(twitterUserGrab("kanyewest"))
def r(subreddit, thread):
""" """
# if db has stored values, return stored values
stored_sentiments = search_thread_id(thread)
if stored_sentiments != []:
return Response(json.dumps(stored_sentiments),
mimetype="application/json")
# establish client
client = HODClient("http://api.havenondemand.com",
"65f7315d-1189-449f-a839-7a46fd4263be")
# create the intial user agent
user_agent = "reddit-mood-analyzer-scrape by /u/abrarisland"
reddit = praw.Reddit(user_agent=user_agent)
submission = reddit.get_submission(submission_id=thread, comment_limit=15)
# get all of the comments in the thread and flatten the comment tree
flat_comments = praw.helpers.flatten_tree(submission.comments)
results = []
evaluate_comments(client, thread, flat_comments, results)
return Response(json.dumps(results), mimetype="application/json")
import newspaper, json
cnn_paper = newspaper.build('http://cnn.com', language='en', memoize_articles=False)
url = []
for article in cnn_paper.articles:
url.append(article.url)
from havenondemand.hodindex import HODClient
client = HODClient(apikey='6b1f8438-56c7-45e0-98a6-6742c1be0d65', apiversiondefault=1)
cumulative_score = 0.0
count = 0
import multiprocessing as mp
p = mp.Pool(3)
def get_bias(url):
data = {'url': url}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print url + " | " + sentiment + " | " + str(score)
return score
res = p.map(get_bias, url)
for record in res:
cumulative_score += record
from havenondemand.hodindex import HODClient
client = HODClient(apikey='API_KEY', apiversiondefault=1)
text = "I love puppies"
data = {'text': text}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print(text + " | " + sentiment + " | " + str(score))
import os
import os, os.path
from threading import Thread
import time
import picamera
import requests
import shutil
from havenondemand.hodindex import HODClient
##Haven OnDemand client
client = HODClient("http://api.havenondemand.com", "API_KEY")
##Find number of images already saved
##Need for if Raspberry Pi shuts off and im_counter gets set back to zero
DIR = './img' #directory images get saved to
img_counter = len([name for name in os.listdir(DIR) if os.path.isfile(os.path.join(DIR, name))])
##
##Set constants
slideshow_delay = 30
save_and_display_delay = 40
img_name = 'image'
##Initialize camera
camera = picamera.PiCamera()
camera.sharpness = 0
camera.contrast = 0
camera.brightness = 50
camera.saturation = 0
camera.ISO = 0
camera.video_stabilization = False
from flask import Flask
from flask import request
from flask import render_template
import json as js
import tweepy
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
from havenondemand.hodindex import HODClient
client = HODClient("http://api.havenondemand.com/",
"075a1e5f-ad81-4677-9362-7ca4649103f2")
listofData = []
class ClassName(StreamListener):
fuckmylife = 0
"""docstring for ClassName"""
def on_status(self, status):
ClassName.fuckmylife += 1
#parsed_data = "tweet='%s'"%(status.text)
listofData.append(status.text)
if (ClassName.fuckmylife < 30):
return True
else:
return False
def on_error(self, status):
def Calculate():
try:
news = request.form['inputNews'].lower()
topic = request.form['inputTopic']
category = request.form['inputCategory']
print news + "\t" + topic + "\t" + category
from havenondemand.hodindex import HODClient
client = HODClient(apikey='6b1f8438-56c7-45e0-98a6-6742c1be0d65',
apiversiondefault=1)
"""def get_bias(url):
print "Hello"
data = {'url': url}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print url + " | " + sentiment + " | " + str(score)
return score"""
paper = newspaper.build("http://" + news + ".com",
language='en',
memoize_articles=False)
url = []
for article in paper.articles:
url.append(article.url)
cumulative_score = 0.0
countNegative = 0
countPositive = 0
countNeutral = 0
"""import multiprocessing as mp
p = mp.Pool(3)
res = p.map(get_bias, url)"""
print newspaper.category
for u in url:
data = {'url': u}
r = client.post('analyzesentiment', data)
sentiment = r.json()['aggregate']['sentiment']
score = r.json()['aggregate']['score']
print u + " | " + sentiment + " | " + str(score)
cumulative_score += score
if sentiment == 'positive':
countPositive += 1
elif sentiment == 'negative':
countNegative += 1
elif sentiment == 'neutral':
countNeutral += 1
print cumulative_score
print cumulative_score / len(url)
except Exception as e:
return json.dumps({'error': str(e)})
return news + topic + category
