def create():
global FLAG
print("u" * 1000)
print(request.data)
print("u" * 1000)
if (request.data == b'' and FLAG == 1):
search_term = "#twitter"
FLAG = 0
else:
request_data = json.loads(request.data)
search_term = request_data['content']
print(search_term)
print(search_term)
CONSUMER_KEY = "Nea50h6UNQ5MLH6DIiPKxTnaV"
CONSUMER_SECRET = "VszNel95RlnGckBhV9gmKp1N1U92HdKZbTi3nmm9YOFDBcs1ki"
ACCESS_TOKEN = "926179846253723648-PgHMZnAW2wf9ANOmEkLdGUPTmfBEVGP"
ACCESS_TOKEN_SECRET = "SWbmV6apGGeApXr6yTwJU4LNJbNUIPt7AOZ4cjawl1o34"
"""consumer_key = os.environ.get('CONSUMER_KEY')
consumer_secret = os.environ.get('CONSUMER_SECRET')
access_token = os.environ.get('ACCESS_TOKEN')
access_token_secret = os.environ.get('ACCESS_TOKEN_SECRET')"""
consumer_key = CONSUMER_KEY
consumer_secret = CONSUMER_SECRET
access_token = ACCESS_TOKEN
access_token_secret = ACCESS_TOKEN_SECRET
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
print(consumer_key)
print("all good")
tweets = api.search(search_term, count=500)
data = pd.DataFrame(data=[tweet.text for tweet in tweets],
columns=['Tweets'])
head = data.head(10)
listy = []
print("here 1" * 100)
tweetList = [tweets for tweets in data['Tweets']]
print("p" * 1000)
print(tweetList)
print("p" * 1000)
print("here 5" * 100)
scoreList = []
labelList = []
model = load_model("model.h5")
for i in range(len(tweetList)):
scoreList.append(str(round(model(tweetList[i]).polarity, 1)))
print("here 6" * 100)
for i in range(len(tweetList)):
pLabel = 0
print('0')
scr = float(scoreList[i])
print(scr)
if (scr < -1 * 0.3):
pLabel = "Negative"
if (scr >= -1 * 0.3 and scr <= 0.3):
pLabel = "Neutral"
if (scr > 0.3):
pLabel = "Positive"
labelList.append(pLabel)
global arr
tempArr = []
print("here 2" * 100)
for i in range(len(tweetList)):
print('heeee')
print(tweetList[i])
print(scoreList[i])
print(labelList[i])
tempArr.append({
"id": i,
"key_word": search_term,
"tweet": tweetList[i],
"score": scoreList[i],
"label": labelList[i]
})
print(tweetList[i])
arr = tempArr
print("here 3" * 100)
resp = jsonify(json.dumps(arr))
resp.headers.add("Access-Control-Allow-Origin", "*")
resp.status_code = 200
return resp
# loop of the base directory
for idx, target_dir in enumerate(dirs):
print("\n===========================")
print("[{idx}/{total}] {target_dir}".format(target_dir=target_dir,
idx=idx + 1,
total=len(dirs)))
machine_type = os.path.split(target_dir)[1]
print("============== MODEL LOAD ==============")
# load model file
model_file = "{model}/model_{machine_type}.hdf5".format(
model=param["model_directory"], machine_type=machine_type)
if not os.path.exists(model_file):
com.logger.error("{} model not found ".format(machine_type))
sys.exit(-1)
model = keras_model.load_model(model_file)
model.summary()
# load anomaly score distribution for determining threshold
score_distr_file_path = "{model}/score_distr_{machine_type}.pkl".format(
model=param["model_directory"], machine_type=machine_type)
shape_hat, loc_hat, scale_hat = joblib.load(score_distr_file_path)
# determine threshold for decision
decision_threshold = scipy.stats.gamma.ppf(
q=param["decision_threshold"],
a=shape_hat,
loc=loc_hat,
scale=scale_hat)
if mode:
def create():
print("u" * 1000)
print(request.data)
print("u" * 1000)
if (request.data == b''):
search_term = "#twitter"
else:
request_data = json.loads(request.data)
search_term = request_data['content']
print(search_term)
print(search_term)
consumer_key = os.environ.get('CONSUMER_KEY')
consumer_secret = os.environ.get('CONSUMER_SECRET')
access_token = os.environ.get('ACCESS_TOKEN')
access_token_secret = os.environ.get('ACCESS_TOKEN_SECRET')
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
tweets = api.search(search_term, count=500)
data = pd.DataFrame(data=[tweet.text for tweet in tweets],
columns=['Tweets'])
head = data.head(10)
listy = []
print("here 1" * 100)
tweetList = [tweets for tweets in data['Tweets']]
print("p" * 1000)
print(tweetList)
print("p" * 1000)
print("here 5" * 100)
scoreList = []
labelList = []
model = load_model("model.h5")
for i in range(len(tweetList)):
scoreList.append(str(round(model(tweetList[i]).polarity, 1)))
print("here 6" * 100)
for i in range(len(tweetList)):
pLabel = 0
print('0')
scr = float(scoreList[i])
print(scr)
if (scr < -1 * 0.3):
pLabel = "Negative"
if (scr >= -1 * 0.3 and scr <= 0.3):
pLabel = "Neutral"
if (scr > 0.3):
pLabel = "Positive"
labelList.append(pLabel)
global arr
tempArr = []
print("here 2" * 100)
for i in range(len(tweetList)):
print('heeee')
print(tweetList[i])
print(scoreList[i])
print(labelList[i])
tempArr.append({
"id": i,
"key_word": search_term,
"tweet": tweetList[i],
"score": scoreList[i],
"label": labelList[i]
})
print(tweetList[i])
arr = tempArr
print("here 3" * 100)
resp = jsonify(json.dumps(arr))
resp.headers.add("Access-Control-Allow-Origin", "*")
resp.status_code = 200
return resp
