def recognitions():
if request.method == 'POST':
data = request.get_json()
x = data["base64Image"].split(',')
base64_decoded = base64.b64decode(x[1])
image = Image.open(io.BytesIO(base64_decoded))
image_np = np.array(image)
t = time.time()
result = get_emotion(image_np)
print(result)
print(time.time() - t)
return json.dumps(result)
def post(self, id):
args = self.reqparse.parse_args()
text = args["text"]
# getting sentiment analysis from google nlp api
annotations = get_sentiment(text)
sentiment = annotations.document_sentiment.score
# getting emotion from deepaffects text api
emotion = list(json.loads(get_emotion(text).text)["response"].keys())[0]
ketchup = CheckIn(id, text, sentiment, emotion)
self.add_checkin_to_db(ketchup)
most_common, average, slope, r2 = self.get_data(id)
return jsonify({"emotion": emotion, "sentiment": sentiment, "most_freq_emotion": most_common, "average_sentiment": average, "slope": slope, "r2": r2})
def predict():
label = get_emotion()
return redirect('/musicplayer?mood=' + label)
import pandas as pd
import re #清楚数字标点的标准库
from textblob import TextBlob
import emotion
from nltk.corpus import stopwords # 下载之后 载入字典
from nltk.stem.porter import PorterStemmer # stem：词根 PorterStemmer： 词根函数库
positive, negative = emotion.get_emotion()
positive = tuple(positive)
negative = tuple(negative)
table = "microwave"
# dataset = pd.read_csv('data/hair_dryer.tsv', delimiter = '\t', quoting = 3, encoding='utf-8')
# dataset = pd.read_csv('data/pacifier.tsv', delimiter = '\t', quoting = 3, encoding='utf-8')
dataset = pd.read_csv('data/microwave.tsv', delimiter = '\t', quoting = 3, encoding='utf-8')
def get_scores():
corpus = [] # 空list
lens = len(dataset)
listc = []
mp = dict()
k = 0
sum = 0
scorce = []
sum_count = 0
for i in range(0, lens):
def get_emotion_of_text():
if request.method == 'POST':
text = request.json.get('text')
return get_emotion(text)