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from flask import Flask
from keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import re
from tensorflow.keras.preprocessing.text import one_hot as oh
import numpy as np
import tensorflow as tf
app = Flask(__name__)
# Load the saved model
new_model = load_model('m.h5')
stopwords_lst = [ 'i',
'me',
'my',
'myself',
'we',
'our',
'ours',
'ourselves',
'you',
"you're",
"you've",
"you'll",
"you'd",
'your',
'yours',
'yourself',
'yourselves',
'he',
'him',
'his',
'himself',
'she',
"she's",
'her',
'hers',
'herself',
'it',
"it's",
'its',
'itself',
'they',
'them',
'their',
'theirs',
'themselves',
'what',
'which',
'who',
'whom',
'this',
'that',
"that'll",
'these',
'those',
'am',
'is',
'are',
'was',
'were',
'be',
'been',
'being',
'have',
'has',
'had',
'having',
'do',
'does',
'did',
'doing',
'a',
'an',
'the',
'and',
'but',
'if',
'or',
'because',
'as',
'until',
'while',
'of',
'at',
'by',
'for',
'with',
'about',
'against',
'between',
'into',
'through',
'during',
'before',
'after',
'above',
'below',
'to',
'from',
'up',
'down',
'in',
'out',
'on',
'off',
'over',
'under',
'again',
'further',
'then',
'once',
'here',
'there',
'when',
'where',
'why',
'how',
'all',
'any',
'both',
'each',
'few',
'more',
'most',
'other',
'some',
'such',
'no',
'nor',
'not',
'only',
'own',
'same',
'so',
'than',
'too',
'very',
's',
't',
'can',
'will',
'just',
'don',
"don't",
'should',
"should've",
'now',
'd',
'll',
'm',
'o',
're',
've',
'y',
'ain',
'aren',
"aren't",
'couldn',
"couldn't",
'didn',
"didn't",
'doesn',
"doesn't",
'hadn',
"hadn't",
'hasn',
"hasn't",
'haven',
"haven't",
'isn',
"isn't",
'ma',
'mightn',
"mightn't",
'mustn',
"mustn't",
'needn',
"needn't",
'shan',
"shan't",
'shouldn',
"shouldn't",
'wasn',
"wasn't",
'weren',
"weren't",
'won',
"won't",
'wouldn',
"wouldn't"]
import pickle
import random
random.seed(42)
with open ('oh.pkl','rb') as f:
oh = pickle.load(f)
with open ('ps.pkl','rb') as f:
ps = pickle.load(f)
def predict_emotion2(stri):
review = re.sub('[^a-zA-Z]', ' ', stri)
review = review.lower()
review = review.split()
# print(ps.stem(word))
review = [ps.stem(word) for word in review if not word in stopwords_lst]
review = [int(tf.strings.to_hash_bucket_fast(word, 1000)) for word in review]
onehot_repr = [review]
print(onehot_repr)
embed = pad_sequences(onehot_repr,padding='pre',maxlen=35)
# predicti = new_model.predict(embed)
# return np.argmax(predicti)
strs = ["I am surprised of my work", "I am happy of my work", "I am sad of my work", "I love my country and I am happy"]
for s in strs:
predict_emotion2(s)
# print("em: ", predict_emotion2("I am surprised of my work"))
# print("em: ", predict_emotion2("I am happy of my work"))
# print("em: ", predict_emotion2("I am sad of my work"))
# print("em: ", predict_emotion2("I love my country and I am happy"))
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