create_model.py

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"))