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app.py
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app.py
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# Flask utils
from flask import Flask, request, render_template
import numpy as np
import pickle
from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import re
import nltk
nltk.download('wordnet')
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
app = Flask(__name__)
model = load_model('model_BiLSTM.h5')
# loading
with open('tokenizer.pickle', 'rb') as handle:
word_tokenizer = pickle.load(handle)
def model_predict(text):
lemmatizer = WordNetLemmatizer()
#removal of url
text = re.sub(r'https?://\S+|www\.\S+|http?://\S+',' ',text[0])
#decontraction
text = re.sub(r"won\'t", " will not", text)
text = re.sub(r"won\'t've", " will not have", text)
text = re.sub(r"can\'t", " can not", text)
text = re.sub(r"don\'t", " do not", text)
text = re.sub(r"can\'t've", " can not have", text)
text = re.sub(r"ma\'am", " madam", text)
text = re.sub(r"let\'s", " let us", text)
text = re.sub(r"ain\'t", " am not", text)
text = re.sub(r"shan\'t", " shall not", text)
text = re.sub(r"sha\n't", " shall not", text)
text = re.sub(r"o\'clock", " of the clock", text)
text = re.sub(r"y\'all", " you all", text)
text = re.sub(r"n\'t", " not", text)
text = re.sub(r"n\'t've", " not have", text)
text = re.sub(r"\'re", " are", text)
text = re.sub(r"\'s", " is", text)
text = re.sub(r"\'d", " would", text)
text = re.sub(r"\'d've", " would have", text)
text = re.sub(r"\'ll", " will", text)
text = re.sub(r"\'ll've", " will have", text)
text = re.sub(r"\'t", " not", text)
text = re.sub(r"\'ve", " have", text)
text = re.sub(r"\'m", " am", text)
text = re.sub(r"\'re", " are", text)
#removal of html tags
text = re.sub(r'<.*?>',' ',text)
# Match all digits in the string and replace them by empty string
text = re.sub(r'[0-9]', '', text)
text = re.sub("["
u"\U0001F600-\U0001F64F" # removal of emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+",' ',text)
# filtering out miscellaneous text.
text = re.sub('[^a-zA-Z]',' ',text)
text = re.sub(r"\([^()]*\)", "", text)
# remove mentions
text = re.sub('@\S+', '', text)
# remove punctuations
text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), '', text)
# Lowering all the words in text
text = text.lower()
text = text.split()
text = [lemmatizer.lemmatize(words) for words in text if words not in stopwords.words('english')]
# Removal of words with length<2
text = [i for i in text if len(i)>2]
text = ' '.join(text)
common_words = ['via','like','build','get','would','one','two','feel',
'lol','fuck','take','way','may','first','latest','want',
'make','back','see','know','let','look','come','got',
'still','say','think','great','pleas','amp']
text = [' '.join(i for i in text.split() if i not in common_words)]
text = word_tokenizer.texts_to_sequences(text)
# 24 is length of longest train sentence
text = pad_sequences(text,23,padding='post')
preds = model.predict(text)
print(preds)
pred = (preds[0][0] > 0.42).astype(np.int)
return pred
@app.route('/', methods=['Get'])
def index():
#home page
return render_template('home.html')
@app.route('/predict',methods=['POST','GET'])
def predict():
if request.method == 'POST':
tweet = request.form['tweet']
data = [tweet]
my_pred = model_predict(data)
return render_template('result.html',prediction = my_pred)
@app.route('/home', methods=['Get'])
def home():
#home page
return render_template('home.html')
if __name__ == '__main__':
app.run(debug=False)