generated from Micky373/readme-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
spam_classifier.py
62 lines (42 loc) · 1.55 KB
/
spam_classifier.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
import streamlit as st # For the UI
import pickle # For loading the model
# For natural language processing related tasks
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
with open('models/vectorizer.h5','rb') as f:
tfidf = pickle.load(f)
with open('models/spam_classifier_model.h5','rb') as f:
model = pickle.load(f)
def transform_text(message):
# Changing each word into lower case
message = message.lower()
# Creating a token list
tokens = []
message = nltk.word_tokenize(message)
# Creating the stemmer object
ps = PorterStemmer()
# Itterating through all the message tokens
for word in message:
# Removing all the non-alphanumeric and stop words
if word.isalnum() and word not in stopwords.words('english'):
# Stemming all the words(example: changing 'words' --> 'word')
tokens.append(ps.stem(word))
# Returning a string of cleaned tokens
return ' '.join(tokens)
st.title('Email/SMS Messages Spam Classifier')
input_message = st.text_area('Enter the message here:')
if st.button('Check if it is spam'):
# Preprocessing
transformed_message = transform_text(input_message)
# Vectorizing
vectorized_input = tfidf.transform([transformed_message])
# Predicting
result = model.predict(vectorized_input)[0]
# Displaying the result
if result == 1:
st.header('Spam')
else:
st.header('Not Spam')