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from streamlit_extras.let_it_rain import rain
import streamlit as st
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import sklearn
ps = PorterStemmer()
nltk.download('stopwords')
def example():
rain(
emoji="❌",
font_size=64,
falling_speed=1.5,
animation_length="10",
)
def transform_Text(Text):
Text = Text.lower()
Text = nltk.word_tokenize(Text , preserve_line=True)
y = []
for i in Text:
if i.isalnum():
y.append(i)
Text = y[:]
y.clear()
for i in Text:
if i not in stopwords.words('english') and i not in string.punctuation:
y.append(i)
Text = y[:]
y.clear()
for i in Text:
y.append(ps.stem(i))
return " ".join(y)
tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))
st.title("SMS SPAM CLASSIFIER/CHECKER")
st.text("")
st.text("")
input_sms = st.text_area("Enter the message...or Copy and Paste the message to detect!! ",)
st.text("")
st.write(f'You wrote {len(input_sms)} characters.')
st.text("")
if st.button("Let's Check"):
transformed_sms = transform_Text(input_sms)
vector_input = tfidf.transform([transformed_sms])
result = model.predict(vector_input)[0]
if result == 1:
st.warning("OH..NO! IT'S A SPAM !! BEAWARE!")
example()
else:
st.success("RELAX!! IT'S NOT A SPAM !")
st.balloons()
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