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spam.py

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+import streamlit as st 
+import pickle 
+import numpy as np
+import pandas as pd 
+import nltk
+import re
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+from nltk.stem import WordNetLemmatizer
+import sklearn
+from sklearn import feature_extraction
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+model = pickle.load(open('C:\Users\HP\Documents\Model_deployment\spam_model.sav','rb'))
+
+st.title('Spam classifier')
+input = st.text_area('Enter sms here')
+ps = PorterStemmer()
+review = re.sub('[^a-zA-Z]', ' ', input) # to remove punctuation
+review = review.lower()
+review = review.split()
+review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
+review = ' '.join(review)
+
+tf = TfidfVectorizer()
+X = tf.fit_transform(review).toarray()
+
+
+
+if input:
+    output = model.predict(X)
+    st.json(output)