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| import streamlit as st | |
| st.set_page_config( | |
| page_title="NLP WEB APP" | |
| ) | |
| st.title("SMS SPAM CLASSIFIER") | |
| st.sidebar.success("Select a page above") | |
| import pickle | |
| import string | |
| from nltk.corpus import stopwords | |
| import nltk | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| from nltk.stem.porter import PorterStemmer | |
| ps = PorterStemmer() | |
| def transform_text(text): | |
| # Lowering | |
| text = text.lower() | |
| #Tokenizing | |
| text = nltk.word_tokenize(text) | |
| #Removing special characters | |
| new_text=[] | |
| for word in text: | |
| if word.isalnum(): | |
| new_text.append(word) | |
| text = new_text[:] | |
| new_text.clear() | |
| #Removing stopwords and punctuation | |
| for word in text: | |
| if word not in stopwords.words('english') and word not in string.punctuation: | |
| new_text.append(word) | |
| text = new_text[:] | |
| new_text.clear() | |
| # Stemming | |
| for word in text : | |
| new_text.append(ps.stem(word)) | |
| return " ".join(new_text) | |
| tfidf = pickle.load(open('sms-spam-models/vectorizer.pkl','rb')) | |
| model = pickle.load(open('sms-spam-models/model.pkl','rb')) | |
| sms = st.text_input("ENTER THE MESSAGE") | |
| if st.button("PREDICT"): | |
| # PREPROCESS | |
| transformed_sms = transform_text(sms) | |
| # VECTORIZE | |
| vector_input = tfidf.transform([transformed_sms]) | |
| # PREDICTION | |
| result = model.predict(vector_input)[0] | |
| # DISPLAY | |
| if result==1: | |
| st.header("SPAM") | |
| else: | |
| st.header("NOT SPAM") |