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Create app.py
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app.py
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import pickle
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import nltk
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import re
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from nltk.corpus import stopwords
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from nltk.stem import PorterStemmer
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from nltk.stem import WordNetLemmatizer
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import streamlit as st
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stemmer = PorterStemmer()
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lemmatizer = WordNetLemmatizer()
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cv = pickle.load(open('pickle_files/
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count_vectorizer.pkl', 'rb'))
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model = pickle.load(open('pickle_files/spam_model.pkl', 'rb'))
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def spam_or_ham(message):
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message = re.sub('[^a-zA-Z]', ' ', message)
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message = message.lower()
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message = message.split()
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message = [lemmatizer.lemmatize(word) for word in message if word not in set(stopwords.words('english'))]
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message = ' '.join(message)
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X = cv.transform([message]).toarray()
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prediction = model.predict(X)
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if prediction:
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return 'Not Spam'
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else:
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return 'Spam'
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st.title("Spam Classifier")
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message = st.text_input("Type a Message")
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if st.button("Check Spam or Ham"):
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if message:
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spam_check = spam_or_ham(message)
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st.write(spam_check)
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else:
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st.write('Empty Message')
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