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Procfile

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+web: sh setup.sh && streamlit run app.py

README.md

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+# email-spam-classifier-new
+End to end code for the email spam classifier project

app.py

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+import streamlit as st
+import pickle
+import string
+import pickle
+from nltk.corpus import stopwords
+import nltk
+from nltk.stem.porter import PorterStemmer
+nltk.download('stopwords')   # Downloading stopwords data
+nltk.download('punkt')       # Downloading tokenizer data
+
+ps = PorterStemmer()
+
+
+def transform_text(text):
+    text = text.lower()
+    text = nltk.word_tokenize(text)
+
+    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("Email/SMS Spam Classifier")
+
+input_sms = st.text_area("Enter the message")
+
+if st.button('Predict'):
+
+    # 1. preprocess
+    transformed_sms = transform_text(input_sms)
+    # 2. vectorize
+    vector_input = tfidf.transform([transformed_sms])
+    # 3. predict
+    result = model.predict(vector_input)[0]
+    # 4. Display
+    if result == 1:
+        st.header("Spam")
+    else:
+        st.header("Not Spam")

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:063393928e57a326b661f9db1226438e481dc361964048c57a7cc320b35fe083
+size 96602

nltk.txt

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+stopwords
+punkt

requirements.txt

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+streamlit
+nltk
+scikit-sklearn
+pickle
+string

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc65b94b327d6c337ab82d6350660ecce36d02f92bbc08da29ec060ce2485f50
+size 160266