Create app.py

app.py

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+import streamlit as st
+import joblib
+from gensim.models import Word2Vec
+from gensim.utils import simple_preprocess
+import numpy as np
+import pickle
+
+import pickle
+
+# Open the file in binary read mode
+with open("Emailclassifier_model.pkl", "rb") as file:
+    classifier_model = pickle.load(file)
+
+wordvect_model = Word2Vec.load("word2vec_model.model")
+
+def preprocess_sentence(sentence):
+    return simple_preprocess(sentence)
+
+def vectorize_sentence(tokens, wordvect_model):
+    tokens = [token for token in tokens if token in wordvect_model.wv]
+    print('Tokens are ----------------',tokens)
+    if not tokens:
+        return np.zeros(wordvect_model.vector_size)
+    print('avaergae', np.mean([wordvect_model.wv[token] for token in tokens], axis=0))
+    return np.mean([wordvect_model.wv[token] for token in tokens], axis=0)
+
+def predict_sentence(sentence, wordvect_model, classifier):
+    tokens = preprocess_sentence(sentence)
+    vector = vectorize_sentence(tokens, wordvect_model)
+    prediction = classifier.predict([vector])
+    return "spam" if prediction == 1 else "ham"
+
+st.title("Email Spam Classifier")
+st.write("Enter the email content below:")
+
+user_input = st.text_area("Email Content")
+
+if st.button("Classify"):
+    result = predict_sentence(user_input, wordvect_model, classifier_model)
+    st.write(f"The email is classified as: {result}")
+