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example_usage.py

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+import joblib
+import re
+import string
+
+# 🧹 Reuse the same clean_text function
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r'\d+', '', text)  # remove numbers
+    text = text.translate(str.maketrans('', '', string.punctuation))  # remove punctuation
+    text = text.strip()
+    return text
+
+# 💾 Load the saved model and vectorizer
+model = joblib.load("spam_detection_model.pkl")
+vectorizer = joblib.load("spam_detection_vectorizer.pkl")
+
+# 💬 Function to predict a new message
+def predict_message(msg):
+    msg_clean = clean_text(msg)
+    msg_vec = vectorizer.transform([msg_clean])
+    pred = model.predict(msg_vec)[0]
+    return "🚨 Spam" if pred == 1 else "✅ Not Spam"
+
+# 🧪 Test with some examples
+print(predict_message("Congratulations! You have won a car"))
+print(predict_message("Hey, click here to claim your reward"))
+print(predict_message("Exclusive offer! Click here to claim your reward now"))

spam_detection_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a5e8a2b1166412aa0127b7617c5980f9851989cb51dbd7b2077ca300f39c095
+size 234551

spam_detection_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c9f45dcdab87aa12d74f70d68d094f0fc28cfcb5bd8179789f61bca7e31762fa
+size 149879