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app.py

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+import streamlit as st
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load the trained LSTM model
+model = load_model("spam_classifier_lstm.h5")
+
+# Load the tokenizer
+with open("tokenizer.pkl", "rb") as file:
+    tokenizer = pickle.load(file)
+
+def classify_sms(text):
+    """Predict if an SMS is spam or ham."""
+    seq = tokenizer.texts_to_sequences([text])
+    padded_seq = pad_sequences(seq, maxlen=50)
+    prediction = (model.predict(padded_seq) > 0.5).astype("int32")
+    return "Spam" if prediction[0][0] == 1 else "Ham"
+
+# Streamlit UI
+st.title("📩 SMS Spam Detector")
+st.write("Enter an SMS message below to check if it's Spam or Ham.")
+
+# Text input
+sms_text = st.text_area("Enter SMS Message:", "")
+
+if st.button("Classify SMS"):
+    if sms_text.strip():
+        result = classify_sms(sms_text)
+        if result == "Spam":
+            st.error("🚨 This message is Spam!")
+        else:
+            st.success("✅ This message is Ham (Not Spam).")
+    else:
+        st.warning("Please enter a message to classify.")
+
+# Run using: streamlit run filename.py

requirements.txt

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+streamlit
+tensorflow==2.9.1

spam_classifier_lstm.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a174bc03428243455e5d1392e522403e75bee6351d1fd8052b49ce4ce94e6982
+size 8464136

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c14b2e6bb63cef37ba1fd5c12c5581b86bc13f04d81fe74ef4d20ec8f8b72680
+size 333172