raopa/TextClassification

1import streamlit as st2import pandas as pd3from tensorflow.keras.preprocessing.sequence import pad_sequences4from tensorflow.keras.models import load_model5from tensorflow.keras.preprocessing.text import Tokenizer6 7# Load the CNN model8model = load_model('path_to_your_cnn_model')  # Replace with the actual path to your CNN model file9tokenizer = Tokenizer(num_words=10000)  # Assuming the same tokenizer configuration10 11def preprocess_text(text):12    sequences = tokenizer.texts_to_sequences([text])13    padded_sequences = pad_sequences(sequences, maxlen=100)  # Assuming the same max_sequence_length14    return padded_sequences15 16def predict_spam(message):17    preprocessed_message = preprocess_text(message)18    prediction = model.predict(preprocessed_message)[0][0]19    return prediction20 21st.title("SMS Spam Detection App")22 23# User input for message24user_input = st.text_area("Enter your message here:")25 26if st.button("Predict"):27    if user_input:28        prediction = predict_spam(user_input)29        result = "Spam" if prediction > 0.5 else "Not Spam"30        st.success(f"The message is predicted as: {result} (Confidence: {prediction:.2f})")31    else:32        st.warning("Please enter a message for prediction.")33