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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-tiny-finetuned-sms-spam-detection") | |
| model = AutoModelForSequenceClassification.from_pretrained("mrm8488/bert-tiny-finetuned-sms-spam-detection") | |
| def classify_spam(text): | |
| encoded_text = tokenizer(text, truncation=True, padding='max_length', max_length=512, return_tensors='pt') | |
| predictions = model(**encoded_text) | |
| predicted_probabilities = predictions.logits.softmax(dim=1) | |
| predicted_class = "Spam" if predicted_probabilities[0, 1] > 0.5 else "Not Spam" | |
| return predicted_class | |
| def main(): | |
| st.title("SMS Spam Classification App") | |
| st.text("Made by Moneeb Ahmad with Lil Love ❤️ ") | |
| text_input = st.text_area("Enter SMS text for classification:", "") | |
| if st.button("Classify"): | |
| if text_input: | |
| result = classify_spam(text_input) | |
| st.subheader("Predicted Class:") | |
| st.write(result) | |
| else: | |
| st.warning("Please enter some text for classification.") | |
| if __name__ == "__main__": | |
| main() |