sellestas/ScamSlayerApp

1import streamlit as st2import torch3from transformers import BertTokenizer, BertForSequenceClassification4 5# ✅ Ensure set_page_config is the first Streamlit command6st.set_page_config(page_title="Scam Slayer", layout="centered")7 8# Load model from Hugging Face9MODEL_NAME = "sellestas/scam_slayer_model"10 11try:12    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")13    model = BertForSequenceClassification.from_pretrained(MODEL_NAME)14    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")15    model.to(device)16    model.eval()17    st.success("✅ Scam Slayer Model Loaded Successfully!")18except Exception as e:19    st.error(f"❌ Error loading model: {e}")20 21# Function to classify email22def classify_email(text):23    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)24    inputs = {k: v.to(device) for k, v in inputs.items()}25    with torch.no_grad():26        outputs = model(**inputs)27        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)28        confidence, prediction = torch.max(probabilities, dim=-1)29 30    label_map = {0: "Non-Malicious ✅", 1: "Malicious 🚨"}31    return label_map[prediction.item()], confidence.item() * 10032 33# UI Layout34st.image("logo.png", width=150)35st.title("🛡️ Scam Slayer - AI Email Threat Detector")36st.markdown("### 🔍 Detect phishing and malicious emails instantly!")37 38# Sidebar About Button39with st.sidebar:40    if st.button("ℹ️ About Scam Slayer"):41        st.markdown("""42        ## 📌 About Scam Slayer  43        **AI-powered cybersecurity tool** to detect phishing threats.  44 45        ✅ **Purpose**: Identify and stop phishing attacks.  46        ✅ **Model**: Fine-tuned BERT-based classifier.  47        ✅ **Developed for**: **SANS AI Cybersecurity Hackathon 2025**.  48        ✅ **Features**:  49        - Detects **Malicious & Non-Malicious** emails  50        - Uses **NLP** for content analysis  51        - Provides a **confidence score** (1-100%)  52 53        **Version**: 1.0.0  54        """)55 56# Email Input57email_text = st.text_area("✉️ Paste the email content below:", height=200)58 59# Detect Scam Button60if st.button("🚀 Detect Scam", help="Click to analyze the email content"):61    if email_text.strip():62        category, confidence = classify_email(email_text)63        st.success(f"**🔹 Result: {category} ({confidence:.2f}% Confidence)**")64        st.markdown("✅ **Stay vigilant against scams!** 🚀")65    else:66        st.warning("⚠️ Please enter email content to analyze!")67