Create app.py

app.py

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+import re
+import streamlit as st
+from transformers import pipeline
+
+# ---------------- CONFIG ----------------
+# Load models
+pipe1 = pipeline("text-classification", model="ElSlay/BERT-Phishing-Email-Model")
+pipe2 = pipeline("text-classification", model="Eason918/malicious-url-detector-v2", use_fast=False)
+pipe3 = pipeline("text-classification", model="r3ddkahili/final-complete-malicious-url-model")
+
+# Label normalization
+def normalize_label(label):
+    return "benign" if label == "LABEL_0" else "malicious"
+
+# Weighted Ensemble Calculation (only pipeline2 and 3)
+def calculate_weighted_prediction(label2, score2, label3, score3):
+    weights = {"Pipeline2": 0.3, "Pipeline3": 0.7}
+    score_dict = {"benign": 0.0, "malicious": 0.0}
+    score_dict[normalize_label(label2)] += weights["Pipeline2"] * score2
+    score_dict[normalize_label(label3)] += weights["Pipeline3"] * score3
+    final_label = max(score_dict, key=score_dict.get)
+    final_score = score_dict[final_label]
+    return final_label, final_score
+
+# Extract URLs
+def extract_urls(text):
+    url_pattern = r'(https?://[^\s]+|www\.[^\s]+)'
+    return re.findall(url_pattern, text)
+
+# ---------------- UI START ----------------
+st.set_page_config(page_title="📩 Email Malicious Detector", layout="wide")
+st.markdown("<h1 style='text-align: center;'>📩 Malicious Email Detection App</h1>", unsafe_allow_html=True)
+
+st.markdown("### ✉️ Enter your email content:")
+email_text = st.text_area("Paste your email content here:", height=200)
+
+if st.button("🚨 Scan Email & Analyze URL"):
+    if not email_text.strip():
+        st.warning("⚠️ Please input some email content.")
+    else:
+        result1 = pipe1(email_text)[0]
+        label1, score1 = result1['label'], result1['score']
+        pred1 = normalize_label(label1)
+
+        if pred1 == "benign":
+            st.markdown("## 🛡️ **Prediction Result:**")
+            st.success(f"✅ BENIGN EMAIL CONTENT (Confidence Score: {score1:.2%})")
+        else:
+            urls = extract_urls(email_text)
+            if not urls:
+                st.warning("⚠️ Email content is malicious, but no URL found for further analysis.")
+            else:
+                url = urls[0]
+                result2 = pipe2(url)[0]
+                result3 = pipe3(url)[0]
+                label2, score2 = result2['label'], result2['score']
+                label3, score3 = result3['label'], result3['score']
+
+                final_label, final_score = calculate_weighted_prediction(label2, score2, label3, score3)
+
+                st.markdown("## 🛡️ **Prediction Result:**")
+                if final_score < 0.6:
+                    st.warning(f"🤔 URLs in email content are UNCERTAIN - Confidence too low ({final_score:.2%}). Please review manually.")
+                elif final_label == "benign":
+                    st.success(f"✅ URLs in email content are BENIGN (Confidence Score: {final_score:.2%})")
+                else:
+                    st.error(f"⚠️ URLs in email content are MALICIOUS (Confidence Score: {final_score:.2%})")