Update app.py

app.py

@@ -9,6 +9,24 @@ model = joblib.load("ensemble_model.pkl")
 with open("features_to_drop.pkl", "rb") as f:
     features_to_drop = pickle.load(f)
 
+# Map of numerical predictions to attack category strings
+label_map = {
+    0: 'Fuzzers',
+    1: 'Fuzzers',
+    2: 'Reconnaissance',
+    3: 'Shellcode',
+    4: 'Analysis',
+    5: 'Backdoor',
+    6: 'Backdoors',
+    7: 'DoS',
+    8: 'Exploits',
+    9: 'Generic',
+    10: 'Reconnaissance',
+    11: 'Shellcode',
+    12: 'Worms',
+    13: 'normal'
+}
+
 # Column names from the raw 49-column dataset (before feature engineering)
 raw_columns = [
     'srcip', 'sport', 'dstip', 'dsport', 'proto', 'state', 'dur', 'sbytes', 'dbytes',
@@ -56,8 +74,12 @@ if st.button("Predict"):
         processed_df = preprocess_input(values)
 
         # Predict using the preprocessed data
-        prediction = model.predict(processed_df)[0]
-        st.success(f"✅ Predicted Attack Category: **{prediction}**")
+        predicted_label = model.predict(processed_df)[0]
+
+        # Map the prediction to string label
+        predicted_category = label_map.get(predicted_label, "Unknown")
+
+        st.success(f"✅ Predicted Attack Category: **{predicted_category}**")
 
     except Exception as e:
         st.error(f"❌ Error processing input: {e}")