Update app.py

app.py

@@ -9,11 +9,11 @@ model_path = hf_hub_download(
     filename="ids_model.pkl"
 )
 
-# Load model
+# Load the model
 with open(model_path, "rb") as f:
     model = pickle.load(f)
 
-# All NSL-KDD features (41 features)
+# Feature names (41 features of NSL-KDD)
 columns = [
     "duration","protocol_type","service","flag","src_bytes","dst_bytes","land",
     "wrong_fragment","urgent","hot","num_failed_logins","logged_in",
@@ -24,29 +24,30 @@ columns = [
     "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
     "dst_host_same_srv_rate","dst_host_diff_srv_rate",
     "dst_host_same_src_port_rate","dst_host_srv_diff_host_rate",
-    "dst_host_serror_rate","dst_host_srv_serror_rate","dst_host_rerror_rate",
-    "dst_host_srv_rerror_rate"
+    "dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate"
 ]
 
 def predict_intrusion(*inputs):
-    data = pd.DataFrame([inputs], columns=columns)
-    data = data.apply(pd.to_numeric, errors="coerce").fillna(0)
+    df = pd.DataFrame([inputs], columns=columns)
+    df = df.apply(pd.to_numeric, errors='coerce').fillna(0)
+
+    pred = model.predict(df)[0]  # LightGBM outputs probability
 
-    pred = model.predict(data)[0]
     if pred > 0.5:
         return "🔴 ATTACK DETECTED"
     else:
         return "🟢 NORMAL TRAFFIC"
 
-# Build Gradio UI with 41 numeric inputs
+# Dynamic UI (41 numeric inputs)
 inputs_ui = [gr.Number(label=col) for col in columns]
 
 app = gr.Interface(
     fn=predict_intrusion,
     inputs=inputs_ui,
     outputs="text",
-    title="CyberSecurity IDS - NSL KDD",
-    description="Enter feature values to detect intrusion using LightGBM model."
+    title="CyberSecurity Intrusion Detection System",
+    description="Upload network feature values (NSL-KDD dataset) to detect malicious intrusions using LightGBM."
 )
 
 app.launch()