Update app.py

app.py

@@ -61,52 +61,57 @@ def preprocess_input(df, features_to_drop, category_encodings):
     return df
 
 ##############################################
-# Streamlit User Interface - Manual Row Input
+# Streamlit Interface - TextArea Input
 ##############################################
 
 st.set_page_config(page_title="Intrusion Detection System - Test", layout="wide")
-st.title("Intrusion Detection System (IDS)")
+st.title("Intrusion Detection System (IDS) - Single Row Input")
 
-st.markdown(
-    """
-    Enter a single row of network traffic data below to test for possible intrusions.
-    """
-)
+st.markdown("""
+Paste a **single row of comma-separated values** below. Include only the relevant features required for prediction.
 
-with st.form("manual_input_form"):
-    Stime = st.number_input("Stime (Start Time)", min_value=0.0, step=1.0)
-    Ltime = st.number_input("Ltime (End Time)", min_value=0.0, step=1.0)
-    sbytes = st.number_input("sbytes (Source Bytes)", min_value=0, step=1)
-    dbytes = st.number_input("dbytes (Destination Bytes)", min_value=0, step=1)
-    Spkts = st.number_input("Spkts (Source Packets)", min_value=0, step=1)
-    Dpkts = st.number_input("Dpkts (Destination Packets)", min_value=0, step=1)
-    # Add more input fields as needed
+**Expected columns (in order):**
+`Stime, Ltime, sbytes, dbytes, Spkts, Dpkts`
+
+You may include additional columns (like `label`) if desired, but they will be ignored.
+""")
 
+with st.form("manual_input_form"):
+    text_input = st.text_area(
+        "Paste a single row of data (comma-separated values):",
+        placeholder="e.g. 1425579984.0,1425579990.0,275,423,10,8"
+    )
     submitted = st.form_submit_button("Run IDS Prediction")
 
 if submitted:
-    # Construct a DataFrame from input
-    user_input = pd.DataFrame([{
-        "Stime": Stime,
-        "Ltime": Ltime,
-        "sbytes": sbytes,
-        "dbytes": dbytes,
-        "Spkts": Spkts,
-        "Dpkts": Dpkts
-    }])
-
-    # Load model artifacts
-    features_to_drop, category_encodings, model = load_model_artifacts()
+    try:
+        # Split and parse the user input into a list of values
+        input_values = [x.strip() for x in text_input.split(',') if x.strip() != '']
+        
+        # Define the column names expected by the model
+        expected_columns = ["Stime", "Ltime", "sbytes", "dbytes", "Spkts", "Dpkts"]
+        
+        if len(input_values) < len(expected_columns):
+            st.error(f"Not enough values provided. Expected at least {len(expected_columns)} values.")
+        else:
+            # Take only the first N values needed
+            input_data = dict(zip(expected_columns, input_values[:len(expected_columns)]))
+            user_input = pd.DataFrame([input_data])
+            
+            # Load model artifacts
+            features_to_drop, category_encodings, model = load_model_artifacts()
 
-    # Preprocess and predict
-    processed_input = preprocess_input(user_input, features_to_drop, category_encodings)
+            # Preprocess and predict
+            processed_input = preprocess_input(user_input, features_to_drop, category_encodings)
 
-    if processed_input is not None:
-        prediction = model.predict(processed_input)[0]
-        st.success(f"Prediction: {prediction}")
-        st.markdown("""
-            - **13** → Normal Traffic  
-            - **Other values** → Intrusion Category (refer to model documentation for exact mappings)
-        """)
-    else:
-        st.error("Preprocessing failed. Please check your inputs.")
+            if processed_input is not None:
+                prediction = model.predict(processed_input)[0]
+                st.success(f"Prediction: {prediction}")
+                st.markdown("""
+                    - **13** → Normal Traffic  
+                    - **Other values** → Intrusion Category (refer to model documentation for exact mappings)
+                """)
+            else:
+                st.error("Preprocessing failed. Please check your input values.")
+    except Exception as e:
+        st.error(f"An error occurred while processing your input: {e}")