Update app.py

app.py

@@ -1,44 +1,46 @@
 import gradio as gr
+import pandas as pd
 import joblib
-import numpy as np
 
-# Load your saved model
-model = joblib.load('cybersecurity_model.joblib')
+# Load your trained model
+model = joblib.load("cybersecurity_model.joblib")
 
-# Replace this list with your actual feature names in the order your model expects
-FEATURE_NAMES = [
+# Exact features the model was trained on
+FEATURES = [
     'A_frequency', 'NS_frequency', 'CNAME_frequency', 'SOA_frequency', 'NULL_frequency',
     'PTR_frequency', 'HINFO_frequency', 'MX_frequency', 'TXT_frequency', 'AAAA_frequency',
     'SRV_frequency', 'OPT_frequency', 'rr_type', 'rr_count', 'rr_name_entropy',
     'rr_name_length', 'distinct_ns', 'distinct_ip', 'unique_country', 'unique_asn',
     'distinct_domains', 'reverse_dns', 'a_records', 'unique_ttl', 'ttl_mean',
-    'ttl_variance', 'timestamp', 'FQDN_count', 'subdomain_length', 'upper',
-    'lower', 'numeric', 'entropy'
+    'ttl_variance', 'FQDN_count', 'subdomain_length', 'upper', 'lower',
+    'numeric', 'entropy', 'special', 'labels', 'labels_max', 'labels_average',
+    'longest_word', 'sld', 'len', 'subdomain'
 ]
 
-def predict(*inputs):
-    # Convert inputs to numpy array, reshape for prediction
-    data = np.array(inputs).reshape(1, -1)
-    
-    # Predict class label
-    prediction = model.predict(data)[0]
-    
-    # Optionally, return class probabilities:
-    # proba = model.predict_proba(data)[0]
-    # return prediction, proba
-    
-    return prediction
-
-# Create Gradio inputs dynamically based on feature names
-inputs = [gr.Number(label=feat) for feat in FEATURE_NAMES]
-
-# Gradio Interface setup
+def predict_from_csv(file):
+    try:
+        df = pd.read_csv(file)
+
+        # Ensure only expected features are used
+        data = df[FEATURES]
+
+        # Make predictions
+        predictions = model.predict(data)
+
+        df['Prediction'] = predictions
+
+        return df[['Prediction']]
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Gradio Interface
 iface = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs="text",
-    title="Cybersecurity Attack Detection",
-    description="Input the feature values and predict whether the network activity is benign or an attack."
+    fn=predict_from_csv,
+    inputs=gr.File(label="📁 Upload CSV File with Network Traffic Features"),
+    outputs="dataframe",
+    title="🚨 Cybersecurity Attack Detector",
+    description="AI-powered model to detect attacks from DNS/network traffic data. Upload a CSV file with preprocessed features to get predictions."
 )
 
 if __name__ == "__main__":