Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load your saved model
+model = joblib.load('cybersecurity_model.joblib')
+
+# Replace this list with your actual feature names in the order your model expects
+FEATURE_NAMES = [
+    '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'
+]
+
+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
+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."
+)
+
+if __name__ == "__main__":
+    iface.launch()