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import gradio as gr
import joblib
import pandas as pd
# Load trained model
model = joblib.load("cybersecurity_model.joblib")
# List of features your model expects (update as per your actual features)
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','FQDN_count','subdomain_length','upper',
'lower','numeric','entropy','special','labels','labels_max','labels_average','longest_word',
'sld','len','subdomain'
]
# Inference function
def predict_from_csv(file):
try:
df = pd.read_csv(file)
# Ensure required columns are present
missing_cols = set(FEATURE_NAMES) - set(df.columns)
if missing_cols:
return f"❌ Missing columns in CSV: {', '.join(missing_cols)}"
# Select and predict
X = df[FEATURE_NAMES]
preds = model.predict(X)
# Format output nicely
result = pd.DataFrame({"Prediction": preds})
return result
except Exception as e:
return f"❌ Error reading file: {str(e)}"
# Gradio UI
interface = gr.Interface(
fn=predict_from_csv,
inputs=gr.File(label="📁 Upload CSV File with Network Traffic Features"),
outputs=gr.Dataframe(label="📊 Predictions"),
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."
),
allow_flagging="never"
)
if __name__ == "__main__":
interface.launch()
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