app.py

import joblib
import gradio as gr
from pathlib import Path

BASE_DIR = Path(__file__).resolve().parent
MODEL_PATH = BASE_DIR / "model.pkl"
model = joblib.load(MODEL_PATH)


def predict_payload(payload: str) -> str:
    if not payload or not payload.strip():
        return "Please enter a payload string."

    pred = int(model.predict([payload])[0])
    label = "Buffer Overflow / Attack" if pred == 1 else "Normal"
    return f"Prediction: {pred} ({label})"


demo = gr.Interface(
    fn=predict_payload,
    inputs=gr.Textbox(lines=8, label="Raw Network Payload"),
    outputs=gr.Textbox(label="IDS Classification"),
    title="Cloud-Native IDS Payload Classifier",
    description="Paste a raw payload string. The TF-IDF + XGBoost pipeline predicts Normal (0) or Attack (1).",
)


if __name__ == "__main__":
    demo.launch()