Upload app.py

app.py

@@ -0,0 +1,57 @@
+import joblib
+import pandas as pd
+import gradio as gr
+import numpy as np
+
+# ======================
+# LOAD MODEL
+# ======================
+artifact = joblib.load("stacking_model.pkl")
+
+base_models = artifact["base_models"]      # list of (name, model)
+meta_model = artifact["meta_model"]
+feature_names = artifact["features"]
+
+# ======================
+# PREDICTION FUNCTION
+# ======================
+def predict_malware(*inputs):
+    # Input → DataFrame
+    X = pd.DataFrame([inputs], columns=feature_names)
+
+    # Level-1 predictions
+    meta_inputs = []
+
+    for name, model in base_models:
+        prob = model.predict_proba(X)[:, 1]
+        meta_inputs.append(prob)
+
+    meta_X = np.column_stack(meta_inputs)
+
+    # Meta prediction
+    pred = meta_model.predict(meta_X)[0]
+    prob = meta_model.predict_proba(meta_X)[0, 1]
+
+    label = "Malware" if pred == 1 else "Benign"
+    return label, float(prob)
+
+# ======================
+# UI
+# ======================
+inputs = [gr.Number(label=feat) for feat in feature_names]
+
+outputs = [
+    gr.Textbox(label="Prediction"),
+    gr.Number(label="Malware Probability")
+]
+
+app = gr.Interface(
+    fn=predict_malware,
+    inputs=inputs,
+    outputs=outputs,
+    title="Stacking-based Malware Detection",
+    description="ExtraTrees + RandomForest + LightGBM + LogisticRegression → XGBoost"
+)
+
+if __name__ == "__main__":
+    app.launch()