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app.py

@@ -1,110 +1,81 @@
-import joblib
-import pandas as pd
-import gradio as gr
-import numpy as np
-import re
-
-# ======================
-# 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"]
-
-# ======================
-# CLEAN FUNCTION (same as training)
-# ======================
-def clean_numeric(val):
-    if pd.isna(val):
-        return None
-
-    val = str(val).strip()
-    val = re.sub(r'\s+', '', val)
-
-    # scientific notation
-    if re.match(r'^-?\d+(\.\d+)?[eE][+-]?\d+$', val):
-        return float(val)
-
-    # remove thousand separators
-    if val.count('.') > 1:
-        val = val.replace('.', '')
-
-    # comma decimal -> dot
-    if ',' in val and '.' not in val:
-        val = val.replace(',', '.')
-
-    try:
-        return float(val)
-    except ValueError:
-        return None
-
-
-# ======================
-# PREDICTION FUNCTION
-# ======================
-def predict_malware_csv(file):
-    df = pd.read_csv(file.name)
-
-    # Check missing features
-    missing = set(feature_names) - set(df.columns)
-    if missing:
-        return f"❌ Missing features: {list(missing)}", None
-
-    # Keep only needed features
-    X = df[feature_names].copy()
-
-    # 🔥 CLEAN NUMERIC FEATURES
-    for col in feature_names:
-        X[col] = X[col].apply(clean_numeric)
-
-    # Optional: fill NaN if needed
-    # X = X.fillna(0)
-
-    # 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
-    preds = meta_model.predict(meta_X)
-    probs = meta_model.predict_proba(meta_X)[:, 1]
-
-    # Append results
-    result_df = df.copy()
-    result_df["Prediction"] = np.where(preds == 1, "Malware", "Benign")
-    result_df["Malware_Probability"] = probs
-
-    return "✅ Prediction completed", result_df
-
-
-# ======================
-# UI
-# ======================
-inputs = gr.File(
-    label="Upload CSV file (features only)",
-    file_types=[".csv"]
-)
-
-outputs = [
-    gr.Textbox(label="Status"),
-    gr.Dataframe(label="Prediction Results")
-]
-
-app = gr.Interface(
-    fn=predict_malware_csv,
-    inputs=inputs,
-    outputs=outputs,
-    title="Stacking-based Malware Detection",
-    description=(
-        "Upload a CSV file.\n\n"
-
-    )
-)
-
-if __name__ == "__main__":
-    app.launch()
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+
+# =========================
+# 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"]
+
+N_FEATURES = len(FEATURE_NAMES)
+
+# =========================
+# PREDICTION FUNCTION
+# =========================
+def predict_csv(file):
+    df = pd.read_csv(file)
+
+    # Chuẩn hóa tên cột để tránh BOM/khoảng trắng
+    df.columns = df.columns.str.strip()
+    df.columns = df.columns.str.replace("\ufeff", "")
+
+    # Drop label columns if exist
+    df = df.drop(columns=["Label", "label", "class", "Class"], errors="ignore")
+
+    # Check missing features
+    missing_features = [f for f in FEATURE_NAMES if f not in df.columns]
+    if missing_features:
+        # Trả về DataFrame báo lỗi thay vì string
+        return pd.DataFrame({
+            "error": [f"Missing required features: {missing_features}"]
+        })
+
+    # Keep only required features & correct order
+    X = df[FEATURE_NAMES].astype(float)
+
+    # =========================
+    # LEVEL-1 (BASE MODELS)
+    # =========================
+    meta_inputs = []
+    for name, model in base_models:
+        probs = model.predict_proba(X)[:, 1]
+        meta_inputs.append(probs)
+
+    meta_X = np.column_stack(meta_inputs)
+
+    # =========================
+    # META MODEL
+    # =========================
+    final_probs = meta_model.predict_proba(meta_X)[:, 1]
+    final_preds = (final_probs > 0.5).astype(int)
+
+    # =========================
+    # BUILD OUTPUT
+    # =========================
+    result = df.copy()
+    result.insert(0, "row_id", range(1, len(df) + 1))
+    result["probability_malware"] = final_probs
+    result["prediction"] = final_preds
+    result["prediction_label"] = result["prediction"].map(
+        {1: "malware", 0: "benign"}
+    )
+
+    return result
+
+# =========================
+# GRADIO INTERFACE
+# =========================
+demo = gr.Interface(
+    fn=predict_csv,
+    inputs=gr.File(label="Upload CSV file"),
+    outputs=gr.Dataframe(label="Prediction Result"),
+    title="Stacking-based Malware Detection",
+    description="ExtraTrees + RandomForest + LightGBM + LogisticRegression → XGBoost"
+)
+
+demo.launch()

requirements.txt

@@ -1,9 +1,7 @@
-numpy
-pandas
-scikit-learn
-xgboost
-lightgbm
-gradio
-joblib
-huggingface_hub
-tensorflow
+numpy
+pandas
+scikit-learn
+xgboost
+lightgbm
+gradio
+joblib