Update app.py

app.py

@@ -9,10 +9,38 @@ import tensorflow as tf
 model = tf.keras.models.load_model("mlp_malware.keras")
 scaler = joblib.load("scaler.pkl")
 
-N_FEATURES = model.input_shape[1]
-
-feature_names = [f"feature_{i+1}" for i in range(N_FEATURES)]
-
+feature_names = [
+    "filesize",
+    "E_file",
+    "E_text",
+    "E_data",
+    "AddressOfEntryPoint",
+    "NumberOfSections",
+    "SizeOfInitializedData",
+    "SizeOfImage",
+    "SizeOfOptionalHeader",
+    "SizeOfCode",
+    "DirectoryEntryImportSize",
+    "ImageBase",
+    "CheckSum",
+    "Magic",
+    "MinorLinkerVersion",
+    "MajorSubsystemVersion",
+    "e_lfanew",
+    "sus_sections",
+    "PointerToSymbolTable",
+    "SectionsLength",
+    "SizeOfStackReserve",
+    "MajorOperatingSystemVersion",
+    "non_sus_sections",
+    "Characteristics",
+    "NumberOfSymbols",
+    "BaseOfData",
+    "MajorImageVersion",
+    "FH_char5",
+    "FH_char8",
+    "OH_DLLchar5"
+]
 
 # PREDICTION FUNCTION
 def predict_malware(*inputs):
@@ -45,7 +73,7 @@ app = gr.Interface(
     inputs=inputs,
     outputs=outputs,
     title="MLP-based Malware Detection",
-    description="Malware detection using MLP neural network + StandardScaler"
+    description="Malware detection using MLP neural network"
 )
 
 if __name__ == "__main__":