{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b156c93b-7114-4401-8956-0bbdf3f55819",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cheikh/anaconda3/lib/python3.12/site-packages/gradio/blocks.py:1049: UserWarning: Cannot load huggingface. Caught Exception: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/huggingface (Request ID: Root=1-6761d652-5bc4d5a26e798b4156071116;691ae8e4-ee45-43b8-8d96-de80ab472888)\n",
      "\n",
      "Sorry, we can't find the page you are looking for.\n",
      "  warnings.warn(f\"Cannot load {theme}. Caught Exception: {str(e)}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7861\n",
      "* Running on public URL: https://9cd0ff2c927f533d29.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://9cd0ff2c927f533d29.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "import joblib\n",
    "import pefile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gradio as gr\n",
    "import hashlib\n",
    "\n",
    "\n",
    "# Charger le modèle pré-entraîné\n",
    "try:\n",
    "    model = joblib.load('random_forest_model.pkl')\n",
    "except Exception as e:\n",
    "    print(f\"Erreur de chargement du modèle : {e}\")\n",
    "    model = None\n",
    "\n",
    "def calculate_file_hash(file_path):\n",
    "    \"\"\"Calculer le hash SHA-256 du fichier.\"\"\"\n",
    "    sha256_hash = hashlib.sha256()\n",
    "    with open(file_path, \"rb\") as f:\n",
    "        for byte_block in iter(lambda: f.read(4096), b\"\"):\n",
    "            sha256_hash.update(byte_block)\n",
    "    return sha256_hash.hexdigest()\n",
    "\n",
    "def extract_pe_attributes(file_path):\n",
    "    \"\"\"Extraction avancée des attributs du fichier PE.\"\"\"\n",
    "    try:\n",
    "        pe = pefile.PE(file_path)\n",
    "\n",
    "        attributes = {\n",
    "            # Attributs PE standard\n",
    "            'AddressOfEntryPoint': pe.OPTIONAL_HEADER.AddressOfEntryPoint,\n",
    "            'MajorLinkerVersion': pe.OPTIONAL_HEADER.MajorLinkerVersion,\n",
    "            'MajorImageVersion': pe.OPTIONAL_HEADER.MajorImageVersion,\n",
    "            'MajorOperatingSystemVersion': pe.OPTIONAL_HEADER.MajorOperatingSystemVersion,\n",
    "            'DllCharacteristics': pe.OPTIONAL_HEADER.DllCharacteristics,\n",
    "            'SizeOfStackReserve': pe.OPTIONAL_HEADER.SizeOfStackReserve,\n",
    "            'NumberOfSections': pe.FILE_HEADER.NumberOfSections,\n",
    "             'ResourceSize':pe.OPTIONAL_HEADER.DATA_DIRECTORY[2].Size\n",
    "        }\n",
    "        \"\"\"## Ressources\n",
    "        data_directory_entries = pe.OPTIONAL_HEADER.DATA_DIRECTORY\n",
    "        # Parcourir la liste pour trouver l'entrée du répertoire des ressources\n",
    "        for entry in data_directory_entries:\n",
    "            if entry.name == \"IMAGE_DIRECTORY_ENTRY_RESOURCE\":\n",
    "                resource_size = entry.Size\n",
    "                attributes['ResourceSize'] = resource_size\n",
    "                break\n",
    "        else:\n",
    "            attributes['ResourceSize'] = 0\"\"\"\n",
    "            \n",
    "        \n",
    "\n",
    "        return attributes\n",
    "    except Exception as e:\n",
    "        print(f\"Erreur de traitement du fichier {file_path}: {str(e)}\")\n",
    "        return f\"Erreur de traitement du fichier {file_path}: {str(e)}\"\n",
    "\n",
    "def predict_malware(file):\n",
    "    \"\"\"Prédiction de malware avec gestion d'erreurs.\"\"\"\n",
    "    if model is None:\n",
    "        return \"Erreur : Modèle non chargé\"\n",
    "\n",
    "    try:\n",
    "        # Extraire les attributs du fichier\n",
    "        attributes = extract_pe_attributes(file.name)\n",
    "        if \"Erreur\" in attributes:\n",
    "            return attributes\n",
    "\n",
    "        # Convertir en DataFrame\n",
    "        df = pd.DataFrame([attributes])\n",
    "\n",
    "        # Prédiction\n",
    "        prediction = model.predict(df)\n",
    "        proba = model.predict_proba(df)[0]\n",
    "\n",
    "        # Résultat avec probabilité\n",
    "        if prediction[0] == 1:\n",
    "            return f\"🚨 MALWARE (Probabilité: {proba[1] * 100:.2f}%)\"\n",
    "        else:\n",
    "            return f\"✅ Fichier Légitime (Probabilité: {proba[0] * 100:.2f}%)\"\n",
    "    except Exception as e:\n",
    "        return f\"Erreur d'analyse : {str(e)}\"\n",
    "\n",
    "# Interface Gradio\n",
    "demo = gr.Interface(\n",
    "    fn=predict_malware,\n",
    "    inputs=gr.File(file_types=['.exe', '.dll', '.sys'], label=\"Télécharger un fichier exécutable\"),\n",
    "    outputs=\"text\",\n",
    "    title=\"🛡️ Détecteur de Malwares\",\n",
    "    theme='huggingface'  # Thème moderne\n",
    ")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    demo.launch(share=True)  # Rend l'interface accessible publiquement\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f87e13b-157d-4105-865f-daa2919c2711",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c23ce0c3-ac81-438b-a8b8-1264ac99dd12",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}