{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Démo Iris: entraînement et prédiction\n",
    "Ce notebook entraîne un modèle Iris et le sauvegarde dans `shared/models/iris_model.pkl` via le script Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.13.3' requires the ipykernel package.\n",
      "\u001b[1;31m<a href='command:jupyter.createPythonEnvAndSelectController'>Create a Python Environment</a> with the required packages.\n",
      "\u001b[1;31mOr install 'ipykernel' using the command: 'c:/Users/brin.amonkonan/AppData/Local/Programs/Python/Python313/python.exe -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "# Exécute le script d'entraînement (Iris)\n",
    "import sys, subprocess\n",
    "subprocess.run([sys.executable, '/work/app/train_iris.py'], check=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.13.3' requires the ipykernel package.\n",
      "\u001b[1;31m<a href='command:jupyter.createPythonEnvAndSelectController'>Create a Python Environment</a> with the required packages.\n",
      "\u001b[1;31mOr install 'ipykernel' using the command: 'c:/Users/brin.amonkonan/AppData/Local/Programs/Python/Python313/python.exe -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "# Vérifie que le modèle est sauvegardé et effectue une prédiction\n",
    "from pathlib import Path\n",
    "import joblib\n",
    "import numpy as np\n",
    "model_path = Path('/work/models/iris_model.pkl')\n",
    "assert model_path.exists(), 'Le modèle n’a pas été sauvegardé.'\n",
    "model = joblib.load(model_path)\n",
    "X = np.array([5.1, 3.5, 1.4, 0.2]).reshape(1, -1)\n",
    "pred = model.predict(X)\n",
    "pred"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}