{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcb69c54-ca55-4865-8b5f-51513e083cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flask import Flask,request,jsonify\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "model = pickle.load(open('random_forest_model_2.pkl','rb'))\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route('/',methods=['POST'])\n",
    "def index():\n",
    "    \n",
    "    age_14          = request.form.get('age_14')\n",
    "    kesehatan_msk   = request.form.get('kesehatan_msk')\n",
    "    jumkamar_msk    = request.form.get('jumkamar_msk')\n",
    "    jumorang_msk    = request.form.get('jumorang_msk')\n",
    "    jumkakak_lk_msk = request.form.get('jumkakak_lk_msk')\n",
    "    jumkakak_pr_msk = request.form.get('jumkakak_pr_msk')\n",
    "    jumadik_lk_msk  = request.form.get('jumadik_lk_msk')\n",
    "    jumadik_pr_msk  = request.form.get('jumadik_pr_msk')\n",
    "    asuransi_kes    = request.form.get('asuransi_kes')\n",
    "    kel_besar_14    = request.form.get('kel_besar_14')\n",
    "    \n",
    "    input_query = np.array([[age_14,kesehatan_msk,jumkamar_msk,jumorang_msk,jumkakak_lk_msk,jumkakak_pr_msk,jumadik_lk_msk,jumadik_pr_msk,asuransi_kes,kel_besar_14]])\n",
    "\n",
    "    #input_query = [[age_14,kesehatan_msk,jumkamar_msk,jumorang_msk,jumkakak_lk_msk,jumkakak_pr_msk,jumadik_lk_msk,jumadik_pr_msk,asuransi_kes,kel_besar_14]]\n",
    "    \n",
    "    result = model.predict(input_query)[0]\n",
    "    \n",
    "    return jsonify({'placement':str(result)})\n",
    "    #return jsonify({'placement':str(input_query)})\n",
    "    #return \"API Pak Amin\"\n",
    "\n",
    "@app.route('/prediksi')\n",
    "def prediksi():\n",
    "    return \"Hello world\"\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(debug=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "388263af-e6c6-4c66-956d-0e1b1ed93922",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('random_forest_model.pkl', 'rb') as file: \n",
    "    model = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a62efe77-ef84-4ba3-8904-30c99f36e494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "\n",
    "#new_data = [[1, 1, 2, 2, 3, 3, 4, 4, 5, 5]]  # Contoh data masukan Anda\n",
    "#new_data = [[14, 1, 2, 2, 3, 3, 4, 4, 5, 5]]  # Ganti nilai-nilai ini sesuai dengan data yang ingin Anda prediksi\n",
    "new_data = [[14, 2, 3, 4, 5, 1, 2, 3, 4, 5]]\n",
    "\n",
    "#new_data = new_data.reshape(-1, 1)  # Mengubah menjadi matriks 2D\n",
    "loaded_model = joblib.load(\"random_forest_model.pkl\")\n",
    "prediction = loaded_model.predict(new_data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8846ad5c-7121-4aa0-8024-52de8116f0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [07/Feb/2024 16:12:15] \"GET / HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [07/Feb/2024 16:12:43] \"POST / HTTP/1.1\" 405 -\n",
      "127.0.0.1 - - [07/Feb/2024 16:13:02] \"POST / HTTP/1.1\" 405 -\n",
      "127.0.0.1 - - [07/Feb/2024 16:13:12] \"GET / HTTP/1.1\" 200 -\n"
     ]
    }
   ],
   "source": [
    "from flask import Flask,request,jsonify\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route('/')\n",
    "def index():\n",
    "    input_query = np.array([[1,2,3,4,5,6,7,8,9,10]])\n",
    "    model = pickle.load(open('random_forest_model_2.pkl','rb'))\n",
    "    \n",
    "    result = model.predict(input_query)[0]\n",
    "    \n",
    "    return jsonify({'placement':str(result)})\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(debug=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec4eb72b-0864-4600-94ec-75093ffb86b7",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}