{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0b8d60a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_palette('RdYlGn')\n",
    "\n",
    "#model\n",
    "from sklearn.preprocessing import LabelEncoder,StandardScaler,MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import gradio as gr\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11273e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Nigerian_Car_Prices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dffa0dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Year of manufacture</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine Size</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Price</th>\n",
       "      <th>Build</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Nigerian Used</td>\n",
       "      <td>166418.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3,120,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138024.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5,834,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Nigerian Used</td>\n",
       "      <td>376807.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3,640,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213362.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3,594,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106199.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8,410,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0           Make  Year of manufacture      Condition   Mileage  \\\n",
       "0           0         Toyota               2007.0  Nigerian Used  166418.0   \n",
       "1           1          Lexus                  NaN            NaN  138024.0   \n",
       "2           2  Mercedes-Benz               2008.0  Nigerian Used  376807.0   \n",
       "3           3          Lexus                  NaN            NaN  213362.0   \n",
       "4           4  Mercedes-Benz                  NaN            NaN  106199.0   \n",
       "\n",
       "   Engine Size    Fuel Transmission      Price Build  \n",
       "0       2400.0  Petrol    Automatic  3,120,000   NaN  \n",
       "1          NaN     NaN    Automatic  5,834,000   NaN  \n",
       "2       3000.0  Petrol    Automatic  3,640,000   NaN  \n",
       "3          NaN     NaN    Automatic  3,594,000   NaN  \n",
       "4          NaN     NaN    Automatic  8,410,000   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30f57450",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4095 entries, 0 to 4094\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Unnamed: 0           4095 non-null   int64  \n",
      " 1   Make                 4095 non-null   object \n",
      " 2   Year of manufacture  3617 non-null   float64\n",
      " 3   Condition            3616 non-null   object \n",
      " 4   Mileage              4024 non-null   float64\n",
      " 5   Engine Size          3584 non-null   float64\n",
      " 6   Fuel                 3607 non-null   object \n",
      " 7   Transmission         4075 non-null   object \n",
      " 8   Price                4095 non-null   object \n",
      " 9   Build                1127 non-null   object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 320.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b138a73",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd78bcc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Build', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60013f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62b833d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3523, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e04b4172",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = df['Price'].str.replace(',', '') \n",
    "df['Price'] = df['Price'].astype(float)  # Converting the \"Price\" column to float\n",
    "\n",
    "df['Year of manufacture'] = df['Year of manufacture'].astype(int)  # Converting the \"Year of manufacture\" column to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c62daca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year of manufacture</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine Size</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3523.000000</td>\n",
       "      <td>3523.000000</td>\n",
       "      <td>3.523000e+03</td>\n",
       "      <td>3523.000000</td>\n",
       "      <td>3.523000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2089.276753</td>\n",
       "      <td>2007.921090</td>\n",
       "      <td>1.901794e+05</td>\n",
       "      <td>3170.591541</td>\n",
       "      <td>4.060590e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1187.608368</td>\n",
       "      <td>4.303771</td>\n",
       "      <td>2.215162e+05</td>\n",
       "      <td>4641.379934</td>\n",
       "      <td>4.520306e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.725000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1066.500000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.070360e+05</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.800000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2085.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.670060e+05</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2.835000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3136.500000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.397715e+05</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>4.500000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4094.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>9.976050e+06</td>\n",
       "      <td>184421.000000</td>\n",
       "      <td>5.880000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Year of manufacture       Mileage    Engine Size  \\\n",
       "count  3523.000000          3523.000000  3.523000e+03    3523.000000   \n",
       "mean   2089.276753          2007.921090  1.901794e+05    3170.591541   \n",
       "std    1187.608368             4.303771  2.215162e+05    4641.379934   \n",
       "min       0.000000          1992.000000  1.000000e+00       3.000000   \n",
       "25%    1066.500000          2005.000000  1.070360e+05    2000.000000   \n",
       "50%    2085.000000          2008.000000  1.670060e+05    2500.000000   \n",
       "75%    3136.500000          2011.000000  2.397715e+05    3500.000000   \n",
       "max    4094.000000          2021.000000  9.976050e+06  184421.000000   \n",
       "\n",
       "              Price  \n",
       "count  3.523000e+03  \n",
       "mean   4.060590e+06  \n",
       "std    4.520306e+06  \n",
       "min    4.725000e+05  \n",
       "25%    1.800000e+06  \n",
       "50%    2.835000e+06  \n",
       "75%    4.500000e+06  \n",
       "max    5.880000e+07  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "910be70f",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03ca468c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_10 = df['Make'].value_counts().head(10)\n",
    "\n",
    "sns.barplot(top_10.index, top_10.values)\n",
    "plt.xlabel('Make')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Top 10 Makes by count')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3afd964b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_price_by_make = df.groupby('Make')['Price'].mean().sort_values(ascending=False)\n",
    "\n",
    "\n",
    "top_10_make = average_price_by_make.head(10)\n",
    "\n",
    "plt.figure(figsize=(12, 6))  \n",
    "plt.bar(top_10_make.index, top_10_make.values)\n",
    "plt.xlabel('Make')\n",
    "plt.ylabel('Average Price')\n",
    "plt.title('Top 10 Makes by Average Price')\n",
    "\n",
    "plt.xticks(rotation=45)  \n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6808c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Year of manufacture'], color = 'Red')\n",
    "plt.title('Year of manufacture')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "909596d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Petrol      3455\n",
       "Diesel        33\n",
       "Hybrid        30\n",
       "Electric       5\n",
       "Name: Fuel, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d729c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_counts = df['Fuel'].value_counts()\n",
    "\n",
    "fuel_counts_modified = fuel_counts.copy()\n",
    "fuel_counts_modified['Others'] = fuel_counts_modified.sum() - fuel_counts_modified['Petrol']\n",
    "fuel_counts_modified = fuel_counts_modified[['Petrol', 'Others']]\n",
    "\n",
    "plt.pie(fuel_counts_modified, labels=fuel_counts_modified.index, autopct='%1.1f%%')\n",
    "plt.title('Fuel Types')\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7554ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Transmission)\n",
    "plt.title('Transmission')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5e2b789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bXUr4UtV4IK0UavfXqF3foAIyuBauJHTDkOFxU/B/iyn48E2zSwlz0tItidq0GHVks7R4w5SEbpgxPG5cyz4jb+FLZpcS/gwJ3dKo1XMhe3+5FlCkpaXRsWNHRo8eXfzPiy++WK66nnjiCY4ePRry6z/88EOefvrpUx67++67WbduXcjXfOedd5g9e3bIry8rGUgLI4bXi3frj+S8OM3sUiKEhG6pVBDju1novaeiEuqi6aH9qDdv3pxFixZVWFmTJ0+usGtFKgndMKGCQYJZGWRPmyADRGdK+nRPL+DF+PpJ9H6PoxxJFbpJzvTp0/nhhx8AGDBgANdffz0TJkwgJyeHnJwc5s2bx2uvvcaGDRtQSnHDDTfQt29fRo8ezdSpU0lOTmb8+PH4fD6aNm3K2rVr+eqrrxg4cCAXXHABO3fuRNM05syZQ0JCwhnVtH//fiZOnIjVasVisTBjxgzq1q3LrFmz/lDHxo0befLJJ0lKSkLXdTp06FBhn82fke6FMKE8bk5MHhsdp/SWg6EUz2Z6uD2tkHuOuDji/2OwegzFyJEj2be/6JDNQl+QGxf+wjX/2s7OdBcAPxzK57WVof8aGzW8+RhfPQEhDqzt2bPnlO6F9PR0li1bRlpaGkuWLOHtt9/mk08+YefOnQB07dqVxYsXs2nTJtLS0li8eDFvvPEGc+fOJS8vr/i6c+fO5fLLL+fNN9+kT58+BINBAAoLC+nfvz9vvvkmderUYcWKFWdUp6ZprF69mjZt2rBgwQJuu+02cnNzWb58eYl1TJs2jVmzZrFgwQIaNWoU0mcTKmnphgHD6yHrsXsJpktIrCoM4FOKFxvF8bMnyNwTXh6rH1P89Z2eIM+d8JKdcJjfuhdW782lR8sadD4rkQ9+zGBin7NYtO44Tw1JNeldhJmCDIxvn0LvOanMc3hL6l5YunQpnTt3RtM0bDYb7du3Z+/evQA0bdoUgF27drF9+3ZGjx4NQCAQOKUvd+/evQwZMgSAzp07n3L91q1bA1C/fn283lMHA51OJz7fqf3ULpcLp9PJ0KFDefXVV/n73/9OQkICd999d6l1pKenF9fasWNHDh06VKbPpTykpWsyw+Mmd8FsvFt+MLuUsLDVE6RLTFFboLXTwk5v8JSv+5XikQbxNGvWrPixWLuOy2fg9hvE2C18sjWLnq2ScVjlj3ex7P2oH96qkH0aUlNTi7sW/H4/P/30E2eddRZA8SnGzZo148ILL2TRokUsXLiQvn37ntKibNGiBT/99BMAmzZtOuX6pzsJuVWrVqxevZrCwkIAcnJy2L17N6mpqXzzzTd06tSJhQsX0qdPH1577bVS66hdu3bxXxRbt24t92dSFtLSNZHh8+LZsIrCf79rdinhQddxO2Ko0bwpcee3x9roLGxPv0TKK+9iT6mNZrXSULcU9U2OHg1JjdGveZ1ugQDLn3ySd49lMm7sHcycPY+xV3bh4f98T5NEC2MuTIag3+x3Zzq19ztodD7Ua4tmsYV8nR49erB+/XpGjBiB3++nT58+tGnT5pTnXHbZZaxfv55Ro0bhcrno2bMn8fHxxV+/+eabuf/++/nss8+oU6cOVuuZRVGzZs0YNWoUo0aNIi4ujkAgwOTJk4mLi6Nt27bcd999zJ49G13XmThxIq1bty6xjpkzZ/LAAw8QFxdHXFwcSUlJIX8eZaUpJaM2ZgmezOL4369CuQvNLsUUmjMGR7vOxHS9FEeni7DUrMW0J6fRvk0b+vbri+Zwcumll5bYr/fbgExq6qldCPPmzaNTp0689eabTJl4H7Nfnsfo0X+jaUos6uhm1PFtkLk75D7OiGeLRR/4FJqz6kKmJMuXLyc5OZl27dqxevVq5s6dyxtvvGFqTVVFWromMbwesh6/r9oFru3s5ji6dCP24p7Yzk5F+XxoMbHFI+udLriAZcuW0f+qq9i0aRMtWrQ442tnZWVx4MABbr31Vv71r39hccajW2x4vD60pOaQUA8t9VKw2KEgvWgBwdFNkLGTajP9zO/CWPE8+mUT0KzmnTDcqFEjJk2ahMViwTCMajWVTELXBEUrzt6pNsfsWM9KJeGq64i56K9gsaBZLGj2ogEdzXbqD/4VV1zBqlWrGDlyJEopnnzySZYuXYrL5WLEiBGnvc/LL7/MbbfdBsCoUaMYM2YMDRo0oFWrVkX30nWwxxY9OakhJNRHO6dH0ZHnu79F7V0OrqyKffPh6MQe1C+fwrl9TdscJzU1lXffrZ7datK9UMVUMIj/8H4ybr8WjOCfvyBSaRrOLheTMHIMtqbN0axWNEv4/h2vgn5AwclDqJ1foA7/CEYU9wNrFvR+TxS1/uWQyyoloVvFDI+b9LHXEDyWZnYplUKLiSOu9yAShv4NzRmDHhtndkllpvxuQEPt/gb18yfgi9IuoKRG6L2nmtrNUB1J6FYhw+Mm751/UfDe62aXUuH0GjVJvPYWYnsOAKXQnTF//qIwV7RvgULt/KoofP0us0uqcFrbQWjn9kezOc0updqQ0K1CgfSjHP/7EAhGUbeC1Ub8kGtJvGZMUV+tLfpaTSrgAxVEbfsYtfMriKbjzzX9126G+tLNUEUkdKuI4XFz4qE78W37yexSKoyz819IvmsKWlx8VLRs/4zye4r2M1j/LziyyexyKk6Nxui9HpZuhioSviMbUUT5fXg2rIqawNWTkkm+YzKOjhdWi7D9jWZzgs2J3m0sKu0H1PrXo2O+b87hoqlzjTqh6Razq4l60tKtAobbxfExgzFyss0updxiL+tHjX8+gGazRWVXwplSAR8E3BjfvwiZO80up/ziUtD7T5fWbhWQTpxKZrhd5L3xcuQHrtVK8r2PUmPsBPTYuGoduACa1Y7mTELvMR6t03Wgh76sNiwUnkDtX/nr1DlRmSR0K5lyuyj4z3tml1Eueo2a1Hl2ITHdLkOPiTW7nLCiWR1oqZeiD5gGCfXMLqdc1JYPZI/iKiChW4kMt4uc156FQOSOdtuat6Luy0uwnZWK7pRpRSXRrA6ITUHv8wiknGN2OaHz5qN2fC5nq1UyCd1KZORk417+pdllhCzmr32oPeNVLEk10M5wF6jqStN1NFsM+mX3Q+MuZpcTMvXzf6J7pWQYkNCtJEWt3Oci8+gdXSfp5ntIvnNytZqdUBE0qwP9olvQWvUxu5TQBDyorR9WyL67omQSupUkePIEnjXfmV1GSJLHPURc3yESuCHSrA60dlejdRoNlL4hd7hSu7+FoHQxVBYJ3UpguArJW1C+o6rNknTz3cRcfLkEbjkVDbB1R/vLrWaXUnZGAPXjO9LarSQSupVA+by4I7CVmzByDHF9r5LArSCa1YHWqCNa+2Fml1Jm6uCa6N3ox2QSuhXM8LjJ/2ARGJE19Sau/1ASht8ggVvBNKsTrWUvtOaXm11K2SiF2vEZKhpW3IUZCd2KpmkUfv6R2VWUScxfe5M0ZpwEbiXRrA60jiOhYUezSykTtX81aBIRFU0+0QqkAgHcyz5HFRaYXcoZ+23TGpmDW7k0qwO92z8gpbnZpZw5XwHq2DaULJioULL3QgUyvB4ybr+WwJGDZpdyRiwpdak77z1ZZVZOwWCQBx98kP3792OxWJg2bRpKKSZMmICmaZxzzjk8/PDD6LqO8rkwPn+IYF46Dy3dz4EsN7qm8cSgZjSp6eT7PTm8uCyN+kl2nhl2Drqm8finB7jxL/VpWMOEo3XqtkbvfheaTX4LqijS0q1A/r07IyZw0XVqTp5R7fdQqAjLli0DYPHixdx5551MmzaNadOmMW7cON5++22UUnzzzTdFT7Y60LuPY9nuXADeuqkNd/RoxFNfFv25WbwhnVdHt6JOgp0dx13sSncR57CYE7gA6b9A0GfOvaOUhG4FMVyFFHwSOXssxA+9HluTZrLSrAL07NmTxx57DICjR4+SkpLC9u3bueCCCwDo3r07q1evBijaOjG+Nldc808eGdi06DU5XlLiijbMibVbcPsM3H6DWLvOq6uO8vdu9U14V79RqF3f/HqKhqgIEroVRLNY8axdbnYZZ8SW2pLEa8agx8ivjBXFarXywAMP8Nhjj9G7d2+UUmha0cKIuLg48vPzi5+rWR1ozXtgrd+aiR/v5YnPDtCrdU0AbuvekCc/P0DDGg4OZns5v1E8/9mWxdRP9rPpcH6J965sau/ySFzjEbYkdCuAUgrPD2tQHrfZpfwpzeGg1pRZxUegi4rz1FNP8cUXXzBlyhS83v+u6CosLCQxMfGU5xYNrP2TaUPb8Okd7Xlo6X5cviCptWN4fngLbr64AR/+lEH/81JYtTeXB/udzcsrjlT1WyriPgkn9plz7ygkoVsBlLswYqaJ1bjtfvTEGsWtMFF+H3/8MfPmzQMgJiYGTdNo27Yt69atA2DFihV07tz5j69Z8BZa59HE2HR0TcOi//d78t4PGQxuXxsAQyk0wO03bxaBsfMLlC/8GxWRQEK3Qmh4flprdhF/ytHhAmIu7SXTwypYr169+Pnnn7n22msZM2YMkyZN4qGHHmL27NmMGDECv99P7969Abj//vs5evQovXr14pcdOxn94BxuXnyQCb2b4LAW/TgWeAOsP5hHj5bJJMVYSYmzc+387Vx9fm3z3uSRTYBMdKoIMmWsnFQwSOFXS8l54XGzSzk9Xafeqx9grd/Y7ErE7yilIOcQxmdTzC7lT2mdr0dr3kNODS4n+fTKSXncuL//yuwy/lTsZf3Qa9QyuwzxPzRNg/i60KCD2aX8KXV0c3QcxGkyCd1y0mx2vOF+yq/dQdLfx8kiiDCl2Zzona+DcO9nz9gBFpnXXV4SuuXk2/0z+MN7DmP8gKEyWyHcORLRzv6L2VWcXsADBelmVxHxJHTLwfB6cK/8xuwyTs9mJ3HEGNnMJsxpNifa+deAHt6LVdSRTSg5zqdcJHTLQyk8G1ebXcVpxfUZDLLqLDJY7GjN/2p2Faeljm0DObiyXCR0y0F53OG914LFQuK1t0hfboTQbE60Vv3MLuP0TuyWft1yktAtB++WjWaXcFrO8y+UvRUijSMeajUzu4rSBX2Qm2Z2FRFNQjdEhseNd8sPZpdxWnF9hqDFxJldhigLiw2txRVmV3Fa6shPqGDA7DIiloRuqIJBfLu2m11FqTSHE2env8hy3wij6Ra0xl3AGr6zTdTx7bLdYzlI6IZIszvwH9hjdhmlcl50qbRGIpUy0JpcaHYVpcvaCxab2VVELAndEAWOpUEgfEMtru/V6LHStRCJigbUeptdRumMIJw8ZHYVEUtCNwRKKbzbN5ldRqn0xBo4WrY1uwxRHgl1Ibam2VWUSmXJVo+hktANgfK48f28yewyShXTvZdMYI90RhCt7rlmV1G6nMMoma8bEgndUBhBAmkHzK6iVHE9+8sKtAin2WKg4flml1EqlXesqJtBlJmEbgg0u4PA0TCdq6hp2M6OoGO+RanCuqWbfwwsMgc8FBK6oTAMjPxcs6sokbVhE1RQWiBRwWKH+DpmV1EyTx7IVtwhkdANQTAr0+wSSmVv0VZ+GKKGQqvb2uwiSufKNruCiCShGwL/kfCdLmNv0wFN9lqICprVCY06ml1G6QqzzK4gIknolpEyjLBeFOFo00FWoUURrebZZpdQKpV/3OwSIpKEbhkpn49g+lGzyyiZ1Yq1fiOzqxAVyZEQvnvsFmTIqscQSOiWVTCAkZdjdhUlsp3dHOWTuZNRJegL28E0VZglezCEQEK3rJTCyM8zu4oS2Zo0C/9ztkTZKAWJ9cyuomQykBYSCd2y0sAoCM/Q1RMSZf/caGOxosWG6SnO3nz5Sz4EErplpVvCtqWrJySBTXb1jyaaxQ7xtc0uo2TKACR0y0pCt4w0qy18Qzc5RWYuRKPE+mZXUDJlSOaGQEK3rCwWlLvQ7CpKZKmRbHYJohJo9jDdolNauiGR0C2rMF5iqydJ6EYlLUx/TI0gErplF6bfTREKPSHR7BJEZQjX0FWGDKSFIEy/myIUelyC2SWIyhDOoSst3TIL0++mCIlNzq2KSrrF7ApKZgQlc0MgoVtmYbyDl09WB0UlaelGlTD9bopQKK/H7BJEZQjn0A3X2sKYfGJlFcYDB4bHbXYJojKE61lkypB54SGQ0C0r3QJ6eH5syhWe84dF+ShX+O5bq2TD/DILz/QIZ8FA2G4SHs4nWohyyM8wu4KS2WLlcMoQSOiWkQoG0ePizS6jRIH0o9LyiDIq6IfCE2aXUbLYZDD8ZlcRcSR0yyoYRI8Pz0UIRnYm+GUGQ1QxAuA+aXYVJYtJlvP4QiChW1ZKoSfWMLuKEgUy01F+aXlEFaVQrvAMXS02WWYvhEA+sbLS9bANXf/eHSD76UYXXQ/jlm7NomPiRZlI6JaRZrNhqZlidhklCmYcD+sNeUQINEv4ntCQUActTGfyhDP5xMpIs9mLjsUJU/79u8wuQVSknMO/rvwKP1pceDY+wp2EbgisZzU1u4RSeTdvlBNao4Qygqhj28wuo3SxNc2uICJJ6IbAWreh2SWUyrdzG8ojy4GjQsCLytxpdhWlc8iudqGQ0A2BnlQjbFel+Xb9jGaXwY2oYLXDib1mV1EyzQJWp9lVRKTwTI4wp/x+LCl1zS6jREbuSQyXy+wyREVw5YA/TL+XMUmyMCJEErqhCASwNmhsdhWl8m79QVamRTilFCpjh9lllC6+jiwBDpGEbgg0uwNb03PMLqNUri//L2wPzxRnyO9GHd5gdhWl0mqfAxbZND8UEroh0Ox2HO06m11GqTw/rUM2l45wugXCeOaCVv88NAndkEjohsjeorXZJZQuGMS14iuULJSISEopOLEnjPtMNah5ttlFRCwJ3RDp8YloYbrxDfzaxeAL082vxWkZHjckN0Uf8gJa+6GQUM/skk6VWC+sT60KdxK6IVI+L/ZzzjW7jFL5dmxFyUkSEUkLBDhydQ+yX5hJwHkOep9H0Qc8hdaqNziTzC4PLSV8xzMigYRuiDSHE3vLtmaXcVqFXy2VXccijPL7KfziYzCCuJd/QcYdozly9V/Jff99jAaXoA+ahX7FFLSm3cybJ1uvDZpN5uiGSkI3RJrVirPjhWaXcVqur5dKv26EUUaQws8/OvXBQICC9xdy/KarODaqDwWrNqBaDUG/+kW07uOgQYeixQpVRKvTssruFY1kH8BysLdoU7SVYiA89zoIpB3Et3s7jjbny25QEUAZBr4dWwkcPVzqc4z8PHJfeYbcV57B2rAxCdfeSkyXm9DsDtTB9ah9y4sG4SqLPQ4c4TuWEQk0JbPoQ2YUFpD12L14t/xgdimlsjVvRe0Zr6E75dfBcGd4PWTeexP+fWXfKc7e5nwSR/0dR+u2YARQ+1ag9q+EvGMVW2SD9uh/+QeaPTzPCYwE0tItB80Zg/PC7mEduv49O/Bu+xHn+ReiWaruV1BRNioYxLtpQ0iBC+Db/hMnJo8FIObS3iQMvQ5b7yvAfRK1ZxnqwBrw5Ja7Tq1OS9lzoZykpVtOgeNHOH7TILPLOC3rWanUeW4hukN+WMKV8npIv+NaAmkHK+6iVivxg64hvt8QLHXqQfbBogA+vBECoe1Ep/d9DC35rIqrsRqSjr5ystRMQQ/TkyR+Ezi4F+8Pa1Bh2vdc3alAAPfaFRUbuFA0APfBIo6PuYqjo3r/OgA3GP2qXwfgGnYoWvl2phwJkNigYmushqSlW06Gq5CceU/j+mqp2aWclrVBY+q89I60dsOQ8no4fuswghkV3P9aCkuDxiRedysxXboWDcAdWo/auwJO7D7t67TmPdA6XoMm3QvlIi3dctJj44jtOdDsMv5U4Ohh3KuXoQIybzecKL8P17LPqyxwAYJHD3NyxoMcHdaTzEl34stzone/B33IbLT2w0ttzWqpf5XArQDS0q0Ayufj2N/6YeTlmFbD2ye9rC4MEFBwZZKN5g4Lz2V6sGkaze06Y1McWGvVpt6rH6DHxAEwePBgEhKKdv9v1KgR06ZNY8WKFbzwwgs0aNCA5557Dl3XefTRR7npppto1KiRae8vWhmFBRy/+SqMHPMPn4y5tBcJV1+H7axmfxyAcyaiD3pWNrmpADJ7oQIoI0hM914UfrLElPtvcgfY7gnyQsNYvAqW5PhYmufh9hQnbZwW5md5+aYgwBXaCXJeeooaYyfg/7Uvb9GiRadc6+2332b+/Pm88MIL7NixA4vFQnx8vARuJTA8brKffigsAhfAvfxL3Mu//N0A3GAs7YdC9iFUQTqoICChW17SvVABdGcM8f2uMu3+G11BmtotPHzcw+RjbrrGWskMKNo4i4K1jdPCNk/RyjTXt5/i27GVX7Ztw+12c9NNN/G3v/2NTZs2ARAXF4fb7cbtdhMTE8Mrr7zCzTffbNZbi1qG14N75Td41q0wu5Q/Kh6Au5oj1/SmYOU6aNBBuhYqiHQvVBDD6yH9HyMIHj9S5feeleEhPWDwRP0YjvsVDx53kaBr3FzLQfsYK89nenAZiol1YwDQk2uRd990tuzazbBhwzhw4AA333wzn3/+OQcPHuS5556jdevWtG7dmrS0NHRd55dffmHIkCGcf/75Vf7+oo1SimBWBuk3X43yhv8hopZ6Dan78rsyCFtBpKVbQTRNI/ayfqbcO9Gi0TnWik3TaGzXsWsaY1OcvHPSx6RjLmpYNJIs/93U3DiZReJ7/2JAryvQNI2mTZtSo0YNMjMzSU1NZfbs2dxyyy28//77DBgwgJUrV/LQQw8xZ84cU95ftFE+L1mP3hsRgQsQd8WVaJpERUWRT7KCaHYH8f2HmnJKcFunhQ2uAEopTgQMPIZiqyfI+DpOnqwfS15Q0Snm1O77j79bwRN33Y7hcZOenk5BQQG1a9cu/vq7777LkCFDADAMA03TcLtlq8jyMjxu8pe8jn9PGJ9/9nu6hbh+V8kJ0xVIBtIqkOZw4rzgEjxrl1fpfS+Ks7LFE2DsEReGgjtrO/ErmHTMjUODDjFWLowr+lZPT3dzY00HfRNtzNi+mVHXjEJ3OnnyySexWoueU1BQwPr163nuuecAqF27Ntdccw2jRo2q0vcVbZTfR+DwfvLfnW92KWfM2bU7mk0CtyJJn24F8+7cRubdN5hdxhnTE2tQZ/abWGqmoFnk7+DKogIBglkZZNw1GiOv/HsgVJU6s9/CnipbOVYk6V6oYLazm2M9O9XsMs6YkZdD5r1jMPLzUIZhdjlRSRlBjPxcMsaPiajAtTVvhbVhE7PLiDoSuhVMs1pJGHq92WWUSfBEOpn3/R3lKkR+8alYyjAwCgvIuPcmjKxMs8spk4ThN0jXQiWQ0K1gmsVKbLfL0RPNP8uqLAJHDpE54VaU22V2KVFFedxk3n+LKVMJy0NPrkVMl0tkO9BKIKFbCRSK+CHXml1Gmfn37eLEQ3cWnUYrys1wuzgx+Z8EDu41u5QySxhyrRz4W0kkdCuB7nASP+ga9ITIau0C+H7eTNYT90vwlpPhcZP16L34dm43u5Qy05OSiRswHN3hMLuUqCShW1k0nYSRY8yuIiTeH9aQef/NBHNzZA/eMlLBIEZ+HpkTbsO7eYPZ5YQk8dqb5Uy9SiSfbCXRHQ7i+l2FnlzL7FJC4t+zg/R/jsB/cC+GJzJWTpnN8HkJZhwj/fZr8O+KvBYugKVWHWKvuFIWQ1QiCd1KpOk6idfdanYZITNOZpFx9w24Vy+T7oY/YXjc+LZvIv32awlmpptdTsgSr/8nWllOkxBlJosjKpnh9ZB+y9UR/YMIEDdoJEnX3y6nCpfA8HjIf3c++UsWQAT/OMnGNlVDWrqVTLNYqHHreLPLKLfC/1tM1iPjMAryUT6v2eWEBeXzYhTkkfXIuKKlvREcuAA1brlHViVWAQndSqZZbTg6dsXRrpPZpZSbd/NGjo8ZjGvFVxgeT7VdSKGUwvB6cC3/kmM3Dca7eaPZJZWbo8MFODpcgGaV0K1s0r1QRQIZxzn+98EQJbMB7C3bkjz+0aLTkGNizS6nyhhuF8ETGWTPejhiB8v+wGqj3vyPsabUNbuSakFaulVET0gk4arRZpdRYXw7t5F+6zBy35iD4Xah/NF94KXy+zDcLnLnzyb9H8OjJ3CBhBE3oscnml1GtSEt3SpkeNyk3zo04gfV/peeXIvksRNwdOyKZrdH1ei3CgZRfh+e9d+T8/JMjNyTZpdUoSz1GlJ3zrsyQFqFJHSrkAr48W7eyIkpd5hdSqWwNW9F4nW34mh/AZrFEtH9g4bXU7Rx+9rlRZuO79tldkmVImXaXBxtO8gAWhWS0K1ihsfNyecfKzp1NUpZ6jUkceRNxF7aG2UYEdXna7gKwTDI/793KPzkvahr2f5ebM8B1PjH/RH1/YkGEromMFyFHL91aMRt9VdWWmwcsT0HkDDkWvTEGkVdD2HYolLBAMrnI5h5nLzF/8K98puoGfAsjaVeQ+q+tBg9JsbsUqodCV0TGH4//p3byHzgloif23mm7Oe2I+aiv+L8Sw+sKXVRAT96bJxp9RiuQjSrjcCJdDxrvsO98uuI3JwmJLqFOrMXYWuSKls3mkBC1ySG20XeW69Q8OGbZpdS5fTkWjjP70rMxZfhaN8FlEKz2dFstkq7p/L7imZYaODdtAH3qm/x/LgWIye70u4ZrhJH30b8kGvRndLKNYOErokMr4fMe8fg37fT7FLMo+vYmp+Ls31nrM1aYGt0NtY69dFiY1FeLxgGmt1xRhuwqIAf5fOBMsBiQ7PoBDKO4z+4F//O7Xg2rSs6hbca/5G3t2xLyvS5stTXRJUauuvWrWPs2LEsXbqU+vXrA/D000/TrFkzLrnkEl566SWmTp0a8vVXrFjBsWPHGDFiRMjX6NatG6tWrTrlmp9++inTp08P6Xper5e+ffvy7bff/ulzlVIEszJJ/8dwVGFBSPeLWnYH1noNsdZv9Os/DeG3qWiaduq/AQIBAhnHCGamEzyRTjDjOMGsjGodsP9Li0+k3twlWGqmmF1KtVbpoxo2m42JEyeyYMECtN/9kNSuXbtcgQvQvXv3clZnLk3TsCQmkfLQLDIn/gPkYMj/8nkJHNpH4NA+syuJDrqFlKnPoifIIgizVXrodu3aFcMweOutt7juuuuKH09LS+Oee+5hyZIlLFu2jBdeeIH4+HiSkpJo2bIld9xxB7NmzWLDhg0opbjhhhvo27cvo0ePJjk5mby8PPr378/BgwcZP348s2bNYtu2bRQWFpKamsq0adOYPXs2aWlpZGVlcfToUSZOnMgll1xyxrVPmDCBQ4cO4fV6GTNmDP369WP9+vU8++yzWCwWGjduzKOPPorP52P8+PHk5eXRpEnZTk/V7A5s57Qmacxd5L76bJleK8SZqnHbvdiatZCDJsNAlczfmTp1KsOGDePiiy/+w9eCwSCPP/447777LikpKdx7770ALF++nLS0NBYvXozX62X48OF069YNgIEDB3LFFVfw4YcfAlBQUEBiYiILFizAMAz69+9PenrRqi+73c5rr73GqlWrmD9//hmFrqZpFBQUsG7dOj744AMAVq1ahVKKKVOm8Pbbb1OrVi2ee+45PvroI3w+Hy1atODuu+9m8+bNrFu3rkyfj+6MIa7vVfh2/4L7u8/L9Foh/kxsz4HE9hwoA2dhokpCNzk5mUmTJjFhwgQ6dux4yteys7OJj48nJaWon6lz586cOHGCXbt2sX37dkaPLtqvIBAIcPToUQCaNm16yjUcDgfZ2dncc889xMbG4nK58P+6F8C5554LQL169fD5fH+o7fddHgAulwuHw0F8fDxTpkxhypQpFBQUcOWVV5KdnU1GRgbjxo0DwOPx0K1bN06ePFkc5u3bt8cawkos3RlD8p2TCaQdKBrsEaIC2Fu2ocY/75fADSNVtuHNZZddRtOmTfnoo49OebxWrVoUFhaSnV00dWfz5s0ANGvWjAsvvJBFixaxcOFC+vbtS6NGjYA/BuVvA2rPPPMM99xzD57fbTv4v8/9X40aNWLNmjXF///9999z3nnnkZGRwfbt23nppZd45ZVXmDlzJgkJCdSrV485c+awaNEibrvtNi688EKaNWvGpk2bAPj5558JhDixXnM4SXnsRSy16oT0eiF+z1K7LrUefUECN8xU6fKgyZMns3bt2lMe03WdKVOmcPPNN5OQkIBhGJx11llcdtllrF+/nlGjRuFyuejZsyfx8fElXrddu3bMmTOH4cOHY7fbady4MRkZGWdU0+OPP84jjzzCs88+i2EYdOjQgUGDBmGxWMjMzGTw4MHExsZy0003YbfbmTx5MrfccgtKKeLi4pgxYwZdunRh4sSJXHPNNTRr1gxbiPNNNU1Dj4+n9qx/kXHndRh5uSFdRwg9KZnaT89Hjy35Z0aYJyzm6c6bN48bb7wRu93O+PHjufjiixk8eLDZZZlG+f0EjqWRcfcNKHeh2eWICKPFxVPnuYVY6zZAs1beghMRmrDYTzcuLo7hw4czcuRIlFL069fP7JJMpdlsWOs1oPa0OWB3mF2OiCCaw0Ht6XOx1q4vgRumwqKlK0pmeD34ft7CiYfugGDQ7HJEuLNaqf3kHGzntJYVZ2EsLFq6omS6w4n93POoNXkmRPDetKIKWG3UmjILW3MJ3HAnLd0IYHjc+Hb9TNbDdxbtRyDE7xTNepmNrfm5cgJEBJDQjRCG10Pg0H4yJ96GcsngmiiixSdQe/o8rA3PQndI/38kkNCNIMrnI5BxjMzxYzDycswuR5hMT65F7ZmvYa1dV5b3RhAJ3QijAn6CJ7PIHD8m6g64FGfOUqc+dWb9Cz0pWWYpRBgZSIswmtWGpWZt6r70DvZz25ldjjCBvdV51H3xbfQatSRwI5C0dCOY4fWQM/dpXF98bHYpoorEXt6fGmMnyNLeCCahG+EMjxvXss/ImfOUzOWNZrqFpFvuJq7XIAncCCehGwUMjxv/gT1kPTwOI1/2a4g2emINak19DtvZqRK4UUBCN0oovw/DVUjWEw/g2/aj2eWICuJo14maE59Cj42r1IM7RdWR0I0yhtdD4acfkLtgNoS4xaQIA1YbSWPuJK73EFnwEGUkdKOQ4XETzEwn67F7CaQdNLscUUbWs1KpNeVpLDVTpDshCknoRillGCifl9z5L1D4yXtmlyPOhKYRP/gaEv/2TzSbHU2XGZ3RSEI3yhluF4EjB8meNZXAwb1mlyNKYTu7Ocn3TsXaoAl6TKzZ5YhKJKFbDSjDQPl9FH7+EXkL56A8brNLEr/SYuJIunEssVdcKa3bakJCtxoxvB6U18PJF57As3qZ2eVUezHde5E89gGwO2Q7xmpEQrcaMtwu/Pt2kfPyTPz7dppdTrVjS21JjbETsJ2VKl0J1ZCEbjX1W5eDd8sP5L72LIHDB8wuKepZz0olacxdONp2RLNLV0J1JaFbzalgEBXw41n/PbkLXiR4/IjZJUUda4PGJN10J45OF6FZbWgWi9klCRNJ6AoAVCAAwSCulV+Tv3g+gSMyv7e8rI2bkjByDDF/+SuaxYomRy4JJHTF/1CBACoYwPfLFvLefk2WFJeVpuHs/BcSRo7B1qyFhK34AwldUSKlFMrjxsg9Sf57C3F9+ynK6zG7rLClxcQSe8WVJA67Hi0mFj02zuySRJiS0BV/ynC7QNNxr/0O1xcf4936IxiG2WWZT9dxnNeJ2N6DiOl6KSglsxHEn5LQFWdMGUbRwopgkMJvP8X11dJqOeXM1rwVsT0HEndZX9B1NGeszEQQZ0xCV4REBQKogB8jLxfX8i/wrP8e346t0bmRusWC/dx2xFxwCTF/7YMeF1805csifbWi7CR0RbmpQKCov9dqxbd9E+6V3+D5YXVEH5xpqdsAZ6eLiOl2OY7W7VCBAJrTKUEryk1CV1Q4w+1G03WMwnx8O7fh3bIR387t+PbtAp/X7PL+QHM4sKW2wn5OaxztOmFv2RYtLr6oj1a2VhQVTEJXVDrD64WAH83hJJh5HO8vW/Dv/oXAsTQCxw4TOH4U/L7KL0TXsdSpj7VhE6wNmmBv1RbHue2wpNT9taVuQ3c4Kr8OUa1J6ApTKJ8P5feCpqM5nBiFBQQzjhE4ephgdiZGTjZGQT5Gfi5Gfh5Gfi7K54Xf/rgW/6lVgIYWG4sen4gen4CekFT076RkLLVqY63fGEvtuugJSSifD4IBsNklYIUpJHRFWFLBIMrvKxqYUwo0reifX0P2jy8wir6ka6Bb0Gw26X8VYUlCVwghqpBMLhRCiCokoSuEEFVIQlcIIaqQhK4QQlQhCV0hhKhCErpCCFGFJHSFEKIKSegKIUQVktAVQogqJKErhBBVSEJXCCGqkISuEEJUIQldIYSoQhK6QghRhSR0hRCiCknoCiFEFZLQFUKIKvT/zq9Q8KQt908AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition_counts = df['Condition'].value_counts()\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(condition_counts, labels=condition_counts.index, autopct='%1.1f%%', startangle=90, wedgeprops=dict(width=0.4))\n",
    "circle = plt.Circle((0, 0), 0.3, color='white')\n",
    "ax.add_artist(circle)\n",
    "\n",
    "plt.title('Car Condition')\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76bf6e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Engine Size'])\n",
    "plt.title('Engine Size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b23c09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qas1vvvmmGhsbVVRUpLVr12r79u3asmVLpKYaNqHW7PV6NXr0aKWnp2vQoEGaPHlyu7vCa1GoNb///vvavXu3/vjHP2rXrl367LPP9MYbb0Rqqib6omG9ju6tt96qvXv3SpLeeecdjRs3zn1vzJgxOnbsmHw+n86fP6/9+/drwoQJvT1lxIVasySVlJTo3LlzWrNmjfuY4VoXas0PPvigtmzZovLychUVFWnmzJnKy8uL1FTDJtSak5OT1dzc7P5G0/79+zV27NiIzDOcQq05ISFBcXFxGjx4sGJiYjR8+HCdOnUqUlM10RcNa38/3U0zZsxQdXW17rvvPjmOoxUrVmjbtm1qaWlRfn6+Fi1apIcffliO42j27Nm68cYbe3vKiAu15vHjx+vVV1/VxIkT9e1vf1vSxSjNmDEjwrPunc5+ngeiztZcVlamBQsWyHEcTZgwQdOmTYv0lHutszXn5+eroKBAgwYNUkpKinJzcyM95T7Rlw3jox0BwBDfHAEAhoguABgiugBgiOgCgCGiCwBBdPRZDJft3btXhYWFKiws1Ny5c/XVr35VtbW1nR6X6CIi3n77bWVkZGjHjh1ttmdnZ2vRokWaN2+eJKmwsLBLv5CBcHr++ee1ZMmSkB9iNGXKFJWXl6u8vFzTpk3T97//fY0ZM6bTYxNdRExaWpq2b9/uvq6pqdHZs2clSatXr47UtIB2n8VQU1Pj3tU+/vjjOn36tPvexx9/rK1bt7o3Cp3p9TdHAD110003qa6uTqdOndKwYcNUUVGh7OxsffTRR8rMzFR1dbW77+nTp7V48WI1NjZKkpYsWaKMjAy9/PLL2rlzp/x+vxISErRq1SoFAgE99dRTamho0Je//GX95S9/UVVVlWpqalRaWirp4rfxrlixYkB8WzrC784779SJEyfc10uXLtWKFSuUnp6uzZs3a926dXryySclSS+99JK+853vKDY2tkvH5k4XETVjxgz94Q9/kOM4evfddzv8Fstf/epX+trXvqby8nItX75cy5YtUyAQkM/n069//WutX79efr9fR44c0caNGzVq1Cht2LBB8+bN06effirp4oXzk5/8ROXl5ZoyZYrWrVtnuVRcw2pra/XTn/5UhYWF+t3vfqeGhgZJFz+bYvfu3brnnnu6fCzudBFR2dnZWrZsmZKTkzVx4sQO9/v73/+ut956y/2AlVOnTik6OlqDBg3S/PnzFR8fr48//lh+v1+1tbWaMmWKpIvfOz98+HBJ/7lwJOnChQtKTU3t49VhoEhNTdXKlSuVlJSkAwcO6JNPPpF08ddlamqq4uLiunwsoouISk5OVktLi8rLyzV//nwdP3486H5paWnKyclRdna2Pv30U23evFnvv/++KisrtXnzZp09e1Z5eXlyHEfjxo3ToUOHdMcdd6i+vt59JNHRhQN0ZtmyZVq4cKFaW1slSWVlZZIufq5ycnJyt45FdBFxd999t7Zu3arU1NQOo/voo49q8eLF2rRpk86cOaN58+Zp9OjRGjJkiPLy8hQbG6sRI0aooaFB9957rxYtWqQHHnhASUlJ7ofId3ThAMGMGjXK/et5xo8fr/Ly8nb7ZGVlKSsrq1vH5QNvMOAcPHhQLS0tmjRpkurq6vS9731PlZWVkZ4WIInoYgD65JNPNH/+fF24cEF+v1/FxcXuM14g0oguABjij4wBgCGiCwCGiC4AGCK6AGCI6AKAof8H8BScQrxW2REAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Mileage'],color = 'Red')\n",
    "plt.title('Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbe9f792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Price'], color = 'Red')\n",
    "plt.title('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a0ab0949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year of manufacture</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine Size</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.349871</td>\n",
       "      <td>0.059657</td>\n",
       "      <td>-0.061578</td>\n",
       "      <td>-0.336367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year of manufacture</th>\n",
       "      <td>-0.349871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.199826</td>\n",
       "      <td>0.019179</td>\n",
       "      <td>0.563691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mileage</th>\n",
       "      <td>0.059657</td>\n",
       "      <td>-0.199826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>-0.141418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine Size</th>\n",
       "      <td>-0.061578</td>\n",
       "      <td>0.019179</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.336367</td>\n",
       "      <td>0.563691</td>\n",
       "      <td>-0.141418</td>\n",
       "      <td>0.046112</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0  Year of manufacture   Mileage  Engine Size  \\\n",
       "Unnamed: 0             1.000000            -0.349871  0.059657    -0.061578   \n",
       "Year of manufacture   -0.349871             1.000000 -0.199826     0.019179   \n",
       "Mileage                0.059657            -0.199826  1.000000     0.001305   \n",
       "Engine Size           -0.061578             0.019179  0.001305     1.000000   \n",
       "Price                 -0.336367             0.563691 -0.141418     0.046112   \n",
       "\n",
       "                        Price  \n",
       "Unnamed: 0          -0.336367  \n",
       "Year of manufacture  0.563691  \n",
       "Mileage             -0.141418  \n",
       "Engine Size          0.046112  \n",
       "Price                1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e49305",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "544f2b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler.joblib']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['Unnamed: 0', 'Price'], axis = 1)\n",
    "y = df.Price\n",
    "\n",
    "make_counts = X['Make'].value_counts()\n",
    "\n",
    "\n",
    "# Get the values to replace with 'Others'\n",
    "make_others = make_counts[make_counts < 14].index.tolist()\n",
    "\n",
    "# Replace values with 'Others'\n",
    "X['Make'] = X['Make'].apply(lambda x: 'Others' if x in make_others else x)\n",
    "\n",
    "X_train,X_test, y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state=10)\n",
    "\n",
    "\n",
    "# Initializing the encoders and scaler for each column\n",
    "make_encoder = LabelEncoder()\n",
    "fuel_encoder = LabelEncoder()\n",
    "transmission_encoder = LabelEncoder()\n",
    "condition_encoder = LabelEncoder()\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Encoding and scaling each column individually\n",
    "X_train['Make'] = make_encoder.fit_transform(X_train['Make'])\n",
    "X_test['Make'] = make_encoder.transform(X_test['Make'])\n",
    "\n",
    "X_train['Fuel'] = fuel_encoder.fit_transform(X_train['Fuel'])\n",
    "X_test['Fuel'] = fuel_encoder.transform(X_test['Fuel'])\n",
    "\n",
    "X_train['Transmission'] = transmission_encoder.fit_transform(X_train['Transmission'])\n",
    "X_test['Transmission'] = transmission_encoder.transform(X_test['Transmission'])\n",
    "\n",
    "X_train['Condition'] = condition_encoder.fit_transform(X_train['Condition'])\n",
    "X_test['Condition'] = condition_encoder.transform(X_test['Condition'])\n",
    "\n",
    "X_train[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.fit_transform(X_train[['Year of manufacture', 'Mileage', 'Engine Size']])\n",
    "X_test[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.transform(X_test[['Year of manufacture', 'Mileage', 'Engine Size']])\n",
    "\n",
    "# Save the encoders and scaler\n",
    "joblib.dump(make_encoder, \"make_encoder.joblib\",compress=3)\n",
    "joblib.dump(fuel_encoder, \"fuel_encoder.joblib\",compress=3)\n",
    "joblib.dump(transmission_encoder, \"transmission_encoder.joblib\",compress=3)\n",
    "joblib.dump(condition_encoder, \"condition_encoder.joblib\",compress=3)\n",
    "joblib.dump(scaler, \"scaler.joblib\",compress=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307eab41",
   "metadata": {},
   "source": [
    "#### Needed Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23aaa0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest RMSE: 1888772.25\n",
      "XGBoost RMSE: 1780817.41\n",
      "Linear Regression RMSE: 2613288.21\n"
     ]
    }
   ],
   "source": [
    "# Initialize the models\n",
    "rf_model = RandomForestRegressor(random_state=42)\n",
    "xgb_model = XGBRegressor(random_state=42)\n",
    "lr_model = LinearRegression()\n",
    "\n",
    "# Fit the models on the training data\n",
    "rf_model.fit(X_train, y_train)\n",
    "xgb_model.fit(X_train, y_train)\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing data\n",
    "rf_preds = rf_model.predict(X_test)\n",
    "xgb_preds = xgb_model.predict(X_test)\n",
    "lr_preds = lr_model.predict(X_test)\n",
    "\n",
    "# Evaluate the models using root mean squared error (RMSE)\n",
    "rf_rmse = mean_squared_error(y_test, rf_preds, squared=False)\n",
    "xgb_rmse = mean_squared_error(y_test, xgb_preds, squared=False)\n",
    "lr_rmse = mean_squared_error(y_test, lr_preds, squared=False)\n",
    "\n",
    "# Print the RMSE scores\n",
    "print(f\"Random Forest RMSE: {rf_rmse:.2f}\")\n",
    "print(f\"XGBoost RMSE: {xgb_rmse:.2f}\")\n",
    "print(f\"Linear Regression RMSE: {lr_rmse:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f9dfda36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['car_model.joblib']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xgb_model, \"car_model.joblib\", compress=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faeff4c7",
   "metadata": {},
   "source": [
    "**Note: Many Models have been built, but only the needed ones were kept**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b6ca9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(xgb_preds, label='prediction',color='red')\n",
    "sns.histplot(y_test, label='actual price', color = 'blue')\n",
    "plt.title('Prediction Vs Actual')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e921f047",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e23ac604",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):\n",
    "    # Load the encoders and scaler\n",
    "    make_encoder = joblib.load(\"make_encoder.joblib\")\n",
    "    fuel_encoder = joblib.load(\"fuel_encoder.joblib\")\n",
    "    transmission_encoder = joblib.load(\"transmission_encoder.joblib\")\n",
    "    condition_encoder = joblib.load(\"condition_encoder.joblib\")\n",
    "    scaler = joblib.load(\"scaler.joblib\")\n",
    "\n",
    "    # Preprocess the input\n",
    "    make_encoded = make_encoder.transform([make])[0]\n",
    "    numerical_value = scaler.transform([[year,mileage, engine_size]])\n",
    "    year_scaled = numerical_value[0][0]\n",
    "    mileage_scaled = numerical_value[0][1]\n",
    "    engine_size_scaled = numerical_value[0][2]\n",
    "    fuel_encoded = fuel_encoder.transform([fuel])[0]\n",
    "    condition_encoded = condition_encoder.transform([condition])[0]\n",
    "    transmission_encoded = transmission_encoder.transform([transmission])[0]\n",
    "\n",
    "    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]\n",
    "    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])\n",
    "\n",
    "    # Make predictions\n",
    "    predicted_price = xgb_model.predict(input_df)\n",
    "    return round(predicted_price[0], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07692f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3857971.8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_car_price('Toyota', 2010,'Nigerian Used', 3000, 2300, 'Petrol', 'Automatic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce6ae74",
   "metadata": {},
   "source": [
    "### Gradio Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e33e3abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7864\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import joblib\n",
    "def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):\n",
    "    # Load the encoders and scaler\n",
    "    make_encoder = joblib.load(\"make_encoder.joblib\")\n",
    "    fuel_encoder = joblib.load(\"fuel_encoder.joblib\")\n",
    "    transmission_encoder = joblib.load(\"transmission_encoder.joblib\")\n",
    "    condition_encoder = joblib.load(\"condition_encoder.joblib\")\n",
    "    scaler = joblib.load(\"scaler.joblib\")\n",
    "\n",
    "    make_encoded = make_encoder.transform([make])[0]\n",
    "    numerical_value = scaler.transform([[year,mileage, engine_size]])\n",
    "    year_scaled = numerical_value[0][0]\n",
    "    mileage_scaled = numerical_value[0][1]\n",
    "    engine_size_scaled = numerical_value[0][2]\n",
    "    fuel_encoded = fuel_encoder.transform([fuel])[0]\n",
    "    condition_encoded = condition_encoder.transform([condition])[0]\n",
    "    transmission_encoded = transmission_encoder.transform([transmission])[0]\n",
    "\n",
    "    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]\n",
    "    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])\n",
    "\n",
    "    # Make predictions\n",
    "    predicted_price = xgb_model.predict(input_df)\n",
    "    return round(predicted_price[0], 2)\n",
    "make_dropdown = gr.inputs.Dropdown(['Acura', 'Audi', 'BMW', 'Chevrolet', 'Dodge', 'Ford', 'Honda',\n",
    "       'Hyundai', 'Infiniti', 'Kia', 'Land Rover', 'Lexus', 'Mazda',\n",
    "       'Mercedes-Benz', 'Mitsubishi', 'Nissan', 'Others', 'Peugeot',\n",
    "       'Pontiac', 'Toyota', 'Volkswagen', 'Volvo'], label=\"Make\")\n",
    "condition_dropdown = gr.inputs.Dropdown(['Foreign Used', 'Nigerian Used','Brand New'], label=\"Condition\")\n",
    "fuel_dropdown = gr.inputs.Dropdown([\"Petrol\", \"Diesel\", \"Electric\"], label=\"Fuel\")\n",
    "transmission_dropdown = gr.inputs.Dropdown([\"Manual\", \"Automatic\", \"AMT\"], label=\"Transmission\")\n",
    "\n",
    "iface = gr.Interface(\n",
    "    fn=predict_car_price,\n",
    "    inputs=[make_dropdown, \"number\", condition_dropdown, \"number\", \"number\", fuel_dropdown, transmission_dropdown],\n",
    "    outputs=\"number\",\n",
    "    title=\"Car Price Prediction\",\n",
    "    description=\"Predict the price of a car based on its details, in Naira\",\n",
    "    examples=[\n",
    "        [\"Toyota\", 2010, \"Nigerian Used\", 80000, 2.0, \"Petrol\", \"Automatic\"],\n",
    "        [\"Mercedes-Benz\", 2015, \"Foreign Used\", 50000, 1000, \"Diesel\", \"AMT\"],\n",
    "    ],css=\".gradio-container {background-color: lightgreen}\"\n",
    ")\n",
    "\n",
    "iface.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135354f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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