pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62686831",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "#relevant ML libraries\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold,StratifiedKFold,RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "\n",
    "\n",
    "\n",
    "#ML models\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "677c8189",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Telco-Customer-Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dc609c3",
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b234ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop(['customerID','PhoneService','SeniorCitizen','StreamingMovies','StreamingTV'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48e580e2",
   "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>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender Partner Dependents  tenure     MultipleLines InternetService  \\\n",
       "0  Female     Yes         No       1  No phone service             DSL   \n",
       "1    Male      No         No      34                No             DSL   \n",
       "2    Male      No         No       2                No             DSL   \n",
       "3    Male      No         No      45  No phone service             DSL   \n",
       "4  Female      No         No       2                No     Fiber optic   \n",
       "\n",
       "  OnlineSecurity OnlineBackup DeviceProtection TechSupport        Contract  \\\n",
       "0             No          Yes               No          No  Month-to-month   \n",
       "1            Yes           No              Yes          No        One year   \n",
       "2            Yes          Yes               No          No  Month-to-month   \n",
       "3            Yes           No              Yes         Yes        One year   \n",
       "4             No           No               No          No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237c7c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17930596",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac6dad39",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a41d968",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d513d4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Churn',axis = 1)\n",
    "Y = data['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fd905a61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'Partner', 'Dependents', 'tenure', 'MultipleLines',\n",
       "       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
       "       'TechSupport', 'Contract', 'PaperlessBilling', 'PaymentMethod',\n",
       "       'MonthlyCharges', 'TotalCharges'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0da38375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronic check', 'Mailed check', 'Bank transfer (automatic)',\n",
       "       'Credit card (automatic)'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['PaymentMethod'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "189294e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test  = train_test_split(X, Y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ab7ccf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "numeric_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'mean')),('scaler',StandardScaler())])\n",
    "categorical_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'most_frequent')),('one_hot_encoder',OneHotEncoder())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57face01",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_numeric =['tenure','MonthlyCharges','TotalCharges']\n",
    "data_categorical =['gender','Partner','Dependents','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','Contract','PaperlessBilling','PaymentMethod']\n",
    "preprocessor =ColumnTransformer(transformers =[('numeric',numeric_transformer,data_numeric),('categoric',categorical_transformer,data_categorical)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fddf96f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the estimator\n",
    "final_pipeline = Pipeline(steps =[('processor',preprocessor),('estimator',GradientBoostingClassifier())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "549b5f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create model\n",
    "ml_model = final_pipeline.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a7834699",
   "metadata": {},
   "outputs": [],
   "source": [
    "#evaluation\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "52e33406",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(ml_model, open('ml_model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb4f243",
   "metadata": {},
   "outputs": [],
   "source": [
    "#open a model saved in pickle file format\n",
    "with open(\"filename.pkl\", \"rb\") as f:\n",
    "\tclf  = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c26657",
   "metadata": {},
   "source": [
    "# Gradio app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eda953d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup variables and constants\n",
    "DIRPATH = os.path.dirname(os.path.realname(__file__))\n",
    "ml_core_fp = os.path.join(DIRPATH,'assets','ml','ml_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d5ef800",
   "metadata": {},
   "outputs": [],
   "source": [
    "#execution\n",
    "ml_components_dict = load_ml_components(fp = ml_core_fp)\n",
    "end2end_pipeline =ml_components_dict['pipeline']\n",
    "print(f\"\\n['info']) ML components loaded:{list(ml_components_dict.keys())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0bc6731",
   "metadata": {},
   "outputs": [],
   "source": [
    "#interface\n",
    "demo =gr.interface(interface_function,['text'],'number', example =[])\n",
    "if __name__=='main':\n",
    "    demo.launch(debug = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c74ceb39",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'gradio'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgradio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgr\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gradio'"
     ]
    }
   ],
   "source": [
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "31ea9f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_prediction(gender, Partner, Dependents, tenure, MultipleLines,\n",
    "       InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,\n",
    "       TechSupport, Contract, PaperlessBilling, PaymentMethod,\n",
    "       MonthlyCharges, TotalCharges):\n",
    "    with open(\"ml_model.pkl\", \"rb\") as f:\n",
    "        model = pickle.load(f)\n",
    "        predt = model.predict([[gender, Partner, Dependents, tenure, MultipleLines,\n",
    "       InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,\n",
    "       TechSupport, Contract, PaperlessBilling, PaymentMethod,\n",
    "       MonthlyCharges, TotalCharges]]) \n",
    "    if pedt == 'Yes':\n",
    "        return 'Customer Will Churn'\n",
    "    return 'Customer Will Not Churn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a0143b8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#create the input components for gradio\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m gender_input \u001b[38;5;241m=\u001b[39m \u001b[43mgr\u001b[49m\u001b[38;5;241m.\u001b[39minputs\u001b[38;5;241m.\u001b[39mDropdown(choices \u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFemale\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMale\u001b[39m\u001b[38;5;124m'\u001b[39m]) \n\u001b[0;32m      3\u001b[0m Partner_input \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39minputs\u001b[38;5;241m.\u001b[39mDropdown(choices \u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYes\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo\u001b[39m\u001b[38;5;124m'\u001b[39m]) \n\u001b[0;32m      4\u001b[0m Dependents_input \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39minputs\u001b[38;5;241m.\u001b[39mDropdown(choices \u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYes\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'gr' is not defined"
     ]
    }
   ],
   "source": [
    "#create the input components for gradio\n",
    "gender_input = gr.inputs.Dropdown(choices =['Female', 'Male']) \n",
    "Partner_input = gr.inputs.Dropdown(choices =['Yes', 'No']) \n",
    "Dependents_input = gr.inputs.Dropdown(choices =['Yes', 'No'])\n",
    "tenure_input = gr.Number()\n",
    "MultipleLines_input = gr.inputs.Dropdown(choices =['No phone service', 'No', 'Yes'])\n",
    "InternetService_input = gr.inputs.Dropdown(choices =['DSL', 'Fiber optic', 'No']) \n",
    "OnlineSecurity_input = gr.inputs.Dropdown(choices =['No', 'Yes', 'No internet service']) \n",
    "OnlineBackup_input = gr.inputs.Dropdown(choices =['Yes', 'No', 'No internet service']) \n",
    "DeviceProtection_input = gr.inputs.Dropdown(choices =['No', 'Yes', 'No internet service'])\n",
    "TechSupport_input = gr.inputs.Dropdown(choices =['No', 'Yes', 'No internet service'])\n",
    "Contract_input = gr.inputs.Dropdown(choices =['Month-to-month', 'One year', 'Two year'])\n",
    "PaperlessBilling_input = gr.inputs.Dropdown(choices =['Yes', 'No']) \n",
    "PaymentMethod_input = gr.inputs.Dropdown(choices =['Electronic check', 'Mailed check', 'Bank transfer (automatic)','Credit card (automatic)'])    \n",
    "MonthlyCharges_input = gr.Number()\n",
    "TotalCharges_input = gr.Number()\n",
    "\n",
    "output = gr.Textbox()\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51a0e929",
   "metadata": {},
   "outputs": [],
   "source": [
    "app = gr.Interface(fn =make_prediction, inputs =[gender_input,\n",
    "                                                 Partner_inpu,\n",
    "                                                 Dependents_input,\n",
    "                                                 tenure_input,\n",
    "                                                 MultipleLines_input,\n",
    "                                                 InternetService_input,\n",
    "                                                 OnlineSecurity_input,\n",
    "                                                 OnlineBackup_input,\n",
    "                                                 DeviceProtection_input,\n",
    "                                                 TechSupport_input,\n",
    "                                                 Contract_input,\n",
    "                                                 PaperlessBilling_inpu,\n",
    "                                                 PaymentMethod_input,\n",
    "                                                 MonthlyCharges_input,\n",
    "                                                 TotalCharges_input], outputs = output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1c49c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "app.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51d6612a",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_down ={}\n",
    "for var in categorical_var:\n",
    "    drop_down_choices[var] = list(df[var].unique())"
   ]
  }
 ],
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