Churn_to_gradio_interface.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOJOO7cwQC7UTtbk1AVnp62",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kumahag/ML-to-App/blob/main/Churn_to_gradio_interface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install dependencies\n",
        "!pip install gradio"
      ],
      "metadata": {
        "id": "vgf0KlO4gRgI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b68d67c7-5f4e-44ec-84a8-149e89275939"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fS49uOMec7Hk"
      },
      "outputs": [],
      "source": [
        "# Importing the base libraries and packages\n",
        "import pickle\n",
        "import os\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "import re\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# ----- Useful Lists\n",
        "expected_inputs = [\"gender\", \"SeniorCitizen\", \"Partner\", \"Dependents\", \"Contract\", \"tenure\", \"MonthlyCharges\", \"TotalCharges\", \"PaymentMethod\", \"PhoneService\",\n",
        "                   \"MultipleLines\", \"InternetService\", \"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\", \"PaperlessBilling\"]\n",
        "categoricals = [\"gender\", \"SeniorCitizen\", \"Partner\", \"Dependents\", \"PhoneService\", \"MultipleLines\", \"InternetService\", \"OnlineSecurity\",\n",
        "                \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\", \"Contract\", \"PaperlessBilling\", \"PaymentMethod\"]\n",
        "columns_to_scale = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]"
      ],
      "metadata": {
        "id": "cO3VIvy0hIz-"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ----- Helper Functions and Imports\n",
        "# Function to load ML toolkit\n",
        "def load_ml_toolkit(filepath=\"//content/ML_tools\"):\n",
        "    with open(filepath, \"rb\") as file:\n",
        "        loaded_toolkit = pickle.load(file)\n",
        "    return loaded_toolkit"
      ],
      "metadata": {
        "id": "ZGryWvq8iyj2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the toolkit\n",
        "loaded_toolkit = load_ml_toolkit()\n",
        "encoder = loaded_toolkit[\"encoder\"]\n",
        "scaler = loaded_toolkit[\"scaler\"]\n",
        "model = loaded_toolkit[\"model\"]"
      ],
      "metadata": {
        "id": "hBHp0eskjpVM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ff72d88-e45b-42d0-d5cc-06ddccc9f582"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator OneHotEncoder from version 1.1.3 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator MinMaxScaler from version 1.1.3 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LogisticRegression from version 1.1.3 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to predict\n",
        "def predict(*args, encoder = encoder, scaler = scaler, model = model):\n",
        "    input_data = pd.DataFrame([args], columns= expected_inputs)\n",
        "    input_data.fillna(0, inplace= True)\n",
        "    \n",
        "    encoder = loaded_toolkit[\"encoder\"]\n",
        "    scaler = loaded_toolkit[\"scaler\"]\n",
        "    model = loaded_toolkit[\"model\"]\n",
        "\n",
        "    # Encoding the categorical columns\n",
        "    encoded_categoricals = encoder.transform(input_data[categoricals])\n",
        "    encoded_categoricals = pd.DataFrame(encoded_categoricals, columns= encoder.get_feature_names_out().tolist())\n",
        "    # Adding the categorical columns to the input dataframe\n",
        "    df_encoded = input_data.join(encoded_categoricals)\n",
        "    df_encoded.drop(columns= categoricals, inplace=True)\n",
        "    \n",
        "    # Scaling the numeric columns\n",
        "    df_encoded[columns_to_scale] = scaler.transform(df_encoded[columns_to_scale])\n",
        "    \n",
        "    # Restricting column names to alpha-numeric characters\n",
        "    df_processed = df_encoded.rename(columns= lambda x: re.sub(\"[^A-Za-z0-9_]+\", \"\", x))\n",
        "    \n",
        "    # Making the prediction\n",
        "    model_output = model.predict(df_processed)\n",
        "    return float(model_output[0])\n"
      ],
      "metadata": {
        "id": "LgS5eZmJi9xT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# ----- App Interface\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown(\"# Telecom Customer Churn Prediction\")\n",
        "\n",
        "    # ----- Phase 1: Receiving Inputs\n",
        "    gr.Markdown(\"**Demographic Data**\")\n",
        "    with gr.Row():\n",
        "        gender = gr.Dropdown(label=\"Gender\", choices=[\"Male\", \"Female\"])\n",
        "        SeniorCitizen = gr.Radio(label=\"Senior Citizen\", choices=[\"Yes\", \"No\"])\n",
        "        Partner = gr.Radio(label=\"Partner\", choices=[\"Yes\", \"No\"])\n",
        "        Dependents = gr.Radio(label=\"Dependents\", choices=[\"Yes\", \"No\"])\n",
        "\n",
        "    gr.Markdown(\"**Service Length and Charges (USD)**\")\n",
        "    with gr.Row():\n",
        "        Contract = gr.Dropdown(label=\"Contract\", choices=[\"Month-to-month\", \"One year\", \"Two year\"])\n",
        "        tenure = gr.Slider(label=\"Tenure (months)\", minimum=1, step=1, interactive=True)\n",
        "        MonthlyCharges = gr.Slider(label=\"Monthly Charges\", step=0.05)\n",
        "        TotalCharges = gr.Slider(label=\"Total Charges\", step=0.05)\n",
        "\n",
        "    # Phone Service Usage\n",
        "    gr.Markdown(\"**Phone Service Usage**\")\n",
        "    with gr.Row():\n",
        "        PhoneService = gr.Radio(label=\"Phone Service\", choices=[\"Yes\", \"No\"])\n",
        "        MultipleLines = gr.Dropdown(label=\"Multiple Lines\", choices=[\n",
        "                                    \"Yes\", \"No\", \"No phone service\"])\n",
        "\n",
        "    # Internet Service Usage\n",
        "    gr.Markdown(\"**Internet Service Usage**\")\n",
        "    with gr.Row():\n",
        "        InternetService = gr.Dropdown(label=\"Internet Service\", choices=[\"DSL\", \"Fiber Optic\", \"No\"])\n",
        "        OnlineSecurity = gr.Dropdown(label=\"Online Security\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "        OnlineBackup = gr.Dropdown(label=\"Online Backup\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "        DeviceProtection = gr.Dropdown(label=\"Device Protection\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "        TechSupport = gr.Dropdown(label=\"Tech Support\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "        StreamingTV = gr.Dropdown(label=\"TV Streaming\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "        StreamingMovies = gr.Dropdown(label=\"Movie Streaming\", choices=[\"Yes\", \"No\", \"No phone service\"])\n",
        "\n",
        "    # Billing and Payment\n",
        "    gr.Markdown(\"**Billing and Payment**\")\n",
        "    with gr.Row():\n",
        "        PaperlessBilling = gr.Radio(\n",
        "            label=\"Paperless Billing\", choices=[\"Yes\", \"No\"])\n",
        "        PaymentMethod = gr.Dropdown(label=\"Payment Method\", choices=[\"Electronic check\", \"Mailed check\", \"Bank transfer (automatic)\", \"Credit card (automatic)\"])\n",
        "\n",
        "    # Output Prediction\n",
        "    output = gr.Number(label=\"Prediction\")\n",
        "    submit_button = gr.Button(\"Submit\")\n",
        "    \n",
        "    submit_button.click(fn= predict,\n",
        "                        outputs= output,\n",
        "                        inputs= [gender, SeniorCitizen, Partner, Dependents, Contract, tenure, MonthlyCharges, TotalCharges, PaymentMethod, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, PaperlessBilling],\n",
        "                        )\n",
        "\n",
        "demo.launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "xkTVwuMYj7db",
        "outputId": "19643ff0-23af-4fcd-cd4c-39426b0e7565"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    }
  ]
}