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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "68bddf84-439e-461b-b7f9-4c9212ca81f5", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "%%capture --no-display\n", | |
| "pip install gradio\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "d692b9ae-c7d4-40b6-9777-992a4303055b", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import gradio as gr" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "9742e1cb-7e40-4af9-8c59-6a341c9d9b38", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pickle\n", | |
| "import pandas as pd\n", | |
| "import shap\n", | |
| "from shap.plots._force_matplotlib import draw_additive_plot\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler # Importing function for scaling the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "7ab0387d-3a31-4164-a46e-b1bb57dbf33a", | |
| "metadata": {}, | |
| "source": [ | |
| "# App Code" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "b168e539-83f7-4bb6-a112-fe8de8fdab52", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "('WellBeing',\n", | |
| " 'SupportiveGM',\n", | |
| " 'Engagement',\n", | |
| " 'Workload',\n", | |
| " 'WorkEnvironment',\n", | |
| " 'Merit')" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "'WellBeing', 'SupportiveGM', 'Engagement', 'Workload', 'WorkEnvironment', 'Merit'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "id": "fc97600d-3c32-4d66-a60e-b416c170293b", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "* Running on local URL: http://127.0.0.1:7860\n", | |
| "* Running on public URL: https://35c8546aedfc1b28e8.gradio.live\n", | |
| "\n", | |
| "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div><iframe src=\"https://35c8546aedfc1b28e8.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import gradio as gr\n", | |
| "import pickle\n", | |
| "import pandas as pd\n", | |
| "import shap\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "# Load model\n", | |
| "filename = 'xgb_h_new.pkl'\n", | |
| "with open(filename, 'rb') as f:\n", | |
| " loaded_model = pickle.load(f)\n", | |
| "\n", | |
| "# Setup SHAP\n", | |
| "explainer = shap.Explainer(loaded_model)\n", | |
| "\n", | |
| "# Employee Profiles (Adjusted Top Performer)\n", | |
| "employee_profiles = {\n", | |
| " \"📈 High Potential Employee\": [4, 5, 5, 3, 4, 5],\n", | |
| " \"🏆 Top Performer\": [5, 5, 5, 3, 5, 5], # Reduced workload\n", | |
| " \"⚠️ At-Risk Employee\": [2, 2, 2, 4, 2, 2],\n", | |
| " \"🔥 Burnt-Out Employee\": [1, 2, 2, 5, 1, 1]\n", | |
| "}\n", | |
| "\n", | |
| "# Define the prediction function\n", | |
| "def main_func(WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit):\n", | |
| " new_row = pd.DataFrame({\n", | |
| " 'WellBeing': [WellBeing],\n", | |
| " 'SupportiveGM': [SupportiveGM],\n", | |
| " 'Engagement': [Engagement],\n", | |
| " 'Workload': [Workload],\n", | |
| " 'WorkEnvironment': [WorkEnvironment],\n", | |
| " 'Merit': [Merit]\n", | |
| " })\n", | |
| "\n", | |
| " # Predict probability\n", | |
| " prob = loaded_model.predict_proba(new_row)\n", | |
| " shap_values = explainer(new_row)\n", | |
| "\n", | |
| " # Calculate probability values\n", | |
| " stay_prob = round((1 - float(prob[0][0])) * 100, 2)\n", | |
| " leave_prob = round(float(prob[0][0]) * 100, 2)\n", | |
| "\n", | |
| " # Dynamic risk label: Changes color & text based on probability\n", | |
| " risk_label = \"🔴 High Risk of Turnover\" if leave_prob > 50 else \"🟢 Low Risk of Turnover\"\n", | |
| " risk_color = \"red\" if leave_prob > 50 else \"green\"\n", | |
| "\n", | |
| " risk_html = f\"\"\"\n", | |
| " <div style='padding: 15px; border-radius: 8px;'>\n", | |
| " <span style='color: {risk_color}; font-size: 26px; font-weight: bold;'>{risk_label}</span>\n", | |
| " <ul style='list-style-type: none; padding-left: 0; font-size: 20px; font-weight: bold; color: #0057B8;'>\n", | |
| " <li>🧲 Likelihood of Staying: {stay_prob}%</li>\n", | |
| " <li>🚪 Likelihood of Leaving: {leave_prob}%</li>\n", | |
| " </ul>\n", | |
| " </div>\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " # Key Insights (Updated for 0.1-point increments)\n", | |
| " insights_html = \"<div style='font-size: 18px;'>\"\n", | |
| " for feature, shap_val in dict(zip(new_row.columns, shap_values.values[0])).items():\n", | |
| " impact = round(shap_val * 10, 2) # Scaling impact for 0.1 changes\n", | |
| " icon = \"📈\" if shap_val > 0 else \"📉\"\n", | |
| " effect = \"raises turnover risk\" if shap_val > 0 else \"improves retention\"\n", | |
| " insights_html += f\"<p style='margin: 5px 0;'> {icon} <b>Each 0.1-point increase in {feature} {effect} by {abs(impact)}%.</b></p>\"\n", | |
| " insights_html += \"</div>\"\n", | |
| "\n", | |
| " # Final Layout (Risk + Key Insights)\n", | |
| " final_layout = f\"\"\"\n", | |
| " <table style='width:100%; border-collapse: collapse; margin-top: 10px; background-color: #FFFFFF;'>\n", | |
| " <tr>\n", | |
| " <td style='width: 33%; padding: 15px; background-color: #FFFFFF; border-radius: 8px; vertical-align: top;'>\n", | |
| " {risk_html}\n", | |
| " </td>\n", | |
| " <td style='width: 67%; padding: 15px; background-color: #FFFFFF; border-radius: 8px; vertical-align: top;'>\n", | |
| " <b style='color: #0057B8; font-size: 22px;'>Key Insights:</b>\n", | |
| " {insights_html}\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " </table>\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " # Retention vs. Turnover Chart\n", | |
| " fig, ax = plt.subplots()\n", | |
| " categories = [\"Stay\", \"Leave\"]\n", | |
| " values = [stay_prob, leave_prob]\n", | |
| " colors = [\"#0057B8\", \"#D43F00\"]\n", | |
| " ax.barh(categories, values, color=colors)\n", | |
| " for i, v in enumerate(values):\n", | |
| " ax.text(v + 2, i, f\"{v:.2f}%\", va='center', fontweight='bold', fontsize=12)\n", | |
| " ax.set_xlabel(\"Probability (%)\")\n", | |
| " ax.set_title(\"Retention vs. Turnover Probability\")\n", | |
| " plt.tight_layout()\n", | |
| " prob_chart_path = \"prob_chart.png\"\n", | |
| " plt.savefig(prob_chart_path, transparent=True)\n", | |
| " plt.close()\n", | |
| "\n", | |
| " # SHAP Chart\n", | |
| " fig, ax = plt.subplots()\n", | |
| " shap.plots.bar(shap_values[0], max_display=6, show=False)\n", | |
| " ax.set_title(\"Key Drivers of Turnover Risk\")\n", | |
| " plt.tight_layout()\n", | |
| " shap_plot_path = \"shap_plot.png\"\n", | |
| " plt.savefig(shap_plot_path, transparent=True)\n", | |
| " plt.close()\n", | |
| "\n", | |
| " return final_layout, prob_chart_path, shap_plot_path\n", | |
| "\n", | |
| "# UI Setup\n", | |
| "with gr.Blocks() as demo:\n", | |
| " gr.Markdown(\"\"\"\n", | |
| " <div style=\"display: flex; justify-content: center; align-items: center;\">\n", | |
| " <img src=\"https://logos-world.net/wp-content/uploads/2021/02/Hilton-Logo.png\" width=\"250px\">\n", | |
| " </div>\n", | |
| " \"\"\")\n", | |
| " gr.Markdown(\"<h1 style='color: #0057B8;'>Hilton Team Member Retention Predictor</h1>\")\n", | |
| " gr.Markdown(\"\"\"\n", | |
| " <div style='font-size: 20px; color: #0057B8;'>\n", | |
| " ✨ <b>Welcome to Hilton’s Employee Retention Predictor</b><br>\n", | |
| " This tool helps <b>HR leaders & managers</b> assess <b>team member engagement</b> \n", | |
| " and predict <b>turnover risk</b> using AI-powered insights.<br> \n", | |
| " 🔍 <b>See what factors drive retention & make data-driven decisions.</b> \n", | |
| " </div>\n", | |
| " \"\"\")\n", | |
| "\n", | |
| " # Dropdown for Employee Profiles\n", | |
| " profile_dropdown = gr.Dropdown(choices=list(employee_profiles.keys()), label=\"Select Employee Profile\")\n", | |
| "\n", | |
| " # Sliders for input features\n", | |
| " with gr.Row():\n", | |
| " WellBeing = gr.Slider(label=\"WellBeing Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| " SupportiveGM = gr.Slider(label=\"Supportive GM Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| " Engagement = gr.Slider(label=\"Engagement Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| " with gr.Row():\n", | |
| " Workload = gr.Slider(label=\"Workload Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| " WorkEnvironment = gr.Slider(label=\"Work Environment Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| " Merit = gr.Slider(label=\"Merit Score\", minimum=1, maximum=5, value=4, step=0.1)\n", | |
| "\n", | |
| " submit_btn = gr.Button(\"🔎 Click Here to Analyze Retention\")\n", | |
| "\n", | |
| " # Output elements\n", | |
| " prediction = gr.HTML()\n", | |
| " with gr.Row():\n", | |
| " prob_chart = gr.Image(label=\"Retention vs. Turnover Probability\", type=\"filepath\")\n", | |
| " shap_plot = gr.Image(label=\"Key Drivers of Turnover Risk\", type=\"filepath\")\n", | |
| "\n", | |
| " # Allow profile selection to update sliders\n", | |
| " def update_sliders(profile):\n", | |
| " if profile in employee_profiles:\n", | |
| " return employee_profiles[profile]\n", | |
| " return [4, 4, 4, 4, 4, 4]\n", | |
| "\n", | |
| " profile_dropdown.change(update_sliders, inputs=[profile_dropdown], outputs=[WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit])\n", | |
| "\n", | |
| " submit_btn.click(main_func, [WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit], [prediction, prob_chart, shap_plot])\n", | |
| "\n", | |
| "demo.launch(share=True)\n" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.11.9" | |
| } | |
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| "nbformat": 4, | |
| "nbformat_minor": 5 | |
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