Upload app.py

app.py

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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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}