Update app.py

app.py

@@ -1,290 +1,154 @@
-{
- "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
+import gradio as gr
+import pickle
+import pandas as pd
+import shap
+import matplotlib.pyplot as plt
+
+# Load model
+filename = 'xgb_h_new.pkl'
+with open(filename, 'rb') as f:
+    loaded_model = pickle.load(f)
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model)
+
+# Employee Profiles (Adjusted Top Performer)
+employee_profiles = {
+    "📈 High Potential Employee": [4, 5, 5, 3, 4, 5],
+    "🏆 Top Performer": [5, 5, 5, 3, 5, 5],  # Reduced workload
+    "⚠️ At-Risk Employee": [2, 2, 2, 4, 2, 2],
+    "🔥 Burnt-Out Employee": [1, 2, 2, 5, 1, 1]
 }
+
+# Define the prediction function
+def main_func(WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit):
+    new_row = pd.DataFrame({
+        'WellBeing': [WellBeing],
+        'SupportiveGM': [SupportiveGM],
+        'Engagement': [Engagement],
+        'Workload': [Workload],
+        'WorkEnvironment': [WorkEnvironment],
+        'Merit': [Merit]
+    })
+
+    # Predict probability
+    prob = loaded_model.predict_proba(new_row)
+    shap_values = explainer(new_row)
+
+    # Calculate probability values
+    stay_prob = round((1 - float(prob[0][0])) * 100, 2)
+    leave_prob = round(float(prob[0][0]) * 100, 2)
+
+    # Dynamic risk label: Changes color & text based on probability
+    risk_label = "🔴 High Risk of Turnover" if leave_prob > 50 else "🟢 Low Risk of Turnover"
+    risk_color = "red" if leave_prob > 50 else "green"
+
+    risk_html = f"""
+    <div style='padding: 15px; border-radius: 8px;'>
+        <span style='color: {risk_color}; font-size: 26px; font-weight: bold;'>{risk_label}</span>
+        <ul style='list-style-type: none; padding-left: 0; font-size: 20px; font-weight: bold; color: #0057B8;'>
+            <li>🧲 Likelihood of Staying: {stay_prob}%</li>
+            <li>🚪 Likelihood of Leaving: {leave_prob}%</li>
+        </ul>
+    </div>
+    """
+
+    # Key Insights (Updated for 0.1-point increments)
+    insights_html = "<div style='font-size: 18px;'>"
+    for feature, shap_val in dict(zip(new_row.columns, shap_values.values[0])).items():
+        impact = round(shap_val * 10, 2)  # Scaling impact for 0.1 changes
+        icon = "📈" if shap_val > 0 else "📉"
+        effect = "raises turnover risk" if shap_val > 0 else "improves retention"
+        insights_html += f"<p style='margin: 5px 0;'> {icon} <b>Each 0.1-point increase in {feature} {effect} by {abs(impact)}%.</b></p>"
+    insights_html += "</div>"
+
+    # Final Layout (Risk + Key Insights)
+    final_layout = f"""
+    <table style='width:100%; border-collapse: collapse; margin-top: 10px; background-color: #FFFFFF;'>
+        <tr>
+            <td style='width: 33%; padding: 15px; background-color: #FFFFFF; border-radius: 8px; vertical-align: top;'>
+                {risk_html}
+            </td>
+            <td style='width: 67%; padding: 15px; background-color: #FFFFFF; border-radius: 8px; vertical-align: top;'>
+                <b style='color: #0057B8; font-size: 22px;'>Key Insights:</b>
+                {insights_html}
+            </td>
+        </tr>
+    </table>
+    """
+
+    # Retention vs. Turnover Chart
+    fig, ax = plt.subplots()
+    categories = ["Stay", "Leave"]
+    values = [stay_prob, leave_prob]
+    colors = ["#0057B8", "#D43F00"]
+    ax.barh(categories, values, color=colors)
+    for i, v in enumerate(values):
+        ax.text(v + 2, i, f"{v:.2f}%", va='center', fontweight='bold', fontsize=12)
+    ax.set_xlabel("Probability (%)")
+    ax.set_title("Retention vs. Turnover Probability")
+    plt.tight_layout()
+    prob_chart_path = "prob_chart.png"
+    plt.savefig(prob_chart_path, transparent=True)
+    plt.close()
+
+    # SHAP Chart
+    fig, ax = plt.subplots()
+    shap.plots.bar(shap_values[0], max_display=6, show=False)
+    ax.set_title("Key Drivers of Turnover Risk")
+    plt.tight_layout()
+    shap_plot_path = "shap_plot.png"
+    plt.savefig(shap_plot_path, transparent=True)
+    plt.close()
+
+    return final_layout, prob_chart_path, shap_plot_path
+
+# UI Setup
+with gr.Blocks() as demo:
+    gr.Markdown("""
+        <div style="display: flex; justify-content: center; align-items: center;">
+            <img src="https://logos-world.net/wp-content/uploads/2021/02/Hilton-Logo.png" width="250px">
+        </div>
+    """)
+    gr.Markdown("<h1 style='color: #0057B8;'>Hilton Team Member Retention Predictor</h1>")
+    gr.Markdown("""
+    <div style='font-size: 20px; color: #0057B8;'>
+        ✨ <b>Welcome to Hilton’s Employee Retention Predictor</b><br>
+        This tool helps <b>HR leaders & managers</b> assess <b>team member engagement</b>  
+        and predict <b>turnover risk</b> using AI-powered insights.<br>  
+        🔍 <b>See what factors drive retention & make data-driven decisions.</b>  
+    </div>
+    """)
+
+    # Dropdown for Employee Profiles
+    profile_dropdown = gr.Dropdown(choices=list(employee_profiles.keys()), label="Select Employee Profile")
+
+    # Sliders for input features
+    with gr.Row():
+        WellBeing = gr.Slider(label="WellBeing Score", minimum=1, maximum=5, value=4, step=0.1)
+        SupportiveGM = gr.Slider(label="Supportive GM Score", minimum=1, maximum=5, value=4, step=0.1)
+        Engagement = gr.Slider(label="Engagement Score", minimum=1, maximum=5, value=4, step=0.1)
+    with gr.Row():
+        Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=0.1)
+        WorkEnvironment = gr.Slider(label="Work Environment Score", minimum=1, maximum=5, value=4, step=0.1)
+        Merit = gr.Slider(label="Merit Score", minimum=1, maximum=5, value=4, step=0.1)
+
+    submit_btn = gr.Button("🔎 Click Here to Analyze Retention")
+
+    # Output elements
+    prediction = gr.HTML()
+    with gr.Row():
+        prob_chart = gr.Image(label="Retention vs. Turnover Probability", type="filepath")
+        shap_plot = gr.Image(label="Key Drivers of Turnover Risk", type="filepath")
+
+    # Allow profile selection to update sliders
+    def update_sliders(profile):
+        if profile in employee_profiles:
+            return employee_profiles[profile]
+        return [4, 4, 4, 4, 4, 4]
+
+    profile_dropdown.change(update_sliders, inputs=[profile_dropdown], outputs=[WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit])
+
+    submit_btn.click(main_func, [WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit], [prediction, prob_chart, shap_plot])
+
+demo.launch()