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Hilton app_1.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import joblib
+import pandas as pd
+import gradio as gr
+
+
+# In[11]:
+
+
+model = joblib.load("cat.joblib")
+
+FEATURES = [
+    "Engagement",
+    "SupportiveGM",
+    "ManagementLevel",
+    "WellBeing",
+    "Voice",
+    "DecisionAutonomy",
+    "AppreciatedAtWork",
+]
+
+def predict_focus_level3(
+    Engagement,
+    SupportiveGM,
+    WellBeing,
+    Voice,
+    DecisionAutonomy,
+    AppreciatedAtWork
+):
+
+    results = []
+
+    # Predict for all levels (for comparison)
+    for mgmt in [1,2,3,4]:
+        X = pd.DataFrame([[
+            Engagement,
+            SupportiveGM,
+            mgmt,
+            WellBeing,
+            Voice,
+            DecisionAutonomy,
+            AppreciatedAtWork,
+        ]], columns=FEATURES)
+
+        p = float(model.predict_proba(X)[0,1])
+
+        results.append({
+            "ManagementLevel": mgmt,
+            "Prob_HighIntent": p
+        })
+
+    df = pd.DataFrame(results)
+
+    # ⭐ Focus result
+    level3_prob = df.loc[df["ManagementLevel"]==3,"Prob_HighIntent"].iloc[0]
+
+    # Bar chart comparison
+    fig, ax = plt.subplots()
+    colors = ["gray","gray","orange","gray"]  # highlight level 3
+    ax.bar(df["ManagementLevel"], df["Prob_HighIntent"], color=colors)
+    ax.set_xlabel("Management Level")
+    ax.set_ylabel("Probability High Intent")
+    ax.set_title("Management Level 3 Highlighted")
+
+    headline = f"Predicted probability of HIGH intent to stay for Management Level 3: {level3_prob:.2%}"
+
+    return headline, df, fig
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Intent to Stay — Management Level 3 Focus")
+
+    with gr.Row():
+        with gr.Column():
+            Engagement = gr.Slider(1,5,step=1,label="Engagement")
+            SupportiveGM = gr.Slider(1,5,step=1,label="Supportive GM")
+            WellBeing = gr.Slider(1,5,step=1,label="Well Being")
+            Voice = gr.Slider(1,5,step=1,label="Voice")
+            DecisionAutonomy = gr.Slider(1,5,step=1,label="Decision Autonomy")
+            AppreciatedAtWork = gr.Slider(1,5,step=1,label="Appreciated At Work")
+
+            btn = gr.Button("Predict")
+
+        with gr.Column():
+            headline = gr.Markdown()
+            table = gr.Dataframe(label="All Management Levels")
+            plot = gr.Plot()
+
+    btn.click(
+        fn=predict_focus_level3,
+        inputs=[
+            Engagement,
+            SupportiveGM,
+            WellBeing,
+            Voice,
+            DecisionAutonomy,
+            AppreciatedAtWork
+        ],
+        outputs=[headline, table, plot]
+    )
+
+
+demo.launch(share=True)
+

cat.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be1987e9f56cdf5871e6e6dcbd6b4cec5aef9a719c1475a87447a47f6b13b558
+size 4374820

requirements.txt

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+gradio
+pandas
+joblib
+catboost
+scikit-learn
+matplotlib