Update app.py

app.py

@@ -78,50 +78,69 @@ ALL_DRIVER_LABELS = [
     "Workload",
 ]
 
+
 # =========================
 # Helpers
 # =========================
 def clamp_1_5(x):
     return max(1.0, min(5.0, float(x)))
 
+
 def build_X(vals: dict) -> pd.DataFrame:
     row = {f: vals[f] for f in FEATURES}
     return pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
 
+
 def prob_at_risk(X: pd.DataFrame) -> float:
     probs = model.predict_proba(X)[0]
     classes = list(model.classes_)
     idx = classes.index(1)  # class 1 = At Risk
     return float(probs[idx])
 
+
 def risk_label(p: float) -> str:
     return "At Risk" if p >= 0.5 else "Not At Risk"
 
+
+def not_at_risk_threshold_from_cluster3():
+    """
+    As requested:
+    threshold line = MIN of Cluster 3 across the driver vars in the averages chart,
+    labeled "Not at-risk threshold".
+    """
+    return min(CLUSTER_3[v] for v in ALL_DRIVER_VARS)
+
+
 # =========================
-# Plot: Average of key drivers
-# NOTE: fixed figsize + fixed container height prevents "vibration"
+# Plot: Average of key drivers + threshold
 # =========================
 def make_driver_plot(driver_vals: dict):
     values = [driver_vals[v] for v in ALL_DRIVER_VARS]
+    th = not_at_risk_threshold_from_cluster3()
 
-    fig, ax = plt.subplots(figsize=(8.8, 3.2))
+    fig, ax = plt.subplots(figsize=(8.6, 3.1))
     ax.bar(ALL_DRIVER_LABELS, values)
 
+    # threshold line
+    ax.axhline(th, linestyle="--", linewidth=2)
+    ax.text(len(ALL_DRIVER_LABELS) - 0.1, th, "Not at-risk threshold", va="center", ha="right")
+
     ax.set_ylim(1, 5.4)
     ax.set_yticks([1, 2, 3, 4, 5])
     ax.set_ylabel("Score (1–5)")
     ax.set_title("Average of key drivers")
 
-    ax.margins(x=0.08)
+    ax.margins(x=0.06)
     plt.tight_layout()
     plt.subplots_adjust(bottom=0.30)
     return fig
 
+
 # =========================
 # TRUE SHAP using CatBoost native SHAP values
 # =========================
 def make_catboost_shap_plot(X: pd.DataFrame):
-    fig, ax = plt.subplots(figsize=(8.8, 3.2))
+    fig, ax = plt.subplots(figsize=(8.6, 3.1))
 
     try:
         from catboost import Pool
@@ -131,7 +150,7 @@ def make_catboost_shap_plot(X: pd.DataFrame):
         contrib = shap_vals[0, :-1]  # drop expected value
 
         s = pd.Series(contrib, index=X.columns)
-        s = s.drop(labels=["ManagementLevel"], errors="ignore")  # hide mgmt level from story
+        s = s.drop(labels=["ManagementLevel"], errors="ignore")
         s = s.reindex(s.abs().sort_values(ascending=False).index).head(8)
 
         ax.barh(s.index[::-1], s.values[::-1])
@@ -142,15 +161,19 @@ def make_catboost_shap_plot(X: pd.DataFrame):
 
     except Exception as e:
         ax.text(
-            0.5, 0.55,
+            0.5,
+            0.55,
             "SHAP chart unavailable.\nInstall 'catboost' in requirements.txt.",
-            ha="center", va="center", fontsize=10
+            ha="center",
+            va="center",
+            fontsize=10,
         )
         ax.text(0.5, 0.40, f"Error: {str(e)[:150]}", ha="center", va="center", fontsize=9)
         ax.set_axis_off()
         plt.tight_layout()
         return fig
 
+
 # =========================
 # Core predict
 # =========================
@@ -184,11 +207,12 @@ def predict(
 
     return headline, drivers_fig, shap_fig
 
+
 # =========================
 # Buttons
 # =========================
 def load_at_risk_group():
-    # At risk group = average of Cluster 1 and Cluster 2 (as you requested)
+    # At risk group = average of Cluster 1 and Cluster 2
     avg = {v: (CLUSTER_1[v] + CLUSTER_2[v]) / 2.0 for v in ALL_DRIVER_VARS}
 
     headline, drivers_fig, shap_fig = predict(
@@ -214,6 +238,7 @@ def load_at_risk_group():
         shap_fig,
     )
 
+
 def apply_recommendation():
     # Apply recommendation = move to Cluster 1 target levels
     target = {v: CLUSTER_1[v] for v in ALL_DRIVER_VARS}
@@ -241,35 +266,37 @@ def apply_recommendation():
         shap_fig,
     )
 
+
 # =========================
-# UI Layout (fix vibration)
-# Key fixes:
-# - Use Textbox (fixed height) instead of Markdown
-# - Wrap plots in fixed-height containers using CSS
+# UI Layout (tight, no big gaps)
 # =========================
 CSS = """
 #app-wrap { max-width: 1200px; margin: 0 auto; }
 
-/* Make output panels stable height so the page doesn't reflow */
-.fixed-plot { height: 360px; overflow: hidden; }
+/* Remove extra padding/margins from blocks */
+.gr-block { padding: 10px 12px !important; }
+.gr-form { gap: 8px !important; }
+.gr-row { gap: 10px !important; }
+
+/* Make plot containers stable but NOT huge (reduces empty space) */
+.fixed-plot { height: 330px; overflow: hidden; }
 
-/* Reduce extra vertical whitespace */
-.compact .gr-box, .compact .gr-panel { padding-top: 8px !important; padding-bottom: 8px !important; }
+/* Make markdown tighter */
+.compact h2 { margin: 0 0 6px 0; }
+.compact p { margin: 0 0 8px 0; }
 """
 
 with gr.Blocks(css=CSS) as demo:
     gr.Markdown(
         "<div id='app-wrap' class='compact'>"
         "<h2>Retention Simulator</h2>"
-        "<p style='margin-top:0;'>Use the sliders and click <b>Predict</b>. "
-        "Or click <b>At risk group</b> / <b>Apply recommendation</b>.</p>"
+        "<p>Use sliders + <b>Predict</b>, or click <b>At risk group</b> / <b>Apply recommendation</b>.</p>"
         "</div>"
     )
 
     with gr.Row():
         # LEFT: sliders + buttons
         with gr.Column(scale=5, min_width=430):
-            # Start at Cluster 3 (most at-risk)
             Engagement = gr.Slider(1, 5, value=CLUSTER_3["Engagement"], step=0.01, label="Engagement")
             SupportiveGM = gr.Slider(1, 5, value=CLUSTER_3["SupportiveGM"], step=0.01, label="Supportive GM")
             WellBeing = gr.Slider(1, 5, value=CLUSTER_3["WellBeing"], step=0.01, label="Well-Being")
@@ -283,42 +310,58 @@ with gr.Blocks(css=CSS) as demo:
                 btn_atrisk = gr.Button("At risk group")
                 btn_reco = gr.Button("Apply recommendation")
 
-        # RIGHT: headline + two plots stacked
+        # RIGHT: result + two plots stacked
         with gr.Column(scale=7, min_width=520):
             headline = gr.Textbox(label="Result", value="", interactive=False)
 
             gr.HTML('<div class="fixed-plot">')
             drivers_plot = gr.Plot(label="Average of key drivers")
-            gr.HTML('</div>')
+            gr.HTML("</div>")
 
             gr.HTML('<div class="fixed-plot">')
             shap_plot = gr.Plot(label="Feature Importance (Shap)")
-            gr.HTML('</div>')
+            gr.HTML("</div>")
 
-    # Manual predict (does NOT change sliders)
+    # Predict (does NOT change sliders)
     btn_predict.click(
         fn=predict,
         inputs=[Engagement, SupportiveGM, WellBeing, WorkEnvironment, Voice, DecisionAutonomy, Workload],
         outputs=[headline, drivers_plot, shap_plot],
     )
 
-    # Button: At risk group (updates sliders + outputs)
+    # At risk group (updates sliders + outputs)
     btn_atrisk.click(
         fn=load_at_risk_group,
         inputs=[],
         outputs=[
-            Engagement, SupportiveGM, WellBeing, WorkEnvironment, Voice, DecisionAutonomy, Workload,
-            headline, drivers_plot, shap_plot
+            Engagement,
+            SupportiveGM,
+            WellBeing,
+            WorkEnvironment,
+            Voice,
+            DecisionAutonomy,
+            Workload,
+            headline,
+            drivers_plot,
+            shap_plot,
         ],
     )
 
-    # Button: Apply recommendation (updates sliders + outputs)
+    # Apply recommendation (updates sliders + outputs)
     btn_reco.click(
         fn=apply_recommendation,
         inputs=[],
         outputs=[
-            Engagement, SupportiveGM, WellBeing, WorkEnvironment, Voice, DecisionAutonomy, Workload,
-            headline, drivers_plot, shap_plot
+            Engagement,
+            SupportiveGM,
+            WellBeing,
+            WorkEnvironment,
+            Voice,
+            DecisionAutonomy,
+            Workload,
+            headline,
+            drivers_plot,
+            shap_plot,
         ],
     )