Update app.py

app.py

@@ -64,14 +64,10 @@ CLUSTER_PROFILES = {
 }
 
 # Defaults for hidden vars when user adjusts sliders manually (TOTAL means)
-HIDDEN_DEFAULTS = {
-    "Voice": 4.23,
-    "DecisionAutonomy": 4.50,
-    "Workload": 4.13,
-}
-
+HIDDEN_DEFAULTS = {"Voice": 4.23, "DecisionAutonomy": 4.50, "Workload": 4.13}
 VISIBLE_DRIVERS = ["Engagement", "SupportiveGM", "WellBeing", "WorkEnvironment"]
 
+
 # =========================
 # Helpers
 # =========================
@@ -82,8 +78,7 @@ def clamp_1_5(x: float) -> float:
 
 def build_X(vals: dict) -> pd.DataFrame:
     """Build 1-row DataFrame in exact model feature order."""
-    row = {f: vals[f] for f in FEATURES}
-    return pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
+    return pd.DataFrame([[vals[f] for f in FEATURES]], columns=FEATURES)
 
 
 def prob_at_risk(X: pd.DataFrame) -> float:
@@ -117,13 +112,11 @@ def make_driver_plot_midlevel(Engagement, SupportiveGM, WellBeing, WorkEnvironme
     values = [Engagement, SupportiveGM, WellBeing, WorkEnvironment]
     colors = ["seagreen" if v >= threshold else "firebrick" for v in values]
 
-    # Deterministic layout; helps reduce reflow/jitter in Spaces
+    # Deterministic layout (reduces jitter)
     fig, ax = plt.subplots(figsize=(11, 5), constrained_layout=True)
-
     ax.bar(drivers, values, color=colors)
-    ax.axhline(threshold, linestyle="--", linewidth=2)
 
-    # Stable label position in axis coordinates
+    ax.axhline(threshold, linestyle="--", linewidth=2)
     ax.text(
         0.99,
         threshold,
@@ -164,12 +157,9 @@ def predict_dashboard(
 
     # hidden
     Voice = clamp_1_5(HIDDEN_DEFAULTS["Voice"] if Voice is None else Voice)
-    DecisionAutonomy = clamp_1_5(
-        HIDDEN_DEFAULTS["DecisionAutonomy"] if DecisionAutonomy is None else DecisionAutonomy
-    )
+    DecisionAutonomy = clamp_1_5(HIDDEN_DEFAULTS["DecisionAutonomy"] if DecisionAutonomy is None else DecisionAutonomy)
     Workload = clamp_1_5(HIDDEN_DEFAULTS["Workload"] if Workload is None else Workload)
 
-    # Table: risk status by management level
     rows = []
     for mgmt in [1, 2, 3, 4]:
         vals = {
@@ -193,21 +183,14 @@ def predict_dashboard(
 
     df_table = pd.DataFrame(rows)
 
-    # Headline = Mid-level status
     mid_status = df_table.loc[df_table["Management Level"] == MGMT_LABELS[2], "Risk Status"].iloc[0]
     headline = f"Mid-level managers: **{mid_status}**"
 
-    # Plot only the 4 drivers (mid-level focused)
     fig = make_driver_plot_midlevel(Engagement, SupportiveGM, WellBeing, WorkEnvironment)
-
     return headline, df_table, fig
 
 
 def apply_cluster(cluster_name: str):
-    """
-    Load cluster means into the 4 sliders AND adjust hidden vars to that cluster's means,
-    then run prediction dashboard.
-    """
     prof = CLUSTER_PROFILES[cluster_name]
 
     Engagement = prof["Engagement"]
@@ -229,15 +212,23 @@ def apply_cluster(cluster_name: str):
         Workload=Workload,
     )
 
-    # Return sliders first so UI updates smoothly
     return Engagement, SupportiveGM, WellBeing, WorkEnvironment, headline, table, fig
 
 
 # =========================
-# Gradio UI (Gradio 6-safe CSS + fixed heights)
+# Gradio UI (CSS locks layout without Dataframe height=)
 # =========================
 CSS = """
 #headline_box { min-height: 44px; }
+
+/* fixed-height container for the dataframe to prevent page reflow */
+#table_box {
+  height: 240px;
+  overflow: auto;
+  border: 1px solid rgba(0,0,0,0.08);
+  border-radius: 10px;
+  padding: 6px;
+}
 """
 
 with gr.Blocks() as demo:
@@ -262,21 +253,21 @@ with gr.Blocks() as demo:
             btn_predict = gr.Button("Predict (manual)")
 
         with gr.Column(scale=1, min_width=520):
-            # Use elem_id on the Markdown itself (no gr.Box in Gradio 6)
             headline = gr.Markdown(elem_id="headline_box")
 
-            # Fixed heights reduce reflow/jitter
-            table = gr.Dataframe(label="Risk Status by Management Level", height=220)
+            # Wrap dataframe in a fixed-height container using HTML
+            gr.HTML('<div id="table_box">')
+            table = gr.Dataframe(label="Risk Status by Management Level")
+            gr.HTML("</div>")
+
             plot = gr.Plot(height=420)
 
-    # Manual predict
     btn_predict.click(
         fn=predict_dashboard,
         inputs=[Engagement, SupportiveGM, WellBeing, WorkEnvironment],
         outputs=[headline, table, plot],
     )
 
-    # Cluster buttons (load means + predict)
     btn_c1.click(
         fn=lambda: apply_cluster("Cluster 1"),
         inputs=[],