Update app.py

app.py

@@ -49,36 +49,67 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown(description2)
     gr.Markdown("---")
 
+    # 🎛 Preset scenario dropdown
+    scenario = gr.Dropdown(
+        ["Select a Scenario", "Young, Educated, High Hours", "Older, Low Education, Low Hours"],
+        label="📋 Choose a Sample Profile (optional)"
+    )
+
+    # 🎯 Inputs
     with gr.Row():
-        age = gr.Number(label="Age", value=35)
-        education_num = gr.Number(label="Education Level (numeric)", value=10)
+        age = gr.Number(label="🧓 Age", value=35).info("Age of the individual")
+        education_num = gr.Number(label="🎓 Education Level (numeric)", value=10).info("e.g., 12 = HS grad, 16 = college")
     with gr.Row():
-        sex = gr.Radio(["Male", "Female"], label="Sex")  
-        capital_gain = gr.Number(label="Capital Gain", value=0)
-        capital_loss = gr.Number(label="Capital Loss", value=0)
-        hours_per_week = gr.Number(label="Hours per Week", value=40)
+        sex = gr.Radio(["Male", "Female"], label="🧍 Sex")
+        capital_gain = gr.Number(label="📈 Capital Gain", value=0).info("Income from investment gains")
+        capital_loss = gr.Number(label="📉 Capital Loss", value=0).info("Losses reported for tax purposes")
+        hours_per_week = gr.Number(label="⏱ Hours per Week", value=40).info("Average hours worked per week")
+
+    submit_btn = gr.Button("🔎 Analyze")
+
+    # 🔁 Handle preset scenario changes
+    def fill_scenario(scenario_choice):
+        if scenario_choice == "Young, Educated, High Hours":
+            return [28, 16, "Male", 0, 0, 45]
+        elif scenario_choice == "Older, Low Education, Low Hours":
+            return [60, 8, "Female", 0, 0, 25]
+        else:
+            return [35, 10, "Male", 0, 0, 40]
 
-    submit_btn = gr.Button("Analyze")
+    scenario.change(
+        fn=fill_scenario,
+        inputs=[scenario],
+        outputs=[age, education_num, sex, capital_gain, capital_loss, hours_per_week]
+    )
 
+    # 🧠 Prediction output
     with gr.Column(visible=True) as output_col:
-        label = gr.Label(label="Predicted Income")
-        local_plot = gr.Plot(label="Top SHAP Features")
+        label = gr.Label(label="🧠 Predicted Income")
+        confidence = gr.Slider(0, 100, value=50, label="📊 Confidence in > $50K", interactive=False)
+        local_plot = gr.Plot(label="🔍 Top SHAP Features")
+
+    # 🧠 Wrap predict + confidence slider logic
+    def wrapped_main(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
+        result, shap_plot = main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week)
+        return result, float(result["> $50K"]) * 100, shap_plot
 
     submit_btn.click(
-        main_func,
+        wrapped_main,
         [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
-        [label, local_plot],
+        [label, confidence, local_plot],
         api_name="Salary_Predictor"
     )
 
-    gr.Markdown("### Examples:")
-    gr.Examples([
-        [28, 12, "Male", 0, 0, 40],  # Male, younger, more educated
-        [50, 9, "Female", 0, 0, 30]    # Female, mid-education, fewer hours
-    ], 
-    inputs=[age, education_num, sex, capital_gain, capital_loss, hours_per_week], 
-    outputs=[label, local_plot],
-    fn=main_func,
-    cache_examples=True)
+    gr.Markdown("### 🧪 Try Some Examples:")
+    gr.Examples(
+        [
+            [28, 16, "Male", 0, 0, 45],
+            [60, 8, "Female", 0, 0, 25]
+        ],
+        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        [label, confidence, local_plot],
+        wrapped_main,
+        cache_examples=True
+    )
 
 demo.launch()