Update app.py

app.py

@@ -15,75 +15,61 @@ explainer = shap.Explainer(model)
 # Define prediction function
 def predict_salary(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
     sex_num = 0 if sex == "Male" else 1
-    input_data = pd.DataFrame([[age, int(education_num), sex_num, capital_gain, capital_loss, hours_per_week]],
+    input_data = pd.DataFrame([[age, education_num, sex_num, capital_gain, capital_loss, hours_per_week]],
                               columns=['age', 'education-num', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week'])
 
+    # Prediction & confidence
     pred = model.predict(input_data)[0]
     prob = model.predict_proba(input_data)[0][1]
     label = ">50K" if pred == 1 else "<=50K"
     confidence = f"{prob * 100:.2f}%" if pred == 1 else f"{(1 - prob) * 100:.2f}%"
 
-    # SHAP plot with dark background
+    # SHAP values
     shap_values = explainer(input_data)
-    plt.style.use('dark_background')
-    fig, ax = plt.subplots(figsize=(6, 3))
+    fig, ax = plt.subplots(figsize=(6, 2.5))
     shap.plots.bar(shap_values[0], max_display=6, show=False)
     plt.tight_layout()
-
+    
     return label, confidence, fig
 
-with gr.Blocks(css="""
-    body { background-color: #0a0a23; color: white; }
-    .gr-box { background-color: #1a1a3d !important; border-radius: 12px; padding: 20px; }
-    h1, h3, p { color: white; text-align: center; font-family: 'Segoe UI', sans-serif; }
-    .gr-button { background-color: #1db954 !important; color: white !important; border-radius: 10px; font-size: 1.1em; }
-    .gr-radio .gr-form { display: flex; justify-content: center !important; }
-"""
-) as demo:
-
-    gr.Markdown("""
-        <div style='max-width: 700px; margin: 0 auto;'>
-            <h1 style='font-size: 2.5em;'>💼 Income Prediction App</h1>
-            <p style='font-size: 1.2em;'>Predict whether someone earns more than $50K/year using financial and demographic data, with AI explainability via SHAP.</p>
-        </div>
-    """)
+# Build UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 💼 Income Prediction App")
+    gr.Markdown(
+        """
+        This tool uses a trained XGBoost model to predict whether someone earns more than $50K/year based on demographic and financial information.
+        It also shows which features influenced the prediction the most, using SHAP explainability.
+        """
+    )
 
     with gr.Row():
         with gr.Column():
-            gr.Markdown("<h3>Age</h3>")
-            age = gr.Slider(minimum=0, maximum=100, step=1, value=35, label="", interactive=True)
-
-            gr.Markdown("<h3>Education Level</h3>")
-            education = gr.Dropdown(choices=[str(i) for i in range(1, 17)], value="10", label="", interactive=True)
-
-            gr.Markdown("<h3>Sex</h3>")
-            sex = gr.Radio(choices=["Male", "Female"], value="Male", label="", interactive=True)
-
-            gr.Markdown("<h3>Capital Gain</h3>")
-            capital_gain = gr.Number(value=0, label="", interactive=True)
-
-            gr.Markdown("<h3>Capital Loss</h3>")
-            capital_loss = gr.Number(value=0, label="", interactive=True)
-
-            gr.Markdown("<h3>Hours per Week</h3>")
-            hours_per_week = gr.Number(value=40, label="", interactive=True)
+            age = gr.Number(label="Age", value=35)
+            education = gr.Number(label="Education Level (numeric)", value=10)
+            sex = gr.Radio(["Male", "Female"], label="Sex", value="Male")
+            cap_gain = gr.Number(label="Capital Gain", value=0)
+            cap_loss = gr.Number(label="Capital Loss", value=0)
+            hours = gr.Number(label="Hours per Week", value=40)
+            submit_btn = gr.Button("🔮 Predict")
 
-            predict_btn = gr.Button("🔮 Predict", elem_id="predict-button")
-
-    with gr.Row():
         with gr.Column():
-            gr.Markdown("<h3>Prediction Result</h3>")
-            result = gr.Textbox(label="", interactive=False)
-            confidence = gr.Textbox(label="Confidence", interactive=False)
-            shap_plot = gr.Plot(label="SHAP Feature Importance")
-
-    predict_btn.click(
-        fn=predict_salary,
-        inputs=[age, education, sex, capital_gain, capital_loss, hours_per_week],
-        outputs=[result, confidence, shap_plot]
+            result = gr.Label(label="Predicted Income")
+            confidence = gr.Label(label="Prediction Confidence")
+            shap_plot = gr.Plot(label="Feature Importance (SHAP)")
+
+    gr.Markdown("### 🧪 Try Example Inputs")
+    gr.Examples(
+        examples=[
+            [24, 9, "Female", 0, 0, 25],
+            [45, 13, "Male", 5000, 0, 50],
+            [39, 10, "Female", 0, 0, 35],
+            [60, 16, "Male", 0, 0, 40],
+        ],
+        inputs=[age, education, sex, cap_gain, cap_loss, hours],
     )
 
-demo.launch()
-
-
+    submit_btn.click(fn=predict_salary,
+                     inputs=[age, education, sex, cap_gain, cap_loss, hours],
+                     outputs=[result, confidence, shap_plot])
 
+demo.launch()