Update app.py

app.py

@@ -12,13 +12,12 @@ with open("salar_xgb_team.pkl", "rb") as f:
 # Set up SHAP
 explainer = shap.Explainer(model)
 
-# Define prediction function
+# 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]],
                               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"
@@ -32,31 +31,45 @@ def predict_salary(age, education_num, sex, capital_gain, capital_loss, hours_pe
 
     return label, confidence, fig
 
+
 # Build UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 💼 Income Prediction App")
+    gr.Markdown("<div id='pulse'>💼 AI-Powered Income Prediction</div>", elem_id="pulse")
     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.
-        """
+        "Welcome! This app predicts if a person earns more than $50K/year using a trained XGBoost model, based on demographic and financial information.<br>SHAP values help explain the prediction visually.",
+        elem_id="description"
     )
 
     with gr.Row():
-        with gr.Column():
-            age = gr.Slider(0, 100, label="Age", value=35, step=1)
-            education = gr.Dropdown(choices=[str(i) for i in range(1, 17)],
-                                    label="Education Level (1-16)", 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")
-
-        with gr.Column():
-            result = gr.Label(label="Predicted Income")
-            confidence = gr.Label(label="Prediction Confidence")
-            shap_plot = gr.Plot(label="Feature Importance (SHAP)")
+        with gr.Column(scale=1, min_width=400):
+            with gr.Group():
+                age = gr.Slider(0, 100, label="Age", value=35, step=1)
+
+            with gr.Group():
+                education = gr.Dropdown(
+                    choices=[str(i) for i in range(1, 17)],
+                    label="Education Level (1–16)", value="10"
+                )
+
+            with gr.Group():
+                sex = gr.Radio(["Male", "Female"], label="Sex", value="Male")
+
+            with gr.Group():
+                cap_gain = gr.Number(label="Capital Gain", value=0, placeholder="Enter Numerical Value")
+
+            with gr.Group():
+                cap_loss = gr.Number(label="Capital Loss", value=0, placeholder="Enter Numerical Value")
+
+            with gr.Group():
+                hours = gr.Number(label="Hours per Week", value=40)
+
+            with gr.Row():
+                submit_btn = gr.Button("🔮 Predict")
+
+        with gr.Column(scale=2):
+            result = gr.Label(label="📊 Prediction")
+            confidence = gr.Label(label="📈 Confidence Level")
+            shap_plot = gr.Plot(label="📌 Top Feature Contributions")
 
     gr.Markdown("### 🧪 Try Example Inputs")
     gr.Examples(
@@ -75,8 +88,5 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         outputs=[result, confidence, shap_plot]
     )
 
-# Launch
+# Launch app
 demo.launch()
-
-
-