Update app.py

app.py

@@ -1,14 +1,42 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+import shap
+import matplotlib.pyplot as plt
 
+# Load model
+with open("salar_xgb_team.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# SHAP setup
+explainer = shap.Explainer(model)
+
+# Prediction function
 def predict_income_fn(age, education, sex, capital_gain, capital_loss, hours_per_week):
-    # Dummy logic for demonstration
-    return ">$50K" if capital_gain > 5000 else "<=50K"
+    sex_num = 0 if sex == "Male" else 1
+    input_data = pd.DataFrame([[age, int(education), sex_num, capital_gain, capital_loss, hours_per_week]],
+                              columns=['age', 'education-num', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week'])
+
+    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
+    shap_values = explainer(input_data)
+    fig, ax = plt.subplots(figsize=(6, 3))
+    shap.plots.bar(shap_values[0], max_display=6, show=False)
+    plt.tight_layout()
+
+    return label, confidence, fig
 
+# Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
-        <div style='text-align: center; max-width: 700px; margin: 0 auto;'>
-            <h1 style='font-size: 2.5em;'>💼 Income Prediction App</h1>
+        <div style='text-align: center; max-width: 750px; margin: 0 auto;'>
+            <h1 style='font-size: 2.5em; color: #1DB954;'>💼 Income Prediction App</h1>
             <p style='font-size: 1.2em;'>Predict whether someone earns more than $50K/year based on financial and demographic data.</p>
         </div>
         """
@@ -20,12 +48,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             age = gr.Number(value=30, label="", interactive=True)
 
             gr.Markdown("<h3 style='text-align:center;'>Education Level</h3>")
-            education = gr.Dropdown(
-                choices=[str(i) for i in range(1, 17)],
-                value="10",
-                label="",
-                interactive=True
-            )
+            education = gr.Dropdown(choices=[str(i) for i in range(1, 17)], value="10", label="", interactive=True)
 
             gr.Markdown("<h3 style='text-align:center;'>Sex</h3>")
             sex = gr.Radio(choices=["Male", "Female"], value="Male", label="", interactive=True)
@@ -36,7 +59,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             gr.Markdown("<h3 style='text-align:center;'>Capital Loss</h3>")
             capital_loss = gr.Number(value=0, label="", interactive=True)
 
-
             gr.Markdown("<h3 style='text-align:center;'>Hours per Week</h3>")
             hours_per_week = gr.Number(value=40, label="", interactive=True)
 
@@ -45,13 +67,19 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     with gr.Row():
         with gr.Column():
-            gr.Markdown("<h3 style='text-align:center;'>Prediction Result</h3>")
-            output = gr.Textbox(label="", interactive=False)
+            gr.Markdown("<h3 style='text-align:center;'>Prediction</h3>")
+            output_label = gr.Textbox(label="Income", interactive=False)
+            output_confidence = gr.Textbox(label="Confidence", interactive=False)
+
+        with gr.Column():
+            gr.Markdown("<h3 style='text-align:center;'>Feature Importance (SHAP)</h3>")
+            shap_plot = gr.Plot(label="")
 
     predict_btn.click(
         fn=predict_income_fn,
         inputs=[age, education, sex, capital_gain, capital_loss, hours_per_week],
-        outputs=[output]
+        outputs=[output_label, output_confidence, shap_plot]
     )
 
 demo.launch()
+