Update app.py

app.py

@@ -1,4 +1,4 @@
-import pickle
+import pickle 
 import pandas as pd
 import shap
 from shap.plots._force_matplotlib import draw_additive_plot
@@ -6,60 +6,62 @@ import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
 
-# load the model from disk
-loaded_model = pickle.load(open("salar_xgb_team.pkl", 'rb'))
+# Load your regression model (e.g., XGBoost Regressor)
+loaded_model = pickle.load(open("income_regressor_model.pkl", 'rb'))
 
-# Setup SHAP
-explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+# SHAP setup (do not change)
+explainer = shap.Explainer(loaded_model)
 
-# Create the main function for server
-def main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
+# Define the main prediction function
+def predict_affordability(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
     sex = 1 if sex == "Female" else 0
-    new_row = pd.DataFrame.from_dict({'age':age,
-              'education-num':education_num,'sex':sex,'capital-gain':capital_gain,
-              'capital-loss':capital_loss, 'hours-per-week':hours_per_week}, 
-               orient = 'index').transpose()
-    
-    prob = loaded_model.predict_proba(new_row)
+    new_row = pd.DataFrame.from_dict({
+        'age': age,
+        'education-num': education_num,
+        'sex': sex,
+        'capital-gain': capital_gain,
+        'capital-loss': capital_loss,
+        'hours-per-week': hours_per_week
+    }, orient='index').transpose()
     
+    predicted_income = loaded_model.predict(new_row)[0]
+    affordable = predicted_income >= 80000  # Car price threshold
+
     shap_values = explainer(new_row)
-    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
-    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
     plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
-
     plt.tight_layout()
-    local_plot = plt.gcf()
+    plot_fig = plt.gcf()
     plt.close()
-    
-    return {
-        "Chance of Earning > $50K": float(prob[0][1]),
-        "Chance of Earning ≤ $50K": float(prob[0][0])
-    }, local_plot
 
-# Create the UI
-title = "**Household Income Predictor** 💰"
-description1 = """This app uses your input to predict whether a household earns more or less than $50K per year"""
+    affordability_msg = (
+        f"✅ You can likely afford a $40K car! (Predicted Income: ${predicted_income:,.2f})"
+        if affordable else
+        f"❌ A $40K car may be unaffordable. (Predicted Income: ${predicted_income:,.2f})"
+    )
+
+    return affordability_msg, predicted_income, plot_fig
 
-description2 = """Adjust the values below and click 'Analyze' to see the prediction and explanation."""
+# Gradio UI
+title = "**Car Affordability Predictor 🚗**"
+desc1 = "This app uses your input to estimates annual houshold income and tells you if you can afford a $40,000 car."
+desc2 = "Fill in the values below to get a prediction and explanation."
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
-    gr.Markdown(description1)
+    gr.Markdown(desc1)
     gr.Markdown("---")
-    gr.Markdown(description2)
+    gr.Markdown(desc2)
     gr.Markdown("---")
 
-    # 🎛 Preset scenario dropdown
     scenario = gr.Dropdown(
-        ["Select a Sample", 
-     "👨‍💻 Young Tech Worker: 28 yrs, college degree, 45 hrs/week", 
-     "👵 Retired Part-Timer: 65 yrs, no college, 20 hrs/week", 
-     "👩‍🏫 Mid-Career Teacher: 42 yrs, 14 education years, 38 hrs/week", 
-     "👨‍🔧 Manual Laborer: 50 yrs, 9 education years, 60 hrs/week"],
-     label="📋 Choose a Sample Profile (optional — autofills values to explore common cases)"
+        ["Select a Sample",
+         "👨‍💻 Young Tech Worker: 28 yrs, college degree, 45 hrs/week", 
+         "👵 Retired Part-Timer: 65 yrs, no college, 20 hrs/week", 
+         "👩‍🏫 Mid-Career Teacher: 42 yrs, 14 education years, 38 hrs/week", 
+         "👨‍🔧 Manual Laborer: 50 yrs, 9 education years, 60 hrs/week"],
+        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)
@@ -69,9 +71,6 @@ with gr.Blocks(title=title) as demo:
         capital_loss = gr.Number(label="📉 Capital Loss", value=0)
         hours_per_week = gr.Number(label="⏱ Hours per Week", value=40)
 
-    submit_btn = gr.Button("🔎 Analyze")
-
-    # 🔁 Handle preset scenario changes
     def fill_scenario(scenario_choice):
         if scenario_choice == "👨‍💻 Young Tech Worker: 28 yrs, college degree, 45 hrs/week":
             return [28, 16, "Male", 0, 0, 45]
@@ -82,7 +81,7 @@ with gr.Blocks(title=title) as demo:
         elif scenario_choice == "👨‍🔧 Manual Laborer: 50 yrs, 9 education years, 60 hrs/week":
             return [50, 9, "Male", 0, 0, 60]
         else:
-            return [35, 10, "Male", 0, 0, 40]  # Default values
+            return [35, 10, "Male", 0, 0, 40]
 
     scenario.change(
         fn=fill_scenario,
@@ -90,34 +89,27 @@ with gr.Blocks(title=title) as demo:
         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")
-        confidence = gr.Slider(0, 100, value=50, label="📊 Confidence in > $50K", interactive=False)
-        local_plot = gr.Plot(label="🔍 Top SHAP Features")
+    submit_btn = gr.Button("🔍 Predict Affordability")
 
-    # 🧠 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["Chance of Earning > $50K"]) * 100, shap_plot
+    with gr.Column(visible=True):
+        output_label = gr.Textbox(label="💬 Result")
+        predicted_income = gr.Number(label="📈 Predicted Annual Income ($)", interactive=False)
+        shap_plot = gr.Plot(label="🔍 Top SHAP Features")
 
     submit_btn.click(
-        wrapped_main,
+        predict_affordability,
         [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
-        [label, confidence, local_plot],
-        api_name="Salary_Predictor"
+        [output_label, predicted_income, shap_plot],
+        api_name="Car_Affordability"
     )
 
-    gr.Markdown("### 🧪 Try Some Examples:")
+    gr.Markdown("### 🧪 Try Examples:")
     gr.Examples(
-        [
-            [28, 16, "Male", 0, 0, 45],
-            [60, 8, "Female", 0, 0, 25]
-        ],
+        [[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,
+        [output_label, predicted_income, shap_plot],
+        predict_affordability,
         cache_examples=True
     )
 
-demo.launch()
+demo.launch()