Update app.py

app.py

@@ -1,3 +1,9 @@
+"""app
+Automatically generated by Colab.
+Original file is located at
+    https://colab.research.google.com/drive/1B_g2XLYu46kFDIFzNnnJzBQ0GBPssCQw
+"""
+
 import pickle
 import pandas as pd
 import shap
@@ -6,79 +12,76 @@ import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
 
-# load the model from disk
+# Load the model
 loaded_model = pickle.load(open("salar_xgb_team.pkl", 'rb'))
 
-# Setup SHAP
-explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+# Setup SHAP (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, salary_class):
+# Define main prediction function
+def main_func(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,'salary-class':salary_class}, 
-               orient = 'index').transpose()
-    
+    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)
-    
+
     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()
     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"""
+    return {"≤ $50K": float(prob[0][0]), "> $50K": float(prob[0][1])}, local_plot
 
-description2 = """Adjust the values below and click 'Analyze' to see the prediction and explanation."""
+# Gradio UI
+title = "**Household Income Predictor & Interpreter** 💰"
+description1 = """This app takes demographic and economic features to predict whether a household earns ≤ $50K or > $50K annually.🚀"""
+description2 = """Adjust the values and click Analyze to get predictions and feature importance."""
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description1)
-    gr.Markdown("---")
+    gr.Markdown("""---""")
     gr.Markdown(description2)
-    gr.Markdown("---")
+    gr.Markdown("""---""")
 
-    with gr.Row():
-        age = gr.Number(label="Age", value=35)
-        education_num = gr.Number(label="Education Level (numeric)", value=10)
-    with gr.Row():
-        sex = gr.Radio(["Male", "Female"], label="Sex", type="index")  # Male = 0, Female = 1
-        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)
+    gr.Image("Household.png")
+
+    age = gr.Number(label="Age", value=35)
+    education_num = gr.Number(label="Education Level (numeric)", value=10)
+    sex = gr.Radio(choices=["Male", "Female"], label="Sex", value="Female")
+    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)
+    # salary_class = gr.Number(label="(Optional) Salary Class for SHAP Context", value=0)  # Can remove if not needed
 
     submit_btn = gr.Button("Analyze")
 
     with gr.Column(visible=True) as output_col:
         label = gr.Label(label="Predicted Income")
-        local_plot = gr.Plot(label="Top SHAP Features")
+        local_plot = gr.Plot(label='SHAP Interpretation:')
 
     submit_btn.click(
         main_func,
         [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
-        [label, local_plot],
-        api_name="Salary_Predictor"
+        [label, local_plot], api_name="Income_Predictor"
     )
 
-    gr.Markdown("### Examples:")
-    gr.Examples([
-        [28, 12, 0, 0, 0, 40],  # Male, younger, more educated
-        [50, 9, 1, 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 these examples:")
+    gr.Examples(
+        [[39,13, "Male", 0, 0, 40], [52, 9, "Female", 0, 1876, 45]],
+        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        [label, local_plot],
+        main_func,
+        cache_examples=True
+    )
 
 demo.launch()