app.py

import pickle

import pandas as pd

import shap

import gradio as gr

import numpy as np

import matplotlib.pyplot as plt
 
# Load model

loaded_model = pickle.load(open("salar_xgb_team.pkl", "rb"))
 
# SHAP explainer (Do not change)

explainer = shap.Explainer(loaded_model)
 
# Mapping from dropdown labels to numeric education levels

education_map = {

    "Preschool": 1,

    "1st-4th": 2,

    "5th-6th": 3,

    "7th-8th": 4,

    "9th": 5,

    "10th": 6,

    "11th": 7,

    "12th": 8,

    "HS-grad": 9,

    "Some-college": 10,

    "Assoc-voc": 11,

    "Assoc-acdm": 12,

    "Bachelors": 13,

    "Masters": 14,

    "Prof-school": 15,

    "Doctorate": 16

}
 
# Main prediction function

def main_func(age, education_label, sex, capital_gain, capital_loss, hours_per_week):

    # Validate input

    if age < 18 or age > 100 or hours_per_week < 1 or hours_per_week > 100:

        return {"≤50K": 0.0, ">50K": 0.0}, None, "❌ Invalid inputs. Please check your entries."
 
    # Convert to model format

    education_num = education_map.get(education_label, 9)  # default to HS-grad

    sex_binary = 0 if sex == "Male" else 1
 
    # Build dataframe

    new_row = pd.DataFrame({

        'age': [age],

        'education-num': [education_num],

        'sex': [sex_binary],

        'capital-gain': [capital_gain],

        'capital-loss': [capital_loss],

        'hours-per-week': [hours_per_week]

    })
 
    # Predict and explain

    prob = loaded_model.predict_proba(new_row)

    shap_values = explainer(new_row)
 
    # SHAP plot

    plt.figure(figsize=(8, 4))

    shap.plots.bar(shap_values[0], max_display=6, show=False)

    plt.tight_layout()

    local_plot = plt.gcf()

    plt.close()
 
    # Class and confidence

    pred_class = ">50K" if prob[0][1] > 0.5 else "≤50K"

    confidence = round(prob[0][1] if pred_class == ">50K" else prob[0][0], 2)

    interpretation = f"💼 Prediction: **{pred_class}**\nConfidence: {confidence * 100:.2f}%"
 
    return {

        "≤50K": round(prob[0][0], 2),

        ">50K": round(prob[0][1], 2)

    }, local_plot, interpretation
 
# UI layout

title = "**Salary Predictor & SHAP Explainer** 💰"

description1 = "This app uses demographic and financial info to predict whether someone earns more than $50K annually."

description2 = "Adjust the inputs and click **Analyze** to see prediction and SHAP feature contributions."
 
with gr.Blocks(title=title) as demo:

    gr.Markdown(f"## {title}")

    gr.Markdown(description1)

    gr.Markdown("---")

    gr.Markdown(description2)

    gr.Markdown("---")
 
    with gr.Row():

        with gr.Column(scale=1):

            age = gr.Number(label="Age", value=35, precision=0)

            education_label = gr.Dropdown(

                choices=list(education_map.keys()),

                label="Education Level",

                value="HS-grad"

            )

            sex = gr.Radio(["Male", "Female"], label="Sex")

            capital_gain = gr.Number(label="Capital Gain", value=0)

            capital_loss = gr.Number(label="Capital Loss", value=0)

            hours_per_week = gr.Slider(label="Hours Worked per Week", minimum=1, maximum=100, value=40)
 
            submit_btn = gr.Button("🔍 Analyze")
 
        with gr.Column(scale=1):

            label = gr.Label(label="Predicted Probabilities")

            local_plot = gr.Plot(label="SHAP Feature Importance")

            result_text = gr.Textbox(label="Prediction Summary", lines=2)
 
    submit_btn.click(

        main_func,

        [age, education_label, sex, capital_gain, capital_loss, hours_per_week],

        [label, local_plot, result_text],

        api_name="Salary_Predictor"

    )
 
    gr.Markdown("### Try one of the following examples:")

    gr.Examples(

        examples=[

            [28, "Some-college", "Male", 0, 0, 45],

            [52, "Masters", "Female", 7688, 0, 60],

            [35, "HS-grad", "Male", 0, 1902, 40]

        ],

        inputs=[age, education_label, sex, capital_gain, capital_loss, hours_per_week],

        outputs=[label, local_plot, result_text],

        fn=main_func,

        cache_examples=True

    )
 
    gr.Markdown("---")

    gr.Markdown("Built with love by Team 3 for the 2025 AI Applications Project!")
 
 
    gr.Markdown("---")

    gr.Markdown("📊 Thanks for using the Salary Predictor!")

    gr.Image(

        value="https://media.giphy.com/media/l0MYt5jPR6QX5pnqM/giphy.gif",

        label="",

        show_label=False,

        show_download_button=False,

        height=200

    )

demo.launch(server_name="0.0.0.0", server_port=7860)