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app.py
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# -*- coding: utf-8 -*-
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"""app.py
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1B_g2XLYu46kFDIFzNnnJzBQ0GBPssCQw
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"""
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import pickle
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import pandas as pd
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import shap
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from shap.plots._force_matplotlib import draw_additive_plot
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import gradio as gr
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import numpy as np
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import matplotlib.pyplot as plt
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# Load the model
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loaded_model = pickle.load(open("salar_xgb_team.pkl", 'rb'))
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# Setup SHAP (do not change)
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explainer = shap.Explainer(loaded_model)
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# Define main prediction function
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def main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
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sex = 1 if sex == "Male" else 0
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new_row = pd.DataFrame.from_dict({
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'age': age,
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'education-num': education_num,
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'sex': sex,
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'capital-gain': capital_gain,
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'capital-loss': capital_loss,
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'hours-per-week': hours_per_week
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}, orient='index').transpose()
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prob = loaded_model.predict_proba(new_row)
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shap_values = explainer(new_row)
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plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
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plt.tight_layout()
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local_plot = plt.gcf()
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plt.close()
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return {"≤ $50K": float(prob[0][0]), "> $50K": float(prob[0][1])}, local_plot
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# Gradio UI
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title = "**Household Income Predictor & Interpreter** 💰"
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description1 = """This app takes demographic and economic features to predict whether a household earns ≤ $50K or > $50K annually."""
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description2 = """Adjust the values and click Analyze to get predictions and feature importance."""
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"## {title}")
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gr.Markdown(description1)
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gr.Markdown("""---""")
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gr.Markdown(description2)
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gr.Markdown("""---""")
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age = gr.Number(label="Age", value=35)
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education_num = gr.Number(label="Education Level (numeric)", value=10)
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sex = gr.Radio(choices=[0,1], label="Sex", value=0) # ✅ Fixed: checkbox -> radio
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capital_gain = gr.Number(label="Capital Gain", value=0)
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capital_loss = gr.Number(label="Capital Loss", value=0)
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hours_per_week = gr.Number(label="Hours per Week", value=40)
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# salary_class = gr.Number(label="(Optional) Salary Class for SHAP Context", value=0) # Can remove if not needed
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submit_btn = gr.Button("Analyze")
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with gr.Column(visible=True) as output_col:
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label = gr.Label(label="Predicted Income")
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local_plot = gr.Plot(label='SHAP Interpretation:')
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submit_btn.click(
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main_func,
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[age, education_num, sex, capital_gain, capital_loss, hours_per_week],
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[label, local_plot], api_name="Income_Predictor"
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)
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gr.Markdown("### Try these examples:")
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gr.Examples(
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[[39,13, 0, 0, 0, 40], [52, 9, 1, 0, 1876, 45]],
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[age, education_num, sex, capital_gain, capital_loss, hours_per_week],
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[label, local_plot],
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main_func,
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cache_examples=True
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)
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demo.launch()
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