Spaces:
Build error
Build error
| # -*- coding: utf-8 -*- | |
| """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 | |
| from shap.plots._force_matplotlib import draw_additive_plot | |
| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # Load the model | |
| loaded_model = pickle.load(open("salar_xgb_team.pkl", 'rb')) | |
| # Setup SHAP (do not change) | |
| explainer = shap.Explainer(loaded_model) | |
| # 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 | |
| }, orient='index').transpose() | |
| prob = loaded_model.predict_proba(new_row) | |
| shap_values = explainer(new_row) | |
| 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 {"≤ $50K": float(prob[0][0]), "> $50K": float(prob[0][1])}, local_plot | |
| # 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(description2) | |
| gr.Markdown("""---""") | |
| 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='SHAP Interpretation:') | |
| submit_btn.click( | |
| main_func, | |
| [age, education_num, sex, capital_gain, capital_loss, hours_per_week], | |
| [label, local_plot], api_name="Income_Predictor" | |
| ) | |
| 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() |