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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 from disk
loaded_model = pickle.load(open("h22_xgb.pkl", 'rb'))
# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
# Create the main function for server
def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance):
new_row = pd.DataFrame.from_dict({'ValueDiversity':ValueDiversity,'AdequateResources':AdequateResources,
'Voice':Voice,'GrowthAdvancement':GrowthAdvancement,'Workload':Workload,
'WorkLifeBalance':WorkLifeBalance}, 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 {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
# Create the UI
title = "**Employee Turnover Predictor & Interpreter** 🪐"
description1 = """
This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave prediction.✨
"""
description2 = """
To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. 🤞
"""
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}")
# gr.Markdown("""""")
gr.Markdown(description1)
gr.Markdown("""---""")
gr.Markdown(description2)
gr.Markdown("""---""")
ValueDiversity = gr.Slider(label="ValueDiversity Score", minimum=1, maximum=5, value=4, step=1)
AdequateResources = gr.Slider(label="AdequateResources Score", minimum=1, maximum=5, value=4, step=1)
Voice = gr.Slider(label="Voice Score", minimum=1, maximum=5, value=4, step=1)
GrowthAdvancement = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=1)
Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=1)
WorkLifeBalance = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
submit_btn = gr.Button("Analyze")
with gr.Column(visible=True) as output_col:
label = gr.Label(label = "Predicted Label")
local_plot = gr.Plot(label = 'Shap:')
submit_btn.click(
main_func,
[ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
[label,local_plot], api_name="Employee_Turnover"
)
gr.Markdown("### Click on any of the examples below to see how it works:")
gr.Examples([[4,4,4,4,5,5], [5,4,5,4,4,4]], [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance], [label,local_plot], main_func, cache_examples=True)
demo.launch() |