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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("heart_xgb.pkl", 'rb'))
# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
# Create the main function for server
def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall):
new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
'cp':cp,'trtbps':trtbps,'chol':chol, 'fbs':fbs, 'restecg':restecg,
'thalachh':thalachh, 'exng':exng, 'oldpeak':oldpeak, 'slp':slp, 'caa':caa, 'thall':thall}, 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 = "**Heart Attack Predictor & Interpreter** 🪐"
description1 = """
This app takes info from subjects and predicts their heart attack likelihood. Do not use for medical diagnosis.
"""
description2 = """
To use the app, click on one of the examples, or adjust the values of the 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("""---""")
age = gr.Slider(label="age score", minimum=15, maximum=90, value=40, step=5)
sex = gr.Slider(label="sex score", minimum=0, maximum=1, value=1, step=1)
cp = gr.Slider(label="cp score", minimum=1, maximum=5, value=4, step=1)
trtbps = gr.Slider(label="trtbps Score", minimum=1, maximum=5, value=4, step=1)
chol = gr.Slider(label="chol Score", minimum=1, maximum=5, value=4, step=1)
fbs = gr.Slider(label="fbs Score", minimum=1, maximum=5, value=4, step=1)
restecg = gr.Slider(label="restecg Score", minimum=1, maximum=5, value=4, step=1)
thalachh = gr.Slider(label="thalachh Score", minimum=1, maximum=5, value=4, step=1)
exng = gr.Slider(label="exng Score", minimum=1, maximum=5, value=4, step=1)
oldpeak = gr.Slider(label="oldpeak Score", minimum=1, maximum=5, value=4, step=1)
slp = gr.Slider(label="slp Score", minimum=1, maximum=5, value=4, step=1)
caa = gr.Slider(label="caa Score", minimum=1, maximum=5, value=4, step=1)
thall = gr.Slider(label="thall 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,
[age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall],
[label,local_plot], api_name="Employee_Turnover"
)
gr.Markdown("### Click on any of the examples below to see how it works:")
gr.Examples([[24,0,4,4,5,4,4,5,5,1,2,3,4], [20,0,3,4,5,4,4,5,5,1,2,3,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall], [label,local_plot], main_func, cache_examples=True)
demo.launch() |