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Update app.py
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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=8, order=shap.Explanation.abs, show_data='auto', show=False)
plt.tight_layout()
local_plot = plt.gcf()
plt.close()
return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
# Create the UI
title = "**Heart Attack Predictor & Interpreter** πŸͺ"
description1 = """This app takes info from subjects and predicts their heart attack likelihood. Do not use these results for an actual medical diagnosis."""
description2 = """
To use the app, simply adjust the inputs and click the "Analyze" button. You can also click one of the examples below to see how it's done!
"""
with gr.Blocks(title=title) as demo:
with gr.Row():
with gr.Column():
gr.Markdown(f"# {title}")
gr.Markdown(f"## How does it work?")
gr.Markdown(description1)
gr.Markdown("""---""")
gr.Markdown(description2)
gr.Markdown("""---""")
with gr.Row():
with gr.Column():
gr.Markdown(f"## Edit the Inputs Below:")
gr.Markdown("""---""")
with gr.Row():
age = gr.Number(label="Age", info="How old are you?", value=40)
# sex = gr.Radio(["Male", "Female"], label = "What Gender are you?", type = "index")
sex = gr.Radio(["Male", "Female"], label="Sex", info="What gender are you?", type="index")
# sex = gr.Radio(choices=["Male", "Female"])
cp = gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest Pain", info="What kind of chest pain do you have?", type="index")
# cp = gr.Slider(label="Chest Pain Type", minimum=1, maximum=5, value=4, step=1)
# trtbps = gr.Slider(label="Resting blood pressure (in mm Hg)", minimum=1, maximum=200, value=4, step=1)
trtbps = gr.Number(label="trtbps", value=100)
chol = gr.Number(label="chol", value=70)
fbs = gr.Radio(["False", "True"], label="fbs", info="Is your fasting blood sugar > 120 mg/dl?" , type="index")
# restecg = gr.Slider(label="Resting ECG Score", minimum=1, maximum=5, value=4, step=1)
restecg = gr.Dropdown(["Normal", "Having ST-T wave abnormality", "Showing probable or definite left ventricular hypertrophy by Estes' criteria"], label="rest_ecg", type="index")
thalachh = gr.Slider(label="thalach Score", minimum=1, maximum=205, value=4, step=1)
exng = gr.Radio(["No", "Yes"], label="Exercise Induced Angina", type="index")
oldpeak = gr.Slider(label="Oldpeak Score", minimum=1, maximum=10, value=4, step=1)
slp = gr.Slider(label="Slp Score", minimum=1, maximum=5, value=4, step=1)
caa = gr.Slider(label="Number of Major Vessels", minimum=1, maximum=3, value=3, step=1)
thall = gr.Slider(label="Thall Score", minimum=1, maximum=5, value=4, step=1)
with gr.Column():
gr.Markdown(f"## Output:")
gr.Markdown("""---""")
with gr.Column(visible=True) as output_col:
label = gr.Label(label = "Predicted Label")
local_plot = gr.Plot(label = 'Shap:')
gr.Markdown(f"## Examples:")
gr.Markdown("""---""")
gr.Markdown("### Click on any of the examples below to see how it works:")
gr.Examples([[24,"Male","Typical Angina",4,5,"True","Normal",4,"No",5,1,2,3], [24,"Female","Asymptomatic",4,5,"False","Normal",2,"Yes",1,1,2,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
submit_btn = gr.Button("Analyze", variant="primary")
gr.Markdown("""---""")
gr.Markdown(f"## Data Dictionary:")
gr.Markdown("""
Age : Age of the patient
Sex : Sex of the patient
trtbps : resting blood pressure (in mm Hg)
chol : cholestoral in mg/dl fetched via BMI sensor
fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
rest_ecg : resting electrocardiographic results
Value 0: normal
Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
thalach : maximum heart rate achieved
target : 0 = less chance of heart attack 1= more chance of heart attack""")
submit_btn.click(
main_func,
[age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
[label,local_plot], api_name="Heart_Predictor"
)
demo.launch()