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# Necessary imports
import gradio as gr
import pandas as pd
from pycaret.classification import load_model, predict_model
# Load the tuned model
tuned_gbc_classifier = load_model('tuned_gbc_classifier')
def predict_ten_year_chd(male, age, education, currentSmoker, cigsPerDay, BPMeds, prevalentStroke,
prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
try:
# Convert categorical variables to numerical representation
male = 1 if male == "Male" else 0
education_mapping = {
"Some High School": 0,
"High School Graduate": 1,
"Some College": 2,
"College Graduate": 3
}
education = education_mapping.get(education, 0)
# Create a DataFrame with the input values
data = pd.DataFrame(
data=[[male, age, education, currentSmoker, cigsPerDay, BPMeds, prevalentStroke,
prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]],
columns=['male', 'age', 'education', 'currentSmoker', 'cigsPerDay', 'BPMeds', 'prevalentStroke',
'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
)
# Make a prediction
pred = predict_model(tuned_gbc_classifier, data=data)
# Extract the prediction and the confidence using the correct keys
prediction = pred['prediction_label'].iloc[0]
confidence = pred['prediction_score'].iloc[0]
# Return the prediction with 'At Risk' category for No CHD with confidence < 0.8
if prediction == 0 and confidence < 0.8:
return f"Prediction: No CHD (At Risk), Confidence: {confidence:.2f}"
else:
return f"Prediction: {'Has CHD' if prediction == 1 else 'No CHD'}, Confidence: {confidence:.2f}"
except Exception as e:
return f"An error occurred: {str(e)}"
# Create the Gradio interface
iface = gr.Interface(
fn=predict_ten_year_chd,
inputs=[
gr.inputs.Radio(["Male", "Female"], label="Gender"),
gr.inputs.Slider(minimum=18, maximum=100, label="Age"),
gr.inputs.Dropdown(["Some High School", "High School Graduate", "Some College", "College Graduate"], label="Education"),
gr.inputs.Checkbox(label="Current Smoker"),
gr.inputs.Slider(minimum=0, maximum=50, default=0, label="Cigarettes Per Day"),
gr.inputs.Checkbox(label="On Blood Pressure Medication"),
gr.inputs.Checkbox(label="History of Prevalent Stroke"),
gr.inputs.Checkbox(label="History of Prevalent Hypertension"),
gr.inputs.Checkbox(label="Diabetes"),
gr.inputs.Slider(minimum=100, maximum=400, default=200, label="Total Cholesterol"),
gr.inputs.Slider(minimum=90, maximum=200, default=120, label="Systolic BP"),
gr.inputs.Slider(minimum=60, maximum=120, default=80, label="Diastolic BP"),
gr.inputs.Slider(minimum=15, maximum=50, default=25, label="BMI"),
gr.inputs.Slider(minimum=40, maximum=120, default=75, label="Heart Rate"),
gr.inputs.Slider(minimum=40, maximum=300, default=100, label="Glucose Level")
],
outputs=gr.outputs.Textbox(),
live=False, # set live to False to add a submit button
title="CHD Prediction",
description="By Abderrahim Benmoussa, Ph.D."
)
# Run the app
iface.launch()
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