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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(share=True) | |