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 color = gr.themes.Default(primary_hue="pink").set( background_fill_primary="#ADD8E6", block_background_fill="#FFFFFF", ) # 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. gender = {"Female":1,"Male":0} chestpain = {"Typical angina":0,"Atypical angina":1,"Non-anginal pain":2,"Asymptomatic":3} bloodsuguar = {"True":1, "False":0} rest = {"Probable or Definite Left Ventricular Hypertrophy by Estes' Criteria":0, "Having ST - T Wave Abnormality":1,"Normal Value":2} ex = {"Yes":1, "No":0} slope = {"Upsloping":0, "Flat":1,"Downsloping":2} thl = {"Reversible Defect":0, "Normal Blood Flow":1, "Fixed Defect":2} # 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':gender[sex], 'cp':chestpain[cp],'trtbps':trtbps,'chol':chol, 'fbs':bloodsuguar[fbs], 'restecg':rest[restecg],'thalachh':thalachh,'exng':ex[exng], 'oldpeak':oldpeak,'slp':slope[slp],'caa':caa,'thall':thl[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 {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot # Create the UI title = "🩺**Heart Attack Risk Calculator** 🏥" title_length = len(title) title = title.center(title_length + 6) description1 = """ This app retrieves data from individuals and anticipates their probability of experiencing a heart attack. However, it should not be utilized for medical diagnosis purposes. """ description2 = """ Please adjust the values of the factors. Then it will analyze your probability of getting a heart attack. **Age**: Age of the patient **Sex**: Sex of the patient **Chest Pain Type:** Type of chest pain experienced by the patient **Resting Blood Pressure:** Blood pressure of the patient at rest, measured in millimeters of mercury (mm Hg) **Cholesterol:** Cholesterol level of the patient, measured in milligrams per deciliter (mg/dl) and fetched via BMI sensor **Fasting Blood Sugar:** Fasting blood sugar level of the patient, measured in milligrams per deciliter (mg/dl) and indicating whether it is greater than 120 mg/dl **Resting Electrocardiographic Results:** Results of the resting electrocardiogram test **Maximum Heart Rate Achieved:** Maximum heart rate achieved by the patient **Exercise Induced Angina:** Whether the patient experienced exercise-induced angina or not **Short-Term Depression Induced by Exercise Relative to Rest:** Amount of short-term depression in the ST segment induced by exercise relative to rest **Slope of the Peak Exercise Short-Term Segment:** Slope of the peak exercise ST segment **Number of Major Vessels:** Number of major vessels colored by fluoroscopy (0-3) **Thalassemia:** Type of thalassemia the patient has, if any. """ with gr.Blocks(title=title, theme=color) as demo: gr.Markdown(f"## {title}") gr.Markdown(description1) gr.Markdown("""---""") gr.Markdown(description2) gr.Markdown("""---""") age = gr.Slider(label="Age", minimum=0, maximum=100, value=40, step=1) sex = gr.Radio(label="Sex", choices =["Female","Male"]) cp = gr.Dropdown(label="Chest Pain Type", choices = ["Typical angina", "Atypical angina", "Non-anginal pain", "Asymptomatic"]) trtbps = gr.Slider(label="Resting Blood Pressure (in mm Hg)", minimum=90, maximum=200, value=90, step=1) chol = gr.Slider(label="Cholestoral in mg/dl", minimum=120, maximum=570, value=120, step=1) fbs = gr.Radio(label="Is your fasting blood sugar greater than 120mg/dl ", choices = ["True", "False"]) restecg = gr.Radio(label="Resting Electrocardiographic Results", choices = ["Probable or Definite Left Ventricular Hypertrophy by Estes' Criteria", "Having ST - T Wave Abnormality","Normal Value"]) thalachh = gr.Slider(label="Maximum Heart Rate", minimum=70, maximum=210, value=70, step=1) exng = gr.Radio(label="Do You Have Exercise Induced Angina?", choices = ["Yes","No"]) oldpeak = gr.Slider(label="Short-Term Depression Induced by Exercise Relative to Rest", minimum=0.0, maximum=6.5, value=0, step=0.1) slp = gr.Radio(label="Slope of the Peak Exercise", choices = ["Upsloping", "Flat", "Downsloping"]) caa = gr.Slider(label="Number of Major Vessels", minimum=0, maximum=3, value=0, step=1) thall = gr.Radio(label="Type of Thalassemia", choices = ["Reversible Defect", "Normal Blood Flow", "Fixed Defect"]) 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="Risk Predictor" ) gr.Markdown("### Click on any of the examples below to see how it works:") gr.Examples([[24,"Male","Atypical angina",200,500,"True","Normal Value",210,"Yes",6.5,"Upsloping",3,"Fixed Defect"], [22,"Female","Non-anginal pain",70,200,"False","Normal Value",120,"No",3.5,"Flat",3,"Fixed Defect"], [23,"Female","Asymptomatic",90,250,"True","Normal Value",200,"Yes",5,"Upsloping",3,"Normal Blood Flow"]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall], [label,local_plot], main_func, cache_examples=True) demo.launch()