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("db_xgb.pkl", 'rb')) # Setup SHAP explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS. # Define mapping functions def map_HighBP(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_HighChol(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_CholCheck(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_Smoker(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_Stroke(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_HeartDiseaseorAttack(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_PhysActivity(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_Fruits(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_Veggies(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_HvyAlcoholConsump(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_AnyHealthcare(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_NoDocbcCost(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_DiffWalk(value): mapping = {'No': 0, 'Yes': 1} return mapping[value] def map_Sex(value): mapping = {'Female': 0, 'Male': 1} return mapping[value] def map_Education(value): mapping = { "Never attended school": 1, "Grades 1-8": 2, "Grades 9-11": 3, "Grade 12 or GED": 4, "College 1-3 years": 5, "College 4+ years": 6 } return mapping[value] def map_Income(value): mapping = { "> $10,000": 1, "> $20,000": 2, "> $25,000": 3, "> $30,000": 4, "> $35,000": 5, "> $50,000": 6, "> $60,000": 7, "< $75,000": 8 } return mapping[value] # Create the main function for server def main_func(HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income): new_row = pd.DataFrame.from_dict({ 'HighBP': map_HighBP(HighBP), 'HighChol': map_HighChol(HighChol), 'CholCheck': map_CholCheck(CholCheck), 'BMI': BMI, 'Smoker': map_Smoker(Smoker), 'Stroke': map_Stroke(Stroke), 'HeartDiseaseorAttack': map_HeartDiseaseorAttack(HeartDiseaseorAttack), 'PhysActivity': map_PhysActivity(PhysActivity), 'Fruits': map_Fruits(Fruits), 'Veggies': map_Veggies(Veggies), 'HvyAlcoholConsump': map_HvyAlcoholConsump(HvyAlcoholConsump), 'AnyHealthcare': map_AnyHealthcare(AnyHealthcare), 'NoDocbcCost': map_NoDocbcCost(NoDocbcCost), 'GenHlth': GenHlth, 'MentHlth': MentHlth, 'PhysHlth': PhysHlth, 'DiffWalk': map_DiffWalk(DiffWalk), 'Sex': map_Sex(Sex), 'Age': Age, 'Education': map_Education(Education), 'Income': map_Income(Income) }, orient='index').transpose() prob = loaded_model.predict_proba(new_row) shap_values = explainer(new_row) 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 of Diabetes": float(prob[0][0]), "High Chance of Diabetes": 1-float(prob[0][0])}, local_plot # Create the UI title = "**Diabetes Predictor Application** 🪐" description1 = """This app takes information from subjects and predicts their diabetes 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(description1) gr.Markdown("""---""") gr.Markdown(description2) gr.Markdown("""---""") with gr.Row(): CholCheck = gr.Radio(label="Did you check your cholestorol in the past 5 years?", choices=["No", "Yes"]) HighChol = gr.Radio(label="Do you have high cholesterol?", choices=["No", "Yes"]) with gr.Row(): DiffWalk = gr.Radio(label="Do you have serious difficulty walking or climbing stairs?", choices=["No", "Yes"]) BMI = gr.Number(label="BMI") with gr.Row(): Smoker = gr.Radio(label="Are you a smoker?", choices=["No", "Yes"]) HvyAlcoholConsump = gr.Radio(label="Do you drink often?", choices=["No", "Yes"]) with gr.Row(): Stroke = gr.Radio(label="Have you had a stroke?", choices=["No", "Yes"]) HighBP = gr.Radio(label="Do you have high blood pressure?", choices=["No", "Yes"]) HeartDiseaseorAttack = gr.Radio(label="Do you have coronary heart disease or myocardial infarction?", choices=["No", "Yes"]) with gr.Row(): PhysActivity = gr.Radio(label="Did you partake in physical activity in the past 30 days?", choices=["No", "Yes"]) Fruits = gr.Radio(label="Do you consume fruit 1 or more times per day?", choices=["No", "Yes"]) Veggies = gr.Radio(label="Do you consume vegetables 1 or more times per day?", choices=["No", "Yes"]) with gr.Row(): AnyHealthcare = gr.Radio(label="Do you have any kind of health care coverage?", choices=["No", "Yes"]) NoDocbcCost = gr.Radio(label="Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?", choices=["No", "Yes"]) with gr.Row(): MentHlth = gr.Number(label="How many days in the past 30 days did you have poor mental health?") PhysHlth = gr.Number(label="How many days in the past 30 days did you have poor physical health?") GenHlth = gr.Slider(label="In general, rank your overall health on a scale: 1(excellent)-5(poor)", minimum=1, maximum=5) with gr.Row(): Sex = gr.Dropdown(label="Sex", choices=["Female", "Male"]) Age = gr.Number(label="Age") with gr.Row(): Education = gr.Dropdown(label="Education Level", choices=["Never attended school", "Grades 1-8", "Grades 9-11", "Grade 12 or GED", "College 1-3 years", "College 4+ years"]) Income = gr.Dropdown(label="Income Level", choices=["> $10,000", "> $20,000", "> $25,000", "> $30,000", "> $35,000","> $50,000","> $60,000", "< $75,000"]) with gr.Column(visible=True) as output_col: label = gr.Label(label = "Predicted Label") local_plot = gr.Plot(label = 'Shap:') submit_btn = gr.Button("Analyze") submit_btn.click( main_func, [HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income], [label,local_plot],api_name="Diabetes Predictor" ) gr.Markdown("### Click on any of the examples below to see how it works:") gr.Examples( [["No", "No", "Yes", 22, "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", 3, 25, 23, "No", "Female", 22, "Grade 12 or GED", "> $35,000"], ["Yes", "Yes", "Yes", 30, "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", "No", 2, 20, 23, "No", "Male", 21, "College 4+ years", "< $75,000"]], [HighBP, CholCheck, HighChol, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income], [label, local_plot], main_func, cache_examples=True ) demo.launch()