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import pickle |
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import pandas as pd |
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import shap |
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from shap.plots._force_matplotlib import draw_additive_plot |
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import gradio as gr |
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import numpy as np |
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import matplotlib.pyplot as plt |
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loaded_model = pickle.load(open("db_xgb.pkl", 'rb')) |
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explainer = shap.Explainer(loaded_model) |
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def map_HighBP(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_HighChol(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_CholCheck(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_Smoker(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_Stroke(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_HeartDiseaseorAttack(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_PhysActivity(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_Fruits(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_Veggies(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_HvyAlcoholConsump(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_AnyHealthcare(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_NoDocbcCost(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_DiffWalk(value): |
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mapping = {'No': 0, 'Yes': 1} |
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return mapping[value] |
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def map_Sex(value): |
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mapping = {'Female': 0, 'Male': 1} |
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return mapping[value] |
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def map_Education(value): |
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mapping = { |
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"Never attended school": 1, |
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"Grades 1-8": 2, |
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"Grades 9-11": 3, |
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"Grade 12 or GED": 4, |
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"College 1-3 years": 5, |
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"College 4+ years": 6 |
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} |
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return mapping[value] |
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def map_Income(value): |
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mapping = { |
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"> $10,000": 1, |
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"> $20,000": 2, |
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"> $25,000": 3, |
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"> $30,000": 4, |
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"> $35,000": 5, |
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"> $50,000": 6, |
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"> $60,000": 7, |
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"< $75,000": 8 |
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} |
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return mapping[value] |
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def main_func(HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income): |
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new_row = pd.DataFrame.from_dict({ |
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'HighBP': map_HighBP(HighBP), |
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'HighChol': map_HighChol(HighChol), |
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'CholCheck': map_CholCheck(CholCheck), |
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'BMI': BMI, |
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'Smoker': map_Smoker(Smoker), |
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'Stroke': map_Stroke(Stroke), |
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'HeartDiseaseorAttack': map_HeartDiseaseorAttack(HeartDiseaseorAttack), |
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'PhysActivity': map_PhysActivity(PhysActivity), |
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'Fruits': map_Fruits(Fruits), |
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'Veggies': map_Veggies(Veggies), |
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'HvyAlcoholConsump': map_HvyAlcoholConsump(HvyAlcoholConsump), |
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'AnyHealthcare': map_AnyHealthcare(AnyHealthcare), |
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'NoDocbcCost': map_NoDocbcCost(NoDocbcCost), |
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'GenHlth': GenHlth, |
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'MentHlth': MentHlth, |
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'PhysHlth': PhysHlth, |
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'DiffWalk': map_DiffWalk(DiffWalk), |
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'Sex': map_Sex(Sex), |
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'Age': Age, |
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'Education': map_Education(Education), |
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'Income': map_Income(Income) |
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}, orient='index').transpose() |
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prob = loaded_model.predict_proba(new_row) |
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shap_values = explainer(new_row) |
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plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False) |
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plt.tight_layout() |
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local_plot = plt.gcf() |
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plt.close() |
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return {"Low Chance of Diabetes": float(prob[0][0]), "High Chance of Diabetes": 1-float(prob[0][0])}, local_plot |
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title = "**Diabetes Predictor Application** 🪐" |
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description1 = """This app takes information from subjects and predicts their diabetes likelihood. Do not use for medical diagnosis.""" |
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description2 = """ |
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To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞 |
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""" |
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with gr.Blocks(title=title) as demo: |
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gr.Markdown(f"## {title}") |
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gr.Markdown(description1) |
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gr.Markdown("""---""") |
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gr.Markdown(description2) |
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gr.Markdown("""---""") |
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with gr.Row(): |
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CholCheck = gr.Radio(label="Did you check your cholestorol in the past 5 years?", choices=["No", "Yes"]) |
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HighChol = gr.Radio(label="Do you have high cholesterol?", choices=["No", "Yes"]) |
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with gr.Row(): |
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DiffWalk = gr.Radio(label="Do you have serious difficulty walking or climbing stairs?", choices=["No", "Yes"]) |
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BMI = gr.Number(label="BMI") |
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with gr.Row(): |
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Smoker = gr.Radio(label="Are you a smoker?", choices=["No", "Yes"]) |
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HvyAlcoholConsump = gr.Radio(label="Do you drink often?", choices=["No", "Yes"]) |
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with gr.Row(): |
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Stroke = gr.Radio(label="Have you had a stroke?", choices=["No", "Yes"]) |
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HighBP = gr.Radio(label="Do you have high blood pressure?", choices=["No", "Yes"]) |
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HeartDiseaseorAttack = gr.Radio(label="Do you have coronary heart disease or myocardial infarction?", choices=["No", "Yes"]) |
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with gr.Row(): |
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PhysActivity = gr.Radio(label="Did you partake in physical activity in the past 30 days?", choices=["No", "Yes"]) |
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Fruits = gr.Radio(label="Do you consume fruit 1 or more times per day?", choices=["No", "Yes"]) |
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Veggies = gr.Radio(label="Do you consume vegetables 1 or more times per day?", choices=["No", "Yes"]) |
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with gr.Row(): |
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AnyHealthcare = gr.Radio(label="Do you have any kind of health care coverage?", choices=["No", "Yes"]) |
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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"]) |
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with gr.Row(): |
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MentHlth = gr.Number(label="How many days in the past 30 days did you have poor mental health?") |
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PhysHlth = gr.Number(label="How many days in the past 30 days did you have poor physical health?") |
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GenHlth = gr.Slider(label="In general, rank your overall health on a scale: 1(excellent)-5(poor)", minimum=1, maximum=5) |
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with gr.Row(): |
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Sex = gr.Dropdown(label="Sex", choices=["Female", "Male"]) |
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Age = gr.Number(label="Age") |
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with gr.Row(): |
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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"]) |
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Income = gr.Dropdown(label="Income Level", choices=["> $10,000", "> $20,000", "> $25,000", "> $30,000", "> $35,000","> $50,000","> $60,000", "< $75,000"]) |
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with gr.Column(visible=True) as output_col: |
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label = gr.Label(label = "Predicted Label") |
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local_plot = gr.Plot(label = 'Shap:') |
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submit_btn = gr.Button("Analyze") |
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submit_btn.click( |
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main_func, |
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[HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, |
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HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income], |
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[label,local_plot],api_name="Diabetes Predictor" |
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) |
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gr.Markdown("### Click on any of the examples below to see how it works:") |
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gr.Examples( |
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[["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"], |
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["Yes", "Yes", "Yes", 30, "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", "No", 2, 20, 23, "No", "Male", 21, "College 4+ years", "< $75,000"]], |
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[HighBP, CholCheck, HighChol, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, |
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AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income], |
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[label, local_plot], |
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main_func, |
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cache_examples=True |
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) |
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demo.launch() |
