Create app.py

app.py

@@ -0,0 +1,84 @@
+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("heart_xgb.pkl", 'rb'))
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+
+# Create the main function for server
+    def main_func(ID, Diabetes_binary, HighBP, HighCol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHealth, DiffWalk, Sex, Age, Education, Income):
+    new_row = pd.DataFrame.from_dict({'ID':ID,'Diabetes_binary':Diabetes_binary,
+              'HighBP':HighBP,'HighCol':HighCol,'CholCheck':CholCheck,
+                  'BMI':BMI, 'Smoker':Smoker,'Stroke':Stroke,'HeartDiseaseorAttack':HeartDiseaseorAttack,
+                                     'PhysActivity':PhysActivity,'Fruits':Fruits,'Veggies':Veggies,'HvyAlcoholConsump':HvyAlcoholConsump, 'AnyHealthcare':AnyHealthcare, 'NoDocbcCost':NoDocbcCost, 'GenHlth':GenHlth, 'MenHlth': MenHlth, 'PhysHealth':PhysHealth, 'DiffWalk':DiffWalk, 'Sex':Sex, 'Age':Age, 'Education':Education, 'Income':Income}, 
+                                     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 Predictor & Interpreter** 🪐"
+description1 = """This app takes info from subjects and predicts their heart attack 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("""---""")
+    
+    age = gr.Number(label="age Score", value=40)
+    sex = gr.Slider(label="sex Score", minimum=0, maximum=1, value=1, step=1)
+    cp = gr.Slider(label="cp Score", minimum=1, maximum=5, value=4, step=1)
+    trtbps = gr.Slider(label="trtbps Score", minimum=1, maximum=5, value=4, step=1) 
+    chol = gr.Slider(label="chol Score", minimum=1, maximum=5, value=4, step=1)
+    fbs = gr.Slider(label="fbs Score", minimum=1, maximum=5, value=4, step=1)
+
+    restecg = gr.Slider(label="restecg Score", minimum=1, maximum=5, value=4, step=1)
+    thalachh = gr.Slider(label="thalachh Score", minimum=1, maximum=5, value=4, step=1)
+   
+    exng = gr.Slider(label="exng Score", minimum=1, maximum=5, value=4, step=1)
+    oldpeak = gr.Slider(label="oldpeak Score", minimum=1, maximum=5, value=4, step=1)
+    slp = gr.Slider(label="slp Score", minimum=1, maximum=5, value=4, step=1)
+    caa = gr.Slider(label="caa Score", minimum=1, maximum=5, value=4, step=1)
+    thall = gr.Slider(label="thall Score", minimum=1, maximum=5, value=4, step=1)
+    
+    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,
+        [ID, Diabetes_binary, HighBP, HighCol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHealth, 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([[24,0,4,4,5,5,4,4,5,5,1,2,3], [24,0,4,4,5,3,3,2,1,1,1,2,3]], [ID, Diabetes_binary, HighBP, HighCol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHealth, DiffWalk, Sex, Age, Education, Income], [label,local_plot], main_func, cache_examples=True)
+
+demo.launch()