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app.py

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+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(age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall):
+    new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
+              'cp':cp,'trtbps':trtbps,'chol':chol,
+              'fbs':fbs, 'restecg':restecg,'thalachh':thalachh,'exng':exng,
+                                     'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':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 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,
+        [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
+        [label,local_plot], api_name="Heart_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]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
+
+demo.launch()

requirements.txt

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+gradio==3.41.2
+pandas
+scikit-learn
+shap
+xgboost
+matplotlib
+numpy
+streamlit