Create app.py

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
+from xgboost import XGBClassifier
+
+# load the model from disk
+loaded_model = xgb.XGBClassifier()
+loaded_model.load_model("heart_xgb.json")
+
+# 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=7, order=shap.Explanation.abs, show_data='auto', show=False)
+
+    plt.tight_layout()
+    local_plot = plt.gcf()
+    plt.rcParams['figure.figsize'] = 7,4
+    plt.close()
+    
+    return {"Normal Heart Condition": float(prob[0][0]), "Critical Heart Condition": 1-float(prob[0][0])}, local_plot
+
+# Create the UI
+title = "**Heart Condition Predictor & Interpreter** 🪐"
+description1 = """
+This app takes inputs about patients' demographics and medical history to predict whether the patient has heart condition. There are two outputs from the app: 1- the predicted probability of normal condition or heart condition, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the prediction.   
+"""
+
+description2 = """
+To use the app, click on one of the examples, or adjust the values of the patient factors, and click on Analyze. ✨ 
+""" 
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    # gr.Markdown("""![marketing](types-of-employee-turnover.jpg)""")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+    gr.Markdown(description2)
+    gr.Markdown("""---""")
+    with gr.Row():        
+        with gr.Column():
+            age = gr.Slider(label="age", minimum=17, maximum=74, value=24, step=1)
+            sex = gr.Slider(label="sex", minimum=0, maximum=1, value=1, step=1)
+            cp = gr.Slider(label="cp Score", minimum=1, maximum=4, value=3, step=.1)
+            trtbps = gr.Slider(label="trestbps Score", minimum=94, maximum=200, value=150, step=.1)
+            chol = gr.Slider(label="chol Score", minimum=126, maximum=564, value=400, step=.1)
+            fbs = gr.Slider(label="fbs Score", minimum=0, maximum=1, value=0, step=.1)
+            restecg = gr.Slider(label="restecg Score", minimum=0, maximum=2, value=1, step=.1)
+            thalachh = gr.Slider(label="thalach Score", minimum=71, maximum=202, value=90, step=.1) 
+            exng = gr.Slider(label="exang Score", minimum=0, maximum=1, value=1, step=.1)
+            oldpeak = gr.Slider(label="oldpeak Score", minimum=0, maximum=6, value=4, step=.1)
+            slp = gr.Slider(label="slope Score", minimum=1, maximum=3, value=2, step=.1)
+            caa = gr.Slider(label="ca Score", minimum=0, maximum=3, value=2, step=.1)
+            thall = gr.Slider(label="thal Score", minimum=3, maximum=7, value=4, step=.1) 
+            
+            submit_btn = gr.Button("Analyze")
+        with gr.Column(visible=True,scale=1, min_width=600) 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_Condition"
+            )
+    
+    gr.Markdown("### Click on any of the examples below to see how it works:")
+    gr.Examples([[33,0,1,100,230,1,1,150,0,.9,2,1,6], [39,1,0,170,200,1,1,150,0,1.4,2,1,6]], 
+                [age, sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall], 
+                [label,local_plot], main_func, cache_examples=True)
+
+demo.launch()