Update app.py

app.py

@@ -1,3 +1,43 @@
+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.
+
+# 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': HighBP, 'HighChol': HighChol, 'CholCheck': CholCheck, 'BMI': BMI, 'Smoker': Smoker, 'Stroke': Stroke, 'HeartDiseaseorAttack': HeartDiseaseorAttack, 'PhysActivity':PhysActivity, 'Fruits':Fruits, 'Veggies':Veggies, 'HvyAlcoholConsump': HvyAlcoholConsump, 'AnyHealthcare': AnyHealthcare, 'NoDocbcCost': NoDocbcCost, 'GenHlth': GenHlth, 'MentHlth': MentHlth, 'PhysHlth': PhysHlth, '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 = "**Diabetes Predictor & Interpreter** 🪐"
+description1 = """This app takes info 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)
@@ -50,4 +90,4 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown("### Click on any of the examples below to see how it works:")
     gr.Examples([[0,0,1,0,22,0,0,0,1,1,1,0,0,1,3,25,23,1,1,21,5,3], [1,1,1,1,30,1,1,1,0,0,0,1,1,0,2,20,23,0,0,21,3,2]], [HighBP, HighChol, CholCheck, 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()
+demo.launch()