Update app.py

app.py

@@ -1,43 +1,3 @@
-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)
@@ -45,10 +5,10 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown(description2)
     gr.Markdown("""---""")
     
-    input_block_left() == gr.Column(
+    input_block_left = gr.Column(
         HighBP = gr.Radio(label="Do you have high Blood Pressure?", choices=["No", "Yes"], default="Yes", description="0 = no high BP, 1 = high BP"),
         HighChol = gr.Radio(label="Do you have high Cholesterol?",  choices=["No", "Yes"], default="Yes", description="0 = no high cholesterol, 1 = high cholesterol"),
-        CholCheck = gr.Radio(label="Did you have a Cholestorol check in past 5 years?", choices=["No", "Yes"], default="Yes", description="No = not checked in 5 years, Yes = checked in 5 years"),
+        CholCheck = gr.Radio(label="Did you have a Cholesterol check in past 5 years?", choices=["No", "Yes"], default="Yes", description="No = not checked in 5 years, Yes = checked in 5 years"),
         BMI = gr.Number(label="BMI", minimum=0, maximum=98, default=1),
         Smoker = gr.Radio(label="Are you a smoker?", choices=["No", "Yes"], default="Yes", description="No = never smoked, Yes = smoked at least 100 cigarettes"),
         Stroke = gr.Radio(label="Have you had a stroke?", choices=["No", "Yes"], default="Yes", description="No = never had a stroke, Yes = had a stroke"),
@@ -60,8 +20,8 @@ with gr.Blocks(title=title) as demo:
         AnyHealthcare = gr.Radio(label="Do you have any kind of health care coverage? (e.g., health insurance, prepaid plans such as HMO)", choices=["No", "Yes"], default="Yes", description="No = no coverage, Yes = coverage"),
         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"], default="Yes", description="No = no barrier, Yes = cost barrier")
     )
-                    
-    input_block_right == gr.Column(
+    
+    input_block_right = gr.Column(
         GenHlth = gr.Slider(label="In general, rank your health on a scale: 1(excellent)-5(poor)", minimum=1, maximum=5, default=1, step=1, description="1 = excellent, 5 = poor"),
         MentHlth = gr.Number(label="Poor Mental Health in the Past 30 Days", minimum=0, maximum=30, default=1, description="Days not good out of last 30"),
         PhysHlth = gr.Number(label="Poor Physical Health in the Past 30 Days", minimum=0, maximum=30, default=1, description="Days not good out of last 30"),
@@ -72,9 +32,9 @@ with gr.Blocks(title=title) as demo:
         Income = gr.Dropdown(label="Income Level", choices=["< $10,000", "$10,000 - $24,999", "$25,000 - $49,999", "$50,000 - $74,999", "$75,000 or more"], default="< $10,000", description="Income level")
     )
     
-    input_block == gr.Row([input_block_left, input_block_right])
+    input_block = gr.Row([input_block_left, input_block_right])
         
-    output_block == gr.Column(
+    output_block = gr.Column(
         gr.Label(label="Predicted Label"),
         gr.Plot(label="SHAP Plot")
     )