adeledududu commited on
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307484a
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Create finalapp

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  1. finalapp +79 -0
finalapp ADDED
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+ import pickle
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+ import pandas as pd
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+ import shap
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+ from shap.plots._force_matplotlib import draw_additive_plot
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+ import gradio as gr
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+ import numpy as np
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+ import matplotlib.pyplot as plt
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+
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+ # load the model from disk
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+ loaded_model = pickle.load(open("heart_xgb.pkl", 'rb'))
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+
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+ # Setup SHAP
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+ explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
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+
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+ # Create the main function for server
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+ def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall):
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+ new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
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+ 'cp':cp,'trtbps':trtbps,'chol':chol,
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+ 'fbs':fbs, 'restecg':restecg, '
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+ thalachh':thalachh, 'exng':exng, 'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':thall}, orient = 'index').transpose()
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+
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+ prob = loaded_model.predict_proba(new_row)
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+
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+ shap_values = explainer(new_row)
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+ # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
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+ # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
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+ plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
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+
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+ plt.tight_layout()
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+ local_plot = plt.gcf()
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+ plt.close()
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+
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+ return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
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+
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+ # Create the UI
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+ title = "**Heart Attack Predictor & Interpreter** 🪐"
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+ description1 = """ This app takes info from subjects and predicts their heart attack likelihood. Do not use it for medical.
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+ """
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+
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+ description2 = """
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+ To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. 🤞
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+ """
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+
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+ with gr.Blocks(title=title) as demo:
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+ gr.Markdown(f"## {title}")
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+ # gr.Markdown("""![marketing](file/marketing.jpg)""")
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+ gr.Markdown(description1)
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+ gr.Markdown("""---""")
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+ gr.Markdown(description2)
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+ gr.Markdown("""---""")
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+ age = gr.Slider(label="age Score", minimum=15, maximum=90, value=4, step=5)
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+ sex = gr.Slider(label="sex Score", minimum=0, maximum=1, value=4, step=1)
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+ cp = gr.Slider(label="cp Score", minimum=1, maximum=5, value=4, step=1)
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+ trtbps = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=1)
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+ chol = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=1)
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+ fbs = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ restecg = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ thalachh = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ exng = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ oldpeak = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ slp = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ caa = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ thall = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
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+ submit_btn = gr.Button("Analyze")
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+
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+ with gr.Column(visible=True) as output_col:
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+ label = gr.Label(label = "Predicted Label")
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+ local_plot = gr.Plot(label = 'Shap:')
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+
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+ submit_btn.click(
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+ main_func,
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+ [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall],
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+ [label,local_plot], api_name="heart_prediction"
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+ )
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+
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+ gr.Markdown("### Click on any of the examples below to see how it works:")
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+ gr.Examples([[24,0,4,4,5,5,4,4,5,5,1,2,3], [5,4,5,4,4,4,5,5,4,4,5,5,1,2,3]]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall], [label,local_plot], main_func, cache_examples=True)
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+
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+ demo.launch()