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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 Change": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot | |
| # Create the UI | |
| title = "**Heart Attack Predictor & Interpreter** 🪐" | |
| description1 = """ | |
| This app predicts heart attacks using various factors from subjects. Do not use for medical diagnosis.✨ | |
| """ | |
| description2 = """ | |
| To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. 🤞 | |
| """ | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(f"## {title}") | |
| # gr.Markdown("""""") | |
| gr.Markdown(description1) | |
| gr.Markdown("""---""") | |
| gr.Markdown(description2) | |
| gr.Markdown("""---""") | |
| age = gr.Slider(label="age Score", minimum=15, maximum=90, value=4, step=1) | |
| sex = gr.Slider(label="sex Score", minimum=1, maximum=5, value=4, step=1) | |
| cp = gr.Slider(label="cp Score", minimum=1, maximum=5, value=4, step=1) | |
| trtbps = gr.Slider(label="GrowthAdvancement 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) | |
| thalacch = gr.Slider(label="thalacch 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([[24,0,4,4,4,4,5,5,1,2,3], [24,0,4,4,3,2,5,5,1,2,3]], [age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True) | |
| demo.launch() |