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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("classroom_xgb.pkl", 'rb'))
# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
# Create the main function for server
def main_func(Target, Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment):
new_row = pd.DataFrame.from_dict({'Target':Target, 'Admission grade':Admission_grade,'Curricular units 2nd sem (grade)':Curricular_units_2nd_sem_grade,
'Previous qualifications':Previous_qualifications,'Curricular units 1st sem (grade)':Curricular_units_1st_sem_grade, 'Course':Course,'Curricular units 2nd sem (approved)':Curricular_units_2nd_sem_approved,
'Age at enrollment':Age_at_enrollmentenrollment}).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 = "**Heart Attack Predictor & Interpreter** 🪐"
description1 = """This app takes info from subjects and predicts their heart attack 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)
gr.Markdown("""---""")
gr.Markdown(description2)
gr.Markdown("""---""")
Curricular_units_2nd_sem_grade = gr.Number(label="Curricular units 2nd sem (grade)", value=40)
Admission_grade = gr.Slider(label="Admission grade", minimum=0, maximum=1, value=1, step=1)
Previous_qualifications = gr.Slider(label="Previous qualifications", minimum=1, maximum=5, value=4, step=1)
Curricular_units_1st_sem_grade = gr.Slider(label="Curricular units 1st sem (grade)", minimum=1, maximum=5, value=4, step=1)
Course = gr.Slider(label="Course", minimum=1, maximum=5, value=4, step=1)
Curricular_units_2nd_sem_approved = gr.Slider(label="Curricular units 2nd sem (approved)", minimum=1, maximum=5, value=4, step=1)
Age_at_enrollment = gr.Slider(label="Age_at_enrollment", 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,
[Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment],
[label,local_plot], api_name="Dropout_Predictor"
)
gr.Markdown("### Click on any of the examples below to see how it works:")
gr.Examples([[24,0,4,4,5,5,4,4,5,5,1,2,3], [24,0,4,4,5,3,3,2,1,1,1,2,3]], [Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment], [label,local_plot], main_func, cache_examples=True)
demo.launch() |