app.py

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(Admission_Grade, Second_Sem_Grades, Previous_Qualification_Grade, First_Sem_Grades, Course, Second_Sem_Units_Approved, Age_at_Enrollment):
    new_row = pd.DataFrame.from_dict({'Admission_Grade':Admission_Grade,
              'Second_Sem_Grades':Second_Sem_Grades,'Previous_Qualification_Grade':Previous_Qualification_Grade,'First_Sem_Grades':First_Sem_Grades,
              'Course':Course,'Second_Sem_Units_Approved':Second_Sem_Units_Approved,'Age_at_Enrollment':Age_at_Enrollment},
                                      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 {"Dropout": float(prob[0][0]), "Graduate": 1-float(prob[0][0])}, local_plot

# Create the UI
title = "**Student Graduation Predictor & Interpreter** 🪐"
description1 = """This app takes information from subjects and predicts their graduation likelihood."""

description2 = """
To use the app, click on one of the examples or adjust the values of the factors, then click Analyze.
""" 
    
with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    gr.Markdown(description2)
    gr.Markdown("""---""")
    with gr.Row():
        with gr.Column():
            Admission_Grade = gr.Slider(label="Admission Grade", minimum=0, maximum=200, value=100, step=1)
            Age_at_Enrollment = gr.Slider(label="Age at Enrollment", minimum=10, maximum=80, value=40, step=1)
            Previous_Qualification_Grade = gr.Slider(label="Previous Qualification Grade", minimum=0, maximum=200, value=100, step=1)
            First_Sem_Grades = gr.Slider(label="First Semester Grade", minimum=0, maximum=20, value=10, step=1)
            Second_Sem_Grades = gr.Slider(label="Second Semester Grade", minimum=0, maximum=20, value=10, step=1)   
            Course = gr.Dropdown(label="Select a Course:", choices=[33,171,8014,9003,9070,9085,9119,9130,9147,9238,9254,9500,9556,9670,9773,9853,9991], value=33)
            Second_Sem_Units_Approved = gr.Slider(label="Second Semester Units Approved", minimum=0, maximum=20, value=10, 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, Second_Sem_Grades, Previous_Qualification_Grade, First_Sem_Grades, Course, Second_Sem_Units_Approved, Age_at_Enrollment],
        [label,local_plot], api_name="Graduation_Predictor"
    )
    
    gr.Markdown("### Click on any of the examples below to see how it works:")
    gr.Examples([[119,13,122,12,8014,0,18],[100,20,90,50,33,2,20], [150,15,102,46,171,8,25]], [Admission_Grade, Second_Sem_Grades, Previous_Qualification_Grade, First_Sem_Grades, Course, Second_Sem_Units_Approved, Age_at_Enrollment]
                 , [label,local_plot], main_func, cache_examples=True)

demo.launch()