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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(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() |