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#Bismillahir Rahmaanir Raheem
#Almadadh Ya Gause RadiAllahu Ta'alah Anh - Ameen
import gradio as gr
import pandas as pd
from pycaret.classification import load_model, predict_model
# load the trained model for predictions
model = load_model('tuned_blend_specific_model_19112021')
# define the function to call
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predict_label = predictions_df["Label"][0] # either 1 (amputation yes) or 0 (amputation no)
predict_score = predictions_df["Score"][0] # the prediction (accuracy)
amputation_risk = ""
if predict_label == 1:
amputation_risk = "YES"
output = "<div style='background-color:green;'>"+"AMPUTATION RISK: " + amputation_risk + " SCORE: "+str(predict_score)+"</div>"
else:
amputation_risk = "NO"
output = "<div style='background-color:green;'>"+"AMPUTATION RISK: " + amputation_risk + " SCORE: "+str(predict_score)+"</div>"
return output#"AMPUTATION RISK: " + amputation_risk + " SCORE: "+str(predict_score)
# the parameters in this function, actually gets the inputs for the prediction
def predict_amputation(age, gender, race, diabetes_type):
diabetes_class = "Type "+str(diabetes_type)+" diabetes"
gender = gender[0]
input_dict = {"AGE": age, "GENDER": gender, "RACE": race, "DIABETES_CLASS":diabetes_class, "AMPUTATION":''}
input_df = pd.DataFrame([input_dict])
# output
return str(predict(model=model, input_df=input_df)) # calls the predict function when 'submit' is clicked
title = "DIabetes-related Amputation Risk Calculator (DIARC)"
description = "A diabetes-related amputation machine learning model trained on the diabetes dataset from the Inkosi Albert Luthuli Central Hospital (IALCH) in Durban, KwaZulu-Natal, South Africa."
article = "<p style='text-align: center'><span style='font-size: 15pt;'>Copyright © DIARC. 2021. All Rights Reserved. Contact Us: <a href='mailto:smtshali@wol.co.za'>Dr Sifisiso Mtshali</a> or <a href='mailto:mahomedo@ukzn.ac.za'>Dr Ozayr Mahomed</a></span></p>"
iface = gr.Interface(
fn=predict_amputation,
title=title,
description=description,
article=article,
inputs=[gr.inputs.Slider(minimum=0,maximum=100, step=1, default=0, label="Age"),
gr.inputs.Dropdown(["Female", "Male"], default="Female", label="Gender"),
gr.inputs.Dropdown(["Asian", "Black", "Coloured", "White", "Other"], default="Asian", label="Race"),
gr.inputs.Dropdown(["1", "2"], default="1", label="Diabetes Type")],
outputs="text",
theme="huggingface",
examples=[
[50, "Male", "Black", 2],
[76, "Female", "Asian", 2],
[12, "Female", "White", 1],
[30, "Male", "Coloured", 1],
[65, "Female", "Other", 2],
],
)
iface.test_launch()
if __name__ == "__main__":
iface.launch()
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