File size: 2,562 Bytes
1e20656 5c5b397 aae6493 c05f598 50c8486 c05f598 5c5b397 c05f598 dd43c79 a52d12d cb04dfa a52d12d 8716dd5 a52d12d 12b6b1a 9dee9a0 9198215 4920d02 a52d12d 4920d02 12cd932 16b729b 7d9a734 6ed263b c7a8f72 7d9a734 12cd932 8181851 12cd932 c7a8f72 5ad04f6 27bf8b4 3319690 5e90216 a61a907 0707d46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
#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)
predictions = predictions_df["AMPUTATION"][0]
return predictions
#input_dict = {"AGE": age, "GENDER_F": gender, "RACE_Asian": ,"RACE_Black": , "RACE_Coloured":, "RACE_Other":, "RACE_White":, "DIABETES_CLASS_Type 1 diabetes":}
# the parameters in this function, actually gets the inputs for the prediction
def predict_amputation(age, gender, race, diabetes_type):
#input_dict = {"AGE": 70.0, "GENDER_F": 0.0, "RACE_Asian": 1.0, "RACE_Black": 0.0, "RACE_Coloured": 0.0, "RACE_Other": 0.0, "RACE_White": 0.0, "DIABETES_CLASS_Type 1 diabetes":0.0}
#input_dict = {"AGE": 70.0, "GENDER": 0.0, "RACE": 1.0, "DIABETES_CLASS":0.0, "AMPUTATION":0}
input_dict = {"AGE": 70, "GENDER": "F", "RACE": "Asian", "DIABETES_CLASS":"Type 2 diabetes", "AMPUTATION":0}
input_dict = {"AGE": 80, "GENDER": "F", "RACE": "Asian", "DIABETES_CLASS":"Type 2 diabetes", "AMPUTATION":0}
input_df = pd.DataFrame([input_dict])
return "ALLAH: "+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;'>© DIARC. 2021. All Rights Reserved.</span></p>"
iface = gr.Interface(
fn=predict_amputation,
title=title,
description=description,
article=article,
inputs=[gr.inputs.Slider(minimum=0,maximum=100, step=1, label="Age"), gr.inputs.Dropdown(["Female", "Male"], default="Male", 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="darkhuggingface",
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() |