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import numpy as np
import tensorflow as tf
import gradio as gr
from huggingface_hub import from_pretrained_keras
# download the already pushed model
model = from_pretrained_keras("buio/structured-data-classification")
def convert_and_predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
# some conversions from the gradio interface are needed
sample_converted = {
"age": age,
"sex": sex,
"cp": cp+1,
"trestbps": trestbps,
"chol": chol,
"fbs": 0 if fbs<=120 else 1,
"restecg": restecg,
"thalach": thalach,
"exang": exang,
"oldpeak": oldpeak,
"slope": slope+1,
"ca": ca,
"thal": thal,
}
input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample_converted.items()}
predictions = model.predict(input_dict)
return f'{predictions[0][0]:.2%}'
## gradio interface elements
inputs = [
gr.Slider(minimum=1, maximum=120, step=1, label='age', value=60),
gr.Radio(choices=['female','male'], label='sex', type='index',value='male'),
gr.Radio(choices=['typical angina',
'atypical angina',
'non-anginal pain',
'asymptomatic'],
type='index', label=f'chest pain type', value='typical angina'),
gr.Number(label='blood pressure in mmHg', value=145),
gr.Number(label='serum cholestoral in mg/dl', value=233),
gr.Number(label='fasting blood sugar in mg/dl', value=150),
gr.Radio(choices=['normal','T-T wave abnormality','probable or definite left ventricular hypertrophy'],
label='resting ecg', type='index',value='probable or definite left ventricular hypertrophy'),
gr.Number(label='maximum heart rate achieved', value=150),
gr.Radio(choices=['no','yes',], type='index', label='exercise induced angina',value='no'),
gr.Number(label='ST depression induced by exercise relative to rest', value=2.3),
gr.Radio(choices=['psloping','flat','downsloping'], label='slope of the peak exercise ST segment', type='index', value='downsloping'),
gr.Number(minimum=0, maximum=3, label ='number of major vessels (0-3) colored by flourosopy',value=0),
gr.Radio(['normal','fixed','reversable'],label ='thal', value='fixed')
]
# the app outputs text
output = gr.Textbox(label='Probability of having a heart disease, as evaluated by our model:')
# it's good practice to pass examples, description and a title to guide users
examples = []
title = "Heart Disease Classification"
description = "Play with the clinical values or select examples below"
examples = [[63, 1, 1, 145, 233, 1, 2, 150, 0, 2.3, 3, 0, 'fixed'],
[67, 1, 4, 160, 286, 0, 2, 108, 1, 1.5, 2, 3, 'normal'],
[67, 1, 4, 120, 229, 0, 2, 129, 1, 2.6, 2, 2, 'reversible']]
gr.Interface(convert_and_predict, inputs, output, examples= examples, allow_flagging=False, analytics_enabled=False,
title=title, description=description).launch() |