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import numpy as np |
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import tensorflow as tf |
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import gradio as gr |
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from huggingface_hub import from_pretrained_keras |
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model = from_pretrained_keras("keras-io/structured-data-classification") |
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def convert_and_predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal): |
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sample_converted = { |
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"age": age, |
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"sex": sex, |
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"cp": cp+1, |
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"trestbps": trestbps, |
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"chol": chol, |
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"fbs": 0 if fbs<=120 else 1, |
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"restecg": restecg, |
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"thalach": thalach, |
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"exang": exang, |
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"oldpeak": oldpeak, |
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"slope": slope+1, |
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"ca": ca, |
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"thal": thal, |
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} |
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input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample_converted.items()} |
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predictions = model.predict(input_dict) |
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return f'{predictions[0][0]:.2%}' |
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inputs = [ |
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gr.Slider(minimum=1, maximum=120, step=1, label='age', value=60), |
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gr.Radio(choices=['female','male'], label='sex', type='index',value='male'), |
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gr.Radio(choices=['typical angina', |
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'atypical angina', |
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'non-anginal pain', |
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'asymptomatic'], |
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type='index', label=f'chest pain type', value='typical angina'), |
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gr.Number(label='blood pressure in mmHg', value=145), |
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gr.Number(label='serum cholestoral in mg/dl', value=233), |
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gr.Number(label='fasting blood sugar in mg/dl', value=150), |
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gr.Radio(choices=['normal','T-T wave abnormality','probable or definite left ventricular hypertrophy'], |
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label='resting ecg', type='index',value='probable or definite left ventricular hypertrophy'), |
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gr.Number(label='maximum heart rate achieved', value=150), |
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gr.Radio(choices=['no','yes',], type='index', label='exercise induced angina',value='no'), |
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gr.Number(label='ST depression induced by exercise relative to rest', value=2.3), |
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gr.Radio(choices=['psloping','flat','downsloping'], label='slope of the peak exercise ST segment', type='index', value='downsloping'), |
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gr.Number(label ='number of major vessels (0-3) colored by flourosopy',value=0), |
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gr.Radio(['normal','fixed','reversable'],label ='thal', value='fixed') |
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] |
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output = gr.Textbox(label='Probability of having a heart disease, as evaluated by our model:') |
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title = "Structured Data Classification 🧮" |
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description = "Binary classification of structured data including numerical and categorical features for Heart Disease prediction." |
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article = "Author: <a href=\"https://huggingface.co/buio\">Marco Buiani</a>. Based on this <a href=\"https://keras.io/examples/structured_data/structured_data_classification_from_scratch/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">François Chollet.</a> HuggingFace Model <a href=\"https://huggingface.co/buio/structured-data-classification\">here</a> " |
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examples = [[41, 'female', 'atypical angina', 130, 204, 100, 'normal', 150, 'yes', 1.4, 'psloping', 2, 'reversible'], |
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[63, 'male', 'typical angina', 145, 233, 150, 'T-T wave abnormality', 150, 'no', 2.3, 'flat', 0, 'fixed']] |
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gr.Interface(convert_and_predict, inputs, output, examples= examples, allow_flagging='never', |
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title=title, description=description, article=article, live=True).launch() |
