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import gradio as gr |
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import pandas as pd |
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from pycaret.classification import load_model, predict_model |
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model = load_model('tuned_blend_specific_model_19112021') |
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def predict(model, input_df): |
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predictions_df = predict_model(estimator=model, data=input_df) |
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predict_label = predictions_df["Label"][0] |
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predict_score = predictions_df["Score"][0] |
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amputation_risk = "" |
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if predict_label == 1: |
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amputation_risk = "YES" |
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amputation_risk_output = "Amputation Risk: " + amputation_risk |
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score_output = "Score: "+str(predict_score) |
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html = "<div style='background-color:rgb(153, 0, 0);color:white;font-size:20px;'>" + amputation_risk_output + "<br>" + score_output + "<br>" + "</div>" |
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else: |
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amputation_risk = "NO" |
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amputation_risk_output = "Amputation Risk: " + amputation_risk |
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score_output = "Score: "+str(predict_score) |
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html = "<div style='background-color:rgba(16, 185, 129, var(--tw-bg-opacity));color:white;font-size:20px;'>" + amputation_risk_output + "<br>" + score_output + "<br>" + "</div>" |
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return html |
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def predict_amputation(age, gender, race, diabetes_type): |
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diabetes_class = "Type "+str(diabetes_type)+" diabetes" |
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gender = gender[0] |
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input_dict = {"AGE": age, "GENDER": gender, "RACE": race, "DIABETES_CLASS":diabetes_class, "AMPUTATION":''} |
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input_df = pd.DataFrame([input_dict]) |
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return str(predict(model=model, input_df=input_df)) |
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title = "DIabetes-related Amputation Risk Calculator (DIARC)" |
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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." |
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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 Sifiso Mtshali</a> or <a href='mailto:mahomedo@ukzn.ac.za'>Dr Ozayr Mahomed</a></span></p>" |
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iface = gr.Interface( |
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fn=predict_amputation, |
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title=title, |
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description=description, |
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article=article, |
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inputs=[gr.inputs.Slider(minimum=0,maximum=100, step=1, default=0, label="Age"), |
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gr.inputs.Dropdown(["Female", "Male"], default="Female", label="Gender"), |
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gr.inputs.Dropdown(["Asian", "Black", "Coloured", "White", "Other"], default="Asian", label="Race"), |
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gr.inputs.Dropdown(["1", "2"], default="1", label="Diabetes Type")], |
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outputs="html", |
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theme="grass", |
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examples=[ |
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[77, "Female", "Asian", 2], |
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[28, "Male", "Black", 1], |
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[75, "Male", "White", 2], |
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[59, "Male", "Coloured", 1], |
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[73, "Female", "Other", 1], |
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[4, "Female", "Black", 2], |
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[65, "Male", "Coloured", 2], |
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], |
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) |
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iface.test_launch() |
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if __name__ == "__main__": |
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iface.launch() |
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