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import gradio as gr
import skops.io as sio
pipe = sio.load("./Model/drug_pipeline.skops", trusted=True)
def predict_drug(age, sex, blood_pressure, cholesterol, na_to_k_ratio):
"""Predict drugs based on patient features.
Args:
age (int): Age of patient
sex (str): Sex of patient
blood_pressure (str): Blood pressure level
cholesterol (str): Cholesterol level
na_to_k_ratio (float): Ratio of sodium to potassium in blood
Returns:
str: Predicted drug label
"""
features = [age, sex, blood_pressure, cholesterol, na_to_k_ratio]
predicted_drug = pipe.predict([features])[0]
label = f"Predicted Drug: {predicted_drug}"
return label
inputs = [
gr.Slider(15, 74, step=1, label="Age"),
gr.Radio(["M", "F"], label="Sex"),
gr.Radio(["HIGH", "LOW", "NORMAL"], label="Blood Pressure"),
gr.Radio(["HIGH", "NORMAL"], label="Cholesterol"),
gr.Slider(6.2, 38.2, step=0.1, label="Na_to_K"),
]
outputs = [gr.Label(num_top_classes=5)]
examples = [
[30, "M", "HIGH", "NORMAL", 15.4],
[35, "F", "LOW", "NORMAL", 8],
[50, "M", "HIGH", "HIGH", 34],
]
title = "Drug Classification"
description = "Enter the details to correctly identify Drug type?"
article = "This app is a part of the **[Beginner's Guide to CI/CD for Machine Learning](https://www.datacamp.com/tutorial/ci-cd-for-machine-learning)**. It teaches how to automate training, evaluation, and deployment of models to Hugging Face using GitHub Actions."
gr.Interface(
fn=predict_drug,
inputs=inputs,
outputs=outputs,
examples=examples,
title=title,
description=description,
article=article,
theme=gr.themes.Soft(),
).launch()
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