File size: 2,167 Bytes
70e062f 8e201b2 70e062f 8e201b2 70e062f 21e3c04 14b29ac 21e3c04 14b29ac 21e3c04 14b29ac 21e3c04 8e201b2 70e062f 8e201b2 95dc8c0 8e201b2 14b29ac 95dc8c0 8e201b2 21e3c04 8e201b2 21e3c04 8e201b2 95dc8c0 8e201b2 21e3c04 8e201b2 |
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 |
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 drug based on patient features.
Args:
age (int): Age of patient
sex (int): Sex of patient (0 for female, 1 for male)
blood_pressure (int): Blood pressure level
cholesterol (int): 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 = """<center>
[](https://github.com/kingabzpro/CICD-for-Machine-Learning)[](https://huggingface.co/kingabzpro)
**This app is a part of the Beginner's Guide to CI/CD for Machine Learning.**
**It teaches how to automate training, evaluation, and deployment of models to Hugging Face using GitHub Actions.**
[](https://www.datacamp.com/portfolio/kingabzpro)
</center>"""
gr.Interface(
fn=predict_drug,
inputs=inputs,
outputs=outputs,
examples=examples,
title=title,
description=description,
article=article,
theme=gr.themes.Soft(),
).launch()
|